United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS-GTR-31

July 1999

Abundance and

Characteristics of

Snags in Western

Montana Forests

Richard B. Harris
Rocky Mountain Research Station

324 25th Street

Ogden, UT 84401

Abstract _____________________________________________

Harris, Richard B. 1999. Abundance and characteristics of snags in western Montana forests. Gen Tech.

Rep. RMRS-GTR-31. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain

Research Station. 19 p.

Plot data from the U.S. Forest Service’s Forest Inventory and Analysis program was used to characterize

the abundance and selected characteristics of snags from forests in western Montana. Plots were grouped

by whether they had a history of timber harvest, and the U.S. Forest Service classifications of forest type,

habitat type, and potential vegetation group were used to characterize plot conditions. Snag abundance was

classified by d.b.h. class and species. On uncut plots (no evidence of previous timber harvest or other

mechanized human activity), total snag (9 inches or greater) density varied from under three per acre on dry

Ponderosa pine (Pinus ponderosa) types to approximately 21 per acre on mesic spruce and fir types as well

as warmer sites supporting grand fir (Abies grandis) and western redcedar (Thuja plicata). Higher snag

densities occurred in dry subalpine types (over 30 per acre), but these figures may be anomalous because

of recent, uncharacteristically high mortality of whitebark pine (Pinus albicaulis). Abundance of large (21

inches or greater) snags was much lower, but showed similar trends, varying from as low as 0.4 per acre on

dry ponderosa pine sites and 0.2 per acre on lodgepole pine (Pinus contorta) dominated sites to 2 per acre

on warm, mesic sites. Similar trends were observed when stands were categorized by habitat type groups

and potential vegetation groups. Snag abundance on young, recently disturbed stands was only slightly

lower than on older, sawtimber stands, and in general, snag dynamics differed from those of live trees during

the process of stand aging. Stands lacking a history of timber harvest had significantly higher snag

abundance than those with a history of timber harvest. The generally higher snag abundances in uncut

stands reflect not only an unharvested condition, but also a lack of fire and probably an attendant excess of

mortality from insects and disease, which would be manifested more strongly in smaller d.b.h. classes. Snag

abundances in larger d.b.h. classes of uncut stands should closely mimic natural conditions on the landscape

in the absence of timber harvest if accounting for a small upward bias caused by fire suppression. These

estimates for larger d.b.h. classes can be used as rough targets for landscapes where managing for

biodiversity or emulating natural disturbance patterns is an important objective.

Keywords: cover type, habitat type, logging, snag, snag abundance

The Author ___________________________________________

Richard B. Harris is a Research Associate with the Wildlife Biology Program at the University of

Montana, Missoula. He was Programmatic Wildlife Biologist for the Montana Department of

Natural Resources and Conservation 1995 to 1998. He received a B.A. degree in music from

Bennington College in 1974, an M.S. degree in wildlife biology from the University of Montana in

1984, and a Ph.D. degree in forestry from the University of Montana in 1993.

Cover photos are by Milo Burcham
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-31. 1999 1

Introduction ____________________

Snags (standing dead trees) are recognized as a vital

component of wildlife habitat and a normal and necessary part of forested landscapes (Bull and others 1997;

Thomas and others 1979). During the past two decades, considerable research has been devoted to characterizing the numerous relationships among various

wildlife species and snags (Martin and Barrett 1983;

Marzluff and Lyon 1983; Raphael and White 1984).

Much of this research has been motivated by the

concern that silvicultural practices aimed solely at

maximizing wood production would, in time, produce

entire forests deficient in snags, particularly largediameter snags. The most frequently articulated result of such a condition would be extirpation of snagdependent species (often woodpeckers), although the

long-term effects on other aspects of the forest ecosystem of snag loss remain largely unknown.

Because earlier forestry practices often removed

snags without such an awareness, and because even

modern forestry practices can easily reduce snag abundance further, the question arises: How many snags

should we target for our landscapes? This question has

recently been brought into sharper focus by the involvement of the Federal Occupational Safety and

Health Administration (OSHA) in the safety aspects

of snag management. In short, the pressures on both

“sides” of the snag management issue—more snags

suggested by wildlife or ecosystem integrity concerns,

fewer snags suggested by human safety concerns—

have intensified, clarifying the need for better data on

current and desired snag abundance.

Wildlife researchers have taken principally a “species-centered” approach to this question. They have

studied the life-history requirements of individual

species known to be dependent on, or associated with,

snags and estimated the number and type of snags

needed by individuals of these species. Then, they

have combined these results with theory from the

population dynamics literature to estimate the abundance and characteristics of snags needed on a landscape scale to ensure persistent populations (Menasco

1983; Neitro and others 1985; Thomas and others

1979).

Abundance and Characteristics

of Snags in Western Montana

Forests

Richard B. Harris

While this approach has great merit and has undoubtedly contributed to more enlightened snag management than in the past, it suffers from at least three

drawbacks:

1. Estimates of population sizes needed to ensure

persistence can only be developed through mathematical models, but species-specific data to parameterize

the extremely detailed and complex models needed

to estimate population viability are nearly always

inadequate.

2. Even if the abundance and characteristics of

snags required for a chosen “indicator” or “umbrella”

species are known with precision, questions remain

about how many snags are required to ensure persistence of the entire range of snag-dependent species.

3. Focus on individual species is inconsistent with

the recent move toward focusing on general ecosystem

patterns and processes (Franklin 1993). This “coarsefilter” approach has the advantage that, at least theoretically, it lends itself to management objectives that,

while including the needs of individual wildlife species, embrace a more fundamental concept of ecosystem integrity.

For example, in considering the closely related (and

even more contentious) issue of “old-growth,” most

research has been devoted to elucidating the natural

processes that create and eliminate old-growth

(Johnson and others 1995; Lesica 1996), and on the

resultant natural patterns present on the historical

landscape (Losensky 1993, 1996), rather than on questions of how much old-growth any given species needs

for persistence. These general patterns of forest age

are highly correlated to patterns among local indigenous fauna (Bunnell 1995), providing additional confidence that maintaining a similar abundance and

pattern of age classes should generally support all

species, and obviating the (clearly impossible) task of

calculating the individual needs of hundreds or perhaps thousands of such species.

The same ecological knowledge that allows us to

draw general conclusions about “natural” age distributions within differing forest types can be used to

make similar broad statements about snag abundance

and dynamics (USFS n.d.). Although we can analyze
2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-31. 1999

historical records in the Northern Rockies with respect to age distribution (Losensky 1993, 1997), no

such comparable data exist with respect to snags.

Foresters working during the initiation of the era of

large-scale logging and fire suppression (about 1900 to

1930) documented ages and species mixes of stands in

ways we can now use, but they did not keep similar

records about snags. Thus, we have no way to go back

in time to examine the “naturally” occurring abundance and pattern of snags. (Although see the recent

work of Harrod and others 1998, for an estimate of

historical snag densities in an assumed steady-state

system.)

Fortunately, we have access to modern data that

allow estimation of snag abundances in various forest

types and in forests subject to varying levels of human

intervention. We can gain some insight into the patterns of snags that “should” characterize our forests by

careful examination of current conditions, even if we

are unable to determine exactly what “would” have

been on the landscape some 100 years ago.

This report is a preliminary attempt to examine

those patterns. I have used data obtained from the

U.S. Forest Service’s Rocky Mountain Research Station, Ogden, UT, as part of the Forest Inventory and

Analysis (FIA) program to estimate abundance and

characteristics of snags in western Montana forests,

with the objectives of:

1. Estimating abundance of snags in various forest

types in the absence of timber harvesting.

2. Comparing these data with those obtained from

forest stands already subject to logging.

3. Suggesting possible management approaches to

snag management that follow a coarse-filter philosophy, rather than a species-specific one.

Methods _______________________

Data used in this report came from the most recent

(generally 1988 through 1995) periodic forest inventory

developed and administered by the Forest Service’s

Western FIA program, in Ogden, UT. Briefly, these

were permanent, systematically located point clusters

encompassing approximately 0.5 to 1 acre (0.2 to 0.4 ha)

on which trees were sampled. For all tallied trees,

attributes such as species, size, age, and condition

were documented. Plots were sampled on all forested

land, regardless of ownership or forest condition, and

constituted an objective representation of forests on

unsampled locations. Clusters (plots) consisted of up

to 10 points. Only point centers were fixed (that is, plot

boundaries were not fixed), and a variable radius was

used to select individual trees for sampling. For trees

of diameters that are the focus of interest in this

report, a basal-area-factor 40 prism was used. The raw

data entering these analyses were not number of trees

tallied per plot, but rather trees-per-acre projections

based on the diameter of each tallied tree and its

distance from the point center. During some years, the

FIA program additionally estimated snags counted

within circular 0.5 acre plots superimposed on the

center of the cluster, with the rationale that variable

basal-area-factor 40 plots dealt ineffectively with the

clumpy distribution typically characterizing snags. I

elected, however, to use the point-cluster data in

preference to the snag count estimates because the

estimation procedures used in these additional fixed

plots were prone to measurement errors (Harris, unpublished data), species was not recorded for snags

counted, and the area effectively sampled by the cluster of basal-area-factor 40 plots was equal to or larger

than 0.5 acre for the larger diameter snags of interest

here (table 1). For information on sampling and documentation procedures used in the FIA program, see

Woudenberg and Farrenkopf (1995) and USDA (1988).

The key attributes in classifying plots were (1) stand

history (for example, human intervention, fire, and

insect damage); (2) forest cover type (by species); (3)

habitat type group (Green and others 1992; Pfister

and others 1977); and (4) stand-size class (for example,

poletimber and sawtimber). Further description of

grouped plots was made possible by reference to variables describing plot elevation, aspect, and slope.

Key attributes used in classifying individual trees

within plots were (1) species; (2) diameter at breast

height (d.b.h.); (3) tree history (for example, snag, live

tree, and dead or down tree); and (4) trees per acre

(that is, extrapolation factor) indicated for the species

per d.b.h. per tree history combination.

Plots used in this report originated from three different land owners or managers:

1. Lands held in trust for educational beneficiaries

of the State of Montana and managed by the Montana

Department of Natural Resources and Conservation.

Table 1—Area effectively sampled by basal-area-factor 40

variable plots, assuming trees of various d.b.h. and

differing numbers of points within each cluster.

Number of points sampled at each plot are those

used in the present analysis.

Number of points sampled at each plot

D.b.h. 5 7 10

inches - - - - - - - - Proportion of acre - - - - - - - - 

12 0.092 0.129 0.185

18 0.208 0.291 0.415

24 0.369 0.517 0.739

30 0.577 0.808 1.154

n 630 602 698
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-31. 1999 3

2. The Bitterroot, Flathead, Kootenai, and Lolo National Forests.

3. Nonindustrial private owners of forested lands.

Plots were grouped into two stand history categories: cut and uncut.

1. Plots were classified as “cut” if:

a. “...most important man-caused or natural

action...on the sample acre” (USDA 1988:37) was any

form of timber cutting.

b. The plot was identified as having been initiated through planting or seeding.

c. The most important action was documented as

a separate human source (for example, road-building,

chaining, land-clearing).

2. Plots were classified as “uncut” if none of the

above characteristics were documented. The uncut

classification included plots on which the “most important action” was fire, insect damage, disease, wind

damage, and so forth (table 2). It also, necessarily,

included plots influenced by fire-suppression. I use the

word “uncut” throughout as a shorthand, but recognize that, even in the absence of timber harvest,

human activities have influenced these stands and

trees.

No attempt was made to alter or reclassify forest

cover types from those assigned by the FIA program.

These were generally in the form of single-species

identifiers. For sparsely stocked locations (less than 6

live trees 5 inches or greater d.b.h. tallied), field

workers were instructed to “Estimate which species

has the plurality (highest amount) of basal area stocking....” For more densely stocked stands, cover-type

designations were produced by automated, in-office

programs that considered the entire documented tree

tally for the plot. I did, however, lump together existing cover types to form aggregate types when sample

sizes were obviously too small for meaningful analysis. A “mesic conifer” type was created consisting of the

FIA’s grand fir, western redcedar, western hemlock

(Tsuga heterophylla), and western white pine (Pinus

monticola) forest types; a “hardwood” type, consisting

of the FIA’s aspen, (Populus tremuloides), cottonwood

(P. trichocarpa), and birch (Betula spp.) forest types;

and a “subalpine” type, consisting of the FIA’s whitebark pine, and limber pine (Pinus flexilis) forest types.

Eleven plots originally coded as mountain hemlock

(Tsuga mertensiana), accounting for about 3.5 percent

of the resultant cover type, were included within the

“spruce/fir” type.

The “primary” habitat type designation (Pfister and

others 1977) was retained for each plot as assigned by

the field crew. Habitat types were then aggregated

into habitat type groups, following Green and others

(1992). I modified their classification system slightly

by lumping all “Montana east-of-the-Continental-Divide” habitat types with their corresponding “west-ofthe-Divide” reducing the total number of habitat type

groups in Montana from 19 to 10. For example, habitat

types designated by Green and others (1992) as “cool

and moist” east of the Divide were grouped together

with those designated “cool and moist” west of the

Divide (appendix B). Habitat types coded by the FIA

program were used to classify plots into six potential

vegetation groups as suggested by M. Hillis (unpublished work). The objective of classifying plots into

potential vegetation groups was to integrate underlying climatic and physical attributes with fire regimes

and dominant cover types (for example, splitting out

forests that are typically characterized by lodgepole

pine, regardless of potential climax species)

(appendix C).

To classify individual trees, I simply retained the

species, d.b.h., and trees per acre provided by the FIA

program for each tree record. Snags were classified as

all trees designated with a tree-history code of either

4 (hard) “standing salvable dead...5.0 inches d.b.h. or

larger with less than 67 percent rotten or missing

volume” (USDA 1988 p. 55) or 6 (soft) “nonsalvable

dead...5.0 inches d.b.h. or larger with 67 percent or

more rotten or missing volume” that were not indicated by the damage-code field as being on the ground.

Snags can also be categorized (Ohmann and others

1994) as being “remnant” (formed in a previous stand,

usually from large old-growth trees) or “recruited”

(originating in the current stand, usually from suppression mortality during the stem-exclusion stage of

stand development) (Oliver and Larson 1990). I made

no attempt at such a classification, but discuss this

issue following presentation of the results.

Table 2—Proportion of plots in field location history categories,

as recorded in the Forest Inventory Analysis data.

Category Uncut Cut

None 0.471 0.161

Animal damage 0.014 0.004

Insect damage 0.066 0.033

Disease 0.211 0.079

Weather 0.061 0.004

Wind 0.044 0.004

Fire 0.133 0.057

Clearcut 0.000 0.061

Selective cut 0.000 0.315

Other (selective) cut 0.000 0.193

Post/pole cut 0.000 0.004

Clearing 0.000 0.008

Road building 0.000 0.041

Type conversion 0.000 0.018

Other human alteration 0.000 0.018
4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-31. 1999

Trees per acre for each plot were summarized by

6-inch d.b.h. classes, beginning with a d.b.h. of 9

inches (small = 9 to 14.99 inches; medium = 15 to 20.99

inches; large = 21 to 26.99 inches) plus an unbounded

d.b.h. class (for example, more than 27 inches), as well

as by snag/nonsnag status and tree species. All statistics and summaries reported here are by plot (sum of

individual points); none are summarized on a by-tree

basis.

Snag abundances and characteristics were examined among plots classified by cover type, habitat type

groups, and potential vegetation groups. The most

precise designation would have been to examine each

cover type/habitat type group or cover type/potential

vegetation group combination separately. However,

sample sizes were too small in most cases to allow

meaningful inference (I imposed a lower limit of 25

plots whenever possible) (see also Ohmann and others

1994), and the resultant matrix of snag-size distributions for 90 different types of forest may have produced

more confusion than clarity.

The density from each plot represented a single

sample, even though each consisted of cumulative

data from 5 to 10 sample points within each cluster. I

assumed that cover types, habitat type groups, potential vegetation groups and stand-size classes represented meaningful classifications, and thus did not

test for significant differences in snag densities among

them. I did not lump samples from different classes to

enlarge sample sizes, even when it appeared that snag

densities did not differ significantly between classes.

To compare elevation and slope of plots that were

classified as cut and uncut, standard 2-factor ANOVA

(Norusis 1993) was used, using cover type as a covariate.

The Mann-Whitney test was used to compare snag

densities among samples classified as cut and uncut.

To compare frequency distributions of snags per acre

between cut and uncut plots, the Kolmogorov-Smirnov

test was used (Norusis 1993). To compare snag and

live tree densities among stand-size classes, standard

one-way ANOVA was used, considering pairwise comparisons significantly different if P < 0.05 (Tukey’s

Honestly Significant Difference, Sokal and Rohlf

1981 p. 245).

Throughout this report, the English measurement

system is used because raw data were reported using

these units, and because American foresters and land

managers continue to use it.

Results ________________________

Sample Plots and Trees

Data consisted of a total of 1,949 forested plots

(Montana Department of Natural Resources and Conservation - 232 plots; National Forest - 1,237 plots;

nonindustrial private owners of forested lands - 480

plots). These plots were classified as uncut (1,158 plots

- 59 percent) and cut (791 plots - 41 percent). A history of

fire, insect damage, disease damage, wind damage,

and other factors characterized both cut and uncut

plots with different frequencies (table 2). A total of

54,096 individual stems more than 5.0 inches d.b.h.

were tallied on these plots, of which 5,167 (9.6 percent)

were classified as snags. Of 23,955 stems more than

9.0 inches d.b.h., 3,279 (13.7 percent) were classified

as snags.

The most common forest type among the 1,949 plots

was Douglas-fir (Pseudostuga menziesii)—(almost 41

percent of all plots). Other common forest types included spruce/fir (almost 19 percent), lodgepole pine

(over 16 percent), and ponderosa pine (about 7 percent). Very few were classified as limber pine, western

redcedar, grand fir, western hemlock, mountain hemlock, aspen, cottonwood, or birch, thus necessitating

the lumping of some cover types (table 3).

Among the 1,158 plots classified as uncut, a similar

pattern emerged: Douglas-fir types were the most

common (roughly 36 percent), followed by spruce/fir

(about 24 percent) and lodgepole pine (20 percent;

table 3).

Ninety-five plots were not classified into a habitat

type group, because no habitat type was assigned to

the plot by the FIA program or because I could not

confidently group the habitat type into a habitat type

group (nor did Green and others 1992). Of the remaining 1,854 plots, the most common habitat type groups

represented were the “moderately-warm-dry-B” (23

percent) and “cool-moist-E” (22 percent) types. Habitat type groups “wet-F,” “cold-moderately-dry-I,” and

“cold-J” were all represented by less than 3 percent of

sample plots (table 3).

Among the 1,116 plots classified as uncut for which

habitat type groups could be assigned, patterns were

similar, although some groups—presumably those

with high productivity and accessibility and thus

attractiveness for timber production—were less well

represented than they were when considering all plots.

In particular, the “moderately-warm-dry-B” group fell

to a 16 percent representation (from 23 percent), and

the “warm-moist-D” group fell to 9 percent (from 11

percent). At the other extreme, only a single plot

classified as “cold-J” had experienced timber harvesting or other significant human intervention (table 3).

Roughly 44 percent of plots classified as uncut fell

within the “cool, infrequent-but-stand-replacing fire

regime” potential vegetation group. Similar numbers

of plots were classified as “warm, moist, mixed-fire

regime” (18 percent) and “warm, dry, frequent-butlow-intensity-fire regime” (17 percent). Fewer uncut

plots were classified as “very moist, maritime climate,

very-infrequent-fire-regime” (7 percent).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-31. 1999

5

Table 3—Plot sample sizes by cover type and habitat type group.

Habitat type groups

ABCDEFGH I J

Moderately Moderately Moderately Cool , Cold, No

Warm, warm, cool, Warm, Cool, cool, moderately moderately habitat

Cover type dry dry dry moist moist Wet moist dry dry Cold type groups Total

Cut plotsa

Ponderosa 33 42 2 2 1 9 89

Douglas-fir 28 180 55 24 20 2 38 9 21 377

Larch 4 4 14 19 3 9 5 58

Mesic conifer 1 36 3 2 42

Spruce 5 10 6 3 1 3 28

Spruce/fir 1 3 10 55 5 8 4 6 92

Lodgepole 1 14 13 5 16 1 8 19 3 5 85

Dry subalpine 213

Hardwood 1 4 1 2 3 1 5 17

No cover type

Total 63 245 79 99 126 15 52 50 8 1 53 791

Uncut plots only

Ponderosa 23 15 1 1 3 43

Douglas-fir 48 151 78 34 37 2 13 43 1 15 456

Larch 2 4 14 20 7 4 2 1 54

Mesic conifer 3 27 1 1 1 33

Spruce 6 18 5 3 1 2 35

Spruce/fir 1 1 2 142 14 65 27 20 4 276

Lodgepole 2 9 32 18 57 2 13 77 15 4 5 231

Dry subalpine 1 1 4 6 8 10 0 30

Hardwood 3 2 1 3 7 18

No cover type 3 3

Total 74 181 122 102 284 27 33 202 51 40 42 1,158

aPlots subject to cutting or other human disturbance.
6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-31. 1999

To further understand differences among cover types,

habitat type groups, and potential vegetation groups,

I summarized each by mean elevation, slope, and

aspect. These summaries are presented in appendix

D. Stands with a history of timber harvest were not,

however, a random sample of all stands, as characterized by mean elevation and slope. Cut stands had

significantly lower elevations (F = 273.9, df = 1, 1946,

P < 0.001) and gentler slopes (F = 224.1, df = 1, 1946,

P < 0.001) than did uncut stands after they were

controlled for the effects of cover type.

Over 72 percent of plots classified as uncut were in

the sawtimber size-class, with most of the remainder

(18 percent) being in the poletimber class; about 8

percent were classified as seedling/sapling and the

remaining as nonstocked (table 4). Lodgepole pine

cover types formed the major exception to this general

trend (table 5): only 45 percent of such stands were

classified as sawtimber, while almost 47 percent were

poletimber. Spruce/fir stands had a substantial component (15 percent) of seedling/sapling plots.

Overall Natural Patterns of Snag

Abundance

Overall Means—Assessed across the entire range

of cover types, habitat type groups, potential vegetation groups, and stand-size classes, there were an

average of 10.41 (standard error [SE] = 0.43) snags per

Table 4—Sample sizes of plots by stand-size class.

Category Cut stands Uncut stands

Nonstocked 24 14

Seedlings/saplings less than 5 inches d.b.h. 170 96

Poletimber 5 to 9 inches d.b.h. 119 213

Sawtimber greater than 9 inches d.b.h. 477 833

(Sawtimber greater than 15 inches d.b.h.) (446) (826)

Total 790 1,158

Table 5—Proportion of uncut plots in the four stand-size classes by cover type.

Cover typea

Category PP DF WL ES SF LP MC SA HW Total

Nonstocked 0.05 0.02 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.01

Seedling/saplings

(less than 5 inches d.b.h.) 0.07 0.05 0.00 0.06 0.15 0.08 0.06 0.10 0.06 0.08

Poletimber (5 to 9 inches d.b.h.) 0.02 0.08 0.22 0.06 0.14 0.47 0.03 0.17 0.44 0.19

Sawtimber

(greater than 9 inches d.b.h.) 0.86 0.85 0.78 0.89 0.70 0.45 0.91 0.72 0.50 0.74

N 43 422 54 35 276 234 34 29 16 1,108

a

PP = ponderosa pine; DF = Douglas-fir; WL = western larch; ES = Englemann spruce; SF = subalpine fir; LP = lodgepole pine; MC = mesic conifer;

SA = subalpine; HW = hardwoods.

acre (more than 9.0 inches d.b.h.) on all plots. Of these,

a mean of some 8.12 (SE = 0.39) snags per acre were

small (9 to 14.9 inches d.b.h.), 1.69 (SE = 0.09) were

medium (15 to 20.9 inches d.b.h.), 0.44 (SE = 0.03)

were large (21 to 26.9 inches d.b.h.), and 0.16 (SE =

0.02) were very large (27 inches d.b.h. or larger). On

these same plots, total stems per acre (live trees and

snags 9 inches or greater d.b.h.) averaged 74.57 (SE =

1.12). Considering uncut plots only, total snags averaged 13.98 per acre (SE = 0.63), with 11.05 (0.57), 2.21

(0.14) , 0.56 (0.04), and 0.17 (0.02) per acre in the small,

medium, large, and very large categories, respectively. On these same plots, total stems per acre

averaged 86.85 (SE = 1.51). On average, snags had a

density roughly 14 percent of all 9 inch or greater

d.b.h. stems (about 16 percent on uncut plots). This

total tree/snag proportion remained relatively constant throughout the diameter distribution (fig. 1),

with all size classes having from 14 to 17 percent

snags. The largest proportion representation by snags

was in the 9 to 14.9 inch (small) diameter class, where

snags made up just over 16 percent of the total stems.

Spatial Distribution of Snags—The distribution

of snag densities among uncut plots was highly variable; plots with an average snag density were rarely

encountered. In fact, the modal category for snags per

acre was zero (table 6). Considering all 9 inch or

greater snags on uncut plots, over half the plots had

densities of over 5 snags per acre, while 38 percent had
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-31. 1999 7

none. Again considering uncut plots, over 77 percent

had no snags of 21 inches or greater (while 17 percent

had more than 2 per acre). As quantified by plots of

approximately 1 acre, the geographic distribution of

snags was highly clumped. Plots with no snags were

generally more common among those classified as cut,

while plots with many (10 or more) snags per acre were

more common among those classified as uncut; however, the overall frequency distributions of snags per

acre on cut and uncut plots did not differ significantly.

Differences Between Uncut and Cut Plots—

Snags were significantly (P < 0.0001) more abundant

on uncut than on cut plots (table 7) in all diameter

classes except the largest. The pattern of snags being

reduced on cut plots relative to uncut plots was general across cover types, habitat type groups, and

Small Medium Large Very large

12

10

8

6

4

2

0

0.20

0.15

0.10

Snags per acre

Proportion of stems/snags

Figure 1—Snags per acre (open bars) and the

proportion of all standing stems made up by

snags (solid squares connected by solid line)

and by d.b.h. class (small = 9 to 14.9 inches,

medium = 15 to 20.9 inches, large = 21 to 26.9

inches, very large = 27 inches or greater)

among all plots classified as “uncut.”

Table 6—Frequency distribution of snags per acre across all

cover type habitat type groups and potential vegetation groups.

9 inches 21 inches

Density or greater or greater

0 442 895

0.001 to 1 6 11

1.001 to 2 31 73

2.001 to 3 40 87

3.001 to 4 32 30

4.001 to 5 26 25

5.001 to 6 25 13

6.001 to 7 17 21

7.001 to 8 27 3

8.001 to 9 26 3

9.001 to10 16 4

10 or greater 470 6

Table 7—Snag abundance on cut and uncut plots by d.b.h.

class. Also shown are approximate z and probability

values (2-sample Mann-Whitney tests).

D.b.h. class Uncut Cut z p

inches

9 to 14.9 11.05 3.83 –10.78 0.0000

15 to 20.9 2.21 0.92 –6.66 0.0000

21 to 26.9 0.56 0.28 –4.71 0.0000

27 or greater 0.17 0.15 –0.47 ns

All 13.98 5.18 –11.68 0.0000

potential vegetation groups. All pairwise comparisons

(cover type x d.b.h. class) displayed the same directional trend of fewer snags on cut than uncut plots,

although the magnitude differed among cover types.

For example, ponderosa pine-dominated plots with no

history of timber cutting averaged 1.6 times more

snags than did previously cut ponderosa pine plots,

whereas uncut western larch (Larix occidentalis) plots

averaged 3.9 times more snags than larch plots with a

history of timber harvest (table 8).

Differences Among (Successional) Stand-Size

Classes—On uncut plots, snags were more abundant

on plots classified as sawtimber than on plots classified as seedling/sapling or poletimber. Snag abundance on sawtimber plots was not significantly different from that on nonstocked plots, with about 86

percent as many snags on nonstocked plots as on

sawtimber plots (fig. 2). Considering larger snags only

(21 inches or greater), the largest number of snags

occurred on nonstocked plots, although the only significant difference occurred between sawtimber and

poletimber plots (fig. 3). The dynamics of snag creation

and attrition differed from the analogous dynamics of

live stems (fig. 2, 3). Snags constituted the majority of

total stems on uncut plots classified as nonstocked,

gradually declining in their proportional representation with time (fig. 2). The absolute number of snags

also declined from the nonstocked to seedling/sapling

stages, while the absolute number of live stems increased. Among the largest diameter-class snags, this

decline continued into the poletimber stage (fig. 3); it

was not until the sawtimber stage that new largediameter snags were again recruited.

Snag Abundance by Individual Cover

Types, Habitat Type Groups, and Potential

Vegetation Groups

While interesting, background information summarizing snag characteristics across all types of forests

found in western Montana is not terribly useful, because these characteristics differ markedly depending

on site quality, elevation, time since disturbance, and
8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-31. 1999

Table 8—Mean snag abundance by cover type and d.b.h. class.

Small Medium Large Very large

(9 to 14.9 (15 to 20.9 (21 to 26.9 (more than

Cover type N Total inches) inches) inches) 27 inches)

- - - - - - - - - - - - - - - - - - - - - - - - - - Snags per acre - - - - - - - - - - - - - - - - - - - - - - - - - - - 

Cut plots

Ponderosa pine 89 1.57 1.13 0.17 0.20 0.08

Douglas-fir 377 3.36 2.32 0.75 0.18 0.12

Western larch 58 4.79 3.66 0.98 0.25 0.10

Englemann spruce 28 4.58 2.80 1.12 0.54 0.12

Spruce/fir 92 11.75 9.35 1.83 0.44 0.13

Lodgepole pine 85 6.80 6.08 0.50 0.12 0.11

Mesic conifer 42 12.51 8.21 2.61 0.98 0.71

Dry subalpine 3 1.59 1.59 0.00 0.00 0.00

Hardwoods 17 4.72 2.12 1.18 0.90 0.52

Uncut plots

Ponderosa pine 43 2.51 1.51 0.61 0.24 0.15

Douglas-fir 422 8.96 6.78 1.62 0.46 0.10

Western larch 54 18.79 15.23 2.45 0.81 0.30

Englemann spruce 35 21.32 17.13 3.18 0.72 0.29

Spruce/fir 276 21.09 16.06 3.79 0.92 0.32

Lodgepole pine 234 12.18 11.13 0.85 0.17 0.03

Mesic conifer 34 21.68 12.57 7.20 1.38 0.53

Dry subalpine 30 31.44 27.62 2.78 0.98 0.06

Hardwoods 16 5.38 5.33 0.00 0.00 0.05

Figure 2—Standing stems per acre (9 inches or

greater) in uncut plots by the four successional

stages identified by the FIA program, showing

both snags (dark shading) and live trees (light

shading). Bars with the same letter are not

significantly different at P < 0.05 (Tukey’s

Honestly Significant Difference Post-Hoc Test).

A

A A

CD

CD

C D

B

Nonstocked Seedling/

sapling

Poletimber Sawtimber

7

6

5

4

3

2

1

0

Stems per acre, uncut plots

Snag Live tree

Figure 3—Standing stems per acre (21 inches or

greater) in uncut plots, by the four successional

stages identified by the FIA program, showing

both snags (dark shading) and live trees (light

shading). Bars with the same letter are not

significantly different at P < 0.05 (Tukey’s Honestly

Significant Difference Post-Hoc Test).

A AB

C

D

EF E E F

Nonstocked Seedling/

sapling

Poletimber Sawtimber

120

100

80

60

40

20

0

Stems per acre, uncut plots

Snag Live tree
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-31. 1999 9

other factors. The overall mean reflects the weight

given to each of these factors by the underlying sampling regime.

Table 8 summarizes mean snag abundance by cover

type. As already discussed, snags were more abundant

on uncut (table 8) than cut (table 8) plots. Table 9

summarizes mean snag abundance by habitat type

group. Table 10 summarizes mean snag abundance by

potential vegetation group.

Viewed simply, the abundance of sawtimber-size

snags on any given cover type, habitat type group or

potential vegetation group, will be determined by

(1) the abundance of sawtimber-size live trees from

which snags can be recruited, (2) the recruitment rate

of snags (mortality rate of trees), and (3) the rate at

which snags fall to the ground. Forest Inventory Assessment data were ill equipped to add insight into the

second and third factors, but they did allow inference

regarding the first, as well as the product of the

second and third (that is, the proportion of total stems

that are snags at any given time). Figures 4 and 5

summarize these relationships by plotting the mean

density of live trees per acre by cover type as well as the

mean density of snags.

Considering all diameter classes, live trees were

most dense on mixed conifer types, and somewhat less

so on Englemann spruce (Picea englemannii) and

subalpine types (fig. 4). Live tree density was least on

ponderosa pine and hardwood cover types. The proportion of total stems consisting of snags was highest on

subalpine, spruce/fir, and western larch cover types,

and least on ponderosa pine and hardwood cover

types. For example, although overall stem density was

similar among Douglas-fir, western larch, and lodgepole pine cover types, snags were relatively more

abundant on western larch types (fig. 4; table 8)

because proportionally more of those stems were snags.

The pattern was modified somewhat when consideration was given only to the largest diameter classes of

stems (21 inches and over, in this case). Large stems

were most dense in mesic conifer types, followed by

Englemann spruce, ponderosa pine, and western larch

types (fig. 5). Ponderosa pine types ranked much

higher when viewing only these large stems than

when viewing all stems, and lodgepole pine and subalpine types had by far the lowest density of largediameter stems. However, a high proportion of these

few large-diameter stems in the subalpine type were

Table 9—Mean snag abundance by habitat type groups and d.b.h. class.

Small Medium Large Very large

(9 to 14.9 (15 to 20.9 (21 to 26.9 (more than

Habitat type group N Total inches) inches) inches) 27 inches)

- - - - - - - - - - - - - - - - - - - - - - - - - - Snags per acre - - - - - - - - - - - - - - - - - - - - - - - - - - - 

Cut plots

A = Warm, dry 63 1.70 1.28 0.20 0.16 0.06

B = Moderate, warm, dry 245 2.90 2.08 0.51 0.17 0.13

C = Moderate, cool, dry 79 3.10 2.09 0.76 0.17 0.08

D = Warm, moist 99 9.80 6.69 2.01 0.64 0.46

E = Cool, moist 126 5.62 4.13 1.06 0.30 0.12

F = Wet 15 15.33 10.60 3.36 0.89 0.49

G = Moderate, cool, moist 52 4.62 3.52 0.97 0.12 0.02

H = Cool, moderate,.dry 50 9.24 8.66 0.37 0.13 0.08

I = Cold, moderate, dry 8 8.23 7.94 0.00 0.29 0.00

J = Dry 1 0.00 0.00 0.00 0.00 0.00

Uncut plots

A = Warm, dry 74 2.92 1.71 0.87 0.24 0.10

B = Moderate, warm, dry 181 6.66 3.95 1.42 0.39 0.09

C = Moderate, cool, dry 122 9.45 7.56 1.42 0.39 0.08

D = Warm, moist 102 18.32 13.24 3.86 0.90 0.32

E = Cool, moist 284 17.47 14.10 2.43 0.70 0.24

F = Wet 27 14.58 10.80 2.33 0.94 0.51

G = Moderate, cool, moist 33 12.97 10.61 1.39 0.90 0.07

H = Cool, moderate, dry 202 17.39 14.53 2.35 0.43 0.08

I = Cold, moderate, dry 41 28.72 23.38 4.56 0.61 0.17

J = Dry 40 16.58 13.27 2.34 0.64 0.33
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-31. 1999

Table 10—Mean snag abundance by potential vegetation groups and d.b.h. class.

Small Medium Large Very Large

Potential (9 to 14.9 (15 to 20.9 (21 to 26.9 (more than

vegetation group N Total inches) inches) inches) 27 inches)

- - - - - - - - - - - - - - - - - - - - - - - - - - Snags per acre - - - - - - - - - - - - - - - - - - - - - - - - - - - 

Cut plots

Warm, dry, frequent fire 222 2.39 1.59 0.56 0.16 0.08

Warm, moist, mixed fire 195 3.09 2.24 0.54 0.19 0.12

Cool, stand-replacing fire 221 7.26 5.71 1.12 0.30 0.12

Very moist, maritime 66 11.41 7.44 2.60 0.73 0.63

Lodgepole types 83 6.25 4.90 0.85 0.37 0.13

Whitebark pine types 4 21.44 19.48 1.33 0.64 0.00

Uncut plots

Warm, dry, frequent fire 194 4.53 2.94 1.07 0.40 0.12

Warm, moist, mixed fire 212 8.17 6.14 1.53 0.44 0.06

Cool, stand-replacing fire 510 17.42 14.14 2.46 0.63 0.19

Very moist, maritime 78 18.36 13.68 3.52 0.81 0.34

Lodgepole types 57 15.49 13.92 1.42 0.12 0.03

Whitebark pine types 106 22.13 17.28 3.86 0.75 0.25

Figure 4—Density of standing live trees

(light shading) and standing snags (dark

shading) in d.b.h. classes 9 inches or

greater, within uncut plots on classified FIAdesignated cover types. Cover type

abbreviations are: PP = ponderosa pine;

DF = Douglas-fir; WL = western larch; ES =

Englemann spruce; SF = spruce/fir; LP =

lodgepole pine ; MC = mesic conifer (grand

fir, western redcedar, western hemlock,

western white pine); SA = subalpine

(whitebark pine, limber pine, alpine larch [L.

lyallii]); HD = hardwood (cottonwood, aspen,

paper birch).

Figure 5—Density of standing live trees

(light shading) and standing snags (dark

shading) in d.b.h. classes 21 inches or

greater, within uncut plots on classified FIAdesignated cover types. Cover type

abbreviations are: PP = ponderosa pine;

DF = Douglas-fir; WL = western larch; ES =

Englemann spruce; SF = spruce/fir; LP =

lodgepole pine; MC = mesic conifer (grand

fir, western redcedar, western hemlock,

western white pine); SA = subalpine

(whitebark pine, limber pine, alpine larch);

HD = hardwood (cottonwood, aspen, paper

birch).

PP DF WL ES SF LP MC SA HD

140

120

100

80

60

40

20

0

Stems per acre

Live trees Snags

PP DF WL ES SF LP MC SA HD

12

10

8

6

4

2

0

Stems per acre

Live trees Snags
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-31. 1999 11

snags, whereas snags remained a relatively low proportion of all stems in the ponderosa pine cover type.

Snag Characteristics by Cover Types

Next, I summarized snag characteristics by cover

type and provided data on the predominant species of

snags, particularly those that tend to be fire-resistant

(western larch, ponderosa pine, Douglas-fir) (Fischer

and Bradley 1987), and that frequently persisted on

historical landscapes. These cover-type descriptions

are limited to uncut stands.

Density of snags on ponderosa pine cover types was

relatively low (fig. 6), but density of all trees was also

the lowest of any cover type. Most (23 of 43 plots

assigned to habitat type groups) uncut ponderosa pine

cover type plots were in group ‘A’ (warm, dry), which

are typically quite open, with relatively low tree density. Sample size was also small in this cover type, so

inferences should be made with some caution.

Total snags were much more common on Douglas-fir

types than on ponderosa pine types, averaging 8.96

per acre (SE = 0.74) (table 8) but very large (27 inches

or greater d.b.h.) snags were relatively uncommon

(0.10 per acre). However, Douglas-fir cover types occur

on a wide variety of habitat types and potential

vegetation groups, with a correspondingly wide variation in snag characteristics. Figure 7 displays the

variation in snag abundance (21 inches or greater) and

representation of four characteristic species within

the Douglas-fir cover type. Ponderosa pine was well

represented in the “warm, dry, frequent-fire regime”

potential vegetation group, but essentially absent

elsewhere. Western larch was well represented in the

“warm, moist, mixed-fire regime” group, but less so

elsewhere. Unsurprisingly, Douglas-fir was well represented in all potential vegetation groups.

Snag abundance was considerably greater among

stands classified as western larch than either ponderosa pine or Douglas-fir (table 8; fig. 8). Total snags per

acre averaged 18.79 (SE = 3.09), of which 3.56 per acre

were 15 inches or larger. Most of the large snags were

larch, with a smaller number of Douglas-fir. Most of

the small snags were other species, notably lodgepole

pine.

Snags were abundant in Englemann spruce cover

types, averaging 21.32 per acre, with 4.19 per acre of

these 15 inches or greater d.b.h. (table 8). Relatively

few of these snags were fire-resistant species (western

larch, ponderosa pine, Douglas-fir). Most of the small

snags were lodgepole pine, and most of the large snags

were Englemann spruce.

Snags in spruce/fir cover types were only slightly

less abundant than in Englemann spruce cover types,

with a mean of 21.09 snags per acre, of which 5.03 per

acre were 15 inches d.b.h. or greater (table 8).

Snags were moderately common (10.73 per acre) in

lodgepole pine cover types, but large (0.15 per acre)

and very large (0.05 per acre) snags were rare. This

result was unsurprising, considering the generally

small diameter that typifies the species. Most of the

largest diameter snags in this cover type were Douglas-fir and western larch.

Mesic cover types (grand fir, western redcedar, western hemlock, and western white pine) displayed similar overall snag abundance to that of western larch,

Englemann spruce, and spruce/fir cover types (21.68

per acre). However, considering the larger diameter

Figure 6—Snag (d.b.h. 9 inches or greater)

density on uncut plots within FIA-designated

ponderosa pine cover types by d.b.h. class,

showing the proportion made up by ponderosa

pine, Douglas-fir, and all other species.

Figure 7—Snag (d.b.h. 21 inches or greater)

density on uncut plots within FIA-designated

Douglas-fir cover types by the three most

common potential vegetation groups,

showing the proportion made up by

ponderosa pine, Douglas-fir, western larch,

whitebark pine, and all other species.

 

9-14.9 15-20.9 21-26.9 27+

D.b.h. class (inches)

1.2

1

0.8

0.6

0.4

0.2

0

Snags per acre

Ponderosa pine

Douglas-fir Other species





 

Warm, dry Warm, moist Cool



Potential vegetation group

0.8

0.6

0.4

0.2

0

Snags per acre

Other species

Ponderosa pine

Whitebark pine

Douglas-fir  Western larch
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-31. 1999

Snags were less common in aspen, cottonwood, and

birch cover types than in most other cover types, and

large snags were particularly uncommon (table 8).

Snag Characteristics by Habitat Type

Group

Total stem density was substantially higher on the

warm/moist ‘D’ habitat type group than all others, and

it was by far the lowest on the warm/dry ‘A’ and cold ‘J’

habitat type groups (table 9; fig. 10). The proportion of

all stems consisting of snags generally increased from

the warm/dry ‘A’ habitat type group on through the

cold ‘J’ habitat type group (fig. 10). The cold/moderately dry habitat type group ‘I’ had the highest snag

density because both total stems and proportion snags

were high. As with the parallel analysis by cover types,

this pattern changed somewhat when considering

only the largest (21 inches and larger) stems (fig. 11).

Stem density was substantially greater on the wet ‘F’

habitat type group. Surprisingly, the proportion of all

stems constituted by snags in the wet habitat type

group was the lowest of any habitat type group, resulting in a moderate total density of snags. Large snags

were most common on the warm/moist ‘D’ habitat type

group because both total stem density and proportion

snags were relatively high.

Mean snag abundances on uncut plots, classified by

habitat type group, varied from as few as 2.92 per acre

on warm, dry ‘A’ habitat type groups to as many as

28.72 on cold, moderately dry ‘I’ habitat type groups.

A BC DE F GH I J

130

120

110

100

90

80

70

60

50

40

30

20

Stems per acre

Live trees Snags

 15-20-9 21-26.9 27+

D.b.h. class (inches)

2.5

2

1.5

1

0.5

0

Snags per acre

Other species

Ponderosa pine Douglas-fir 

Western larch



8

6

4

2

0

Snags per acre

15-20-9 21-26.9 27+

D.b.h. class (inches)

Other species

Ponderosa pine Douglas-fir  Western larch

Figure 8—Snag density on uncut plots within

FIA-designated western larch cover types by

d.b.h. class, showing the proportion made up

by ponderosa pine, Douglas-fir, western larch,

and all other species.

Figure 9—Snag density in mesic conifer

cover types (see text for definition) by d.b.h.

class, showing the proportion made up by

ponderosa pine, Douglas-fir, western larch,

and all other species.

snags, this cover type had the highest snag density of

any defined cover type. A mean of 9.11 snags per acre

were 15 inches d.b.h. or greater, and of these, a mean

of 1.91 per acre were over 21 inches d.b.h. (table 8).

Over half of these large-size snags were western larch

(fig. 9).

Snag abundance in whitebark pine and limber pine

cover types was the highest of any defined type; however, relatively few of these were in the larger diameter classes (table 8). As suggested below, current snag

abundance in this type may be higher than was historically the case, due to high mortality in whitebark

pine from blister rust (Cronartium ribicola) (Keane

and Arno 1993).

Figure 10—Density of standing live trees (light

shading) and standing snags (dark shading) in

d.b.h. classes 9 inches or greater, classified by

habitat type groups. A = warm, dry; B = moderately

warm, dry; C = moderately cool, dry; D = warm,

moist; E = cool, moist; F = wet; G = moderately

cool, moist; H = cool, moderately dry; I = cool,

moderately dry, J = cold.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-31. 1999 13

A BC DE F GH I J

12

10

8

6

4

2

0

Stems per acre

Live trees

Snags

Figure 11—Density of standing live trees (light

shading) and standing snags (dark shading) in

d.b.h. classes 21 inches or greater, classified by

habitat type groups. A = warm, dry; B = moderately

warm, dry; C = moderately cool, dry; D = warm,

moist; E = cool, moist; F = wet; G = moderately

cool, moist; H = cool, moderately dry; I = cool,

moderately dry, J = cold.

Generally, dry habitat type groups had low snag abundances, and moist or wet habitat type groups had high

snag abundances (table 9).

Subtle differences may be obscured by the means

displayed in table 9. For example, large (greater than

21 inches) live tree and snag abundances on habitat

type groups ‘B’ and ‘E’ were similar (fig. 11). However,

habitat type group ‘B’ was characterized by the presence of ponderosa pine and Douglas-fir snags (fig. 12),

whereas ponderosa pine was virtually lacking in habitat type group ‘E,’ and western larch was more common. The largest number of snags occurred in habitat

type group ‘I,’ a cold and dry habitat type group, and

the highest proportion of large stems consisting of

snags occurred in habitat type group ‘J,’ a cold, dry

habitat type group (fig. 10). This seeming anomaly is

most likely explained by the presence of numerous

dead whitebark pines (fig. 13), resulting from the

current outbreak of white-pine blister rust (Keane and

Arno 1993). Presumably, the historical pattern would

have resulted in fewer of these whitebark pine stems

becoming snags than was evident in these recent plots.

Snag Characteristics by Potential

Vegetation Group

Mean snag abundances on uncut plots, classified by

potential vegetation group, varied from as few as 4.53

per acre on warm, dry, nonlethal burn regimes, to as

many as 22.13 on cold, whitebark pine sites (table 10).







2.5

2

1.5

1

0.5

0

Snags per acre

15-20.9 B 21-26.9 B 27+ B 15-20.9 E 21-26.9 E 27+ E

D.b.h. class (inches)/habitat type

Other species

Ponderosa pine Douglas-fir 

Western larch

 

9-14.9 15-20.9 21-26.9 27+

D.b.h. class (inches)

12

8

4

0

Snags per acre

Whitebark pine

Douglas-fir 

Other species

Figure 12—Snag density in two habitat type

groups with a similar number of standing live

trees per acre (B = moderately warm, dry;

E = cool, moist) by d.b.h. class, showing the

species proportion of ponderosa pine,

Douglas-fir, western larch, and all other

species.

Figure 13—Snag density in the cold habitat

type group by d.b.h. class, showing the

predominance of whitebark pine snags.

Generally, snag abundance increased with fire intensity and fire-free intervals. Plots in the very moist/

maritime potential vegetation group (usually dominated by grand fir, western redcedar, or western

hemlock) had similar snag numbers as those in the

cool, stand-replacing burn potential vegetation group

(18.36 versus 17.42), but 15 inch or greater and 21 inch

or greater snags were more abundant on the former

than the latter (4.67 versus 3.28 and 1.15 versus 0.82,

respectively). Species representation also differed

among potential vegetation groups, with Douglas-fir

and ponderosa pine dominating the “warm, dry,
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-31. 1999

frequent-fire regime,” western larch dominating the

“warm, moist, mixed-fire regime,” potential vegetation group, and the “cool, stand-replacing fire regime”

having a variety of species (fig. 14).

Discussion _____________________

The use of these data are largely dependent on the

degree to which the uncut plots can be reasonably

interpreted as representing conditions prior to industrialized human intervention, or to which approximate

corrections can be developed if this is impossible.

Clearly, all plots, uncut or cut, have experienced some

human influence for at least the past 60 years. Thus,

some interpretation is called for before equating uncut

plots, documented in the late 1980’s and 1990’s, with

“historical conditions.”

Quantifiable factors other than timber harvest that

may have been important in altering snag abundance

were fire frequency, insect and disease damage, wind

damage, and their associated attributes of stand-size

class. Although fires initially create snags, when of

high intensity they also move stands out of the sawtimber class and into smaller size-classes (for example, seedling or poletimber) where snag density is

generally lower (fig. 2, 3). Snags created by fire may

persist for shorter times than snags created by other

mortality agents (Morrison and Raphael 1993). Fires

may also weaken existing snags to the point that they

are more susceptible to windfall. Winds generally

reduce snag abundance by blowing down trees. I would

expect insect and disease damage to increase snag

abundance.

Although uncut stands were influenced by fire suppression, fire was not entirely excluded from these

plots. Fire was recorded as the “most important mancaused or natural action...evident on the sample acre”

(USDA 1988 p. 37) on 13.3 percent of uncut plots

compared with less than 6 percent on cut plots (table 2).

Wind damage was recorded on over 4 percent of additional uncut plots compared with less than 1 percent

of cut plots. Insect and disease damage was recorded

for over twice as many (27.7 percent) uncut plots as

with fire. Therefore, about 17 percent of uncut plots

were documented as having attributes tending to

reduce snag abundance, while almost 28 percent had

attributes tending to increase snag abundance. However, in assessing the how “natural” these uncut stands

were, the more important question is how these percentages would differ from those in stands sampled

100 years ago. Unfortunately, such comparative data

are not available.

In general, it seems reasonable to assume that the

number of small-diameter snags would increase with

unnaturally reduced fire frequency because stocking

levels would increase, leading to many early deaths

during the “stem-exclusion” stage (Oliver and Larsen

1990), particularly given the likely association with

fire suppression of increased insects and disease. It

seems equally logical to hypothesize that the abundance of large snags (over 21 inches d.b.h.) would

generally be much less influenced by unnaturally

reduced fire frequency. Current density of these largediameter snags may be similar to the prefire suppression era, especially in cover types, habitat type groups,

or potential vegetation groups in which stand-replacement fires occur but at long intervals, or in which fires

occur frequently but with an intensity that is incapable of killing substantial numbers of largediameter trees. If nothing else, most large-diameter

snags must have lived a substantial portion (and in

some cases, the predominance) of their lives prior to

effective fire suppression. If stocking densities have

increased due to fire suppression, one would expect

tree growth rates to decline. An additional possible

complication is that fewer trees would have grown

large to begin with, reducing the potential for recruitment of large-size snags.

One additional factor that would tend to produce a

downward bias in snag abundance observed on uncut

stands is that such stands were biased samples of all

possible stands within their respective cover types and

habitat types. Uncut stands were generally at higher

elevations and on steeper slopes within corresponding

cover types than were cut stands. While not specifically indicated by these data, I would deduce that

timber harvesting has historically occurred where

stands are both easily accessible and relatively productive. Because snag abundance is partly a function

Figure 14—Density of snags 21inches or greater

in the three most common potential vegetation

groups, showing the species proportion of

ponderosa pine, Douglas-fir, western larch,

whitebark pine, and all other species.





 Warm, dry Warm, moist Cool 

D.b.h. class (inches)

1

0.8

0.6

0.4

0.2

0

Snags per acre

Whitebark pine

Ponderosa pine

Douglas-fir Western larch

Other Species
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-31. 1999 15

of overall stem abundance (fig. 4-5, 10-11) and uncut

plots are located disproportionally in areas with lower

stem abundance, snag abundance may likewise be

biased downwards.

Given these data, deducing the likely effects of

changes in the natural disturbance regimes on snag

abundance is complex and uncertain. We do, however,

have access to estimates of the proportion of western

Montana forest cover types in various stand-size classes

around 1900 (Losensky 1993, 1997), which may be

useful as an approximation to more specific questions

regarding disturbance history (compare with table 4).

Because snags are more abundant on sawtimber-size

plots than in earlier successional stages (fig. 2, 3), it is

useful to know if mean snag abundances from stands

classified as uncut are biased because the sawtimbersize class is “unnaturally” common. Table 11 compares

the proportion of stands in the combined nonstocked

plus seedling/sapling, poletimber, and sawtimber

stages from uncut plots with those estimated to characterize western Montana forests around 1900

(Losensky 1993, 1997). I combined the two early stages

because sample sizes were small and because snag

abundances on these plots were similar in those two

classes (fig. 2, 3). I also adjusted the number of FIA

plots in poletimber and sawtimber categories to more

nearly reflect the classification procedures used during the 1930’s and adopted by Losensky (1993, 1997).

This comparison must be viewed as approximate, not

only because both data sets contained uncertainty, but

because cover types were not necessarily defined in

the same way. Bearing this caveat in mind, it appears

that nonstocked and seedling/sapling plots were less

abundant in the present data than was historically the

case, while sawtimber stands were more abundant

than around 1900. Differences in proportions varied

from almost none to over 40 percent (table 11).

Considering the various, sometimes conflicting

sources of bias in using recent uncut stands as a

substitute for prefire-suppression averages, I estimate that the average snag densities in tables 8

through 10 may be higher than historic levels by about

10 percent.

Management Implications ________

In asking how many snags a forestry operation

should leave behind, there are at least two considerations that must be added to the tables presented. The

first question to be asked is that of the desired landscape, for example, how many snags are desirable to

maintain through time? The second question is one of

implementation, for example, how does one assure

that the desired number are actually present at any

given time? Treating the second question first, snag

abundances treated in these analyses are “standing

snags.” In actuality, snags have recruitment and mortality rates (snags are recruited when living trees die,

and die themselves when they fall over) that vary

depending on species and site characteristics (Bull

1983; Lyon 1984; Morrison and Raphael 1993). In

these published studies, most snags standing at the

Table 11—Comparisons (where they could be made) between proportions of uncut plots in various stand-size classes and those

estimated to characterize western Montana forests around 1900 (Losensky 1993, 1997). The “nonstocked” and

“seedling/sapling” classes were combined for both data sets. Losensky’s “larch/Douglas-fir” category was used to

compare with the FIA “larch” category, and Losensky’s “spruce/fir” category to compare with FIA’s “Englemann

spruce” category because the latter did not include subalpine (“noncommercial”) forests, which are probably more

nearly comparable with the FIA “spruce/fir” type. For PP, DF, and WL types, FIA plots were classified as “poletimber”

if they were initially classed as “sawtimber” but lacked trees 14.9 inches d.b.h. or larger.a For ES, LP, and MC types,

FIA plots were classified as “poletimber” if they were initially classed as “sawtimber” but lacked trees 12.9 inches

d.b.h. or larger.a

PP DF WL ES LP MC

Uncut plots

Nonstocked/seedlings/saplings 0.12 0.07 0.00 0.06 0.08 0.06

Poletimber 0.05 0.11 0.30 0.09 0.65 0.12

Sawtimber 0.84 0.82 0.70 0.85 0.27 0.82

Total 1.01 1.00 1.00 1.00 1.00 1.00

About 1900

Nonstocked/seedlings/saplings 0.18 0.28 0.37 0.06 0.57 0.25

Poletimber 0.11 0.33 0.13 0.07 0.31 0.22

Sawtimber 0.71 0.41 0.50 0.87 0.12 0.53

Total 1.00 1.02 1.00 1.02 1.00 1.00

a

PP = ponderosa pine; DF = Douglas-fir; WL = western larch; ES = Englemann spruce; LP = lodgepole pine; MC = mesic conifer.
16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-31. 1999

beginning of the study had fallen by 10 to 20 years

later, although larger snags (more valuable for vertebrates) lasted longer, and few studies followed the fate

of extremely large, persistent species (such as ponderosa pine, western larch, western redcedar) for long

time periods. Allowance must be made for the fact that

snags standing following a particular management

activity will not necessarily last until the subsequent

management activity. Snag recruits must be figured

into the snag management picture, and snag retention

in practice may fall short of the targeted abundance

due to logging, skidding and cable practices, and

firewood cutting (legal or otherwise) subsequent to

logging (Hillis 1993). Adjustments to targeted snag

retention amounts, therefore, may be necessary to

ensure that realized snag maintenance regimes approximate desired regimes.

The answer to the question of what desired snag

abundances ought to be depends crucially on overall

management objectives. If biodiversity and long-term

forest health are highly valued, it seems clear that, at

least on a landscape scale, characteristics of standing

snags (abundances, species, distribution of size classes)

should approximate those occurring historically. Results presented here show that historical cutting practices have reduced snag densities on western Montana

forests, most likely to levels below those that prevailed

prior to fire suppression. Attention must be paid to

snag retention and recruitment in existing and future

silvicultural plans to ensure that snag density does

not continue to decline. There are several ways the

results presented here can be used to that end:

1. Targets for standing snag density can be developed for individual timber cutting units (that is, anywhere timber is cut, snags should be left on the unit in

numbers sufficient to approximate the target amounts).

In this case, snag retention and recruitment targets

would probably best approximate those for uncut

stands in nonstocked or seedling/sapling stages, because these snags would emulate “legacies” (Franklin

and others 1997) remaining some time after a natural

disturbance;

2. Targets for standing snag density can be developed on a landscape scale, considering that fewer

snags may be left following a given timber harvest, but

this may be acceptable if sufficient snags are present

on nearby stands to compensate. This is consistent

with the “clumpy” distribution of snags suggested by

these data. However, it should be kept in mind, if

adopting such a strategy, that sufficient snags should

include not merely the total number, but the number

in various size classes and species. While it is true that

two snags per acre does not mean that every acre must

have two snags, the spatial scale of these underlying

data must be kept firmly in mind. (Although a substantial number of plots used here had 0 snags per

acre, this might not have been the case had sampling

been conducted with larger plots.) The “clumpiness” of

snags observed is highly dependent on the scale used

for sampling. In other words, managing for forests in

which many individual acres have no snags (or large

snags) is perfectly consistent with these data; managing for forests in which entire stands have no snags (or

large snags) is not. In this case, standing snag abundance can be determined empirically for proposed sale

areas during timber cruising, and can be estimated by

extrapolation from tables 8 through 10 for a specified

landscape analysis area that is not cruised.

3. Targets for standing snag density can reflect a

mix of the two scales. One possibility is to consider that

previously treated stands are likely to have lower than

uncut snag densities, and that stands targeted for

treatment should have a higher than uncut snag

abundance to compensate. Another possibility is to

consider that management objectives may not sufficiently prioritize biodiversity to mandate maintaining

100 percent of historically occurring abundance.

Any of these strategies should be seen as useful

interim approaches, because crucial data on snag

recruitment and survival (such as length of time

standing) are, with the exception of Lyon (1984),

lacking for western Montana forests. Such data will

ultimately be useful in developing a more holistic

approach toward snag management (particularly in

guiding snag recruitment strategies) that attempts to

retain biodiversity while simultaneously producing

wood products for human use.

Acknowledgments ______________

This project was supported by the Forest Management Bureau, Montana Department of Natural Resources and Conservation. Thanks to S. W. Woudenberg

and staff at the U.S. Forest Service’s Rocky Mountain

Research Station in Ogden, UT, for assistance in data

retrieval and interpretation, and to J. M. Hillis, B. D.

Long, S. D. McLeod, D. Sirucek, and W. L. Wood for

advice and consultation. Improvements to this manuscript were suggested by J. Adams, D. Adkins, G. R.

Baty, G. N. Gray, J. M. Hillis, W. Johnson, T. M. Kelly,

L. J. Lyon, B. D. Long, S. D. McLeod, D. E. Pearson,

F. B. Samson, and S. W. Woudenberg.

References _____________________

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Station: 64-67.

Bull, E. L.; Parks, C. G.; Torgerson, T. R. 1997. Trees and logs

important to wildlife in the Interior Columbia River Basin.
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Portland, OR: Gen. Tech. Rep. PNW-391. U.S. Department of

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fire regimes in British Columbia: patterns and implications for

conservation. Conservation Biology. 9(3): 636-644.

Fischer, W. C.; Bradley, A. F. 1987. Fire types of western Montana

forest habitat types. Ogden, UT: Gen. Tech. Rep. INT-223. U.S.

Department of Agriculture, Forest Service, Intermountain Research Station. 95 p.

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or landscapes? Ecological Applications. 3: 202-205.

Franklin, J. F.; Berg, D. R.; Thornburgh, D. A.; Tappeiner, J. C.

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J. F., eds. Creating a forestry for the 21st Century: the science of

ecosystem management. Washington, DC: Island Press. 475 p.

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Hillis, Mike. 1998. Unpublished work on file at: U.S. Department of

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disturbance, and ecological management. Canadian Journal of

Botany. 73(6): 918-926.

Keane, R. E.; Arno, S. F. 1993. Rapid decline of white-bark pine in

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climatic section. Version 3. Missoula, MT: U.S. Department of

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change. Res. Pap. INT-330. Ogden, UT: U.S. Department of

Agriculture, Forest Service, Intermountain Forest and Range

Experiment Station. 17 p.

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marten habitat in the Northern Sierra Nevada. In: Davis, J. W.;

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

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18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-31. 1999

Appendix A: dBASE Filter Used to Distinguish Cut and Uncut Stands ______

* Program STNDHIST

* This program assigns a four-character name for each STAND identified in the USFS FIA Plot data

* Based on the location history

* Coded “HIST” only if all other variables indicate a ‘pristine’ stand

* Coded “HUMN” only if timber cutting is not primary human use, but others are

* Coded “CUT” if any timber cutting evident

* For codes, see ‘variable LHIS’ in Woudenberg and Farrenkopf (1988)

replace all STATUS with ‘HIST’ for STNDORG = 1 .and. CUTHISTORY = 0

* OK to use for historical pattern if no human disturbance (code 1) or code is

* 610 (animal damage), 620 (insect damage), 640 (disease), 680 (weather damage), 681 (wind damage)

* 690 (fire damage) AND Cuthistory = 0 (no cutting history)

replace all STATUS with ‘HUMN’ for STNDORG <> 1 .or. LHIS > 699

* Stand has artificial regen (stand or not 1), or is road, cleared, converted, and so forth.

* but not predominately affected by timber harvesting

replace all STATUS with ‘CUT’ for LHIS = 13 .or. LHIS = 14 .or. LHIS = 15 .or. LHIS = 16 .or. LHIS = 154 .or.

LHIS = 153 .or. LHIS = 10 .or. CUTHISTORY > 0

* Stand has been cut, either clearcut (13), selective (14), other (15), salvage (16), xmas tree (153), or post/pole (154),

or (10) This designation takes precedence (for example, can overwrite either of the previous two)

Appendix B: Habitat Type Groups ____________________________________

(Green and others 1992) Codes are habitat types (Pfister and others 1977).

Habitat type group A: warm, dry

104, 105, 130, 140, 141, 142, 160, 161, 162, 170, 171, 172, 180, 181, 210, 220, 230, 350, 320, 321, 311

Habitat type group B: moderately warm, dry

324, 340, 010, 250, 260, 261, 262, 282, 310, 312, 131, 322, 324, 340, 330, 313, 320, 430

Habitat type group C: moderately cool, dry

360, 370, 356, 351, 311, 390, 380, 280, 281, 283, 292, 323, 360, 370, 510, 750

Habitat type group D: warm, moist

520, 521, 522, 523, 530, 531, 533, 570, 571, 572, 532

Habitat type group E: cool, moist

421, 422, 420, 620, 621, 622, 623, 624, 625, 660, 661, 662, 670, 680, 832, 740

Habitat type group F: wet

410, 440, 480, 550, 610, 630, 631, 632, 650, 651, 653, 654

Habitat type group G: moderately cool, moist

290, 291, 591, 293, 590, 592, 470

Habitat type group H: cool, moderately dry

450, 640, 641, 663, 690, 691, 692, 710, 720, 731, 920, 930, 940, 792, 910, 920, 930, 940, 950, 960

Habitat type group I: cold, moderately dry

733, 780, 791, 730, 732, 820, 830, 831, 791

Habitat type group J: dry

840, 850, 870, 860, 810
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-31. 1999 19

Appendix C: Potential Vegetation Groups _____________________________

Codes are habitat types (Pfister and others 1977)

Warm, dry, frequency low intensity fire potential vegetation group

All habitat types < 260, plus 311, 321, 323, 324, 262, and 340

Warm, moist mixed fire regime potential vegetation group

260, 261, 262, 281, 282, 283, 290, 291, 292, 312, 313, 330, 350, 505, 506, 507, 508, 510, 512, 515, 592

Cool, moist-to-dry, infrequent, stand-replacing fire potential vegetation group

511, 590, 591, 517, 518, 519, 520, 521, 522, 524, 526, 529, 420, 421, 470, 620, 621, 622, 623, 624, 625, 635, 636,

637, 660, 661, 662, 670, 671, 672, 673, 674, 675, 676, 677, 680, 681, 682, 685, 686, 687, 691, 692

Maritime, very moist cedar/hemlock potential vegetation group

530, 531, 532, 533, 534, 535, 541, 542, 545, 546, 547, 548, 550, 555, 560, 570, 571, 572, 573, 574, 576, 577, 578

Lodgepole pine sites (climax or long-term disclimax) potential vegetation group

450, 640, 663, 693, 713, 732, all over 900

Very cold whitebark pine, infrequent, low intensity fires

800-899

Appendix D: Attributes in Different Cover Types, Habitat Type Groups, and

Potential Vegetation Groups ________________________________________

Cover type PP DF WL ES SF LP MC SA HW

Mean elevation (x100) 43 49 43 52 60 56 39 71 44

Mean slope 30 40 28 27 36 31 34 38 8

Percent cool aspects (E, NE, N) 17 36 43 43 46 28 51 18 9

Percent warm aspects (S, SW, W) 54 34 25 19 29 39 25 55 15

n 132 799 112 63 365 319 76 33 33

Habitat type group AB CD EF G H IJ

Moderate Moderate Moderate Cool, Cold,

Warm, warm, cool, Warm, Cool, cool, moderate moderate

dry dry dry moist moist Wet moist dry dry Cold

Mean elevation (x100) 50 46 55 39 54 50 45 60 73 72

Mean slope 36 38 38 35 34 22 31 37 30 45

Percent cool aspects (E, NE, N) 23 32 30 50 43 33 49 28 36 41

Percent warm aspects (S, SW, W) 50 40 38 22 27 12 20 44 31 39

n 137 426 201 201 410 42 85 252 59 41

Potential vegetation groups Warm, Warm, Cool, Cold

dry, moist, stand- Very Whitefrequent mixed replacing moist Lodgepole bark

fire fire fire maritime pine pine

Mean elevation (x100) 48 49 54 39 52 70

Mean slope 37 38 34 36 27 39

Percent cool aspects (E, NE, N) 28 33 38 51 36 42

Percent warm aspects (S, SW, W) 43 37 31 21 27 32

n 416 407 731 144 140 110

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