USDA Forest Service RMRS-RN-35. 2008. 1

United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

Research Note

RMRS-RN-35

September 2008

Introduction

For the purpose of vegetation management, soil seed 

banks are viable seeds stored in litter (Oi horizon), 

duff (Oe+a horizon), or mineral soil that can germinate when germination requirements are met (Bakker 

and others 1996, Thompson 1987). Seed banks provide 

propagules that may influence plant community changes after disturbance events and management activities 

(van der Valk and Pederson 1989). Not all plant species 

form large and persistent soil seed banks. Species that 

are short-lived aboveground and associated with disturbance often develop the largest and most persistent seed 

banks (Koniak and Everett 1982; Roberts 1981; Wienk 

and others 2004). For example, the native biennial 

spreading fleabane (Erigeron divergens) and the nonnative biennial common mullein (Verbascum thapsus) 

are abundant in seed banks of ponderosa pine (Pinus 

ponderosa) forests near Flagstaff, Arizona (Abella and 

others 2007, Korb and others 2005). However, some native perennials, such as White Mountain sedge (Carex 

geophila), mountain muhly (Muhlenbergia montana), 

and muttongrass (Poa fendleriana), also occur in these 

seed banks.

Soil seed banks are important for vegetation management in ponderosa pine forests in at least four ways. 

First, seed banks may provide on-site seed sources for 

species considered undesirable (for example, exotic 

species) for site colonization after management activities such as tree thinning. Second, they may provide 

seed sources for early successional species and species 

considered desirable (for example, perennial grasses) 

that hasten vegetation recovery after disturbance. Third, 

seed banks can provide seedlings for the establishment 

and maintenance of some aboveground plant populations in the absence of major disturbance. Fourth, 

seed banks lacking desirable species may indicate that 

seeding or other treatments are necessary to meet management objectives for understory vegetation. Locally 

calibrated estimates of seed bank size and composition 

could assist resource managers in determining possible understory responses to management activities. 

Knowledge of these seed bank characteristics could be 

used to predict which exotic species could become management concerns or for deciding whether to seed native 

species based on their abundance in seed banks (Glass 

1989). Although on- or off-site seed dispersal from existing vegetation is also important (Wienk and others 

2004), several studies have found partial correlations 

between seed bank composition and species colonizing 

sites after disturbance (Korb and others 2005, Springer 

and Hastings 2004).

Estimating Soil Seed Bank Characteristics 

in Ponderosa Pine Forests Using 

Vegetation and Forest-Floor Data

Scott R. Abella and Judith D. Springer

Abstract—Soil seed banks are important for vegetation management because they contain propagules of 

species that may be considered desirable or undesirable for site colonization after management and disturbance 

events. Knowledge of seed bank size and composition before planning management activities facilitates proactive 

management by providing early alerts of exotic species presence and of abilities of seed banks to promote 

colonization by desirable species. We developed models in ponderosa pine (Pinus ponderosa) forests in northern 

Arizona to estimate the size and richness of mineral soil seed banks using readily observable vegetation and forestfloor characteristics. Regression models using three or fewer predictors explained 41 to 59 percent of the variance in 

0- to 2-inch (0- to 5-cm) seed densities of total and native perennial seed banks. Key predictors included aboveground 

plant species richness/10.8 ft2

 (1 m2), litter weight and thickness, and tree canopy type (open or closed). Both total 

and native perennial seed banks were larger and richer in plots containing: (1) species-rich understories, (2) sparse 

litter, and (3) tree canopy openings. A regression tree model estimated that seed bank density of native perennials is 

14-fold greater if aboveground plant richness exceeds eight species/10.8 ft2, forest-floor leaf litter is < 1 inch (2.5 cm) 

thick, and tree canopies are open.

Scott R. Abella, Assistant Research Professor, Public Lands 

Institute and School of Life Sciences, University of Nevada Las 

Vegas, Las Vegas, NV.

Judith D. Springer, Research Specialist, Sr., Ecological 

Restoration Institute, Northern Arizona University, Flagstaff, AZ.
2 USDA Forest Service RMRS-RN-35. 2008.

Our objective was to develop a first approximation 

of models to estimate seed bank density and species 

richness using soil and aboveground vegetation variables that are more easily measured in the field than the 

seed bank characteristics themselves. Our models were 

developed for ponderosa pine forests in a local study 

area. Thus, the models require more extensive testing 

before extrapolation to other ponderosa pine forests 

that potentially differ from our study area in species 

pools and in other factors affecting seed banks.

Methods

Study Area

We conducted this study on forty-five, 82 × 66 ft (25 

× 20 m), 0.12-acre (0.05-ha) plots in the southern half 

of the Northern Arizona University Centennial Forest. 

The Centennial Forest consists of 640-acre (259-ha) 

sections that alternate with U.S. Forest Service sections within the Coconino National Forest, about 5 to 

15 mi (8 to 24 km) southwest of the city of Flagstaff 

in northern Arizona. The maximum distance between 

plots was 10 mi (16 km). Forests in the study area 

are pure ponderosa pine with occasional Gambel oak 

(Quercus gambelii). Understory vegetation is dominated by grasses, sedges, and forbs. Plot elevations 

ranged from 7,072 to 7,472 ft (2,156 to 2,278 m), with 

slope gradients less than 10 percent. Plots were located 

and stratified by a terrestrial ecosystem classification 

following methods in Abella and others (2007). Five 

plots were located within each of three randomly 

selected mapping units of each of the 536, 570, and 

585 terrestrial ecosystem survey soil types. Soils 

in these types are primarily classified as Typic and 

Mollic Eutroboralfs (Miller and others 1995). Annual 

precipitation at the Flagstaff airport, about 10 mi 

(16 km) east of the study area, averages 21 inches 

(54 cm). Approximately 50 percent of this precipitation is snow (1950 to 2006 records; Western Regional 

Climate Center, Reno, NV). July high temperatures 

average 82o

F (28oC), and January low temperatures 

average 16o

F (-9oC).

Field Sampling

Plots spanned a range of overstory characteristics 

and were located either in openings or in dense patches 

of young trees (fig. 1). Locations for plots were established by randomly selecting a geographic coordinate 

within each soil mapping unit, and then selecting 

vegetation patches closest to that coordinate using 

field reconnaissance. Density of trees greater than 0.4 

inches (1 cm) in diameter at breast height ranged from 

0 to 1,392 trees/acre (3,440 trees/ha) among plots. 

Basal area ranged from 0 to 274 ft2

/acre (63 m2/ha). 

We measured the diameter of every tree (> 0.4 inches 

[1 cm] in diameter) on each plot and classified tree 

canopy types as primarily open (usually near old trees 

> age 120 years based on increment boring) or closed 

(dense tree patches). These general canopy types are 

readily recognized in the field (fig. 1) and have been 

used in several previous studies (for example, Vose 

and White 1987).

In each plot, we established a total of fifteen, 10.8-ft2

(1-m2

) subplots per plot. Subplots were systematically 

located at 2, 16, 41, 66, and 80 ft (0.5, 5, 12.5, 20, 

and 24.5 m) along the southern, central, and northern 

82-ft (25-m) plot lines. In these subplots, we measured 

litter (Oi horizon) thickness to the nearest 0.04 inch 

(0.1 cm) and visually estimated areal percent cover (at 

0.1, 0.25, 1, or 5 percent intervals) of each plant species rooted in the subplots. We estimated tree canopy 

cover to the nearest 5 percent using a densitometer 

(Geographic Resource Solutions, Arcata, CA). We 

also collected a 0- to 2-inch (0- to 5-cm) sample of the 

mineral soil seed bank in each subplot using a 1.6-inch 

(4.2-cm) diameter metal corer. Each sample was 4.3 in3

(70 cm3

), and we composited samples on a plot basis 

for a plot seed bank sample of 64 in3

 (1,050 cm3). O 

horizons can trap seeds (Abella and Covington 2007; 

Korb and others 2005), but we focused on mineral soil 

seed banks because O horizons were sparse on some 

plots. From soil pits at the southwestern and northeastern plot corners, we collected a composite 0- to 

6-inch (0- to 15-cm) mineral soil sample for determining soil texture (hydrometer method; Dane and Topp 

2002). We measured litter weight by collecting litter 

samples in 5.4-ft2

 (0.5-m2) frames at soil pit locations. 

We then oven dried these samples at 158o

F (70oC) for 

24 hours.

Plot and seed bank sampling occurred from June 

through August 2003. Based on published phenologies in the region (Clary and Kruse 1979), seedbank 

collections occurred before most species disperse 

seeds in late summer or fall. Thus, collections likely 

primarily represent the persistent seed bank (Baskin 

and Baskin 1998), although it is possible that some 

early flowering species contributed current-year seeds 

to samples.

Greenhouse Seed Bank Procedures

On the same day each sample was collected, we 

placed 7 in3

 (120 cm3) of the composite seed bank sample from each plot in a greenhouse maintained at 75o

F 
USDA Forest Service RMRS-RN-35. 2008. 3

(24oC). We placed samples in a layer about 0.4 inches 

(1 cm) thick on top of 18 in3

 (300 cm3) of sterile potting soil in 43-in3

 (700-cm3) square pots. We randomly 

arranged pots on greenhouse benches and watered the 

samples daily. The greenhouse was maintained with 

natural lighting, but we also provided samples with 

four hours of supplemental artificial lighting during 

fall and winter months (October through February). 

We identified and counted emerging seedlings (as 

a measure of seed density) every two weeks for six 

months. To meet possible chilling requirements for 

some species, we stored the remainder of samples 

(57 in3

, 930 cm3 from each plot) at 23oF (-5oC) for 

four to six months. We then placed 7 in3

 (120 cm3) of 

chilled sample from each plot in each of four separate pots and treated these samples procedurally the 

same as the unchilled samples. Seed density between 

chilled and unchilled samples was not appreciably different, so we pooled data from these separate samples 

into 37-in3

 (600-cm3) plot samples for calculating 0- 

to 2-inch (0- to 5-cm) seed densities/ft2

 (0.09 m2) and 

species richness/0.13 ft2

 (0.01 m2). We report richness 

for this sample area computed from our sample volume because species-accumulation curves are needed 

for extrapolating richness to larger volumes and areas 

(Bigwood and Inouye 1988). Plant nomenclature and 

classification of species as native or exotic followed 

NRCS (2004).

Statistical Analysis

The seed bank characteristics we analyzed in this 

study included: (1) total seed densities/ft2 (0.09 m2

), 

(2) total species richness/0.13 ft2 (0.01 m2

), (3) native 

perennial seed densities/ft2

, and (4) native perennial 

species richness/0.13 ft2

. We first examined bivariate 

correlations between these seed bank characteristics and soil and aboveground vegetation variables 

(table 1). We then used multiple regression and regression trees as exploratory models to estimate seed 

bank characteristics from these variables with a sample size of 45 plots. Analyses were performed with 

the software JMP (SAS Institute 2002). For multiple 

regression, we used a forward stepwise procedure 

for identifying predictor variables. Regression trees 

are nonparametric models that partition data into 

increasingly homogenous subsets and provide dichotomous keys to estimate a dependent variable at 

different values of predictor variables (Breiman and 

others 1984). The JMP version allows users to determine the number of splits, so we stopped splitting 

Figure 1. Examples of ponderosa pine canopy types below which we collected soil seed bank samples 

southwest of Flagstaff, Arizona. Foreground: open patch dominated by large, old trees. Background: dense 

canopy of small-diameter trees. Open patches contained larger and richer soil seed banks than patches of 

dense, small trees. Photo by S.R. Abella, June 11, 2003 (35o08’54”N, 111o43’43”W).
4 USDA Forest Service RMRS-RN-35. 2008.

when adding more predictors resulted in increases of 

< 0.05 in R2

. We employed JMP’s k-fold crossvalidation (k = 5) to compute a cross-validated overall R2

(1 - [crossvalidated sum of squares error / corrected 

sum of squares]).

Results

Of 49 total species detected in seed bank samples, 

10 percent were exotic and 43 percent were native 

perennials. Total average seed density was 54 seeds/

ft2

 (0- to 2-inch layer; 583 seeds/m2) on closed tree 

canopy plots and 149 seeds/ft2

 (1,611/m2) on open 

plots. Exotic species contributed 19 percent of these 

total seed densities on both closed and open plots. 

Common mullein constituted 78 (closed plots) and 

88 (open plots) percent of total average exotic seed 

density. Native perennials comprised 15 (closed plots) 

and 22 (open plots) percent of the total average seed 

density for all species. The most abundant native perennials in seed banks included White Mountain sedge, 

spreading sandwort (Arenaria lanuginosa), mountain 

muhly, muttongrass, and lobe-leaf groundsel (Packera 

multilobata).

Total seed densities in mineral soil seed banks were 

positively correlated with aboveground plant species 

richness/10.8 ft2

 (1 m2; Pearson r = 0.54) and plant 

cover (r = 0.34). However, these seed densities were 

negatively correlated with ponderosa pine tree density 

(r = -0.40), basal area (r = -0.39), and litter thickness 

and weight (r = -0.47 for both). Similar to total seed 

densities, native perennial seed densities exhibited 

the following correlations with vegetation and forestfloor variables: aboveground plant richness (r = 0.54) 

and cover (r = 0.45), ponderosa pine tree density (r = 

-0.38) and basal area (r = -0.27), and litter thickness 

(r = -0.39) and weight (r = -0.40). Total and native 

perennial species richness in seed banks had relationships with these vegetation and forest-floor variables 

similar to those of seed densities. Additionally, neither 

seed density nor species richness were strongly correlated (|r| <0.32) with 0- to 6-inch (0- to 15-cm) percent 

sand, silt, or clay.

Aboveground species richness, tree canopy type 

(open or closed), and litter weight explained 49 percent of the variance in seed bank total seed densities 

and species richness in multiple regression models (table 2). These three predictors explained 41 percent of 

the variance in native perennial seed densities. Fiftytwo percent of the variance in native perennial species 

richness was explained by these predictors and tree 

canopy percent cover.

Regression trees explained 47 to 59 percent of the 

variance in seed density using two or three predictors 

(fig. 2). Similar to multiple regression, aboveground 

species richness and tree canopy type were important 

predictors in these models. Litter thickness, rather than 

litter weight as in multiple regression, also was an important predictor. The greatest seed densities occurred 

on plots containing thin litter layers, high aboveground 

plant richness, and open canopies.

Discussion

The basic insights gained from the regression models include: (1) densities and species richness of seeds 

in mineral soils increased with decreasing forest-floor 

litter weight and thickness; (2) both total and native 

perennial seed densities and species richness in mineral soils were highest in areas with high aboveground 

plant species richness/10.8 ft2

 (1 m2); and (3) patches 

with open canopies, typically near old trees, supported 

Table 1. Correlation matrix (Pearson r) between seed bank response (variables 1 through 4) and explanatory variables (5 through 9) 

included in final multiple regression or regression tree models. Descriptive statistics by tree canopy type are provided for each 

variable at the bottom of the table.

1 2 3 4 5 6 7 8 9

1 Total seeds/ft2 1

2 Total species/0.13 ft2 0.89 1

3 Native perennial seeds/ft2 0.53 0.55 1

4 Native perennial species/0.13ft2 0.44 0.57 0.76 1

5 Plant species/10.8 ft2 0.54 0.59 0.54 0.59 1

6 Litter wt. (tons/acre) -0.47 -0.51 -0.40 -0.39 -0.34 1

7 Litter thickness (inches) -0.47 -0.38 -0.39 -0.25 -0.53 0.22 1

8 Canopy type (open, closed) -0.53 -0.43 -0.48 -0.49 -0.50 0.11 0.54 1

9 Canopy cover (percent) -0.29 -0.19 -0.26 -0.18 -0.42 0.10 0.76 0.67 1

Open canopy, mean±SD 139±98 7.1±4.1 33±32 2.1±1.6 7.2±2.3 2.9±2.3 0.8±0.4 – 27±15

Closed canopy, mean±SD 49±46 3.7±3.0 8±13 0.8±0.8 4.5±2.5 3.3±1.5 1.3±0.4 – 52±14
USDA Forest Service RMRS-RN-35. 2008. 5

Table 2. Multiple regression models estimating seed densities and species richness in 0- to 2-inch (0- to 5-cm) soil seed 

banks in ponderosa pine forests southwest of Flagstaff, Arizona.

Predictora Coefficient SE t-statistic Prob. >t Cum. R2(b)

–––––—–––––––––––––––––– Total seeds/ft2 –––––––––––––––––––––––

Interceptc 100.9756 35.1 2.88 <0.01 –––

Plant species/10.8 ft2 7.0217 4.2 1.68 0.10 0.29

Tree canopy type (0, 1) 32.2178 10.9 2.96 <0.01 0.39

Litter weight (tons/acre) -15.5909 5.3 -3.19 <0.01 0.49

–––––––––––––––––––––– Total species/0.13 ft2 ––––––––––––––––––––

Intercept 4.7100 1.6 2.91 <0.01 –––

Plant species/10.8 ft2 0.5047 0.2 2.63 0.01 0.35

Litter weight (tons/acre) -0.7338 0.3 -2.99 <0.01 0.45

Tree canopy type (0, 1) 0.8156 0.5 1.63 0.11 0.49

–––––—––––––––––––– Native perennial seeds/ft2 ––––––––––––––––––

Intercept 15.0924 11.4 1.32 0.19 –––

Plant species/10.8 ft2 2.7957 1.4 2.06 0.04 0.29

Tree canopy type (0, 1) 7.5809 3.5 2.15 0.04 0.35

Litter weight (tons/acre) -3.5714 1.7 -2.07 0.04 0.41

–––––—–––––––––––– Native perennial species/0.13 ft2 –––––––––––––

Intercept -0.2001 0.7 -0.27 0.78 –––

Plant species/10.8 ft2 0.2038 0.1 3.05 <0.01 0.35

Tree canopy type (0, 1) 0.6667 0.2 3.13 <0.01 0.40

Tree canopy cover (percent) 0.0245 0.0 2.31 0.03 0.47

Litter weight (tons/acre) -0.1662 0.1 -1.96 0.06 0.52

a

Plant species richness represents aboveground vegetation. Tree canopy type corresponds to open old-tree patches (0) or dense 

patches of small trees (1). b

Cumulative proportion of variance explained for the dependent (y) variable based on stepwise predictor selection.

c

As an example, the equation for estimating total seeds/ft2 is the following: y = 100.9756 + 7.0217(aboveground plant species/10.8 ft2) 

+ 32.2178(canopy type) – 15.5909(litter weight). Metric conversions are as follows: seed bank seeds/ft2 = seeds/0.09 m2, seed 

bank species/0.13 ft2 = species/0.01 m2, and aboveground plant species/10.8 ft2 = species/m2.

Figure 2. Regression trees estimating 0- to 2-inch (0- to 5-cm) soil seed bank: (a) total seeds/ft2 (0.09 m2) and 

(b) native perennial seeds/ft2

 (0.09 m2) based on litter thickness (Oi horizon), aboveground plant species 

richness/10.8 ft2 (1 m2), and tree canopy type. The cumulative proportion of variance explained (R2) is shown 

at each division. Estimated values at each node are the mean ± 1 standard deviation. Cross-validated R2

 for 

whole models was 0.41 for (a) and 0.39 for (b).
6 USDA Forest Service RMRS-RN-35. 2008.

larger and richer soil seed banks than patches of denser 

canopies containing many closely spaced small trees 

(figs. 1, 2).

Several factors could cause mineral soil seed 

banks to be smaller in areas with thick and heavy litter. Seeds may become trapped in litter so that fewer 

reach mineral soil (Korb and others 2005) or retain 

viability. Litter abundance increases in areas of high 

pine density, which may decrease seed banks by decreasing aboveground vegetation that provides seed 

inputs. This conjecture is consistent with the strong 

positive relationship we observed between seed bank 

density and aboveground plant richness. Aboveground 

richness itself was negatively correlated with pine 

density (r = -0.66) and basal area (r = -0.52). Our finding of larger and richer seed banks in openings usually 

near large, old trees concurs with Vose and White’s 

(1987) data on seed rain by understory vegetation. 

These authors found that grass + forb seed rain averaged more than 10 times greater below openings than 

below dense tree canopies in ponderosa pine forests 

near Flagstaff, Arizona (fig. 3). These data suggest 

that many seeds in our seed bank samples probably 

originated from relatively recent inputs by aboveground vegetation. However, some seeds, such as those 

of common mullein that are long-lived in seed banks, 

were possibly deposited in our samples long ago (Warr 

and others 1993).

Regression models explained 41 to 59 percent of 

the variance in seed bank densities using three or 

fewer predictor variables (table 2, fig. 2). Imperfect 

measurement of predictors, plot-scale disturbance history, or other unmeasured factors could have resulted 

in unexplained variance. Our method of locating plots 

was not intended to assess the effects of disturbance 

history on seed banks. Increasing our understanding of 

relationships between disturbance history and contemporary seed banks would probably improve our ability 

to estimate seed bank size and composition (Korb and 

others 2005).

Management Implications

Our study and previous research (Vose and White 

1987) suggests that positive feedbacks exist between 

soil seed banks and aboveground vegetation. Seed 

banks, overall and specifically for native perennials, are 

richer and larger in patches where aboveground understory vegetation is most abundant. Unfortunately, this 

indicates that seed banks are less useful for increasing 

Figure 3. Seed rain by grasses and forbs among canopy types from fall 1982 to fall 1983 after a 

prescribed burn in ponderosa pine forests in the Fort Valley Experimental Forest, northern Arizona. 

Seed rain was greatest in openings that contained abundant understory vegetation. Data from Vose 

and White (1987).
USDA Forest Service RMRS-RN-35. 2008. 7

native understory vegetation in dense pine stands 

where existing understory vegetation is most depauperate. Our findings suggest that seed banks of some 

native perennials can be enhanced by increasing the 

vigor and richness of understory vegetation containing 

these species. Management techniques for accomplishing this could include tree thinning, reducing grazing 

pressure especially during periods of seed production, 

prescribed burning timed to maintain seed production, 

or actively planting or seeding understory species.

Acknowledgments

We thank several individuals for assistance during this study: students and staff at the Ecological 

Restoration Institute helped collect seed bank samples; Brad Blake and Phil Patterson maintained the 

Northern Arizona University research greenhouse; 

Keith Pajkos, Arizona State Lands forester, and J.J. 

Smith, Centennial Forest manager, permitted us to 

perform this study and provided valuable insights on 

local forest ecology; Randi Walker and Sharon Altman 

formatted tables 1 and 2; Julie Korb and Jill Craig 

reviewed the manuscript; and Rudy King, Rocky 

Mountain Research Station statistician, provided a 

statistical review and helped with statistical analyses. 

We also value Wally Covington’s encouragement and 

support of distributing research findings to resource 

managers. This study was funded by the U.S. Forest 

Service and the Ecological Restoration Institute.

References

Abella, Scott R.; Covington, W. Wallace. 2007. Forest-floor 

treatments in Arizona ponderosa pine restoration ecosystems: no short-term effects on plant communities. 

Western North American Naturalist. 67:120-132.

Abella, Scott R.; Springer, Judith D.; Covington, W. Wallace. 

2007. Seed banks of an Arizona Pinus ponderosa landscape: responses to environmental gradients and fire 

cues. Canadian Journal of Forest Research. 37:552-567.

Bakker, J.P.; Poschlod, P.; Strykstra, R.J.; Bekker, R.M.; 

Thompson, K. 1996. Seed banks and seed dispersal: 

important topics in restoration ecology. Acta Botanica 

Neerland. 45:461-490.

Baskin, Carol C.; Baskin, Jerry M. 1998. Seeds: ecology, 

biogeography, and evolution of dormancy and germination. San Diego, CA: Academic Press. 666 p.

Bigwood, Douglas W.; Inouye, David W. 1988. Spatial pattern analysis of seed banks: an improved method and optimized sampling. Ecology. 69:497-507.

Breiman, Leo; Friedman, Jerome H.; Olshen, Richard A.; 

Stone, Charles J. 1984. Classification and regression 

trees. Belmont, CA: Wadsworth Inc. 358 p.

Clary, Warren P.; Kruse, William H. 1979. Phenology and rate 

of height growth of some forbs in the southwestern ponderosa pine type. Res. Note RM-376. Fort Collins, CO: 

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

Mountain Forest and Range Experiment Station. 8 p.

Dane, Jacob H.; Topp, G. Clarke, eds. 2002. Methods of 

soil analysis. Part 4. Physical methods. Soil Science 

Society of American Book Series No. 5. Madison, WI: 

Soil Science Society of America, Inc. 1692 p.

Glass, Steve. 1989. The role of soil seed banks in restoration and management. Restoration and Management 

Notes. 7:24-29.

Koniak, Susan; Everett, Richard L. 1982. Seed reserves 

in soils of successional stages of pinyon woodland. 

American Midland Naturalist. 108:295-303.

Korb, Julie E.; Springer, Judith D.; Powers, Stephanie R.; 

Moore, Margaret M. 2005. Soil seed banks in Pinus ponderosa forests in Arizona: clues to site history and restoration potential. Applied Vegetation Science. 8:103-112.

Miller, Greg; Ambos, Norm; Boness, Pat; Reyher, Deanna; 

Robertson, George; Scalzone, Ken; Steinke, Rory; 

Subirge, Tom. 1995. Terrestrial ecosystems survey 

of the Coconino National Forest. U.S. Department of 

Agriculture, Forest Service, Southwestern Region. 

405 p.

NRCS [U.S. Department of Agriculture, Natural Resources 

Conservation Service]. 2004. The PLANTS database. 

Baton Rouge, LA: National Plant Data Center. http://

plants.usda.gov/

Roberts, H.A. 1981. Seed banks in soils. Advances in 

Applied Biology. 6:1-55.

SAS Institute. 2002. JMP version 5 user’s guide. Cary, NC: 

SAS Institute, Inc. 263 p.

Springer, Judy; Hastings, Rob. 2004. Soil seed banks in 

coniferous, fire-adapted, and southwestern U.S. ecosystems: an annotated bibliography. Flagstaff, AZ: 

Ecological Restoration Institute. 67 p.

Thompson, K. 1987. Seeds and seed banks. New Phytologist. 

106 (Suppl.):23-34.

van der Valk, A.G.; Pederson, Roger L. 1989. Seed banks 

and the management and restoration of natural vegetation. In: Leck, Mary Allessio; Parker, V. Thomas; 

Simpson, Robert L., eds. Ecology of soil seed banks. 

NY, NY: Academic Press: 329-346.

Vose, James M.; White, Alan S. 1987. Processes of understory seedling recruitment 1 year after prescribed fire in 

an Arizona ponderosa pine community. Canadian Journal 

of Botany. 65:2280-2290.

Warr, Susan J.; Thompson, Ken; Kent, Martin. 1993. Seed 

banks as a neglected area of biogeographic research: a 

review of literature and sampling techniques. Progress in 

Physical Geography. 17:329-347.

Wienk, Cody L.; Sieg, Carolyn Hull; McPherson, Guy R. 

2004. Evaluating the role of cutting treatments, fire and 

soil seed banks in an experimental framework in ponderosa pine forests of the Black Hills, South Dakota. Forest 

Ecology and Management. 192:375-393.
You may order additional copies of this publication by sending your 

mailing information in label form through one of the following media. 

Please specify the publication title and series number.

Fort Collins Service Center

Telephone (970) 498-1392

FAX (970) 498-1122

E-mail rschneider@fs.fed.us

Web site http://www.fs.fed.us/rm/publications

Mailing address Publications Distribution

Rocky Mountain Research Station

240 West Prospect Road

Fort Collins, CO 80526

Rocky Mountain Research Station

240 W. Prospect Road

Fort Collins, Colorado 80526

The Rocky Mountain Research Station develops scientific information 

and technology to improve management, protection, and use of the 

forests and rangelands. Research is designed to meet the needs of 

the National Forest managers, Federal and State agencies, public and 

private organizations, academic institutions, industry, and individuals. 

Studies accelerate solutions to problems involving ecosystems, range, 

forests, water, recreation, fire, resource inventory, land reclamation, 

community sustainability, forest engineering technology, multiple use 

economics, wildlife and fish habitat, and forest insects and diseases. 

Studies are conducted cooperatively, and applications may be found 

worldwide.

Station Headquarters

Rocky Mountain Research Station

240 W Prospect Road

Fort Collins, CO 80526

(970) 498-1100

Research Locations

Rocky

 Mountain

 Research Station

Reno, Nevada

Albuquerque, New Mexico

Rapid City, South Dakota

Logan, Utah

Ogden, Utah

Provo, Utah

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its 

programs and activities on the basis of race, color, national origin, age, disability, 

and where applicable, sex, marital status, familial status, parental status, religion, 

sexual orientation, genetic information, political beliefs, reprisal, or because all 

or part of an individual’s income is derived from any public assistance program. 

(Not all prohibited bases apply to all programs.) Persons with disabilities who 

require alternative means for communication of program information (Braille, 

large print, audiotape, etc.) should contact USDA’s TARGET Center at (202) 

720-2600 (voice and TDD). To file a complaint of discrimination, write to USDA, 

Director, Office of Civil Rights, 1400 Independence Avenue, S.W., Washington, 

DC 20250-9410, or call (800) 795-3272 (voice) or (202) 720-6382 (TDD). USDA 

is an equal opportunity provider and employer.

Flagstaff, Arizona

Fort Collins, Colorado

Boise, Idaho

Moscow, Idaho

Bozeman, Montana

Missoula, Montana