![](media/image1.wmf){width="3.083464566929134in"
height="5.9417322834645665in"}

**Executive Summary**

This project investigated the relationships among fuels, wildfire
severity, and non-native plants in three mixed conifer forests in the
Rocky Mountains of Colorado and New Mexico. Our research addressed the
following questions:

-   *To what extent is fire severity related to stand conditions?*

The relationship between fuel variables and fire severity varied
substantially among our three study sites, but fuel treatment by
prescribed fire consistently appeared as an informative and significant
mitigating variable. Stand conditions that can be managed by mechanical
thinning, such as tree density and mean tree diameter, also
significantly influenced the severity of all three fires: fire severity
decreased with increases in mean tree diameter and/or decreases in stand
density. Our models explained a significant portion of the variability
in fire severity, but explanatory power varied among sites. Historic
fire regime may influence the degree to which fire behavior and severity
can be explained by fuel variables versus weather variables and suggests
caution in applying fuel treatments outside of short fire return
interval systems.

-   *Is fire severity a predictor of non-native abundance?*

Fire severity was a consistent predictor of higher non-native species
cover at all three fires and was a more important predictor of
non-native species establishment than other abiotic variables. Our
results suggest that wildfires may be one of the more important
mechanisms for continued spread of non-native species in the western US.

-   *Do disturbances created by pre-fire treatments provide a source for
    post-fire invasion by non-native species?*

The influence of fuel treatments is less than that of wildfire on the
establishment of non-native species, but there is a potential for higher
establishment of non-native species in thinned areas. We found a
slightly higher incidence of non-native species in the thinned areas of
one of our study sites. However, we found no difference in non-native
species cover between treated and untreated areas at the other two
sites.

-   *Does post-fire seeding mitigate invasion risk?*

Establishment and spread of non-native species following wildfire may be
deterred through appropriate post-fire management actions. However seed
mixes are often contaminated with non-native species and their
application on landscape scales may introduce a large number of
non-native weed seeds. Seed mixes were extensively applied by air at two
of our sites where we found a positive association between the cover of
non-native species and seeded grasses. However, at our other site, seed
mixes were intensively applied by hand or seed drill on a more limited
scale. Here post-fire seeding successfully deterred non-native species
establishment, similar to the effect of abundant native cover. Post-fire
treatments that encourage high vegetative cover, without introducing new
non-native species in contaminated seed mixes, would best prevent
further spread of non-native species.

-   *Do invaded areas represent greater post-fire fuel hazard?*

The influence of non-native plant species on post-fire flammability was
not consistent across our three study sites. Non-native species
significantly increased post-fire flammability at one site, decreased
flammability at another site, but had no effect at the third site. The
suite of non-native species found at each site varied and different
species will uniquely influence the fuel bed parameters that determine
flammability, such as surface-area-to-volume ratio and relative packing.
However, differential species influences on flammability may become more
pronounced as total vegetation cover increases over time.

Our findings highlight some of the ecological risks and benefits
associated with pre- and post-fire management activities and our
multi-scale sampling method provided an excellent data source and input
for spatial models. Integration of remotely sensed data, geographic
information systems, and field data can be used to produce spatial
statistical prediction maps; a cost-effective means to identify areas
vulnerable to non-native plant species invasion and increased potential
for wildfire.

**Acknowledgements**

We have many people to thank for cooperation and assistance in the
completion of this project. Data collection was made possible by the
assistance of Mike Alexander, Don Carpenter, Ben Chemel, Steve Desipio,
Andrew Irvin, Jennifer Jackson, Li-Ming Liang, Amy Randell, John Rizza,
Ian Roblee-Herzog, Rod Rochambeau, Francisco Senra, Jodi Stevens,
Suzanne Terry, Emily White Hat, and Victoria Williams. Rick Shory and
Sara Simonson provided botanical and database support. Ran Ye managed
our project website. Acquisition of spatial data layers and field
sampling was facilitated by Bandelier National Monument, the Santa Fe
and Pike National Forests, and the US Forest Service's Rocky Mountain
Research Station: we thank in particular Bill Armstrong, Kay Beeley,
Cindy Dean, Paula Fornwalt, Brian Jacobs, Tom Mellon, Marla Merkel,
Darryl Murphy, Theresa Nallick, Al Sandoval, Diane Strohm, and Steve
Tapia. The spatial statistical library used for our spatial modeling was
provided and developed by Dr. Robin M. Reich and Dr. Richard A. Davis of
Colorado State University. We thank the Joint Fire Science Program for
funding this research.

**Table of Contents**

  ------------------------------------------------------------------- --------
  **Executive Summary**                                               i

  **Acknowledgements**                                                ii

  **1. Project Background**                                           **1**

  1.1 ***Rationale***                                                 2

  1.2 ***Objectives***                                                2

  1.3 ***Hypotheses***                                                2

  1.4 ***Deliverables***                                              3

  **2. Methods**                                                      **3**

  2.1 ***Study Areas***                                               4

  2.2 ***Landscape Stratification***                                  5

  2.3 ***Data Collection***                                           5

  2.3.1 *Stand Structure*                                             7

  2.3.2 *Fire Severity*                                               7

  2.3.3 *Floristics*                                                  7

  2.3.4 *Fuel Flammability*                                           7

  2.4 ***Data Analysis***                                             7

  2.4.1 *Fire Severity*                                               8

  2.4.2 *Non-native Species*                                          9

  2.4.3 *Soil Analysis*                                               9

  2.4.4 *Post-fire Flammability*                                      10

  2.4.5 *Spatial Modeling*                                            10

  **3. Results**                                                      **12**

  3.1 ***Predictors of Fire Severity***                               12

  3.1.1 *Cerro Grande*                                                12

  3.1.2 *Hi Meadow*                                                   13

  3.1.3 *Hayman*                                                      15

  3.2 ***Predictors of Non-native Presence and Abundance***           15

  3.2.1 *Cerro Grande*                                                15

  3.2.2 *Hi Meadow*                                                   18

  3.2.3 *Hayman*                                                      19

  3.3 ***Predictors of Post-fire Fuel Flammability***                 21

  3.3.1 *Cerro Grande*                                                21

  3.3.2 *Hi Meadow*                                                   22

  3.3.3 *Hayman*                                                      22

  3.4 ***Spatial Prediction Models***                                 24

  **4. Discussion**                                                   **27**

  4.1 ***Influence of Stand Structure and Management Activities on    27
  Fire Severity***                                                    

  4.2 ***Influence of Management Activities and Fire Severity on      29
  Non-native Plants***                                                

  4.3 ***Influence of Non-Native Plants on Post-fire Flammability***  30

  4.4 ***Potential of Spatial Prediction Models***                    32

  **5. Conclusions**                                                  **34**

  **6. Literature Cited**                                             **35**

  **Appendix A: Dissemination**                                       **40**

  **Appendix B: Non-native Species List**                             **43**

  **Appendix C: Database Metadata**                                   **45**

  **Appendix D: Outline for Potential Workshop**                      **56**
  ------------------------------------------------------------------- --------

### List of Tables and Figures

  -------- ----------------------------------------------------------- ----
  Table 1  Products proposed and delivered by this project.            3

  Figure 1 Study site locations.                                       4

  Figure 2 Stratifed random sampling design.                           6

  Figure 3 Modified-Whittaker plot design.                             6

  Table 2  Standard environment used for flammability modeling.        10

  Table 3  Summary statistics for fire severity models.                12

  Table 4  Parameter estimates for models selected to explain Cerro    13
           Grande fire severity.                                       

  Table 5  Parameter estimates for models selected to explain Hi       14
           Meadow fire severity.                                       

  Table 6  Parameter estimates for models selected to explain Hayman   15
           fire severity.                                              

  Table 7  Summary statistics for non-native species models.           16

  Table 8  Parameter estimates for models selected to explain          17
           non-native species in treated areas of the Cerro Grande     
           fire at the 1000-m^2^ scale.                                

  Table 9  Parameter estimates for models selected to explain          17
           non-native species in untreated areas of the Cerro Grande   
           fire at the 1000-m^2^ scale.                                

  Table 10 Parameter estimates for models selected to explain          18
           non-native species in treated areas of the Cerro Grande     
           fire at the 1-m^2^ scale.                                   

  Table 11 Parameter estimates for models selected to explain          18
           non-native species in untreated areas of the Cerro Grande   
           fire at the 1-m^2^ scale.                                   

  Table 12 Parameter estimates for models selected to explain          19
           non-native species in the Hi Meadow fire at the 1000-m^2^   
           scale.                                                      

  Table 13 Parameter estimates for models selected to explain          19
           non-native species in the Hi Meadow fire at the 1-m^2^      
           scale.                                                      

  Table 14 Parameter estimates for models selected to explain          20
           non-native species in the Hayman fire at the 1000-m^2^      
           scale.                                                      

  Table 15 Parameter estimates for models selected to explain          20
           non-native species in the Hayman fire at the 1-m^2^ scale.  

  Table 16 Summary statistics for post-fire flammability models.       21

  Table 17 Parameter estimates for models selected to explain fuelbed  22
           flammability after the Cerro Grande fire.                   

  Table 18 Parameter estimates for models selected to explain fuelbed  23
           flammability after the Hi Meadow fire.                      

  Table 19 Parameter estimates for models selected to explain fuelbed  23
           flammability after the Hayman fire.                         

  Table 20 Summary statistics for spatial statistical models.          25

  Table 21 Summary statistics for large- and small-scale variability   26
           models to predict total, native, and non-native plant       
           species richness and percent cover within the Cerro Grande  
           fire.                                                       

  Figure 4 Effect of prescribed burning on fire severity.              27

  Figure 5 Effect of mean tree diameter and stand density on fire      28
           severity.                                                   

  Figure 6 Effect of non-native species on post-fire flammability.     31

  Figure 7 Examples of predictive spatial statistical maps.            33

  Table B  Non-native species frequency of occurrence on the 1000-m^2^ 43
           plots sampled at each study site.                           
  -------- ----------------------------------------------------------- ----

1\. **Project Background**

1.1 ***Rationale***

Synergistic interactions and positive feedbacks among fuels, extreme
wildfire behavior, and non-native plant species are widely recognized as
major threats to the structure and function of natural ecosystems (Mack
and D'Antonio 1998). Twentieth century land use and management practices
may have unwittingly increased the vertical and horizontal continuity of
fuels and the incidence of large catastrophic wildfires in forest types
that previously experienced more frequent, less-severe fires (Allen et
al. 2002). Disturbances such as fire may promote non-native plant
species invasions by increasing available light and nutrients, as well
as by decreasing competition from native plants for these resources (Fox
1979, Melgoza et al. 1990). Once established, non-native species may
further alter fuel bed characteristics that increase the likelihood of
future catastrophic wildfires (Whisenant 1990a). Despite the recognition
of interactions between fire and non-native invasive plant species,
these interactions have not been well studied; particularly in western
montane coniferous forests (Harrod and Reichard 2001). This project
sought to elucidate some of the spatial relationships among fuel
characteristics, burn severity, non-native plant species, and fuel
flammability after three large wildfires in montane coniferous forests
of the Rocky Mountains in New Mexico and Colorado.

Burn severity is the post-fire manifestation of fire behavior (Rothermel
and Deeming 1980) resulting from complex interactions among fuels,
topography, and weather (Rothermel 1972). A relationship between fuel
conditions and burn severity is therefore expected. However, the
predictive models commonly used to derive this relationship are limited
under the extreme environmental conditions that generally accompany
large wildfire events (Andrews 1986; Rothermel 1991). Empirical evidence
relating fuel modification to fire severity is surprisingly sparse
(Martinson and Omi 2003).

Non-native plant invasion into wildfire burn areas is commonly expected
based on disturbance theory (Fox 1979). Non-native plants have been
found to invade Hawaiian woodland (Hughes et al. 1991) and Great Basin
steppe (Billings 1994) after wildfire, as well as California chaparral
after prescribed fire (Zedler and Scheid 1988; D'Antonio et al. 1993).
But other investigators have found prescribed fire to have either no
effect one growing season post-fire (in Rocky Mountain ponderosa pine
*op. cit.* Petterson 1999) or to inhibit non-native plant invasions (in
Great Plains grasslands *op. cit* Smith and Knapp, 1999; Whisenant
1990b). The Fire Effects Information System (Fischer et al. 1996)
includes more than 80 non-native species, but data on the post-fire
response of most of these are limited and the relationship to fire
severity is rarely investigated. The effect of post-fire seeding and
mulching on mitigating non-native plant invasion is also uncertain
(Robichaud et al. 2000). Non-native invasions may be facilitated by
seeding in some cases, particularly via seed contamination (Beyers
2004).

A current paradigm suggests that post-fire invasion by non-native plants
increases fuel flammability and initiates a positive feedback cycle that
maintains ecosystems in a perpetually altered state of non-native
dominance (D'Antonio and Vitousek 1992). Fire frequency has reportedly
increased since the introduction of non-native grasses in Idaho
shrublands (Whisenant, 1990a) and Hawaiian woodland (Hughes et al.,
1991). Knapp (1998) found that large fires in the Intermountain West are
clustered in areas dominated by non-native annual grasses. Increased
fire frequency in areas invaded by non-native plants (especially
grasses) is presumed to result from physical alterations to fuel bed
complexes that increase their flammability. Altered fuel characteristics
such as increased surface area-to-volume ratio and horizontal continuity
and decreased fuel moisture content might explain increases in fire
intensity, size, and length of seasons (D'Antonio and Vitousek 1992).
However, we are unaware of a study that has actually measured and
calculated fuel bed characteristics to relate predicted post-fire
flammability (after Burgan and Rothermel 1984) to non-native species
presence or abundance.

1.2 ***Objectives***

The goal of this research was to determine the factors that contribute
to post-fire invasion by non-native plants and to identify effective
mitigation options for land managers. We sampled three large wildfires
in montane coniferous forests in the Rocky Mountains of New Mexico and
Colorado to meet the following objectives in support of our research
goal:

-   Relate wildfire severity to manageable pre-fire fuel characteristics
    (e.g., stand density, basal area, canopy base height).

-   Relate plant species (native and non-native) composition to wildfire
    severity.

-   Relate post-fire fuel flammability to non-native plant species
    abundance.

-   Extrapolate plot-level information to landscape scales with
    generalized predictive spatial models derived from remotely sensed
    imagery to allow broader examination and conclusions regarding the
    interactions among fuels, wildfire, and non-native plant invasions.

## 1.3 Hypotheses

We tested four null hypotheses based on our stated objectives:

H~o1~: There is no correlation between wildfire severity and manageable
pre-fire fuel characteristics, rather any observable trends in wildfire
severity will be explained by other variables (e.g., slope, aspect, burn
date (as a proxy for weather conditions)).

H~o2~: There is no correlation between the abundance of non-native
plants and wildfire severity, rather any observable trends in non-native
plant cover will be explained by other variables (e.g., elevation, soil
type, proximity to roads and streams).

H~o3~: There is no correlation between fuel flammability and the
abundance of non-native plants, rather any observable trends in fuel
flammability will be explained by other variables (e.g., fuel load,
topographic influences on curing rates).

H~o4~: Spatial models cannot be developed to accurately predict field
conditions from remotely

sensed information, restricting the above hypothesis tests to
traditional site-specific

analyses.

Rejection of the above null hypotheses would allow us to infer that
non-native plant species contribute to the perpetual alteration of some
ecosystems by increasing their flammability, but that proactive and
strategic fuels management can mitigate post-fire non-native invasions
by reducing wildfire severity. Further, successful development of
predictive spatial models (Chong et al. 2001) would allow us to
determine the landscape conditions at greatest risk to non-native plant
invasions after fire.

## 1.4 Deliverables

The funded proposal for this project included eight deliverables. Table
1 lists the proposed deliverables and the method of product delivery.

**Table 1.** Products proposed and delivered by this project.

+-----------------------------------+-----------------------------------+
| # Proposed Deliverable            | # Product Delivery                |
+-----------------------------------+-----------------------------------+
| 3 Databases of plant species      | Metadata described in Appendix    |
| location and abundance.           | C.2 and submitted for inclusion   |
|                                   | in the JFSP-funded FRAMES online  |
|                                   | catalog                           |
|                                   | ([[http://frames.nbii.gov/portal/ |
|                                   | server.pt]{.underline}](http://fr |
|                                   | ames.nbii.gov/portal/server.pt)). |
+-----------------------------------+-----------------------------------+
| 3 Progress reports.               | Oral presentations delivered at   |
|                                   | JFSP Principal Investigator       |
|                                   | meetings in 2002, 2003, and 2004  |
|                                   | (see Appendix A).                 |
+-----------------------------------+-----------------------------------+
| 3 Arcview shapefiles that map     | Metadata described in Appendix    |
| predicted post-fire invasion by   | C.4 and submitted for inclusion   |
| non-native plant species.         | in the JFSP-funded FRAMES online  |
|                                   | catalog                           |
|                                   | ([[http://frames.nbii.gov/portal/ |
|                                   | server.pt]{.underline}](http://fr |
|                                   | ames.nbii.gov/portal/server.pt)). |
+-----------------------------------+-----------------------------------+
| Models of post-fire invasion risk | Described in Section 3 of this    |
| and flammability.                 | report.                           |
+-----------------------------------+-----------------------------------+
| Outline for potential workshop.   | Presented in Appendix D.          |
+-----------------------------------+-----------------------------------+
| Final report.                     | Submitted May 31, 2005.           |
+-----------------------------------+-----------------------------------+
| Publishable manuscript(s).        | See Appendix A for citations and  |
|                                   | URLs:                             |
|                                   |                                   |
|                                   | 2 papers in published             |
|                                   | proceedings.                      |
|                                   |                                   |
|                                   | 2 chapters in a US Forest Service |
|                                   | General                           |
|                                   |                                   |
|                                   | Technical Report.                 |
|                                   |                                   |
|                                   | 2 papers submitted to             |
|                                   | peer-reviewed journals.           |
|                                   |                                   |
|                                   | 1 doctoral dissertation.          |
|                                   |                                   |
|                                   | 1 master's thesis.                |
+-----------------------------------+-----------------------------------+
| Project web site.                 | <http://www.cnr.co                |
|                                   | lostate.edu/frws/research/weeds/> |
+-----------------------------------+-----------------------------------+

2\. **Methods**

2.1 ***Study Areas***

We sampled three wildfires in the course of this study: the 2000 Cerro
Grande fire, the 2000 Hi Meadow fire, and the 2002 Hayman fire (Figure
1). Each fire occurred in low-elevation forests of the Rocky Mountains,
burned at least 4,000 ha (10,000 acres) with variable severity, and
included both pre- and post-fire management activities.

![](media/image2.wmf){width="5.99251968503937in"
height="4.305511811023622in"}

The Cerro Grande fire was management ignited on May 4, 2000 in Bandelier
National Monument near Los Alamos, New Mexico. The objective of the
prescribed fire was to reduce fuels and restore pre-settlement forest
structure. The fire went out of prescription on May 5 and high winds on
subsequent days forestalled control until June 8. The fire reached a
final size of 193 km^2^. The most active burning days were on May 10 and
11 when the fire burned into the Garcia Canyon watershed where the Santa
Fe National Forest had done extensive fuel-reduction treatments since
1994. The treatments included mechanical thinning and prescribed
burning, both in isolation and in combination. Grass seed was aerially
applied to areas that were classified as high and moderate fire severity
(80 km^2^) to reduce the potential for increased runoff and erosion.
Elevation ranges from 2,000 m to 3,140 m. White fir (*Abies concolor*)
is abundant at higher elevations and ponderosa pine (*Pinus ponderosa*)
is dominant at mid-elevations with pinyon pine (*Pinus edulis*) and
juniper (*Juniperus* spp.) abundant at lower elevations. We sampled this
area in the summer of 2001.

The Hi Meadow fire started on June 12, 2000 from an undetermined
ignition source on private land in Park County, Colorado. Before
containment on June 20 the fire grew to a size of 44 km^2^.
Approximately half the burn area was on the Pike National Forest, which
had accomplished treatments since 1990 involving both prescribed fire
and mechanical thinning on about 12.5 km^2^. Post-fire seeding occurred
on about 12.5 km^2^, as well. Elevation ranges from 2,150 m to 2,500 m.
Ponderosa pine is dominant in the canopy with lodgepole pine (*Pinus
contorta*) and Douglas-fir (*Psuedotsuga menziesii*) also present. We
sampled this area in the summer of 2002.

The Hayman fire ignited on June 8, 2002, from an illegal camp fire on
the Pike National Forest in Park County, Colorado, approximately 48 km
south of the Hi Meadow burn area. The fire remained active until June
28, by which time it had burned 554 km^2^. Approximately one-third of
this area burned on a single day (June 9). Management activities had
occurred on approximately 81 km^2^ within the burn area, though we
focused on the prescribed fire and mechanical thinning activities
conducted since 1990. Post-fire seeding occurred on about 130 km^2^.
Elevation ranges from 2,150 m to 2,500 m. Ponderosa pine and Douglas-fir
are dominant in the canopy with lodgepole pine also present. We sampled
this area in the summer of 2003.

2.2 ***Landscape Stratification***

Sample points within each study area were selected randomly within
several strata. Landscape stratification was accomplished with Arcview
GIS software (ESRI 1999) and existing spatial data layers (Figure 2).
Strata included vegetation type (including pre-fire fuel treatment
status), aspect, burn severity (including unburned reference areas
within 300 m of the fire perimeter), and post-fire mitigation activities
(with a focus on seeding). Fire progression maps were used to restrict
sampling to those burn periods that affected the majority of the fuel
treatment areas (May 10-11, 2000 in the Cerro Grande fire, June 13-15,
2000 in the High Meadow fire, and June 9, 2003 in the Hayman fire).
Areas that had been subject to management activity prior to 1990 were
avoided, as were areas that were subject to post-fire activities other
than seeding (e.g., herbicides or felling). At least three plots were
sampled within each stratum.

2.3 ***Data Collection***

We established a Modified-Whittaker plot at each sampling point
(Stohlgren et al. 1995, 1998). The Modified-Whittaker plot is
multi-scale and has nested within it one 100-m^2^ plot, two 10-m^2^
plots and ten 1-m^2^ quadrats (Figure 3). Our sampling objectives
focused on re-construction of pre-fire stand conditions (timber);
assessment of fire severity; identification, cover and height
measurements of all vascular plant species present post-fire; and
construction of custom fuel models to estimate post-fire flammability.

![](media/image3.wmf){width="5.998031496062992in"
height="4.188976377952756in"}

![](media/image4.wmf){width="6.0in" height="2.7318897637795274in"}

2.3.1 *Stand Structure*

Pre-fire stand conditions were reconstructed from trees within variable
radius plots defined by an angle gauge with a basal area factor of 2
m^2^/ha (Avery and Burkhart 2002) and centered on the Modified-Whittaker
plot. Measured variables included tree height, tree diameter, height to
the pre-fire live crown, position in canopy, species, stand density, and
basal area. Heights were calculated trigonometrically from clinometer
readings. Tree diameters were measured with metric diameter tape 1.4 m
above ground on the uphill side. Lower tree boles were inspected
carefully for evidence of consumed branches to avoid over-estimating
crown base heights in severely burned stands. Each tree's position in
the canopy was rated as dominant, co-dominant, intermediate, or
suppressed. Since variable radius plots are inaccurate for small trees
(Stage and Rennie 1994), trees less than 10 cm in diameter were sampled
separately within the fixed area 100-m^2^ subplot centered within the
Modified-Whittaker plot.

2.3.2 *Fire Severity*

We assessed fire severity with several measures. Bole char height,
needle scorch height, and percentage of crown volume scorched and
consumed were estimated on each tree included in the variable radius
plots. Additionally, we assigned a stand damage rating (adapted from Omi
and Kalabokidis 1991) based on the condition of all trees within the
1000-m^2^ Modified-Whittaker plot used to sample post-fire vegetation.
Depth of ground char ratings (adapted from Ryan and Noste 1985) were
assigned to each of the ten 1-m^2^ quadrats within the
Modified-Whittaker plot.

2.3.3 *Floristics*

Botanists identified and measured the cover and height of all species in
each 1-m^2^ subplot. Cover was also recorded for any non-plant surface
items encountered (i.e., wood, water, rock, roots, duff/litter, lichen,
moss, soil, trail/road, dung, and other (trash, bones, etc.); Bull et
al., 1998). Botanists then searched the 10-m^2^, 100-m^2^, and 1000-m^2^
plots for any species not previously recorded and noted them as present.
Plants that could not be identified to species were collected off-plot
and pressed for later identification. Species codes followed the
standardized USDA Natural Resource Conservation Service (NRCS) PLANTS
Database codes (USDA, NRCS 1999; determined after identification).
Plants that could not be identified because of growth stage retained
their unknown name for use in richness analyses.

2.3.4 *Fuel Flammability*

We assessed the flammability of post-fire fuels in each 1-m^2^ subplot
by collecting information necessary for input into BEHAVE's fuel
modeling subsystem (Burgan and Rothermel 1984). This included separate
estimates of depth, bulk density, percent cover, percent green, and
surface area-to-volume ratios for each fuel class (i.e., grasses, forbs,
shrubs, and litter). Surface area-to-volume ratios were calculated from
diameter measurements made with a digital caliper on randomly selected
fuel particles.

2.4 ***Data Analysis***

We employed standard statistical software (SAS 2001) and an
information-theoretic approach (Burnham and Anderson 1998) for the
development and selection of multiple linear regression models to
explain landscape variations in fire severity, non-native species
abundance, and post-fire flammability. Spatial prediction models were
also developed for the presence and abundance of non-native species
within the Cerro Grande fire. Akaike's Information Criterion (AICc,
corrected for small samples), was used as a guide in selecting the
number of parameters to include in each of the models we developed. When
maximum likelihood is used as a criterion for selecting between models
of different orders, there is the possibility of finding another model
with equal or greater likelihood simply by increasing the number of
parameters. Therefore, AICc penalizes for each increase in the number of
parameters and the model with the smallest AICc value is considered to
have the most support in the data. While models are thus kept as
simplistic as possible, a more complex model could be used if warranted,
or several models with strong support in the data could be averaged with
Akaike weights (Burnham and Anderson 1998).

2.4.1 *Fire Severity*

We developed regression models to explain the crown volume scorch and
consumption observed at each of our plots. Explanatory variables
considered were divided into two groups: those that described pre-fire
fuel conditions and those that described topographic conditions. Though
weather conditions also influence fire behavior and severity, data were
not available that described the exact time and weather conditions at
each of our plot locations as it burned. Instead, we attempted to
minimize weather variability among our sample locations by including
daily fire progression in our landscape stratification (see Section
2.2).

All possible combinations of main effects were explored, as well as
two-way interactions between fuel parameters and topographic parameters.
Models with very strong support in the data (i.e., the difference in
AICc between the candidate model and the model with the lowest AICc
(ΔAICc) was less than 3) were averaged with Akaike weights to arrive at
the final models (Burnham and Anderson 1998). Transformation of
dependent variables followed standard diagnostic checks and power
analysis to maximize conformance with the assumptions of multiple linear
regression (SAS 2001).

We considered the following topographic variables as potential
predictors of fire severity at all three sites: map distances (km) from
the nearest road and the nearest stream, slope steepness (%), elevation
(km) above mean sea level, slope aspect (degrees), direct incident
radiation (MJ/cm^2^/yr), and topographic position (fraction of the local
elevation gradient). Slope aspect was converted to radians and
transformed to increase symmetrically around an axis from northeast to
southwest (after Beers et al. 1966). Direct incident radiation was
calculated from equations provided by McCune and Keon (2002).
Topographic position was initially implemented as dummy variables
indicating whether or not each plot was in a drainage (coded 1,0), on a
ridge (coded 0,1), or neither (coded 0,0). To reduce the number of
parameters necessary to describe topographic position, we instead
calculated this variable as the fraction of the elevation gain between
the nearest drainage and the nearest ridge. Exploratory analysis
suggested a mid-slope moderation of fire severity, so we also considered
a transformation that resulted in a maximum value on ridges and in
drainages and a minimum value at mid-slope (i.e., the absolute value of
the topographic position fraction minus 0.5).

Fuel variables considered at all sites were tree density (#/ha), tree
basal area (m^2^/ha), mean tree diameter (cm), canopy base height (m),
canopy bulk density (kg/m^3^), tree species composition (fraction
comprised of ponderosa pine), age of pre-fire prescribed burn and
mechanical thinning treatments (years inverted with untreated areas set
to 0), and aspen presence (coded 1 if present, 0 if not). Canopy bulk
density was calculated with the maximum running mean method recommended
by Scott and Reinhardt (2001) using Brown's (1978) allometric equations
for crown weights contributed by needles and fine twigs. The effective
canopy base height was calculated as the lower limit of the lowest 1-ft
horizontal section of the canopy in which canopy bulk density exceeded
0.037 kg/m^3^ (Scott and Reinhardt 2001).

Exploratory analysis of the crown base height variable indicated a
counter-intuitive relationship to fire severity, suggesting that our
post-fire reconstructions may have been biased in high severity areas
despite our best efforts to guard against this in the field. We
attempted a post-sampling remedy through the development of an
allometric relationship (*p*\<0.0001, *r*^2^=0.40) based on the species,
diameter, and height of our unscorched sampled trees. The effective
canopy base for each plot was then recalculated after setting the crown
base of each tree to the lowest value between our observation and
allometric prediction. Even after this adjustment, however, our analyses
suggested a positive correlation between fire severity and pre-fire
canopy base height at all three of our study sites. Due to our lack of
confidence in our post-fire measurement of pre-fire canopy base height,
we elected to drop this variable from our analyses rather than conclude
that ladder fuels reduce fire severity.

2.4.2 *Non-native Species*

Species were classified as non-native according to the Natural Resources
Conservation Service Plants Database (<http://plants/usda.gov>). Several
multiple linear regression and analysis of covariance models were tested
to best predict cover of non-native species. Predictor variables were
separated in different models as biotic effects (native grass cover,
seeded grass cover, native species richness), abiotic effects resultant
from wildfire (mean char depth, canopy cover, mulch cover, total
nitrogen), treatment effects (thinned, prescribed burn, and thinned +
prescribed burn), and other abiotic effects (distance from streams,
distance from roads, slope, incident radiation, percent clay content).
Models at the 1000-m^2^ scale included variables from each group and
then combinations of groups. Group variables varied slightly among sites
because of the nature in which the data were collected. Elevation was
included as a factor at Cerro Grande because plot locations spanned a
much wider elevational gradient compared to the other sampled fires.
Separate analyses were conducted for treated and untreated areas at
Cerro Grande, because treated areas encompassed a much smaller portion
of the wildfire and didn't span the entire elevational gradient that was
sampled. Fewer variables were measured and thus tested at the 1-m^2^
scale. At this scale, the structured models were more similar among
fires. Tested models at the 1-m^2^ scale were slightly different for the
Hayman fire because we sampled an extra variable, cover of straw mulch
used for erosion control. Models with strong support in the data (∆AICc
\< 3) were averaged with Akaike weights to obtain averaged parameter
estimates and associated standard error values (Burnham and Anderson
1998). Variables were square-root or arcsine square-root transformed
prior to analysis to correct for non-normal error distribution.

2.4.3 *Soil Analysis*

We collected five soil samples to a depth of 5-15 cm (depending on soil
depth), one in each corner and one in the center, from each
Modified-Whittaker plot and pooled these into one composite sample per
plot. Soils were analyzed for indicators of nutrient (carbon and
nitrogen) and moisture (texture) availability. Samples were air-dried
for at least 48 h and sieved to 2-mm (number 10 standard sieve).
Particle size was determined using the standard hydrometer method (Gee
and Bauder 1986). Samples were ground to a fine powder, oven-dried at
55^o^C for 48 h and analyzed for percent total carbon and nitrogen using
a LECO-1000 CHN analyzer (LECO Corporation, Saint Joseph, Missouri,
USA).

2.4.4 *Post-fire Flammability*

We created and tested custom fuel models for each plot with the fuel
modeling subsystem of BEHAVE (Burgan and Rothermel 1983). We recoded the
New Model and Test Model routines in Visual Basic for Applications to
automate input from our MS Excel datasheets. The Test Model routine was
used to predict fire behavior in each plot based on its custom fuel
model parameters and a standard environment (Table 2). We used the flame
length output of Test Model as the dependent variable in regression
models to assess whether the relative abundance of non-native and seeded
plant species had any influence on predicted post-fire flammability.

> **Table 2**. Standard environment used for flammability modeling.
> Several variables were held constant to produce flame length
> predictions for custom fuel models. The custom fuel models were
> developed from data collected at each plot to assess the influence of
> non-native species on post-fire flammability.

  ---------- ---------- ---------- ---------- ---------- ---------- ----------
  dfmc1      dfmc10     dfmc100    hfmc       sfmc       slope      mfw

  3          4          5          70         70         0          6.4
  ---------- ---------- ---------- ---------- ---------- ---------- ----------

> **Notes:**
>
> dfmc1 = dead fuel moisture content (%) of 1-hr fuels (diameter \< 0.6
> cm).
>
> dfmc10 = dead fuel moisture content (%) of 1-hr fuels (diameter \< 2.5
> cm).
>
> dfmc100 = dead fuel moisture content (%) of 1-hr fuels (diameter \<
> 7.6 cm).
>
> hfmc = live fuel moisture content (%) of herbaceous fuels.
>
> wfmc = live fuel moisture content (%) of woody fuels.
>
> slope = slope steepness (%).
>
> mfw = mid-flame wind speed (km/h).

Non-native and seeded species may increase flammability by altering
fuelbed characteristics such as surface-area-to-volume ratio, fuelbed
depth, horizontal continuity, and the proportion of the live fuel load
that is cured. However, these parameters were not included in our
regression models directly, since there would be little scientific value
in developing a regression model to replicate the output of the fire
behavior model. Rather, we limited additional regression parameters to
those variables that would influence fire behavior independently of the
species that comprised the live fuel load. These included total load and
abiotic variables that influence the curing of live biomass: elevation,
aspect, topographic position, canopy cover, and the date each plot was
sampled.

All possible combinations of available explanatory variables and single
two-way interactions were explored as predictors. Models with very
strong support (∆AICc \< 3) in the data were averaged with Akaike
weights to arrive at the final models (Burnham and Anderson 1998).
Transformation of dependent variables followed standard diagnostic
checks and power analysis to maximize conformance with the assumptions
of multiple linear regression (SAS 2001).

2.4.5 *Spatial Modeling*

Data used in spatial modeling included eight bands of Landsat TM Data,
six different vegetation indices, six bands of transformed tasseled cap
indices (using IMAGINE 8.4 \[ERDAS 2000\]), topographic derived data
(elevation, slope, aspect; ArcInfo 7.4 \[ESRI 2000\]), and vegetation
data (total number of plant species, number of native plant species,
number of non-native plant species, and percent cover for total, native,
and non-native species). All spatial information from remotely sensed
data and GIS layers were converted to a grid using ArcInfo 7.4 (ESRI
2000) and a program written in ARC Macro Language (ESRI 2000) was used
to extract the data points (field plot locations) with respect to their
Universal Transverse Mercator *X*- and *Y*-coordinates within the study
area. All data were then used for the development of the spatial models
using S-Plus software (MathSoft 2000).

The cross-correlation statistic (*I*~YZ~) was used to test the null
hypothesis of no spatial cross-correlation among all pairwise
combinations of vegetation variables and topographic characteristics. In
calculating *I*~YZ~, we used the inverse distance between sample plots
as a weighting factor to give more weight to values in the closest
sample plots and less to those in plots that were farthest away. The
null hypotheses of no spatial cross-correlation were rejected at
*p*\<0.05. Moran\'s *I*, which is a special case of *I*~YZ~ (Czaplewski
and Reich 1993), was used to calculate the spatial auto-correlation
associated with each variable. Cliff and Ord (1981) showed that *I*~YZ~
ranges from --1 to +1, although it can exceed these limits with certain
types of spatial matrices. Data distributions that were strongly skewed
were transformed prior to analysis. Aspect data were transformed using
the absolute value from due south (180°; high solar radiation) (Kalkhan
and Stohlgren 2000).

Stepwise multiple regression analysis was used first to identify the
best linear combination of independent variables. It also allowed us to
explore the variation in predicting total, non-native, and native plant
species richness as a function of the eight TM bands, six derived
vegetation indices, six tasseled cap transformation indices, slope,
aspect, and elevation. The selected independent variables were used in
the ordinary least squares (OLS) procedure to describe large-scale
variability estimates.

Ordinary least squares estimators were used to fit the model if the
variable of interest had a linear relationship with the geographic
coordinates of the sample plots, the digital number value of any of the
Landsat TM bands, and the topographic data. In addition, the least
squares method fits a continuous, univariate response as a linear
function of the predicted variable. This trend surface model represented
continuous first-order spatial variation. We used AICc (Burnham and
Anderson 1988, Akaike 1997) as a guide in selecting the number of model
parameters to include in the regression model.

The residuals from the trend surface models were analyzed for spatial
dependencies in the next stage of the model building process. This was
accomplished using spatial auto-correlation and cross-correlation
statistics. If the residuals were cross-correlated with other variables,
we could use co-kriging to interpolate the residuals. However, if the
residuals were not cross-correlated, we used ordinary kriging. Finally,
the weights associated with the kriging and co-kriging models were
estimated as a function of the spatial continuity of the data (Isaaks
and Srivastiva 1989). This estimation can be accomplished using a sample
variogram to describe spatial continuity. With spatial data, the
variation of the samples generally changes with distance. In other
words, the variogram is a measure of how the variance changes with
distance. The variogram and cross-variogram models used in this analysis
were considered "basic" models, meaning they are simple and isotropic
(Reich et al. 1999). They include Gaussian, spherical, and exponential
models (see Isaaks and Srivastiva 1989). Prior to estimating the sample
variogram and cross-variogram, the data were rescaled by dividing the
individual variables and the residuals by their respective maximum
values. This was necessary to maintain numerical stability (Isaaks and
Srivastiva 1989) by eliminating any differences in the magnitude of the
variables without altering the solution. Although this was not necessary
for kriging, it was important in co-kriging (Isaaks and Srivastiva
1989).

3\. **Results**

3.1 ***Predictors of Fire Severity***

Fire severity within the areas we sampled was variable: the proportion
of our study areas that burned with high severity ranged from 56% in the
Hi Meadow fire to 67% in the Cerro Grande fire to 74% in the Hayman
fire. We sampled areas of high and low severity with equal intensity to
determine how much of the variability in fire severity could be
explained by manageable stand conditions. We developed multiple
regression models for each wildfire in an attempt to explain the fire
severity we observed. Summary statistics for all variables considered in
our models for each fire are presented in Table 3.

> **Table 3**. Summary statistics for all variables considered in
> multiple regression models to explain fire severity at our three study
> sites.

  ------------- --------- ---------- --------- ---------- --------- ----------
  Variable      Cerro                Hi Meadow            Hayman    
                Grande                                              

                Mean      s.d.       Mean      s.d.       Mean      s.d.

  CVS+C         104.48    (82.27)    98.85     (70.79)    133.37    (65.92)

  potr          0.09      (0.29)     0         (0.00)     0.09      (0.29)

  pipo          57.94     (43.78)    70.28     (34.46)    58.14     (41.47)

  burn          3.33      (1.83)     1.74      (1.52)     8.92      (2.78)

  thin          1.45      (0.52)     10.38     (2.78)     2.6       (1.88)

  dbh           27.19     (8.64)     24.53     (8.68)     23.15     (8.83)

  ba            17.08     (8.24)     16.08     (6.46)     14.28     (8.40)

  dns           555.07    (897.00)   569.03    (545.38)   444.63    (502.50)

  cbd           0.07      (0.04)     0.08      (0.04)     0.08      (0.06)

  rd            0.31      (0.45)     0.33      (0.21)     0.63      (0.57)

  strm          0.42      (0.41)     0.23      (0.21)     0.5       (0.36)

  elev          2.34      (0.20)     2.3       (0.09)     2.36      (0.12)

  topo          0.49      (0.32)     0.58      (0.31)     0.53      (0.30)

  slp           25.69     (16.41)    25.07     (12.14)    30.52     (13.45)

  asp           0.7       (0.61)     0.86      (0.72)     0.97      (0.77)
  ------------- --------- ---------- --------- ---------- --------- ----------

> **Notes:**
>
> Parameter abbreviations: *CVS+C* = canopy volume scorch (%) plus
> consumption (%). *potr* = dummy variable indicating the presence
> (coded 1) or absence (coded 0) of aspen. *burn* = age (yr) of
> prescribed burn treatments. *thin* = age (yr) of mechanical thinning
> treatments. *dbh* = mean tree diameter (cm) at breast height (1.4 m
> from tree base on the uphill side). *ba* = tree basal area (m^2^/ha).
> *dns* = tree density (#/ha). *cbd* = canopy bulk density (kg/m^3^).
> *rd* = distance (km) from nearest road. *strm* = distance (km) from
> the nearest stream. *elev* = elevation (km) above mean sea level.
> *topo* = topographic position expressed as the fraction of the
> elevation gradient between the nearest drainage and the nearest ridge.
> *slp* = slope steepness (%). *asp* = slope aspect (degrees)
> transformed to increase continuously from NE (value = 0) to SW (value
> = 2) in both directions (after Beers et al. 1966) with the function
>
> COS(RADIANS(225-asp))+1.

3.1.1 *Cerro Grande*

Regression models for the Cerro Grande fire explained 46% of the
variability in the canopy damage we observed (Table 4). The most
significant variables were elevation, mean tree diameter and the age
(inverted) of prescribed fire treatments. Fire severity increased with
increasing elevation and the transition from pinyon-juniper woodland to
ponderosa pine to mixed conifer forest. Larger trees and prescribed-burn
areas were associated with lower fire severity, though an interaction
term suggests that the effect of tree diameter diminished with
increasing distance from roads. Other important fuel variables were
aspen presence and canopy bulk density. Aspen were associated with
reduced fire severity, as would be expected from their high fuel
moisture content and preference for mesic sites. Fire severity decreased
with increasing canopy bulk density, in contrast to expectation from
fire behavior theory. However, an interaction term indicates that the
effect of canopy bulk density tended to reverse with proximity to
ridgelines. Though stand density was insignificant as a main effect, an
interaction term indicates that this variable did significantly increase
fire severity on southwesterly aspects.

> **Table 4**. Parameter estimates for models selected to explain fire
> severity (percent canopy scorch + consumption) within the Cerro Grande
> burn area (n = 64, adjusted *r*^2^ = 0.46).

+------------------------------+------+--------+------+------+------+
| Selected Models ^a^          | Adju | Akaike | P    |      | p-v  |
|                              | sted | Weight | aram |      | alue |
|                              | r^2^ | ^b^    | eter |      | ^d^  |
|                              |      |        |      |      |      |
|                              |      |        | E    |      |      |
|                              |      |        | stim |      |      |
|                              |      |        | ates |      |      |
|                              |      |        | ^c^  |      |      |
+------------------------------+------+--------+------+------+------+
| bur                          |      |        | I    | -5.  | 0.   |
| n+potr+dbh+cbd+asp+elev+topo |      |        | nter | 3703 | 1939 |
|                              |      |        | cept |      |      |
+------------------------------+------+--------+------+------+------+
| +cbdXtopo                    | 0.46 | 0.35   | elev | 8.   | 0.   |
|                              |      |        |      | 5568 | 0002 |
+------------------------------+------+--------+------+------+------+
| burn+pot                     |      |        | burn | -5.  | 0.   |
| r+dbh++dns+cbd+asp+elev+topo |      |        |      | 3964 | 0009 |
+------------------------------+------+--------+------+------+------+
| +cbdXtopo                    | 0.47 | 0.19   | dbh  | -0.  | 0.   |
|                              |      |        |      | 1160 | 0032 |
+------------------------------+------+--------+------+------+------+
| bur                          |      |        | db   | 0.   | 0.   |
| n+potr+dns+cbd+asp+elev+topo |      |        | hXrd | 0298 | 0116 |
+------------------------------+------+--------+------+------+------+
| +dnsXasp                     | 0.45 | 0.17   | asp  | -1.  | 0.   |
|                              |      |        |      | 3746 | 0117 |
+------------------------------+------+--------+------+------+------+
| burn+po                      |      |        | dns  | 0.   | 0.   |
| tr+dbh+cbd+slp+asp+elev+topo |      |        | Xasp | 0003 | 0145 |
+------------------------------+------+--------+------+------+------+
| +cbdXtopo                    | 0.46 | 0.15   | cbdX | 50.  | 0.   |
|                              |      |        | tpos | 0560 | 0219 |
+------------------------------+------+--------+------+------+------+
| burn+p                       |      |        | potr | -2.  | 0.   |
| otr+dbh+cbd+asp+elev+rd+topo |      |        |      | 6268 | 0351 |
+------------------------------+------+--------+------+------+------+
| +dbhXrd                      | 0.46 | 0.14   | topo | -4.  | 0.   |
|                              |      |        |      | 9182 | 0582 |
+------------------------------+------+--------+------+------+------+
|                              |      |        | cbd  | -39. | 0.   |
|                              |      |        |      | 9756 | 0606 |
+------------------------------+------+--------+------+------+------+
|                              |      |        | dns  | 0.   | 0.   |
|                              |      |        |      | 0002 | 2278 |
+------------------------------+------+--------+------+------+------+
|                              |      |        | slp  | -0.  | 0.   |
|                              |      |        |      | 0036 | 2949 |
+------------------------------+------+--------+------+------+------+
|                              |      |        | rd   | -1.  |      |
|                              |      |        |      | 0713 |      |
+------------------------------+------+--------+------+------+------+

> **Notes:**
>
> ^a^ Selected models were those with strong support in the data (i.e.,
> those within 3 points of the model with the lowest AICc (Akaike's
> Information Criterion) value (after Burnham and Anderson 1998)). All
> selected models were significant at the p\<0.0001 level.
>
> ^b^ Akaike weights indicate the probability that a given model is the
> most informative of the models considered. When no single model has
> overwhelming support in the data, Akaike weights can be used for model
> averaging to account for selection uncertainty (Burnham and Anderson
> 1998).
>
> ^c^ See Table 3 for explanation of parameter abbreviations. However,
> *burn* is inverted (1/yr) with areas that had no record of pre-fire
> prescribed burns set to equal 0. Model parameters predict the square
> root of the arcsine transform (Zar 1984), t(p), of the sum, y, of mean
> crown volume scorch (%) and mean crown volume consumption (%):
>
> t(p) = (360/(2π) ) \* ( arcsin( √p ))
>
> where
>
> p = 1 / (4\*n) if y=0, y/n if 1≤y≤n-1, (n-0.25)/n if y=n
>
> and
>
> n=200.
>
> ^d^ Statistical significance cannot be interpreted and is thus not
> reported for main effects that occur only in conjunction with a
> multiplicitive interaction between it and another variable.

3.1.2 *Hi Meadow*

Regression models for the Hi Meadow fire explained 16% of the
variability in the canopy damage we observed (Table 5). The most
significant variables were tree density, basal area, an interaction
between tree species composition and slope, and an interaction between
the age of prescribed burn treatments and the distance to the nearest
stream. Fire severity increased with increases in tree density, but
decreased with increases in basal area. Basal area is determined by both
the size and number of trees in a stand. The absence of mean tree
diameter from the set of informative variables implies that basal area
acted as a surrogate: at a given density, an increase in basal area
results from larger, more fire-resistant trees that reduce observed fire
severity.

**Table 5**. Parameter estimates for models selected to explain fire
severity (percent canopy scorch + consumption) within the Hi Meadow burn
area (n = 71, adjusted *r*^2^ = 0.16).

+-------------------------------+------+------+--------+------+------+
| Selected Models ^a^           | Adju | Ak   | Par    |      | p-v  |
|                               | sted | aike | ameter |      | alue |
|                               | r^2^ | We   |        |      | ^d^  |
|                               |      | ight | Est    |      |      |
|                               |      | ^b^  | imates |      |      |
|                               |      |      | ^c^    |      |      |
+-------------------------------+------+------+--------+------+------+
| dns+cbd+ba                    | 0.15 | 0.20 | Int    | 5.   | 0.   |
|                               |      |      | ercept | 6687 | 0002 |
+-------------------------------+------+------+--------+------+------+
| dns+pipo+slp+pipoXslp         | 0.17 | 0.17 | pi     | -0.  | 0.   |
|                               |      |      | poXslp | 0242 | 0131 |
+-------------------------------+------+------+--------+------+------+
| dns+cbd+ba+asp                | 0.16 | 0.15 | dns    | 0.   | 0.   |
|                               |      |      |        | 0012 | 0263 |
+-------------------------------+------+------+--------+------+------+
| dns+cbd+ba+rd                 | 0.15 | 0.14 | bur    | -0.  | 0.   |
|                               |      |      | nXstrm | 8697 | 0268 |
+-------------------------------+------+------+--------+------+------+
| dns+burn+strm+burnXstrm       | 0.15 | 0.10 | ba     | -0.  | 0.   |
|                               |      |      |        | 0735 | 0392 |
+-------------------------------+------+------+--------+------+------+
| dns+burn+asp+rd+burnXrd       | 0.17 | 0.08 | b      | 0.   | 0.   |
|                               |      |      | urnXrd | 4509 | 0521 |
+-------------------------------+------+------+--------+------+------+
| dns+rd+dnsXrd                 | 0.13 | 0.08 | cbd    | 10.  | 0.   |
|                               |      |      |        | 5880 | 0841 |
+-------------------------------+------+------+--------+------+------+
| dns+cbd+ba+rd+cbdXrd          | 0.16 | 0.08 | asp    | -0.  | 0.   |
|                               |      |      |        | 1110 | 1589 |
+-------------------------------+------+------+--------+------+------+
|                               |      |      | cbdXrd | -1.  | 0.   |
|                               |      |      |        | 4734 | 2788 |
+-------------------------------+------+------+--------+------+------+
|                               |      |      | dnsXrd | 0.   | 0.   |
|                               |      |      |        | 0001 | 3543 |
+-------------------------------+------+------+--------+------+------+
|                               |      |      | pipo   | 0.   |      |
|                               |      |      |        | 5101 |      |
+-------------------------------+------+------+--------+------+------+
|                               |      |      | burn   | -0.  |      |
|                               |      |      |        | 0222 |      |
+-------------------------------+------+------+--------+------+------+
|                               |      |      | slp    | 0.   |      |
|                               |      |      |        | 0160 |      |
+-------------------------------+------+------+--------+------+------+
|                               |      |      | rd     | 0.   |      |
|                               |      |      |        | 5318 |      |
+-------------------------------+------+------+--------+------+------+
|                               |      |      | strm   | 0.   |      |
|                               |      |      |        | 1637 |      |
+-------------------------------+------+------+--------+------+------+

**Notes:**

^a-b^ See Table 4 notes. All selected models were significant at the
p\<0.01 level.

^c^ See Table 3 for explanation of parameter abbreviations. However,
*burn* is inverted (1/yr) with areas that had no record of pre-fire
prescribed burns set to equal 0. Model parameters predict the square
root of the arcsine transform (see Table 4 for explanation).

^d^ Statistical significance cannot be interpreted and is thus not
reported for main effects that occur only in conjunction with a
multiplicitive interaction between it and another variable.

Though the main effects of ponderosa pine concentration and slope
steepness were insignificant, a multiplicative interaction between these
variables suggests that fire severity was significantly lower on steep
slopes where the concentration of ponderosa pine was high than on steep
slopes where the concentration of ponderosa pine was low (i.e., in
Douglas-fir stands). The age of prescribed burn treatments and distance
from streams also interacted to significantly influence fire severity.
Fire severity was significantly lower within prescribed burn areas that
were removed from streams than in those that were near streams. Higher
productivity in the mesic areas close to streams may have hastened the
recovery of surface fuels to pre-treatment conditions. Prescribed burns
also interacted with distance from roads, though the effect on fire
severity was only marginally significant. Fire severity was lower within
prescribed burn areas that were close to roads than in those that were
removed from roads. Roads likely acted as a fuel break that augmented
the effect of the prescribed burns. However, the main effect of roads
was insignificant: roads had no effect on fire severity outside of
prescribed burn areas where surface fuels had been reduced.

3.1.3 *Hayman*

Regression models for the Hayman fire explained 15% of the variability
in the canopy damage we observed (Table 6). The most significant
variables were the age of prescribed fire treatments, mean tree
diameter, and an interaction between prescribed burn treatments and
distance from roads. Fire severity decreased with increases in mean tree
diameter and recency of the last prescribed fire treatment. The
interaction between prescribed fire treatments and roads suggests that
fire severity was greater in burn treatments that were near roads than
in those that were removed from roads.

**Table 6**. Parameter estimates for models selected to explain fire
severity (percent canopy scorch + consumption) within the Hayman burn
area (n=110, adjusted *r*^2^ = 0.15).

+--------------------------------+------+------+-------+-------+-----+
| Selected Models ^a^            | Adju | Ak   | Para  |       | p   |
|                                | sted | aike | meter |       | -va |
|                                | r^2^ | We   |       |       | lue |
|                                |      | ight | Esti  |       |     |
|                                |      | ^b^  | mates |       |     |
|                                |      |      | ^c^   |       |     |
+--------------------------------+------+------+-------+-------+-----+
| burn+dbh                       | 0.14 | 0.23 | Inte  | 77    | 0.0 |
|                                |      |      | rcept | .0508 | 001 |
+--------------------------------+------+------+-------+-------+-----+
| burn+dbh+rd+strm+burnXrd       | 0.17 | 0.20 | burn  | -33   | 0.0 |
|                                |      |      |       | .2540 | 052 |
+--------------------------------+------+------+-------+-------+-----+
| burn+dbh+topo^1^               | 0.14 | 0.17 | dbh   | -0    | 0.0 |
|                                |      |      |       | .5757 | 157 |
+--------------------------------+------+------+-------+-------+-----+
| burn+dbh+ba                    | 0.14 | 0.15 | bu    | -28   | 0.0 |
|                                |      |      | rnXrd | .5276 | 392 |
+--------------------------------+------+------+-------+-------+-----+
| burn+dbh+topo^1^+dbhXtopo^1^   | 0.15 | 0.14 | strm  | -2    | 0.0 |
|                                |      |      |       | .4091 | 875 |
+--------------------------------+------+------+-------+-------+-----+
| burn+dbh+rd                    | 0.14 | 0.12 | d     | -0    | 0.1 |
|                                |      |      | bhXto | .3383 | 612 |
|                                |      |      | po^1^ |       |     |
+--------------------------------+------+------+-------+-------+-----+
|                                |      |      | to    | 2     | 0.1 |
|                                |      |      | po^1^ | .0690 | 988 |
+--------------------------------+------+------+-------+-------+-----+
|                                |      |      | ba    | 0     | 0.2 |
|                                |      |      |       | .0482 | 352 |
+--------------------------------+------+------+-------+-------+-----+
|                                |      |      | rd    | -0    | 0.3 |
|                                |      |      |       | .6105 | 049 |
+--------------------------------+------+------+-------+-------+-----+

**Notes:**

^a-b^ See Table 4 notes. All selected models were significant at the
p\<0.001 level.

^c^ See Table 3 for explanation of parameter abbreviations. However,
*burn* is inverted (1/yr) with areas that had no record of pre-fire
prescribed burns set to equal 0 and *topo* is transformed to minimize at
mid-slope with the function:

topo^1^ = \|0.5 -- topo\|.

> Model parameters predict the arcsine transform, rather than its square
> root as in Tables 3 and 4 (see Table 4 for explanation).

3.2 ***Predictors of Non-native Presence and Abundance***

We developed multiple regression models to explain the abundance of
non-native species found in each post-burn landscape. Models were
developed for two different scales of observation: 1000-m^2^ and 1-m^2^.
Summary statistics for all variables considered in our models for each
fire are presented in Table 7.

3.2.1 *Cerro Grande*

Since treated plots occurred in ponderosa pine areas only, separate
analyses were conducted for treated and non-treated areas. At the
1000-m^2^ scale in treated areas, the best models accounted for up to
42% of the variability in non-native species cover and included a
treatment effect, seeded grass cover, native grass cover, percent slope,
incident radiation, and percent clay content (Table 8). All parameter
estimates except for native species richness had 95% confidence
intervals that overlapped zero. Non-native species cover was positively
associated with native species richness. Non-native species cover also
had a marginal negative association with native grass cover and percent
slope. The cover of non-native species was significantly greater than
zero in ponderosa pine plots that were thinned prior to the wildfire.
However, the confidence intervals of all treatment types overlapped the
confidence interval for untreated plots in ponderosa pine, indicating no
significant treatment effect.

> **Table 7**. Summary statistics for all variables considered in
> multiple regression models to explain percent cover of non-native
> species at the 1000-m^2^ scale at each of our three study sites.

  ------------ ---------- ---------- ---------- ---------- ---------- ----------
  Variable     Cerro                 Hi Meadow             Hayman     
               Grande                                                 

               mean       s.d.       mean       s.d.       mean       s.d.

  non-native   5.46       (4.76)     7.34       (6.13)     0.66       (2.02)

  seed         7.41       (5.67)     4.60       (8.51)     0.26       (0.76)

  grass        8.57       (7.51)     14.06      (7.08)     1.03       (1.84)

  rich         32.71      (10.20)    42.12      (10.42)    42.07      (12.03)

  mulch        n/a        n/a        n/a        n/a        4.81       (13.90)

  char         1.28       (0.96)     1.19       (0.81)     1.49       (0.62)

  canopy       38.04      (22.72)    44.28      (20.39)    34.32      (21.83)

  soil         35.79      (26.01)    24.30      (27.85)    44.12      (33.76)

  N            0.14       (0.13)     0.20       (0.14)     0.17       (0.07)

  clay         16.52      (5.59)     13.50      (2.32)     13.82      (2.95)

  slope        24.60      (16.5)     24.40      (14.60)    30.20      (13.70)

  elevation    2357.55    (244.68)   n/a        n/a        n/a        n/a

  radiation    0.59       (0.18)     0.67       (0.22)     0.72       (0.24)

  road         288.41     (422.77)   324.85     (209.32)   693.95     (612.86)

  stream       430.67     (431.85)   235.66     (210.96)   507.39     (334.57)
  ------------ ---------- ---------- ---------- ---------- ---------- ----------

> **Notes:**
>
> Explanation of variables: *non-native* = cover (%) of non-native
> species. *seed* = seeded grass cover (%). *grass* = native grass cover
> (%). *rich* = native species richness (#). *mulch* = cover (%) of
> straw mulch. *char* = mean char depth (0-3 rating). *canopy* = canopy
> cover (%). *soil* = exposed bare soil (%). *N* = total soil nitrogen
> (%). *clay* = clay-size particles (%) in soil. *slope* = slope
> steepness (%). *elevation* = elevation (m) above mean sea level.
> *radiation* = direct incident radiation (MJ/cm^2^/yr) calculated from
> percent slope and aspect (McCune and Keon 2002). *road* = plot
> distance (m) from nearest road. *stream* = plot distance (m) from
> nearest stream.

Two models had considerable support in the data at the 1000-m^2^ scale
in areas where no fuel treatments were implemented prior to the wildfire
and explained up to 31% of the variability in non-native species cover
(Table 9). Informative variables in these models included canopy cover,
char depth, total nitrogen, seeded grass cover, native grass cover, and
native species richness. Non-native species cover was positively
associated with seeded grass cover and native species richness. The 95%
confidence intervals for all other parameter estimates overlapped zero.
Though the effects were not significant, non-native species cover was
positively associated with char depth and negatively associated with
native grass cover.

Three models had considerable support in the data at the 1-m^2^ scale in
areas where fuel treatments were implemented prior to the wildfire and
explained up to 6% of the variability in non-native species cover (Table
10). Predictors in these models included native grass cover, seeded
grass cover, native species richness, bare ground exposure, and char
depth. However, only seeded grass cover and native species richness had
95% confidence that did not overlap zero. Non-native species cover was
positively associated with both seeded grass cover and native species
richness. Although estimates were only marginally significant,
non-native species cover was positively associated with char depth and
negatively associated with native grass cover.

Three models had considerable support in the data at the 1-m^2^ scale in
areas where fuel treatments were not implemented prior to the wildfire
and explained up to 4% of the variability in non-native species cover
(Table 11). Informative variables in these models included native grass
cover, seeded grass cover, native species richness, bare ground exposure
(%) and char depth. However, only native species richness and native
grass cover had 95% confidence intervals that did not overlap zero.
Non-native species cover was positively associated with native species
richness and negatively associated with native grass cover.

> **Table 8**. Parameter estimates for models selected to explain
> non-native species cover at the 1000-m^2^ scale in areas treated for
> fuels reduction prior to the Cerro Grande wildfire (n=38 for all
> models).

+----------------------------+-------+------+-------+-----+----------+
| *Selected Models ^a^*      | *Adju | *Aka | *     |     | *95%     |
|                            | sted* | ike* | Param |     | C.I.     |
|                            |       |      | eter* |     | ^d^*     |
|                            | *     | *We  |       |     |          |
|                            | r^2^* | ight | *Esti |     |          |
|                            |       | ^b^* | mates |     |          |
|                            |       |      | ^c^*  |     |          |
+----------------------------+-------+------+-------+-----+----------+
| slope+radi                 | 0.42  | 0.66 | Inte  | -0. | -15.90,  |
| ation+clay+seed+grass+rich |       |      | rcept | 508 | 14.93    |
+----------------------------+-------+------+-------+-----+----------+
| grass+seed+rich            | 0.28  | 0.17 | **r   | **0 | **0.16,  |
|                            |       |      | ich** | .35 | 0.54**   |
|                            |       |      |       | 3** |          |
+----------------------------+-------+------+-------+-----+----------+
| treat+seed+grass+rich      | 0.40  | 0.13 | slope | -0. | -0.45,   |
|                            |       |      |       | 202 | 0.05     |
+----------------------------+-------+------+-------+-----+----------+
| treat                      | 0.24  | 0.02 | grass | -1. | -3.48,   |
|                            |       |      |       | 363 | 0.76     |
+----------------------------+-------+------+-------+-----+----------+
|                            |       |      | clay  | 0.  | -0.15,   |
|                            |       |      |       | 241 | 0.63     |
+----------------------------+-------+------+-------+-----+----------+
|                            |       |      | seed  | 0.  | -0.22,   |
|                            |       |      |       | 077 | 0.38     |
+----------------------------+-------+------+-------+-----+----------+
|                            |       |      | radi  | -0  | -20.20,  |
|                            |       |      | ation | .35 | 19.50    |
+----------------------------+-------+------+-------+-----+----------+
|                            |       |      | thin  | 10. | 7.86,    |
|                            |       |      |       | 485 | 13.11    |
+----------------------------+-------+------+-------+-----+----------+
|                            |       |      | none  | 6.  | 3.44,    |
|                            |       |      |       | 953 | 10.47    |
+----------------------------+-------+------+-------+-----+----------+
|                            |       |      | thin  | 6.  | 2.30,    |
|                            |       |      | /burn | 325 | 10.35    |
+----------------------------+-------+------+-------+-----+----------+
|                            |       |      | burn  | 3.  | -0.51,   |
|                            |       |      |       | 814 | 8.14     |
+----------------------------+-------+------+-------+-----+----------+

> **Notes:**
>
> ^a-b^ See Table 4 notes. All selected models were significant at the
> p\<0.001 level.
>
> ^c^ See Table 7 for explanation of parameter abbreviations. However,
> *grass* was square-root transformed, *burn* = least square mean for
> ponderosa pine that was treated prior to wildfire with prescribed
> burning, *thin* = least square mean for ponderosa pine that was
> treated prior to the wildfire with thinning, *thin/burn* = least
> square mean for ponderosa pine that was treated prior to the wildfire
> with thinning and prescribed burning, *none* = least square mean for
> ponderosa pine that received no fuel treatment prior to wildfire.
> Model parameters predict mean cover (%) of non-native plant species.
>
> ^d^ Confidence intervals that do not include zero indicate a
> significant effect at the p\<0.05 level (emboldened).
>
> **Table 9**. Parameter estimates for models selected to explain
> non-native species cover at the 1000-m^2^ scale in areas that were not
> treated for fuels reduction prior to the Cerro Grande wildfire (n=53
> for all models).

+------------------------------+-------+------+-------+-----+--------+
| *Selected Models ^a^*        | *Adju | *Aka | *     |     | *9     |
|                              | sted* | ike* | Param |     | 5%C.I. |
|                              |       |      | eter* |     | ^d^*   |
|                              | *     | *we  |       |     |        |
|                              | r^2^* | ight | *Esti |     |        |
|                              |       | ^b^* | mates |     |        |
|                              |       |      | ^c^*  |     |        |
+------------------------------+-------+------+-------+-----+--------+
| grass + seed + rich          | 0.31  | 0.83 | Inte  | -0. | -3.68, |
|                              |       |      | rcept | 857 | 1.97   |
+------------------------------+-------+------+-------+-----+--------+
| char + canopy + N + grass +  | 0.31  | 0.17 | **r   | **0 | *      |
| seed + rich                  |       |      | ich** | .14 | *0.06, |
|                              |       |      |       | 0** | 0.22** |
+------------------------------+-------+------+-------+-----+--------+
|                              |       |      | **s   | **0 | *      |
|                              |       |      | eed** | .21 | *0.08, |
|                              |       |      |       | 8** | 0.36** |
+------------------------------+-------+------+-------+-----+--------+
|                              |       |      | char  | 0.  | -0.30, |
|                              |       |      |       | 760 | 1.82   |
+------------------------------+-------+------+-------+-----+--------+
|                              |       |      | grass | -0. | -1.01, |
|                              |       |      |       | 417 | 0.18   |
+------------------------------+-------+------+-------+-----+--------+
|                              |       |      | N     | -1. | -6.70, |
|                              |       |      |       | 721 | 3.25   |
+------------------------------+-------+------+-------+-----+--------+
|                              |       |      | c     | 0.  | -0.03, |
|                              |       |      | anopy | 003 | 0.03   |
+------------------------------+-------+------+-------+-----+--------+

> **Notes:**
>
> ^a-b,d^ See Table 4 notes. All selected models were significant at the
> p\<0.001 level.
>
> ^c^ See Table 7 for explanation of parameter abbreviations. However,
> *grass* and *N* were square-root transformed. Model parameters predict
> mean cover (%) of non-native plant species.
>
> **Table 10**. Parameter estimates for models selected to explain
> non-native species cover at the 1-m^2^ scale in areas that were
> treated for fuels reduction prior to the Cerro Grande wildfire (n=240
> for all models).

+--------------------------+--------+-------+--------+------+---------+
| *Selected Models ^a^*    | *Adj   | *Ak   | *Para  |      | *95%    |
|                          | usted* | aike* | meter* |      | C.I.    |
|                          |        |       |        |      | ^d^*    |
|                          | *r^2^* | *w    | *Est   |      |         |
|                          |        | eight | imates |      |         |
|                          |        | ^b^*  | ^c^*   |      |         |
+--------------------------+--------+-------+--------+------+---------+
| seed + grass + rich      | 0.05   | 0.44  | Int    | 0    | -0.23,  |
|                          |        |       | ercept | .134 | 0.50    |
+--------------------------+--------+-------+--------+------+---------+
| seed + grass + rich +    | 0.06   | 0.38  | **     | *    | **0.04, |
| char + soil              |        |       | seed** | *0.1 | 0.28**  |
|                          |        |       |        | 60** |         |
+--------------------------+--------+-------+--------+------+---------+
| seed                     | 0.05   | 0.19  | **     | *    | **0.01, |
|                          |        |       | rich** | *0.2 | 0.41**  |
|                          |        |       |        | 10** |         |
+--------------------------+--------+-------+--------+------+---------+
|                          |        |       | grass  | -0   | -0.23,  |
|                          |        |       |        | .110 | 0.01    |
+--------------------------+--------+-------+--------+------+---------+
|                          |        |       | char   | 0    | -0.04,  |
|                          |        |       |        | .280 | 0.59    |
+--------------------------+--------+-------+--------+------+---------+
|                          |        |       | soil   | -0   | -0.24,  |
|                          |        |       |        | .100 | 0.04    |
+--------------------------+--------+-------+--------+------+---------+

> **Notes:**
>
> ^a-b,d^ See Table 4 notes. All selected models were significant at the
> p\<0.01 level.
>
> ^c^ See Table 7 for explanation of parameter abbreviations. However,
> all variables were arcsine square-root transformed, including
> non-native species cover (%).
>
> **Table 11**. Selected multiple regression models for non-native
> species cover at the 1-m^2^ scale in areas that were not treated for
> fuels reduction prior to the Cerro Grande wildfire (n=550 for all
> models).

+--------------------------+--------+-------+--------+------+----------+
| *Selected Models ^a^*    | *Adj   | *Ak   | *Para  |      | *95%     |
|                          | usted* | aike* | meter* |      | C.I.     |
|                          |        |       |        |      | ^d^*     |
|                          | *r^2^* | *w    | *Es    |      |          |
|                          |        | eight | timate |      |          |
|                          |        | ^b^*  | ^c^*   |      |          |
+--------------------------+--------+-------+--------+------+----------+
| seed + grass + rich      | 0.04   | 0.75  | Int    | 0    | -0.08,   |
|                          |        |       | ercept | .057 | 0.20     |
+--------------------------+--------+-------+--------+------+----------+
| seed + grass + rich +    | 0.03   | 0.15  | **     | *    | **0.05,  |
| char + soil              |        |       | rich** | *0.1 | 0.19**   |
|                          |        |       |        | 20** |          |
+--------------------------+--------+-------+--------+------+----------+
| grass                    | 0.02   | 0.10  | **g    | **   | **-0.15, |
|                          |        |       | rass** | -0.0 | -0.03**  |
|                          |        |       |        | 88** |          |
+--------------------------+--------+-------+--------+------+----------+
|                          |        |       | char   | 0    | -0.06,   |
|                          |        |       |        | .040 | 0.14     |
+--------------------------+--------+-------+--------+------+----------+
|                          |        |       | seed   | 0    | -0.05,   |
|                          |        |       |        | .030 | 0.11     |
+--------------------------+--------+-------+--------+------+----------+
|                          |        |       | soil   | -0   | -0.06,   |
|                          |        |       |        | .003 | 0.06     |
+--------------------------+--------+-------+--------+------+----------+

> **Notes:**
>
> ^a-b,d^ See Table 4 notes. All selected models were significant at the
> p\<0.001 level.
>
> ^c^ See Table 7 for explanation of parameter abbreviations. However,
> all variables were arcsine square-root transformed, including
> non-native species cover (%).

3.2.2 *Hi Meadow*

Eleven multiple linear regression models were tested to predict cover of
non-native species at the Hi Meadow fire at the 1000-m^2^ scale. Three
models had considerable support in the data and accounted for up to 30%
of the variability in non-native species cover (Table 12). Only three
variables (char depth, native grass cover, and distance from roads) had
parameter estimates with 95% confidence intervals that did not overlap
zero. Non-native species cover was positively associated with char
depth, native grass cover, and distance from roads.

Six multiple linear regression models were tested to predict non-native
species cover in the Hi Meadow fire at the 1-m^2^ scale. All variables
were arcsine-square-root transformed prior to analysis to correct for
non-normal error distribution. Only one model had support in the data
and accounted for 7% of the variability in non-native species cover
(Table 13). Three variables, seeded grass cover, native grass cover, and
char depth, had parameter estimates with 95% confidence intervals that
did not overlap zero. Non-native species cover was negatively associated
with seeded grass cover and native grass cover and positively associated
with char depth.

> **Table 12.** Parameter estimates for models selected to explain
> non-native species cover at the 1000-m^2^ scale in the Hi Meadow fire
> (n=92 for all models).

+--------------------------------+------+------+------+-----+--------+
| *Selected Models ^a^*          | *A   | *Aka | *Pa  |     | *95%   |
|                                | djus | ike* | rame |     | C.I.   |
|                                | ted* |      | ter* |     | ^d^*   |
|                                |      | *We  |      |     |        |
|                                | *r   | ight | *E   |     |        |
|                                | ^2^* | ^b^* | stim |     |        |
|                                |      |      | ates |     |        |
|                                |      |      | ^c^* |     |        |
+--------------------------------+------+------+------+-----+--------+
| char+canopy+N+grass+seed+rich  | 0.29 | 0.56 | I    | -2. | -11.8, |
|                                |      |      | nter | 646 | 6.47   |
|                                |      |      | cept |     |        |
+--------------------------------+------+------+------+-----+--------+
| char+canopy+                   | 0.30 | 0.23 | **ch | **4 | *      |
| N+slope+clay+rd+strm+radiation |      |      | ar** | .31 | *2.53, |
|                                |      |      |      | 1** | 6.09** |
+--------------------------------+------+------+------+-----+--------+
| char+canopy+N                  | 0.25 | 0.21 | **ro | **0 | *      |
|                                |      |      | ad** | .00 | *0.00, |
|                                |      |      |      | 7** | 0.01** |
+--------------------------------+------+------+------+-----+--------+
|                                |      |      | *    | **0 | *      |
|                                |      |      | *gra | .18 | *0.00, |
|                                |      |      | ss** | 1** | 0.36** |
+--------------------------------+------+------+------+-----+--------+
|                                |      |      | st   | -0. | -0.01, |
|                                |      |      | ream | 004 | 0.00   |
+--------------------------------+------+------+------+-----+--------+
|                                |      |      | seed | -0. | -1.09, |
|                                |      |      |      | 410 | 0.27   |
+--------------------------------+------+------+------+-----+--------+
|                                |      |      | clay | 0.  | -0.21, |
|                                |      |      |      | 309 | 0.83   |
+--------------------------------+------+------+------+-----+--------+
|                                |      |      | rich | 0.  | -0.05, |
|                                |      |      |      | 062 | 0.17   |
+--------------------------------+------+------+------+-----+--------+
|                                |      |      | N    | 4.  | -4.6   |
|                                |      |      |      | 172 | 4,13.0 |
+--------------------------------+------+------+------+-----+--------+
|                                |      |      | s    | -0. | -0.12, |
|                                |      |      | lope | 033 | 0.06   |
+--------------------------------+------+------+------+-----+--------+
|                                |      |      | ca   | -0. | -0.08, |
|                                |      |      | nopy | 020 | 0.04   |
+--------------------------------+------+------+------+-----+--------+
|                                |      |      | r    | 1.  | -3.65, |
|                                |      |      | adia | 812 | 7.27   |
|                                |      |      | tion |     |        |
+--------------------------------+------+------+------+-----+--------+

> **Notes:**
>
> ^a-b,d^ See Table 4 notes. All selected models were significant at the
> p\<0.0001 level.
>
> ^c^ See Table 7 for explanation of parameter abbreviations. However,
> *seed* and *N* were square-root transformed. Model parameters predict
> mean cover (%) of non-native plant species.
>
> **Table 13.** Parameter estimates for models selected to explain
> non-native species cover at the 1-m^2^ scale in the Hi Meadow fire
> (n=920 for all models).

+--------------------------+-------+-------+-------+------+----------+
| *Selected Models ^a^*    | *Adju | *Ak   | *     |      | *95%     |
|                          | sted* | aike* | Param |      | C.I.^d^* |
|                          |       |       | eter* |      |          |
|                          | *     | *W    |       |      |          |
|                          | r^2^* | eight | *Esti |      |          |
|                          |       | ^b^*  | mates |      |          |
|                          |       |       | ^c^*  |      |          |
+--------------------------+-------+-------+-------+------+----------+
| seed + grass + rich +    | 0.07  | 1.00  | Inte  | -0   | -0.20,   |
| char + soil              |       |       | rcept | .053 | 0.10     |
+--------------------------+-------+-------+-------+------+----------+
|                          |       |       | **c   | *    | **0.21,  |
|                          |       |       | har** | *0.3 | 0.41**   |
|                          |       |       |       | 10** |          |
+--------------------------+-------+-------+-------+------+----------+
|                          |       |       | **s   | **   | **-0.22, |
|                          |       |       | eed** | -0.1 | -0.06**  |
|                          |       |       |       | 40** |          |
+--------------------------+-------+-------+-------+------+----------+
|                          |       |       | **gr  | **   | **-0.13, |
|                          |       |       | ass** | -0.0 | -0.01**  |
|                          |       |       |       | 70** |          |
+--------------------------+-------+-------+-------+------+----------+
|                          |       |       | rich  | 0    | -0.02,   |
|                          |       |       |       | .100 | 0.22     |
+--------------------------+-------+-------+-------+------+----------+
|                          |       |       | soil  | -0   | -0.06,   |
|                          |       |       |       | .002 | 0.06     |
+--------------------------+-------+-------+-------+------+----------+

> **Notes:**
>
> ^a-b,d^ See Table 4 notes. The selected model was significant at the
> p\<0.0001 level.
>
> ^c^ See Table 7 for explanation of parameter abbreviations. However,
> all variables were arcsine square-root transformed, including
> non-native species cover (%).

3.2.3 *Hayman*

Eleven multiple linear regression models were tested to predict cover of
non-native species at the 1000-m^2^ scale at the Hayman fire. Two models
had considerable support in the data and accounted for up to 26% of the
variation in non-native species cover (Table 14). Only one variable,
native species richness, had parameter estimates with a 95% confidence
interval that did not overlap zero. The association was positive between
non-native species cover and native species richness. The positive
relationship between char depth and non-native species cover was nearly
significant.

**Table 14.** Parameter estimates for models selected to explain
non-native species cover at the 1,000-m^2^ scale in the Hayman fire
(n=102 for all models). Models with Akaike weights greater than 0.01
were used to calculate averaged parameter estimates.

+----------------------------------+------+-----+------+----+--------+
| *Selected Models ^a^*            | *A   | *A  | *Pa  |    | *95%*  |
|                                  | djus | kai | rame |    |        |
|                                  | ted* | ke* | ter* |    | *C.I.  |
|                                  |      |     |      |    | ^d^*   |
|                                  | *r   | *   | *E   |    |        |
|                                  | ^2^* | Wei | stim |    |        |
|                                  |      | ght | ates |    |        |
|                                  |      | ^   | ^c^* |    |        |
|                                  |      | b^* |      |    |        |
+----------------------------------+------+-----+------+----+--------+
| cha                              | 0.26 | 0   | *    | ** | **     |
| r+canopy+N+mulch+grass+seed+rich |      | .97 | *Int | -0 | -1.46, |
|                                  |      |     | erce | .8 | -      |
|                                  |      |     | pt** | 72 | 0.29** |
|                                  |      |     |      | ** |        |
+----------------------------------+------+-----+------+----+--------+
| slope+clay+road+r                | 0.22 | 0   | **ri | *  | *      |
| adiation+rd+strm+grass+seed+rich |      | .03 | ch** | *0 | *0.01, |
|                                  |      |     |      | .0 | 0.03** |
|                                  |      |     |      | 20 |        |
|                                  |      |     |      | ** |        |
+----------------------------------+------+-----+------+----+--------+
|                                  |      |     | strm | 0  | 0.00,  |
|                                  |      |     |      | .1 | 0.30   |
|                                  |      |     |      | 00 |        |
+----------------------------------+------+-----+------+----+--------+
|                                  |      |     | char | 0  | -0.02, |
|                                  |      |     |      | .1 | 0.34   |
|                                  |      |     |      | 60 |        |
+----------------------------------+------+-----+------+----+--------+
|                                  |      |     | N    | 1  | -0.15, |
|                                  |      |     |      | .0 | 2.33   |
|                                  |      |     |      | 90 |        |
+----------------------------------+------+-----+------+----+--------+
|                                  |      |     | ca   | -0 | -0.01, |
|                                  |      |     | nopy | .0 | 0.00   |
|                                  |      |     |      | 02 |        |
+----------------------------------+------+-----+------+----+--------+
|                                  |      |     | rd   | -0 | -0.30, |
|                                  |      |     |      | .1 | 0.10   |
|                                  |      |     |      | 00 |        |
+----------------------------------+------+-----+------+----+--------+
|                                  |      |     | m    | 0  | -0.03, |
|                                  |      |     | ulch | .0 | 0.09   |
|                                  |      |     |      | 30 |        |
+----------------------------------+------+-----+------+----+--------+
|                                  |      |     | clay | 0  | -0.03, |
|                                  |      |     |      | .0 | 0.05   |
|                                  |      |     |      | 10 |        |
+----------------------------------+------+-----+------+----+--------+
|                                  |      |     | g    | 0  | -0.11, |
|                                  |      |     | rass | .0 | 0.17   |
|                                  |      |     |      | 29 |        |
+----------------------------------+------+-----+------+----+--------+
|                                  |      |     | s    | -0 | -0.01, |
|                                  |      |     | lope | .0 | 0.01   |
|                                  |      |     |      | 02 |        |
+----------------------------------+------+-----+------+----+--------+
|                                  |      |     | r    | 0  | -0.36, |
|                                  |      |     | adia | .0 | 0.46   |
|                                  |      |     | tion | 50 |        |
+----------------------------------+------+-----+------+----+--------+
|                                  |      |     | seed | -0 | -0.33, |
|                                  |      |     |      | .0 | 0.26   |
|                                  |      |     |      | 35 |        |
+----------------------------------+------+-----+------+----+--------+

**Notes:**

^a-b,d^ See Table 4 notes. All selected models were significant at the
p\<0.0001 level.

> ^c^ See Table 7 for explanation of parameter abbreviations. However,
> *seed*, *grass*, and *mulch* were square-root transformed and *road*
> and *stream* are expressed in km. Model parameters predict mean cover
> (%) of non-native plant species.

Four multiple linear regression models were tested to predict non-native
species cover at the 1-m^2^ scale at the Hayman fire. All variables were
arcsine square-root transformed prior to analysis to correct for
non-normal error distribution. Only one model had considerable support
in the data and it accounted for 6% of the variability in non-native
species cover (Table 15). Four variables had parameter estimates with
95% confidence intervals that did not overlap zero: non-native species
cover was positively associated with seeded grass cover, native species
richness, and char depth, and negatively associated with bare soil
exposure.

> **Table 15.** Parameter estimates for models selected to explain
> non-native species cover at the 1-m^2^ scale in the Hayman fire
> (n=1060 for all models).

+--------------------------+--------+--------+------+------+----------+
| *Selected Models ^a^*    | *Adj   | *A     | *Pa  |      | *95%     |
|                          | usted* | kaike* | rame |      | C.I.     |
|                          |        |        | ter* |      | ^d^*     |
|                          | *r^2^* | *      |      |      |          |
|                          |        | Weight | *    |      |          |
|                          |        | ^b^*   | Esti |      |          |
|                          |        |        | mate |      |          |
|                          |        |        | ^c^* |      |          |
+--------------------------+--------+--------+------+------+----------+
| Seed mulch grass rich    | 0.06   | 1.00   | Int  | -0   | -0.16,   |
| char soil                |        |        |      | .037 | 0.23     |
+--------------------------+--------+--------+------+------+----------+
|                          |        |        | **ri | **0. | **0.16,  |
|                          |        |        | ch** | 22** | 0.20**   |
+--------------------------+--------+--------+------+------+----------+
|                          |        |        | **se | **0. | **0.04,  |
|                          |        |        | ed** | 14** | 0.24**   |
+--------------------------+--------+--------+------+------+----------+
|                          |        |        | **so | *    | **-0.09, |
|                          |        |        | il** | *-0. | -0.01**  |
|                          |        |        |      | 05** |          |
+--------------------------+--------+--------+------+------+----------+
|                          |        |        | **ch | **0. | **0.01,  |
|                          |        |        | ar** | 11** | 0.21**   |
+--------------------------+--------+--------+------+------+----------+
|                          |        |        | m    | -    | -0.06,   |
|                          |        |        | ulch | 0.02 | 0.18     |
+--------------------------+--------+--------+------+------+----------+
|                          |        |        | g    | -    | -0.07,   |
|                          |        |        | rass | 0.01 | 0.20     |
+--------------------------+--------+--------+------+------+----------+

> **Notes:**
>
> ^a-b,d^ See Table 4 notes. The selected model was significant at the
> p\<0.0001 level.
>
> ^c^ See Table 7 for explanation of parameter abbreviations. However,
> all variables were arcsine square-root transformed, including
> non-native species cover (%).

3.3 ***Predictors of Post-fire Fuel Flammability***

We developed custom fuel models (after Burgan and Rothermel 1984) for
each of the plots sampled in our vegetation surveys to determine whether
non-native species had any influence on post-fire flammability. Summary
statistics for all variables considered in our models for each fire are
presented in Table 16.

> **Table 16**. Summary statistics for all variables considered in
> multiple regression models to explain post-fire fuelbed flammability
> (predicted flame length) at our three study sites.

+----------------+-------+--------+-------+--------+-------+--------+
| > Variable     | Cerro |        | Hi    |        | H     |        |
|                | G     |        | M     |        | ayman |        |
|                | rande |        | eadow |        |       |        |
+----------------+-------+--------+-------+--------+-------+--------+
|                | mean  | s.d.   | mean  | s.d.   | mean  | s.d.   |
+----------------+-------+--------+-------+--------+-------+--------+
| > flame length | 45.49 | (      | 37.43 | (      | 21.57 | (      |
|                |       | 44.32) |       | 33.30) |       | 23.50) |
+----------------+-------+--------+-------+--------+-------+--------+
| > xtc          | 5.84  | (9.33) | 6.39  | (      | 6.62  | (9.90) |
|                |       |        |       | 10.57) |       |        |
+----------------+-------+--------+-------+--------+-------+--------+
| > seed         | 19.80 | (      | 8.22  | (      | 5.32  | (      |
|                |       | 22.41) |       | 17.98) |       | 14.00) |
+----------------+-------+--------+-------+--------+-------+--------+
| > load         | 0.83  | (1.00) | 0.87  | (0.84) | 0.66  | (1.10) |
+----------------+-------+--------+-------+--------+-------+--------+
| > cnpy         | 36.12 | (      | 44.44 | (      | 34.42 | (      |
|                |       | 22.09) |       | 20.35) |       | 21.64) |
+----------------+-------+--------+-------+--------+-------+--------+
| > day          | 26.03 | (      | 22.66 | (      | 28.82 | (      |
|                |       | 12.38) |       | 12.98) |       | 15.22) |
+----------------+-------+--------+-------+--------+-------+--------+
| > elev         | 2.35  | (0.24) | 2.30  | (0.10) | 2.35  | (0.13) |
+----------------+-------+--------+-------+--------+-------+--------+
| > topo         | 0.48  | (0.32) | 0.53  | (0.31) | 0.54  | (0.30) |
+----------------+-------+--------+-------+--------+-------+--------+
| > asp          | 0.75  | (0.63) | 0.82  | (0.70) | 0.95  | (0.76) |
+----------------+-------+--------+-------+--------+-------+--------+

> **Notes:**
>
> Explanation of variables: *Flame length* is the flame length (cm)
> output of the BEHAVE fire behavior prediction and fuel modeling system
> (Burgan and Rothermel 1984). *xtc* = cover (%) of non-native species
> relative to total live surface cover. *seed* = cover (%) of species
> included in the post-fire rehabilitation seed mix relative to total
> live surface cover. *load* = surface biomass (kg/ha) excluding logs
> greater than 7.6 cm in diameter. *cnpy* = canopy cover (%). *day* =
> sampling day (day 1 = June 20 at Cerro Grande, June 24 at Hi Meadow,
> and June 17 at Hayman). *elev* = elevation (km) above mean sea level.
> *topo* = topographic position expressed as the fraction of the
> elevation gradient between the nearest drainage and the nearest ridge.
> *asp* = slope aspect (degrees) transformed to increase continuously
> from NE (value = 0) to SW (value = 2) in both directions (after Beers
> et al. 1966) with the function
>
> COS(RADIANS(225-asp))+1.

3.3.1 *Cerro Grande*

Regression models for the Cerro Grande fire explained 61% of the
variability in post-fire flammability (i.e. BEHAVE (Burgan and Rothermel
1984) flame length prediction). The most significant variables were
elevation, aspect, and an interaction between total fuel load and the
relative cover of non-native species (Table 17). However, a fire will
burn only if there is fuel available: a condition barely met in many of
the severely burned areas we sampled. While fire potential increased
with increasing fuel load as expected at the Cerro Grande fire, the
interaction between total fuel load and the relative cover of non-native
species indicates that the effect of increasing fuel loads was
increasingly exacerbated by the relative abundance of non-native
species. Post-fire flammability also increased with conditions that
facilitated the curing of live biomass, such as xeric conditions
associated with lower elevations and southwesterly aspects. Topographic
position and sample date were also found to be marginally significant
predictors of post-fire flammability: curing of live biomass and
post-fire flammability was enhanced by proximity to ridges and the
progression of summer.

> **Table 17**. Parameter estimates for models selected to explain
> fuelbed flammability within the Cerro Grande burn area 1-year
> post-fire (n = 79, adjusted *r*^2^ = 0.61).

+--------------------------------+------+------+------+-------+------+
| Selected Models ^a^            | Adju | Ak   | P    |       | p-v  |
|                                | sted | aike | aram |       | alue |
|                                | r^2^ | W    | eter |       | ^d^  |
|                                |      | eigh |      |       |      |
|                                |      | t^b^ | Esti |       |      |
|                                |      |      | mate |       |      |
|                                |      |      | s^c^ |       |      |
+--------------------------------+------+------+------+-------+------+
| elev+                          | 0.61 | 0.66 | I    | 14    | 0.   |
| load+xtc+asp+topo+day+xtcXload |      |      | nter | .8782 | 0001 |
|                                |      |      | cept |       |      |
+--------------------------------+------+------+------+-------+------+
| elev+load+                     | 0.61 | 0.18 | elev | -5    | 0.   |
| xtc+asp+topo+day+cnpy+xtcXload |      |      |      | .3939 | 0001 |
+--------------------------------+------+------+------+-------+------+
| elev+load+                     | 0.61 | 0.16 | xtcX | 0     | 0.   |
| xtc+asp+topo+day+seed+xtcXload |      |      | load | .0853 | 0012 |
+--------------------------------+------+------+------+-------+------+
|                                |      |      | asp  | 0     | 0.   |
|                                |      |      |      | .7753 | 0367 |
+--------------------------------+------+------+------+-------+------+
|                                |      |      | topo | 1     | 0.   |
|                                |      |      |      | .3267 | 0636 |
+--------------------------------+------+------+------+-------+------+
|                                |      |      | day  | 0     | 0.   |
|                                |      |      |      | .0329 | 0790 |
+--------------------------------+------+------+------+-------+------+
|                                |      |      | cnpy | 0     | 0.   |
|                                |      |      |      | .0008 | 6965 |
+--------------------------------+------+------+------+-------+------+
|                                |      |      | seed | 0     | 0.   |
|                                |      |      |      | .0004 | 8372 |
+--------------------------------+------+------+------+-------+------+
|                                |      |      | xtc  | -0    |      |
|                                |      |      |      | .0361 |      |
+--------------------------------+------+------+------+-------+------+
|                                |      |      | load | 1     |      |
|                                |      |      |      | .9084 |      |
+--------------------------------+------+------+------+-------+------+

> **Notes:**
>
> ^a,b^ See Table 4 notes. All selected models were significant at the
> p\<0.0001 level.
>
> ^c^ Model parameters predict a square root transformation of flame
> length as predicted by the BEHAVE fire behavior prediction and fuel
> modeling system (Burgan and Rothermel 1984) for the sampled fuel
> conditions and a standard weather environment. See Table 16 for
> explanation of parameter abbreviations.
>
> ^d^ Statistical significance cannot be interpreted and is thus not
> reported for main effects that occur only in conjunction with a
> multiplicitive interaction between it and another variable.

3.3.2 *Hi Meadow*

Regression models for the Hi Meadow fire explained 19% of the
variability in post-fire flammability (Table 18). The most significant
variables were total fuel load, an interaction between fuel load and
canopy cover, and an interaction between sample date and elevation.

The relative cover of non-native species was included in the set of most
informative models, but its influence on flammability was not
significant. Flammability increased significantly with increases in fuel
load. But the effect of fuel load was moderated by canopy cover, as
indicated by the interaction between these two variables. High canopy
cover shades surface fuels and slows the curing rate of live biomass,
reducing flammability. Seasonal curing, as indicated by sample date, was
a flammability enhancer that was moderated by elevation. The interaction
between sample date and elevation indicates that seasonal curing and
consequent increases in flammability progressed fastest at low
elevations.

3.3.3 *Hayman*

Regression models for the Hayman fire explained 7% of the variability in
post-fire flammability (Table 19). The most significant variables were
total fuel load and an interaction between elevation and non-native
species cover. Flammability tended to be lower at high elevations, but
this dampening effect was lessened by non-native species. Flammability
increased significantly with increases in fuel load and increased
marginally with curing associated with southwesterly aspects and later
sampling dates.

> **Table 18**. Parameter estimates for models selected to explain
> fuelbed flammability within the Hi Meadow burn area 2-years post-fire
> (n = 94, adjusted *r*^2^ = 0.19).

+--------------------------------+------+------+-------+------+-----+
| Selected Models ^a^            | Adju | Ak   | Para  |      | p   |
|                                | sted | aike | meter |      | -va |
|                                | r^2^ | We   |       |      | lue |
|                                |      | ight | Es    |      |     |
|                                |      | ^b^  | timat |      |     |
|                                |      |      | es^c^ |      |     |
+--------------------------------+------+------+-------+------+-----+
| load+day+elev+dayXelev         | 0.20 | 0.25 | Int   | 0.   | 0.1 |
|                                |      |      |       | 1362 | 362 |
+--------------------------------+------+------+-------+------+-----+
| load+day+cnpy+loadXcnpy        | 0.19 | 0.23 | load  | 1.   | 0.0 |
|                                |      |      |       | 9451 | 003 |
+--------------------------------+------+------+-------+------+-----+
| load+day+cnpy+asp+loadXcnpy    | 0.20 | 0.15 | day   | -0.  | 0.0 |
|                                |      |      | Xelev | 2469 | 191 |
+--------------------------------+------+------+-------+------+-----+
| load+day+cnpy+elev+loadXcnpy   | 0.19 | 0.08 | load  | -0.  | 0.0 |
|                                |      |      | Xcnpy | 0172 | 198 |
+--------------------------------+------+------+-------+------+-----+
| load+day+elev+cnpy+dayXelev    | 0.19 | 0.08 | day   | 0.   | 0.0 |
|                                |      |      |       | 5340 | 800 |
+--------------------------------+------+------+-------+------+-----+
| load+day+elev+topo+dayXelev    | 0.19 | 0.07 | asp   | 0.   | 0.2 |
|                                |      |      |       | 0629 | 078 |
+--------------------------------+------+------+-------+------+-----+
| load+day+elev+xtc+dayXelev     | 0.19 | 0.07 | elev  | 4.   | 0.4 |
|                                |      |      |       | 3197 | 746 |
+--------------------------------+------+------+-------+------+-----+
| load+day+elev+seed+dayXelev    | 0.19 | 0.07 | cnpy  | 0.   | 0.6 |
|                                |      |      |       | 0104 | 141 |
+--------------------------------+------+------+-------+------+-----+
|                                |      |      | topo  | -0.  | 0.7 |
|                                |      |      |       | 0142 | 936 |
+--------------------------------+------+------+-------+------+-----+
|                                |      |      | xtc   | 0.   | 0.8 |
|                                |      |      |       | 0004 | 075 |
+--------------------------------+------+------+-------+------+-----+
|                                |      |      | seed  | -0.  | 0.9 |
|                                |      |      |       | 0001 | 453 |
+--------------------------------+------+------+-------+------+-----+

> **Notes:**
>
> ^a,\ b^ See Table 4 notes. All selected models were significant at the
> p\<0.001 level.
>
> ^c^ Model parameters predict the log transformation of flame length as
> predicted by the BEHAVE fire behavior prediction and fuel modeling
> system (Burgan and Rothermel 1984) for the sampled fuel conditions and
> a standard weather environment. See Table 16 for explanation of
> parameter abbreviations.
>
> **Table 19**. Parameter estimates for models selected to explain
> fuelbed flammability within the Hayman burn area 1-year post-fire (n =
> 106, adjusted *r*^2^ = 0.07).

+--------------------------------+------+------+-------+-----+------+
| Selected Models ^a^            | Adju | Ak   | Para  |     | p-   |
|                                | sted | aike | meter |     | valu |
|                                | r^2^ | We   |       |     | e^d^ |
|                                |      | ight | Es    |     |      |
|                                |      | ^b^  | timat |     |      |
|                                |      |      | es^c^ |     |      |
+--------------------------------+------+------+-------+-----+------+
| load+asp+day+elev+aspXload     | 0.06 | 0.40 | Inte  | 1.6 | 0.   |
|                                |      |      | rcept | 423 | 0013 |
+--------------------------------+------+------+-------+-----+------+
| load+asp+day+elev+xtc+xtcXelev | 0.08 | 0.39 | load  | 0.0 | 0.   |
|                                |      |      |       | 852 | 0222 |
+--------------------------------+------+------+-------+-----+------+
| load+asp+day+cnpy+aspXcnpy     | 0.06 | 0.21 | xtc   | 0.0 | 0.   |
|                                |      |      | Xelev | 397 | 0322 |
+--------------------------------+------+------+-------+-----+------+
|                                |      |      | day   | 0.0 | 0.   |
|                                |      |      |       | 061 | 0522 |
+--------------------------------+------+------+-------+-----+------+
|                                |      |      | asp   | 0.0 | 0.   |
|                                |      |      |       | 494 | 0611 |
+--------------------------------+------+------+-------+-----+------+
|                                |      |      | cnp   | 0.0 | 0.   |
|                                |      |      | yXasp | 011 | 0831 |
+--------------------------------+------+------+-------+-----+------+
|                                |      |      | asp   | 0.0 | 0.   |
|                                |      |      | Xload | 502 | 1731 |
+--------------------------------+------+------+-------+-----+------+
|                                |      |      | xtc   | -   |      |
|                                |      |      |       | 0.0 |      |
|                                |      |      |       | 940 |      |
+--------------------------------+------+------+-------+-----+------+
|                                |      |      | elev  | -   |      |
|                                |      |      |       | 0.3 |      |
|                                |      |      |       | 529 |      |
+--------------------------------+------+------+-------+-----+------+
|                                |      |      | cnpy  | -   |      |
|                                |      |      |       | 0.0 |      |
|                                |      |      |       | 012 |      |
+--------------------------------+------+------+-------+-----+------+

> **Notes:**
>
> ^a^ See Table 4 notes. All selected models were significant at the
> p\<0.05 level.
>
> ^c^ Model parameters predict a log transformation of flame length as
> predicted by the BEHAVE fire behavior prediction and fuel modeling
> system (Burgan and Rothermel 1984) for the sampled fuel conditions and
> a standard weather environment. See Table 16 for explanation of
> parameter abbreviations.
>
> ^d^ Statistical significance cannot be interpreted and is thus not
> reported for main effects that occur only in conjunction with a
> multiplicitive interaction between it and another variable.

3.4 ***Spatial Prediction Models***

We used the 79 data points from the Cerro Grande fire (based on
Modified-Whittaker nested plots of 1000-m^2^) to represent different
variables that were extracted from Landsat TM data, topographic data,
and vegetation characteristics (Table 20). Total plant species richness,
including species of unknown origin and specimens that could not be
identified, ranged from 14 to 78 per plot. Typically, nonnative species
represented \>10% of the total species at a site and about 5% of the
foliar cover (Table 20).

The results for our field data using Moran's *I* (Moran 1948, Mantel
1967*)* and the bivariate cross correlation-statistic *I*~YZ~
(Czaplewski and Reich 1993, Bonham et al. 1995) to test for spatial
auto-correlation and cross-correlation with residuals suggested that, at
large-scales, the probability of non-native plant species being present
and their percent cover were both spatially independent throughout the
study site (Table 21). That is, the spatial relationships were not
statistically significant. Native species richness was not spatially
independent (Kalkhan and Stohlgren 2000). However, these results may be
different for individual plant species (Kalkhan et al. 2000). In
general, large-scale patterns of species distribution were controlled by
topographic factors such as elevation, aspect, and slope with complex
spatial patterns. This may explain why negative spatial auto-correlation
and cross-correlation resulted when large-scale plots were used (Kalkhan
et al. 2000). These results may have been different if individual native
or non-native plant species had been used in the analysis (Kalkhan et
al. 2000).

The results of modeling the large-scale and small-scale variability in
predicting total, native, and non-native species richness and percent
cover of non-native and native plant species within the Cerro Grande
fire site are shown in Table 21. Models were developed for large-scale
variability of the total number of plants (both native and non-native
species) and percent plant cover (total, native, and non-native). The
trend surface models identified using stepwise multiple regressions
explained 10 to 59 percent of the variability in the dependent variables
and all models were significant.

Small-scale variability models were used to examine the spatial
continuity of variability and were developed using ordinary kriging
based on the Gaussian semi-variogram model which was based on AICc
(Table 21). Model parameters were estimated using weighted least squares
(Cressie 1985). The residuals were also analyzed for spatial
auto-correlation and cross-correlation (Czaplewski and Reich 1993, Reich
et al. 1995) with the geographic variables (e.g., elevation, slope,
aspect, etc.). Inverse distance weighting was used to define the spatial
weights matrix. The kriging models were cross-validated to assess the
variability in the prediction errors. The cross-validation included
deleting one observation from the data set and predicting the deleted
observation using the remaining observations (Reich et al. 1999). This
process was repeated for all observations in the data set. The final
models (trend surface plus the kriged residuals) explained 60 to 84
percent of the variability in our data. In addition, the accuracies of
the kriging models were assessed using the relative mean squared error
suggested by Havesi et al. (1992).

> **Table 20**. Summary statistics for all variables used in developing
> spatial statistical models for the Cerro Grande fire, Los Alamos, New
> Mexico, 2001.

+------------------------+----------+----------+----------+----------+
| Variable               | Minimum  | Median   | # Mean   | Maximum  |
+------------------------+----------+----------+----------+----------+
| Total plant species    | 14       | 44       | 51       | 78       |
+------------------------+----------+----------+----------+----------+
| Native plant species   | 8        | 31       | 40       | 57       |
+------------------------+----------+----------+----------+----------+
| Non-native plant       | 0        | 4        | 4.1      | 9        |
| species                |          |          |          |          |
+------------------------+----------+----------+----------+----------+
| Native cover (%)       | 4.2      | 22.3     | 25.9     | 76.3     |
+------------------------+----------+----------+----------+----------+
| Non-native cover (%)   | 0        | 0.6      | 1.3      | 7.9      |
+------------------------+----------+----------+----------+----------+
| Elevation              | 1972     | 2266     | 2356     | 3023     |
+------------------------+----------+----------+----------+----------+
| Slope                  | 1.4      | 10.02    | 12.46    | 32.5     |
+------------------------+----------+----------+----------+----------+
| Absolute aspect        | 5.2      | 80       | 86.9     | 180      |
+------------------------+----------+----------+----------+----------+
| Thematic Mapper band   |          |          |          |          |
+------------------------+----------+----------+----------+----------+
| > 1                    | 60       | 80       | 81.3     | 116      |
+------------------------+----------+----------+----------+----------+
| > 2                    | 45       | 65       | 66.3     | 106      |
+------------------------+----------+----------+----------+----------+
| > 3                    | 38       | 71       | 73.5     | 131      |
+------------------------+----------+----------+----------+----------+
| > 4                    | 29       | 48       | 49.9     | 111      |
+------------------------+----------+----------+----------+----------+
| > 5                    | 43       | 100      | 98.9     | 168      |
+------------------------+----------+----------+----------+----------+
| > 6                    | 112      | 188      | 185.1    | 222      |
+------------------------+----------+----------+----------+----------+
| > 7                    | 26       | 92       | 92.2     | 169      |
+------------------------+----------+----------+----------+----------+
| > 8                    | 34       | 47       | 49.2     | 85       |
+------------------------+----------+----------+----------+----------+
| Band ratio             |          |          |          |          |
+------------------------+----------+----------+----------+----------+
| > (5/4)                | 63       | 127      | 133.5    | 191      |
+------------------------+----------+----------+----------+----------+
| > (4/3)                | 1        | 1        | 1.038    | 2        |
+------------------------+----------+----------+----------+----------+
| > (3/1)                | 85       | 85       | 88.2     | 170      |
+------------------------+----------+----------+----------+----------+
| > (4--3)               | 22       | 42       | 54.9     | 184      |
+------------------------+----------+----------+----------+----------+
| NDVI^a^                | 0        | 1        | 0.620    | 1        |
+------------------------+----------+----------+----------+----------+
| TNDVI^b^               | 0        | 0        | 0.4975   | 115      |
+------------------------+----------+----------+----------+----------+
| Tassel Cap             |          |          |          |          |
+------------------------+----------+----------+----------+----------+
| > Band 1               | 111      | 168      | 173.4    | 265      |
+------------------------+----------+----------+----------+----------+
| > Band 2               | --80     | --53     | --49.8   | 3        |
+------------------------+----------+----------+----------+----------+
| > Band 3               | --83     | --41     | 38.7     | 7        |
+------------------------+----------+----------+----------+----------+
| > Band 4               | 19       | 27       | 26.7     | 34       |
+------------------------+----------+----------+----------+----------+
| > Band 5               | --71     | --39     | --37.7   | --12     |
+------------------------+----------+----------+----------+----------+
| > Band 6               | --20     | --15     | --15.2   | --11     |
+------------------------+----------+----------+----------+----------+

> **Notes:**
>
> ^a^NDVI = Normalized Difference Vegetation Index.
>
> ^b^TNDVI = Transformed Normalized Difference Vegetation Index.

###### 

> **Table 21.** Summary statistics for large- and small-scale
> variability models to predict total, native, and non-native plant
> species richness and percent cover within the Cerro Grande fire, Los
> Alamos, New Mexico, 2001.

+----------------------+----+-----+------+---+-------------+-----+-----+
|                      | L  |     |      |   | Large- and  |     |     |
|                      | ar |     |      |   | small-scale |     |     |
|                      | ge |     |      |   | variability |     |     |
|                      | -s |     |      |   |             |     |     |
|                      | ca |     |      |   | (OLS and    |     |     |
|                      | le |     |      |   | kriging     |     |     |
|                      | v  |     |      |   | --variogram |     |     |
|                      | ar |     |      |   | model)^b^   |     |     |
|                      | ia |     |      |   |             |     |     |
|                      | bi |     |      |   |             |     |     |
|                      | li |     |      |   |             |     |     |
|                      | ty |     |      |   |             |     |     |
|                      |    |     |      |   |             |     |     |
|                      | (O |     |      |   |             |     |     |
|                      | LS |     |      |   |             |     |     |
|                      | m  |     |      |   |             |     |     |
|                      | od |     |      |   |             |     |     |
|                      | el |     |      |   |             |     |     |
|                      | )^ |     |      |   |             |     |     |
|                      | a^ |     |      |   |             |     |     |
+----------------------+----+-----+------+---+-------------+-----+-----+
| Variable             | *R | SE  | AIC  |   | Model       | *R* | SE  |
|                      | *^ |     | c^c^ |   |             | ^2^ |     |
|                      | 2^ |     |      |   |             |     |     |
+----------------------+----+-----+------+---+-------------+-----+-----+
| Total plant species  | 0. | 1   | 6    |   | Gaussian    | 0   | 7.0 |
|                      | 14 | 1.1 | 10.3 |   |             | .64 |     |
+----------------------+----+-----+------+---+-------------+-----+-----+
| Native plant species | 0. | 8.6 | 5    |   | Gaussian    | 0   | 7.0 |
|                      | 44 |     | 71.6 |   |             | .60 |     |
+----------------------+----+-----+------+---+-------------+-----+-----+
| Non-native plant     | 0. | 1.6 | 3    |   | Gaussian    | 0   | 1.5 |
| species              | 58 |     | 09.5 |   |             | .61 |     |
+----------------------+----+-----+------+---+-------------+-----+-----+
| Probability of       | 0. | 1   | 3    |   | No spatial  |     |     |
| non-native spp.      | 59 | .97 | 42.1 |   | auto-       |     |     |
|                      |    |     |      |   | correlation |     |     |
+----------------------+----+-----+------+---+-------------+-----+-----+
| Total plant cover    | 0. | 1   | 6    |   | Gaussian    | 0   | 7.3 |
| (%)                  | 44 | 3.3 | 39.6 |   |             | .82 |     |
+----------------------+----+-----+------+---+-------------+-----+-----+
| Native plant cover   | 0. | 1   | 6    |   | Gaussian    | 0   | 6.9 |
| (%)                  | 46 | 3.3 | 39.9 |   |             | .84 |     |
+----------------------+----+-----+------+---+-------------+-----+-----+
| Non-native plant     | 0. | 0.5 | 1    |   | No spatial  |     |     |
| cover (%)            | 10 |     | 25.2 |   | auto-       |     |     |
|                      |    |     |      |   | correlation |     |     |
+----------------------+----+-----+------+---+-------------+-----+-----+

> **Notes:**
>
> ^a^ *P* significant at α \< 0.05 for the (Ordinary Least Squares) OLS
> models.
>
> ^b^ *P* significant at α \< 0.01 for the variogram models.
>
> ^c^ AICc = Akaike's Information Criterion corrected for small samples.

4\. **Discussion**

4.1 ***Influence of Stand Structure and Management Activities on Fire
Severity***

Fuel conditions explained a significant portion of the variability in
the severity of each of the fires we sampled. Management actions that
alter fuel conditions can influence the severity of future fires in
montane conifer forests such as those that we sampled. Though the
relationship between fuel variables and fire severity varied
substantially among our three study sites, fuel treatment by prescribed
fire consistently appeared as an informative and significant mitigating
variable at all three sites. Prescribed fires reduce surface and ladder
fuels, at least in the short term. The effect of this fuel modification
on fire severity was most evident in recent prescribed burns at the
Hayman and Cerro Grande sites (Figure 4). Several previous studies
corroborate this result, albeit on a more limited scale (Pollet and Omi
2002, Omi and Martinson 2002, Wagle and Eakle 1979).

![](media/image5.wmf){width="3.8299212598425196in"
height="3.5625984251968505in"}

The only other consistency across all three sites in relation to the
effect of fuels on fire severity was the exclusion of thinning
treatments from all sets of informative models. Mechanical thinning
reduces canopy fuels, but the resultant slash may increase surface fuels
if not followed by prescribed fire or other means of removal. The effect
of mechanical thinning on fire severity is therefore less certain than
that of prescribed fire. Some previous studies have found thinning to
reduce fire severity even without slash treatment (Omi et al. 2004),
while others have observed logging slash to increase fire severity
(Vihanek and Ottmar 1993). But since canopy fuel variables were included
explicitly in our models, the thinning variable represented only the
effect of the alteration to surface fuels when all other variables were
held constant. So we were surprised to find that the additional surface
fuels in thinned areas did not increase the severity of any of the
fires. Nonetheless, the strong effect of recent prescribed burns at all
three sites illustrates the importance of treating surface fuels,
whether natural or activity generated.

Mechanical thinning will also alter canopy fuel variables (Graham et al.
1999), many of which we found to be significant predictors of fire
severity in at least one of the three sampled fires. However, only tree
diameter, stand density, and canopy bulk density were at least
marginally significant at more than one site. Fire severity decreased
significantly with increases in mean tree diameter in both the Cerro
Grande fire and the Hayman fire. Larger trees tend to have thicker bark
and crowns removed from the ground, increasing their resistance to
damage from surface fire (Ryan and Reinhardt 1988). Removal of small
trees effectively increased average tree diameter in thinned areas
within the Cerro Grande fire and lessened its severity (Figure 5a).
However, there was little difference in tree diameters, and thus fire
severity, between thinned and untreated areas of the Hayman fire.

Higher stand density increased fire severity in the Cerro Grande fire as
well as in the Hi Meadow fire. Higher stand density results in a greater
and more continuous canopy fuel load. The effect of thinning on reducing
stand density and fire severity was greatest in the Hi Meadow fire, but
some effect was evident at the Cerro Grande fire, as well (Figure 5b).
Since stand density is determined primarily by small trees, this
variable may also be an indicator of the abundance of ladder fuels.
Canopy base height would be a better measure of ladder fuels, but severe
fire obscured this variable and made post-fire reconstruction
unreliable.

![](media/image6.wmf){width="5.995275590551181in"
height="3.180708661417323in"}

Canopy bulk density is a key variable in crown fire prediction models
(Scott and Reinhardt 2001) and may be a more direct measure of canopy
fuels than stand density. However, we found canopy bulk density to be
only marginally significant as a predictor of fire severity in both the
Cerro Grande and Hi Meadow fires. Also, the effect of increases in
canopy bulk density on fire severity was positive in the Hi Meadow fire
but negative in the Cerro Grande fire. While a reduction in canopy bulk
density would decrease the likelihood of crown fire propagation, greater
exposure of surface fuels to solar radiation and wind could increase
surface fire intensity. But quantification of canopy bulk density was
prone to greater error than our other canopy fuel indicators since it
was not measured directly, rather it was derived allometrically from
imperfect equations based on data from a different geographic region
(Brown 1978).

Fuel variables appeared to have a more consistent influence on fire
severity among our study sites than did topographic variables. The only
topographic variable found to be significant in all three fires was
distance to roads. But roads might be better classified as a fuel
manipulation, albeit not generally installed for the purpose of fire
hazard mitigation. We would expect roads to act as a fuel break and
disrupt fire spread to reduce fire severity. Roads did appear to augment
the mitigating effect of prescribed burns in the Hi Meadow fire and of
large trees in the Cerro Grande fire. However, roads diminished the
mitigating effect of prescribed burns in the Hayman fire. This may have
been due to an association between roads and drainages in the Hayman
area, since there was a marginally significant increase in the severity
of this fire with proximity to streams.

While our models explained a significant portion of the variability in
fire severity, the explanatory power of our models varied among sites.
The explanatory power of our fire severity models was about three times
greater for Cerro Grande than for Hayman and High Meadow. Unexplained
variability in fire severity was presumably caused by vagaries in the
weather.

Weather conditions were most extreme during the Hayman fire and least
extreme during the Hi Meadow fire (we calculated the Energy Release
Component of the National Fire Danger Rating System with Fire Family
Plus software (USDA Forest Service 2000) to be in the 97^th^ percentile
of the historical average during the Hayman fire, 96^th^ percentile
during the Cerro Grande fire, and 87^th^ percentile during the Hi Meadow
fire). Yet the amount of variability left unexplained by our models was
nearly the same for the Hayman and Hi Meadow sites. We hypothesize that
historic fire regime may influence the degree to which fire behavior and
severity can be explained by fuel variables versus weather variables.

The Historic fire regime of the Cerro Grande area was typified by
frequent, small, low intensity fires (Touchan et al. 1996). Fires were
historically less frequent in the Hayman and Hi Meadow area and the fire
regime included occasional extensive crown fire (Brown et al. 1999).
Bessie and Johnson (1995) argue that fuels have little influence over
fire events in systems where the historic fire regime was characterized
by relatively infrequent crown fire occurring only under extreme weather
conditions, such as boreal and subalpine forests. Keeley et al. (1999)
make a similar argument for California chaparral, implying that there is
little benefit to be gained by manipulating fuels in these systems. Most
of the evidence of fuel treatment effectiveness has been found in areas
characterized by short fire return intervals, such as the Cerro Grande
system (Martinson and Omi 2003). Though the Hayman and Hi Meadow area is
not at the opposite end of the fire regime spectrum from the Cerro
Grande area, the relative explanatory power of our models relating fuel
conditions to fire severity suggests caution in applying fuel treatments
outside of short fire return interval systems.

4.2 ***Influence of Management Activities and Fire Severity on
Non-native Plants***

Disturbance theory suggests a high potential for non-native species
establishment in fuel treatment areas (Sieg et al. 2003). But the
literature contains very little empirical evidence demonstrating high
establishment and spread rates of non-native species following fuel
treatments (Griffis et al. 2001; Fornwalt et al. 2003; Albella and
Covington 2004; Wienk et al. 2004). Our results confirm that there is a
potential for higher establishment of non-native species in thinned
areas as we found a slightly higher incidence of non-native species in
thinned areas in the Cerro Grande fire. However, we found no significant
effect of fuel treatments on non-native species cover at either the Hi
Meadow or Hayman fires, indicating that the overall potential for
establishment of non-native species following fuel treatments is low in
these systems.

The influence of fuel treatments is less than that of wildfire on the
establishment of non-native species. There are many documented cases in
which intense wildfires result in increased establishment of invasive
species (Crawford et al. 2001; Griffis et al. 2001; Keeley et al. 2003).
Depth of char, an indicator of the downward heat pulse produced by a
fire, was a consistent predictor of higher non-native species cover at
all fires and both spatial scales. Non-native species cover was
positively associated with char depth at the Hi Meadow and Hayman fires.
Fire severity was a more important predictor of non-native species
establishment than other abiotic variables that have proven impact on
non-native species establishment in other studies. Results from this
study suggest that the occurrence of wildfires may be one of the more
important mechanisms for continued spread of non-native species in the
western US.

Spread and establishment of non-native species following wildfire may be
deterred through appropriate post-fire management actions. However,
there is a potential for introduction of non-native species through
application of seed mixes and straw mulch for erosion control following
wildfire (Beyers 2004). Although seed mixes have acceptable purity, they
are usually contaminated with non-native species. The application of
very large amounts of seed ensures that a large number of non-native
weed seeds are distributed over the landscape. Results from our study
show that non-native species cover is positively associated with seeded
grass cover. The Hi Meadow fire showed a deviation from this trend and
we suspect that is because of the method of seed application. At the
Cerro Grande and Hayman fires, seed mixes were aerially applied and
resulted in very low cover of seeded grasses over broad areas of the
burned landscape. At the Hi Meadow fire, seed mixes were applied by hand
or with a seed drill and resulted in very high cover of seeded grasses
in a smaller portion of the burned landscape. Similarly, when cover of
native grasses was high, establishment of non-native species was
deterred. Post-fire treatments that encourage high vegetative cover,
without introducing new non-native species in contaminated seed mixes,
would best prevent further spread of non-native species. Our results
suggest that hand application of native grass seed mixes would be
preferable to broad aerial application of non-native grass seed. While
we examined the effects of straw mulch at only one fire (Hayman), we
found no effect of mulch application on establishment of non-native
species on our plots.

4.3 ***Influence of Non-native Plants on Post-fire Flammability***

Numerous factors interact to influence the flammability of a fuel bed,
including the characteristic surface-area-to-volume ratio, bulk density,
depth, packing ratio, and the load distribution among live and dead fuel
size classes. Non-native species could influence any of these and all
were included in the creation of custom fuel models to predict post-fire
flammability. However, it was not our intent to determine how non-native
species influence individual fuel components or how individual fuel
components influence flammability. Rather, our objective was to
determine if variations in the abundance of non-native species affected
the output of the BEHAVE (Burgan and Rothermel 1984) fire prediction
system. We therefore refrained from adding to our models any variables
that might have been influenced by the presence of non-native species,
even though their inclusion would have increased the explanatory power
of our models. Fuel bed depth and surface-area-to-volume ratio, in
particular, are two variables we measured that could explain much of the
variability in fire prediction outputs. However, these variables are
also two of the primary means by which non-native species might
influence flammability and their explicit inclusion in our models would
have diminished our ability to attribute variations in flammability to
non-native species.

Though the explanatory power of our flammability models was thus rather
low, the relative cover of non-native species was included in the set of
informative models for all three study areas and had a significant
effect on post-fire flammability at both the Cerro Grande and Hayman
sites. However, the effect of non-native species was not consistent
between the Cerro Grande and Hayman fires. The suite of non-native
species found at each site varied (Appendix B) and different species
will uniquely influence the fuel bed parameters that determine
flammability. Non-native species tended to exacerbate post-fire
flammability in the Cerro Grande area, particularly where the post-fire
fuel load was greater than average (Figure 6a). The main effect of
non-natives was to decrease flammability after the Hayman fire, but
non-natives did act to decrease the rate at which post-fire flammability
was dampened by increases in elevation (Figure 6b).

![](media/image7.wmf){width="3.3346456692913384in"
height="3.3751968503937007in"}![](media/image8.wmf){width="3.266535433070866in"
height="3.3625984251968504in"}

Fuel load and elevation were significant predictors of post-fire
flammability at all three sites. Since severely burned areas may be
completely denuded of fuel, post-fire flammability was determined
primarily by total fuel load, but flammability decreased under
increasingly mesic conditions associated with higher elevations. Though
the BEHAVE (Burgan and Rothermel 1984) inputs for fuel moisture content
were kept constant for all plots (Table 2), greenness of live biomass
was a measured variable influenced by elevation that had a significant
effect on predicted flame length. Sample date also affected greenness
through seasonal curing, which resulted in significant increases in
flammability as summer progressed at all three sites.

We expected seeded species would have an effect on post-fire
flammability similar to that of non-native species, since the seed mixes
used at all three sites included non-native grasses (annual ryegrass
(*Lolium multiflorum*) and common barley (*Hordeum vulgare*) at Cerro
Grande, hard fescue (*Festuca trachyphylla*) at Hi Meadow, common barley
and common wheat (*Triticum aestivum*) at Hayman). Though seeded species
were included in the set of informative models developed for Cerro
Grande and Hi Meadow, their effect on flammability was not significant.
However, the influences that non-native and seeded species have on
flammability may become more pronounced as total vegetation cover
increases over time.

4.4 ***Potential of Spatial Prediction Models***

Investigating spatial relationships among fuels, wildfire severity, and
post-fire invasion by non-native plant species through the linkage of a
multiphase sampling design and multiscale nested sampling field plots,
pre- and post-fire, can be accomplished by the integration of remotely
sensed data, GIS, and spatial statistical models. This technique
provided useful information and tools for describing landscape-scale
patterns of plant diversity within the Cerro Grande fire site. The
various components of vegetation (e.g., forest structure, surface fuels,
non-native species) are not always correlated with existing vegetation
characteristics because of past management activities and random
disturbance in the form of individual tree or plant mortality. Thus,
collecting intensive fuel data and vegetation measurements using
unbiased multiscale sampling within the forest landscape provides an
excellent data source and input for spatial models similar to those
presented here. These spatial models can provide unbiased estimates of
the various components of the landscape, as well as estimates of the
prediction variance associated with individual estimates. Models
covering such areas as the Cerro Grande site enable the spatial
integration of estimates of non-native species and fuel loads to a wide
range of spatial scales, along with estimates of the level of
uncertainty.

The information gained from integration of remotely sensed data, GIS,
and field data can be used to produce spatial statistical maps; for
example of invasive species richness (Figure 7). Integrating spatial
information technology permits predictive modeling on multiple scales
with more focused and unbiased sampling designs, thus reducing cost.
Consequently, natural resource management teams can utilize this as a
cost-effective tool in identifying areas vulnerable to non-native plant
species invasion and increased potential for wildfire.

![](media/image9.wmf){width="5.995275590551181in"
height="4.508267716535433in"}

**5. Conclusions**

Our sampling approach provided comprehensive data sets for the three
individual wildfires we studied, but the low explanatory power of most
of our models necessitates caution in generalizing our results beyond
our study areas. Nonetheless, our findings do highlight some of the
ecological risks and benefits associated with fire management
activities, particularly pre-fire fuel treatments and post-fire seeding.
Wildfires may be one of the more important mechanisms for continued
spread of non-native species in the western US. We found a consistent
positive correlation between fire severity and non-native plants, and
fire severity was a more important predictor of non-native species
establishment than other abiotic variables. Once established, non-native
species increase post-fire flammability in some cases, possibly
increasing the likelihood of severe fires in the future. Thus,
activities that mitigate fire severity may also deter the establishment
and spread of non-native species.

We found fire severity to be most consistently mitigated by recent
prescribed burns. Stand conditions that can be managed by mechanical
thinning, such as tree density and mean tree diameter, also influenced
fire severity: fire severity decreased with increases in mean tree
diameter and/or decreases in stand density. However, fuel variables best
explained fire severity at our study site where historic fire frequency
was greatest. Fuel manipulations may be less effective in mitigating
fire severity in areas where low intensity fires were historically less
common. Also, disturbance caused by fuel treatments may produce
additional opportunities for non-native species establishment. We did
find a slightly higher incidence of non-native species in the thinned
areas of one of our study sites.

Establishment and spread of non-native species following wildfire also
may be deterred through appropriate post-fire management actions. But
seed mixes are often contaminated with non-native species and their
application on landscape scales may introduce a large number of
non-native weed seeds. We found a positive association between the cover
of non-native species and seeded grasses where seed mixes were
extensively applied. However, where seed mixes were intensively applied
on a more limited scale, post-fire seeding successfully deterred
non-native species establishment. Post-fire treatments that encourage
high vegetative cover, without introducing new non-native species in
contaminated seed mixes, would best prevent further spread of non-native
species.

Areas vulnerable to non-native plant species invasion can be identified
with spatial statistical prediction maps. We integrated remotely sensed
data, geographic information systems, and field data to produce spatial
statistical models based on large- and small-scale variability to
predict patterns of non-native plant invasions. The predicted standard
errors for non-native species richness in the Cerro Grande fire were
less than 40% of the mean number of non-native species per plot, even at
the farthest distance from a sampled point. This relatively low error
indicates the potential utility of spatial statistical prediction maps
for directing management activities to deter invasion by non-native
plants.

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**Appendix A: Dissemination**

A.1 ***Manuscripts***

Chong, G., T. Stohlgren., C. Crosier, S. Simonson, G. Newman, and E.
Petterson. 2003. Key invasive non-native plants. Pages 244-249 in R.T.
Graham (Tech. Ed.) Hayman Fire Case Study. USDA Forest Service General
Technical Report RMRS-GTR-114.

URL: http://www.fs.fed.us/rm/pubs/rmrs_gtr_114.html

Hunter, M.E. 2004. Post fire grass seeding for rehabilitation and
erosion control: Implications\
for native plant recovery and establishment of exotic species. Doctoral
Dissertation, Colorado State University, Fort Collins.

Hunter, M.E. and P.N. Omi. *In press*. Seed supply of native and
cultivated grasses in pine forests of the southwestern United States and
the potential for vegetation recovery following wildfire. Plant Ecology.

# Hunter, M.E. and P.N. Omi. *In review*. Response of native and exotic grasses to increased soil nitrogen and relation to recovery in a post-fire environment.

Martinson, E.J., P.N. Omi, and W.D. Shepperd. 2003. Effects of fuel
treatments on fire severity. Pages 96-122 *in* R.T. Graham (Tech. Ed.)
Hayman Fire Case Study. USDA Forest Service General Technical Report
RMRS-GTR-114.

URL: http://www.fs.fed.us/rm/pubs/rmrs_gtr_114.html

Kalkhan, M.A., E.J. Martinson, P.N. Omi, T.J. Stohlgren, G.W. Chong, and
M.E. Hunter. 2004. Integrating remote sensing, GIS, and spatial
statistics: a case study of invasive plants and wildfire on the Cerro
Grande Fire, Los Alamos, New Mexico. Tall Timbers Fire Ecology
Conference Proceedings 22:191-199.

URL: http://www.talltimbers.org/info/pubcategories.html#FECP

Omi, P.N, E.J. Martinson, M.A. Kalkhan, G.W. Chong, M.E. Hunter, and
T.J. Stohlgren. 2004. Fuels, fire severity, and invasive plants within
the Cerro Grande fire, Los Alamos, NM. Tall Timbers Fire Ecology
Conference [Proceedings](../Proceedings) 22:141-148.

URL: http://www.talltimbers.org/info/pubcategories.html#FECP

> Williams, V. 2003. Evaluation of wildfire burn severity
> classification, utilising ground and remote sensing methodologies,
> Southern Colorado, USA. Master's Thesis, University of Edinburgh,
> Great Britian.

A.2 ***Presentations***

Ecological Society of America Annual Meeting, August 2004, Portland,
Oregon:

> *"Effects of pre-fire fuel reduction treatments on post-wildfire
> non-native plant species richness and cover"*

Joint Fire Science Program Annual Principal Investigator Workshop, March
2004, Phoenix, AZ:

*"Spatial Interactions among Fuels, Wildfire, and Invasive Plants"*

Second International Fire Congress, November 2003, Orlando, Florida:

*"[Effects of Fuel Treatments, Post-Fire Rehabilitation Treatments and
Wildfire on Establishment of Invasive
Species](http://ams.confex.com/ams/FIRE2003/2FireEco/abstracts/65546.htm)"*

*"Fuels, Fires, Invasions, and Some Effects of Active Management in the
Eastern Rockies"*

Joint Fire Science Program Annual Principal Investigator Workshop, March
2003, Phoenix, AZ:

*"Spatial Interactions among Fuels, Wildfire, and Invasive Plants"*

Society for Range Management Annual Meeting, February 2003, Casper,
Wyoming:

*"The Effects of Reseeding For Post-Fire Rehabilitation on Native
Vegetation Recovery and Establishment of Invasive Species"*

*"New approach for estimating vegetation distribution, presence,
patterns, and fuel loading: Examples from the Cerro Grande fire and High
Meadow sites, Rocky Mountain Regions"*

Association for Fire Ecology Conference, December 2002, San Diego,
California:

*"New Techniques for Estimating Post-fire Vegetation, Fuels, and
Flammability"*

*"[The Ecological, Hydrological and Geological Consequences of Burn
Severity and Social Application of Those Results: How do we assess and
do all
that?](http://www.cnr.colostate.edu/frws/research/weeds/chongetal.htm)"*

Ecological Society of America Annual Meeting, August 2002, Tucson,
Arizona:

*"Invasive plants and wildfire on the Cerro Grande fire, Los Alamos:
Integration of spatial information and spatial statistics"*

Joint Fire Science Program Annual Principal Investigator Workshop, March
2002, San Antonio, Texas:

*"Spatial Interactions among Fuels, Wildfire, and Invasive Plants"*

Colorado Weed Management Association Meeting, December 2001, Denver,
Colorado:

*"Rapid assessment, inventory, and monitoring tools for managing
non-native species invasions"*

Tall Timbers 22^nd^ Fire Ecology Conference, October 2001, Kananaskis,
Alberta:

## "Fuels, Fire Severity, and Invasive Plants within the Cerro Grande Fire, Los Alamos, NM"

A.3 ***Posters***

Second International Fire Congress, November 2003, Orlando, Florida:

*"[True Mountain Mahogany Sprouting Behavior Following
Fire](http://ams.confex.com/ams/FIRE2003/2FireEco/abstracts/65787.htm)"*

Third USGS Wildland Fire Workshop, November 2002, Denver, Colorado:

*"Fuels, Fire Severity, and Invasive Plants: An Example from the Cerro
Grande Fire, Los Alamos, New Mexico"*

Conference on Fire, Fuel Treatments, and Ecological Restoration, April
2002, Fort Collins, Colorado:

*"Invasive Plants and Wildfire on the Cerro Grande Fire, Los Alamos:
Integration of Spatial Information and Spatial Statistics"*

Tall Timbers 22^nd^ Fire Ecology Conference, October 2001, Kananaskis,
Alberta:

*"Integration of Spatial Information and Spatial Statistics: a Case
Study of Invasive Plants and Wildfire on the Cerro Grande Fire, Los
Alamos, New Mexico, USA"*

A.4 ***Field Trips and Training***

Student: University of Wyoming, October 2003, Hayman Fire, Colorado.

Managers: USFS National Program for Fire Systems Research, August 2003,
Ft. Collins, Colorado.

Student: University of Taiwan, August 2003, Hayman Fire, Colorado.

Student: University of Edinburgh, June-July 2003, Hayman Fire, Colorado.

Researchers: USFS Rocky Mountain Research Station, June 2003, Hayman
Fire, Colorado.

Researchers: CSU Department of Bioagricultural Sciences and Pest
Management, June 2003, Hayman Fire, Colorado.

Researchers: USGS Wildland Fire Workshop, November 2002, Hayman Fire,
Colorado.

Managers: USFS National Fuels Specialists Group, April 2002, Cerro
Grande Fire, New Mexico.

Managers: US Bureau of Land Management, November 2001, Fort Collins,
Colorado.

Researchers: USGS Central Region Water Resources Division, June 2001,
Cerro Grande Fire, New Mexico.

Managers: Los Alamos Volunteer Task Force, July 2001, Los Alamos, New
Mexico.

**Appendix B: Non-native Species List**

> **Table B. Non-native species frequency of occurrence (%) on the
> 1000-m^2^ plots sampled at each of our study sites.**

  ----------------------------- ---------------- ------------- ----------
  Species                       Cerro Grande     Hi Meadow     Hayman

  *Chenopodium album*           73.42            58.51         75.23

  *Verbascum thapsus*           26.58            71.28         80.73

  *Lactuca serriola*            32.91            46.81         47.71

  *Taraxacum officinale*        45.57            39.36         36.7

  *Hordeum vulgare*             70.89\*          0             33.94\*

  *Tragopogon dubius*           32.91            27.66         27.52

  *Bromus tectorum*             49.37            12.77         25.69

  *Lolium multiflorum*          75.95\*          0             0.92

  *Triticum aestivum*           1.27             0             69.72\*

  *Cirsium arvense*             0                63.83         6.42

  *Erysimum cheiranthoides*     0                13.83         40.37

  *Festuca trachyphylla*        1.27             32.98\*       0

  *Polygonum erectum*           32.91            0             0

  *Poa pratensis*               16.46            6.38          5.5

  *Thinopyrum ponticum*         24.05            0             0

  *Conringia orientalis*        0                17.02         0.92

  *Carduus nutans*              0                15.96         0.92

  *Arabis hirsute*              2.53             11.7          1.83

  *Bromus inermis*              15.19            0             0

  *Sisymbrium altissimum*       7.59             6.38          0.92

  *Descurainia Sophia*          7.59             2.13          2.75

  *Chenopodium lanceolatum*     0                1.06          9.17

  *Festuca ovina*               7.59             0             0.92

  *Amaranthus retroflexus*      0                1.06          7.34

  *Linaria vulgaris*            0                2.13          5.5

  *Trifolium pretense*          0                0             7.34

  *Chorispora tenella*          6.33             0             0

  *Bromus mollis*               0                3.19          1.83

  *Salsola iberica*             0                0             4.59

  *Camelina microcarpa*         0                1.06          2.75

  *Euphorbia agrarian*          3.8              0             0

  *Cirsium vulgare*             2.53             1.06          0

  *Trifolium repens*            1.27             1.06          0.92

  *Achillea millefolium*        0                3.19          0

  *Bromus erectus*              0                3.19          0

  *Anthemis cotula*             0                0             2.75

  *Bromus hordaceus*            0                0             2.75

  *Polygonum aviculare*         0                0             2.75

  *Aegilops cylindrical*        2.53             0             0

  *Agropyron cristatum*         2.53             0             0

  *Artemisia biennis*           2.53             0             0

  *Raphanus sativus*            2.53             0             0

  *Ulmus pumila*                2.53             0             0

  *Poa compressa*               1.27             0             0.92

  *Cerastium vulgatum*          0                2.13          0

  *Gypsophila paniculata*       0                1.06          0.92

  *Fallopia convolvulus*        0                0             1.83

  *Polygonum convolvulus*       0                0             1.83

  *Capsella bursa-pastoris*     1.27             0             0

  *Elaeagnus angustifolia*      1.27             0             0

  *Malus sylvestris*            1.27             0             0

  *Medicago lupulina*           1.27             0             0

  *Melandrium album*            1.27             0             0

  *Myosotis alpestris*          1.27             0             0

  *Thlaspi arvense*             1.27             0             0

  *Zea mays*                    1.27             0             0

  *Agropyron repens*            0                1.06          0

  *Asperugo procumbens*         0                1.06          0

  *Avena sativa*                0                1.06          0

  *Dactylis glomerata*          0                1.06          0

  *Myosotis scorpioides*        0                1.06          0

  *Anisantha tectorum*          0                0             0.92

  *Bromus secalinus*            0                0             0.92

  *Erodium cicutarium*          0                0             0.92

  *Matricaria discoidea*        0                0             0.92

  *Onopordum acanthium*         0                0             0.92

  *Panicum miliaceum*           0                0             0.92
  ----------------------------- ---------------- ------------- ----------

> **Notes:**
>
> \* Species included in the seed mix applied for post-fire
> rehabilitation.

**Appendix C: Database Metadata**

C.1 ***Fuels and Fire Severity***

C.1.1 *Cerro Grande*

a.. Project: Spatial interactions among fuels, wildfire, and non-native
plants.

b.. Dataset: Fuels&Severity: Cerro Grande, NM.

c.. Publication Date: Unpublished.

d.. Originator: Western Forest Fire Research Center (WESTFIRE) and the
Natural Resource Ecology Laboratory (NREL), Colorado State University.

e.. Abstract of the data set: The data are from the 2000 Cerro Grande
wildfire on the Santa Fe National Forest. Plots were distributed
randomly across the landscape within strata determined by fire severity
level, aspect, vegetation type, and management activity. Data collected
describe stand conditions and fire severity at each plot.

f.. Purpose: The data were collected to assess the influence of stand
conditions on wildfire severity.

g.. Format: MS Excel database.

h.. Date collected: Summer 2001.

i.. Location: Northern New Mexico between UTM Eastings 371,192 and
390,095 and between UTM Northings 3,966,876 and 3,981,432 (Zone 13S,
NAD27).

j.. Access Constraints: Data are owned by Colorado State University and
will be used in publications currently in preparation. Data access
requests will be considered on a case-by-case basis.

k.. Use Constraints: Data are owned by Colorado State University and
will be used in publications currently in preparation. Data use requests
will be considered on a case-by-case basis. Data use without the express
written consent of either WESTFIRE or NREL is prohibited.

l.. Keywords: Fuel treatments, prescribed fire, mechanical thinning,
stand conditions, wildfire severity, New Mexico, Santa Fe National
Forest, ponderosa pine, pinyon-juniper, mixed conifer.

m.. Contact: Erik J. Martinson

Address: 131 Forestry, Colorado State University, Fort Collins, CO 80523

Telephone: (970) 491-1779

Email: erikm@cnr.colostate.edu

n.. Publications:

> Omi, P.N, E.J. Martinson, M.A. Kalkhan, G.W. Chong, M.E. Hunter, and
> T.J. Stohlgren. 2004. Fuels, fire severity, and invasive plants within
> the Cerro Grande fire, Los Alamos, NM. Tall Timbers Fire Ecology
> Conference [Proceedings](../Proceedings) 22:141-148.

o.. Data quality and accuracy: Error checks have been completed.

p.. Completeness: The dataset contains no omissions.

q.. Methodology: See Section 2 of this report.

r.. Spatial Data:

1\. Plot coordinates are available as points indicating the origin of
each 20m X 50m Modified-Whittaker plot. The bearing from the origin of
the 20m and 50m sides are also indicated.

2\. Coordinates are projected in Universal Transverse Mercator, North
American Datum 1927. Units are in meters.

s.. Attributes:

1\. Plot information: plot name, plot stratum, sample date, measurer,
recorder, GPS coordinates, plot orientation.

2\. Topographic information: aspect (degrees), slope (percent),
elevation (m), distance to nearest road (m), distance to nearest stream
(m).

3\. Pre-fire stand conditions: tree density (#/ha), basal area
(m^2^/ha), mean tree height (m), mean diameter at breast height (cm),
canopy base height (m), canopy bulk density (kg/m3), species composition
(percent ponderosa pine).

4\. Fire severity information: stand damage rating (0-4), depth of char
rating (0-3), needle scorch height (m), bole char height (m), crown
volume scorch (%), crown volume consumption (%).

t.. Distributor: Erik J. Martinson

Address: 131 Forestry, Colorado State University, Fort Collins, CO 80523

Telephone: (970) 491-1779

Email: [erikm@cnr.colostate.edu](mailto:erikm@cnr.colostatel.edu)

C.1.2 *Hi Meadow*

a.. Project:See entry for Section C.1.1.

b.. Dataset: Fuels&Severity: Hi Meadow, CO.

c.. Publication Date: See entry for Section C.1.1.

d.. Originator: See entry for Section C.1.1.

e.. Abstract of the data set: : The data are from the 2000 Hi Meadow
wildfire on the Pike National Forest. Plots were distributed randomly
across the landscape within strata determined by fire severity level,
aspect, vegetation type, and management activity. Data collected
describe stand conditions and fire severity at each plot.

f.. Purpose:See entry for Section C.1.1.

g.. Format: See entry for Section C.1.1.

h.. Date collected: Summer 2002.

i.. Location: Central Colorado between UTM Eastings 465,873 and 474,672
and between UTM Northings 4,357,509 and 4,361,295 (Zone 13S, NAD27).

j.. Access Constraints: See entry for Section C.1.1.

k.. Use Constraints: See entry for Section C.1.1.

l.. Keywords: Fuel treatments, prescribed fire, mechanical thinning,
stand conditions, wildfire severity, Colorado, Pike National Forest,
ponderosa pine, Douglas-fir.

m.. Contact: See entry for Section C.1.1.

n.. Publications: In preparation.

o.. Data quality and accuracy: See entry for Section C.1.1.

p.. Completeness: See entry for Section C.1.1.

q.. Methodology: See Section 2 of this report.

r.. Spatial Data: See entry for Section C.1.1.

s.. Attributes: See entry for Section C.1.1.

t.. Distributor: See entry for Section C.1.1.

C.2.3 *Hayman*

a.. Project: See entry for Section C.1.1.

b.. Dataset: Fuels&Severity: Hayman, CO.

c.. Publication Date: See entry for Section C.1.1.

d.. Originator: See entry for Section C.1.1.

e.. Abstract of the data set: The data are from the 2002 Hayman wildfire
on the Pike National Forest. Plots were distributed randomly across the
landscape within strata determined by fire severity level, aspect,
vegetation type, and management activity. Data collected describe stand
conditions and fire severity at each plot.

f.. Purpose: See entry for Section C.1.1.

g.. Format: See entry for Section C.1.1.

h.. Date collected: Summer 2003.

i.. Location: Central Colorado between UTM Eastings 467,615 and 487,972
and between UTM Northings 4,328,641 and 4,350,786 (Zone 13S, NAD27).

j.. Access Constraints: See entry for Section C.1.1.

k.. Use Constraints: See entry for Section C.1.1.

l.. Keywords: Fuel treatments, prescribed fire, mechanical thinning,
stand conditions, wildfire severity, Colorado, Pike National Forest,
ponderosa pine, Douglas-fir.

m.. Contact: See entry for Section C.1.1.

n.. Publications:

> Martinson, E.J., P.N. Omi, and W.D. Shepperd. 2003. Effects of fuel
> treatments on fire severity. Pages 96-122 *in* R.T. Graham (Tech. Ed.)
> Hayman Fire Case Study. USDA Forest Service General Technical Report
> RMRS-GTR-114.

o.. Data quality and accuracy: See entry for Section C.1.1.

p.. Completeness: Information is unavailable for 2 plots (312 and 616).

q.. Methodology: See Section 2 of this report.

r.. Spatial Data: See entry for Section C.1.1.

s.. Attributes: See entry for Section C.1.1.

t.. Distributor: See entry for Section C.1.1.

C.2 ***Floristics***

C.2.1 *Cerro Grande*

a.. Project: Spatial interactions among fuels, wildfire, and non-native
plants.

b.. Dataset: Plants: Cerro Grande, NM.

c.. Publication Date: Unpublished.

d.. Originator: Western Forest Fire Research Center (WESTFIRE) and the
Natural Resource Ecology Laboratory (NREL), Colorado State University.

e.. Abstract of the data set: The data are from the 2000 Cerro Grande
wildfire on the Santa Fe National Forest. Plots were distributed
randomly across the landscape within strata determined by fire severity
level, aspect, vegetation type, and management activity. Data collected
describe plant species composition including percent cover, height,
nativity, and growth form by species at two plot scales: 1-m^2^ and
1000-m^2^.

f.. Purpose: The data were collected to assess influences on the
presence and abundance of non-native plants in post-fire landscapes,
with foci on fire severity and management activities.

g.. Format: MS Access database.

h.. Date collected: Summer 2001.

i.. Location: Northern New Mexico between UTM Eastings 371,192 and
390,095 and between UTM Northings 3,966,876 and 3,981,432 (Zone 13S,
NAD27).

j.. Access Constraints: Data are owned by Colorado State University and
will be used in publications currently in preparation. Data access
requests will be considered on a case-by-case basis. Once all
publications have been submitted, data will be archived with the USGS
National Institute of Invasive Species Science at
http://129.82.104.51/cwis438/niiss/index.html.

k.. Use Constraints: Data are owned by Colorado State University and
will be used in publications currently in preparation. Data use requests
will be considered on a case-by-case basis. Data use without the express
written consent of either WESTFIRE or NREL is prohibited.

l.. Keywords: Non-native, invasive, exotic, fuel treatments, prescribed
fire, mechanical thinning, seeding, wildfire severity, New Mexico, Santa
Fe National Forest, ponderosa pine, pinyon-juniper, mixed conifer.

m.. Contact: Molly E. Hunter

Address: 131 Forestry, Colorado State University, Fort Collins, CO 80523

Telephone: (970) 491-0614

Email: mhunter@cnr.colostate.edu

n.. Publications:

> Kalkhan, M.A., E.J. Martinson, P.N. Omi, T.J. Stohlgren, G.W. Chong,
> and M.E. Hunter. 2004. Integrating remote sensing, GIS, and spatial
> statistics: a case study of invasive plants and wildfire on the Cerro
> Grande Fire, Los Alamos, New Mexico. Tall Timbers Fire Ecology
> Conference Proceedings 22:191-199.
>
> Omi, P.N, E.J. Martinson, M.A. Kalkhan, G.W. Chong, M.E. Hunter, and
> T.J. Stohlgren. 2004. Fuels, fire severity, and invasive plants within
> the Cerro Grande fire, Los Alamos, NM. Tall Timbers Fire Ecology
> Conference [Proceedings](../Proceedings) 22:141-148.

o.. Data quality and accuracy: Error checks have been completed.

p.. Completeness: Soil information is not available for one plot (plot
#003).

q.. Methodology: See Section 2 of this report.

r.. Spatial Data:

1\. Plot coordinates are available as points indicating the origin of
each 20m X 50m Modified-Whittaker plot. The bearings from the origin of
the 20m and 50m sides are also indicated.

2\. Coordinates are projected in Universal Transverse Mercator, North
American Datum 1927. Units are in meters.

s.. Attributes:

1\. Plot information (plot name, plot stratum, sample date, botanist,
recorder, GPS coordinates, plot orientation).

2\. Plant information (scientific name, USDA Natural Resources
Conservation Service plant species code, growth form, duration (annual,
biennial, perennial), origin (native or introduced), percent cover,
height).

3\. Percent cover of auxiliary items (rock, soil, duff, litter, wood,
moss, lichen).

4\. Soil information (chemistry (percent total nitrogen and carbon) and
texture (percent sand, silt, and clay).

t.. Distributor: Erik J. Martinson

Address: 131 Forestry, Colorado State University, Fort Collins, CO 80523

Telephone: (970) 491-1779

Email: <erikm@cnr.colostate.edu>

C.2.2 *Hi Meadow*

a.. Project: See entry for Section C.2.1.

b.. Dataset: Plants: Hi Meadow, CO.

c.. Publication Date: See entry for Section C.2.1.

d.. Originator: See entry for Section C.2.1.

e.. Abstract of the data set: The data are from the 2000 Hi Meadow
wildfire on the Pike National Forest. Plots were distributed randomly
across the landscape within strata determined by fire severity level,
aspect, and management activity. Data collected describe plant species
composition including percent cover, height, nativity, and growth form
by species at two plot scales: 1-m^2^ and 1000-m^2^.

f.. Purpose: See entry for Section C.2.1.

g.. Format: See entry for Section C.2.1.

h.. Date collected: Summer 2002.

i.. Location: Central Colorado between UTM Eastings 465,873 and 474,672
and between UTM Northings 4,357,509 and 4,361,295 (Zone 13S, NAD27).

j.. Access Constraints: See entry for Section C.2.1.

k.. Use Constraints: See entry for Section C.2.1.

l.. Keywords: Non-native, invasive, exotic, fuel treatments, prescribed
fire, mechanical thinning, seeding, wildfire severity, Colorado, Pike
National Forest, ponderosa pine, Douglas-fir.

m.. Contact: See entry for Section C.2.1.

n.. Publications: In preparation.

o.. Data quality and accuracy: See entry for Section C.2.1.

p.. Completeness: The dataset contains no omissions.

q.. Methodology: See Section 2 of this report.

r.. Spatial Data: See entry for Section C.2.1.

s.. Attributes: See entry for Section C.2.1.

t.. Distributor: See entry for Section C.2.1.

C.2.3 *Hayman*

a.. Project: See entry for Section C.2.1.

b.. Dataset: Plants: Hayman, CO.

c.. Publication Date: See entry for Section C.2.1.

d.. Originator: See entry for Section C.2.1.

e.. Abstract of the data set: The data are from the 2002 Hayman wildfire
on the Pike National Forest. Plots were distributed randomly across the
landscape within strata determined by fire severity level, aspect, and
management activity. Data collected describe plant species composition
including percent cover, height, nativity, and growth form by species at
two plot scales: 1-m^2^ and 1000-m^2^.

f.. Purpose: See entry for Section C.2.1.

g.. Format: See entry for Section C.2.1.

h.. Date collected: Summer 2003.

i.. Location: Central Colorado between UTM Eastings 467,615 and 487,972
and between UTM Northings 4,328,641 and 4,350,786 (Zone 13S, NAD27).

j.. Access Constraints: See entry for Section C.2.1.

k.. Use Constraints: See entry for Section C.2.1.

l.. Keywords: Non-native, invasive, exotic, fuel treatments, prescribed
fire, mechanical thinning, seeding, wildfire severity, Colorado, Pike
National Forest, ponderosa pine, Douglas-fir.

m.. Contact: See entry for Section C.2.1.

n.. Publications:

> Chong, G., T. Stohlgren., C. Crosier, S. Simonson, G. Newman, and E.
> Petterson. 2003. Key invasive non-native plants. Pages 244-249 in R.T.
> Graham (Tech. Ed.) Hayman Fire Case Study. USDA Forest Service General
> Technical Report RMRS-GTR-114.

o.. Data quality and accuracy: See entry for Section C.2.1.

p.. Completeness: Plant information for 16 plots (307-308, 328, 702-704,
720-723, 804-805, 902, 904, 909) was lost due to technical difficulties
with Palm-top computers. Soil information is unavailable for an
additional two plots (404 and 501).

q.. Methodology: See Section 2 of this report.

r.. Spatial Data: See entry for Section C.2.1.

s.. Attributes: See entry for Section C.2.1.

t.. Distributor: See entry for Section C.2.1.

C.3 ***Post-fire Flammability***

C.3.1 *Cerro Grande*

a.. Project: Spatial interactions among fuels, wildfire, and non-native
plants.

b.. Dataset: Flammability - Cerro Grande, NM.

c.. Publication Date: Unpublished.

d.. Originator: Western Forest Fire Research Center (WESTFIRE) and the
Natural Resource Ecology Laboratory (NREL), Colorado State University.

e.. Abstract of the data set: The data are from the 2000 Cerro Grande
wildfire on the Santa Fe National Forest. Plots were distributed
randomly across the landscape within strata determined by fire severity
level, aspect, vegetation type, and management activity. Data collected
describe post-fire fuel model parameters and fire behavior predictions
at each plot.

f.. Purpose: The data were collected to assess the influence of
non-native plant species on fuelbed flammability.

g.. Format: MS Excel database.

h.. Date collected: Summer 2001.

i.. Location: Northern New Mexico between UTM Eastings 371,192 and
390,095 and between UTM Northings 3,966,876 and 3,981,432 (Zone 13S,
NAD27).

j.. Access Constraints: Data are owned by Colorado State University and
will be used in publications currently in preparation. Data access
requests will be considered on a case-by-case basis.

k.. Use Constraints: Data are owned by Colorado State University and
will be used in publications currently in preparation. Data use requests
will be considered on a case-by-case basis. Data use without the express
written consent of either WESTFIRE or NREL is prohibited.

l.. Keywords: Non-native, exotic, invasive, seeded, custom fuel models,
flammability, New Mexico, Santa Fe National Forest, ponderosa pine
pinyon-juniper, mixed conifer.

m.. Contact: Erik J. Martinson

Address: 131 Forestry, Colorado State University, Fort Collins, CO 80523

Telephone: (970) 491-1779

Email: erikm@cnr.colostate.edu

n.. Publications: In preparation.

o.. Data quality and accuracy: Error checks have been completed.

p.. Completeness: The dataset contains no omissions.

q.. Methodology: See Section 2 of this report.

r.. Spatial Data:

1\. Plot coordinates are available as points indicating the origin of
each 20m X 50m Modified-Whittaker plot. The bearing from the origin of
the 20m and 50m sides are also indicated.

2\. Coordinates are projected in Universal Transverse Mercator, North
American Datum 1927. Units are in meters.

s.. Attributes:

1\. Plot information: plot name, plot stratum, sample date, measurer,
recorder, GPS coordinates, plot orientation.

2\. Fuel modeling output: Fuel load (g/m^2^, separated by fuel moisture
timelag class), fuel bed depth (cm), surface area-to-volume ratio
(m^2^/m^3^), relative packing (packing ratio divided by optimal packing
ratio).

3\. Fire behavior prediction output: Fireline intensity (kw/m), rate of
spread (m/min), flame length (cm).

t.. Distributor: Erik J. Martinson

Address: 131 Forestry, Colorado State University, Fort Collins, CO 80523

Telephone: (970) 491-1779

Email: [erikm@cnr.colostate.edu](mailto:erikm@cnr.colostatel.edu)

C.3.2 *Hi Meadow*

a.. Project: See entry for Section C.3.1.

b.. Dataset: Flammability - Hi Meadow, CO.

c.. Publication Date: See entry for Section C.3.1.

d.. Originator: See entry for Section C.3.1.

e.. Abstract of the data set: : The data are from the 2000 Hi Meadow
wildfire on the Pike National Forest. Plots were distributed randomly
across the landscape within strata determined by fire severity level,
aspect, vegetation type, and management activity. Data collected
describe post-fire fuel model parameters and fire behavior predictions
at each plot.

f.. Purpose: See entry for Section C.3.1.

g.. Format: See entry for Section C.3.1.

h.. Date collected: Summer 2002.

i.. Location: Central Colorado between UTM Eastings 465,873 and 474,672
and between UTM Northings 4,357,509 and 4,361,295 (Zone 13S, NAD27).

j.. Access Constraints: See entry for Section C.3.1.

k.. Use Constraints: See entry for Section C.3.1.

l.. Keywords: Non-native, exotic, invasive, seeded, custom fuel models,
flammability, Colorado, Pike National Forest, ponderosa pine,
Douglas-fir.

m.. Contact: See entry for Section C.3.1.

n.. Publications: In preparation.

o.. Data quality and accuracy: See entry for Section C.3.1.

p.. Completeness: See entry for Section C.3.1.

q.. Methodology: See Section 2 of this report.

r.. Spatial Data: See entry for Section C.3.1.

s.. Attributes: See entry for Section C.3.1.

t.. Distributor: See entry for Section C.3.1.

C.3.3 *Hayman*

a.. Project: See entry for Section C.3.1.

b.. Dataset: Flammability - Hayman, CO.

c.. Publication Date: See entry for Section C.3.1.

d.. Originator: See entry for Section C.3.1.

e.. Abstract of the data set: The data are from the 2002 Hayman wildfire
on the Pike National Forest. Plots were distributed randomly across the
landscape within strata determined by fire severity level, aspect,
vegetation type, and management activity. Data collected describe
post-fire fuel model parameters and fire behavior predictions at each
plot.

f.. Purpose: See entry for Section C.3.1.

g.. Format: See entry for Section C.3.1.

h.. Date collected: Summer 2003.

i.. Location: Central Colorado between UTM Eastings 467,615 and 487,972
and between UTM Northings 4,328,641 and 4,350,786 (Zone 13S, NAD27).

j.. Access Constraints: See entry for Section C.3.1.

k.. Use Constraints: See entry for Section C.3.1.

l.. Keywords: Non-native, exotic, invasive, seeded, custom fuel models,
flammability, Colorado, Pike National Forest, ponderosa pine,
Douglas-fir.

m.. Contact: See entry for Section C.3.1.

n.. Publications: See entry for Section C.3.1.

o.. Data quality and accuracy: See entry for Section C.3.1.

p.. Completeness: Information is unavailable for 2 plots (312 and 616).

q.. Methodology: See Section 2 of this report.

r.. Spatial Data: See entry for Section C.3.1.

s.. Attributes: See entry for Section C.3.1.

t.. Distributor: See entry for Section C.3.1.

C.4 ***Spatial Prediction Maps***

C.4.1 *Cerro Grande*

a.. Project: Spatial interactions among fuels, wildfire, and non-native
plants.

b.. Dataset: Invasion Predictions - Cerro Grande, NM.

c.. Publication Date: Unpublished.

d.. Originator: Western Forest Fire Research Center (WESTFIRE) and the
Natural Resource Ecology Laboratory (NREL), Colorado State University.

e.. Abstract of the data set: The data show predicted cover and richness
of non-native species in the post-fire landscape of the 2000 Cerro
Grande wildfire on the Santa Fe National Forest. The predictions were
derived from spatial statistical models based on field sampling
augmented by full-coverage remotely sensed data layers. Sample plots
were distributed randomly across the landscape within strata determined
by fire severity level, aspect, vegetation type, and management
activity.

f.. Purpose: The maps were developed to show the spatial distribution of
predicted hotspots of non-native species invasions into the sampled burn
areas.

g.. Format: Arc GRID export file.

h.. Date collected: Summer 2001.

i.. Location: Northern New Mexico between UTM Eastings 370,723 and
390,260 and between UTM Northings 3,964,358 and 3,985,773 (Zone 13S,
NAD27).

j.. Access Constraints: Data are owned by Colorado State University and
will be used in publications currently in preparation. Data access
requests will be considered on a case-by-case basis.

k.. Use Constraints: Data are owned by Colorado State University and
will be used in publications currently in preparation. Data use requests
will be considered on a case-by-case basis. Data use without the express
written consent of either WESTFIRE or NREL is prohibited.

l.. Keywords: Non-native, exotic, invasive, spatial statistics, trend
surface, variogram, kriging, New Mexico, Santa Fe National Forest,
ponderosa pine pinyon-juniper, mixed conifer.

m.. Contact: Mohammed A. Kalkhan

Address: 1499 Campus Delivery, Colorado State University, Fort Collins,
CO 80523

Telephone: (970) 491-5262

Email: <mohammed@nrel.colostate.edu>

n.. Publications: In preparation.

> Kalkhan, M.A., E.J. Martinson, P.N. Omi, T.J. Stohlgren, G.W. Chong,
> and M.E. Hunter. 2004. Integrating remote sensing, GIS, and spatial
> statistics: a case study of invasive plants and wildfire on the Cerro
> Grande Fire, Los Alamos, New Mexico. Tall Timbers Fire Ecology
> Conference Proceedings 22:191-199.

o.. Data quality and accuracy: Error checks have been completed.

p.. Completeness: The dataset contains no omissions.

q.. Methodology: See Section 2 of this report.

r.. Spatial Data:

1\. Data are in raster format.

2\. Coordinates are projected in Universal Transverse Mercator, North
American Datum 1927. Units are in meters.

s.. Attributes:

1\. Percent cover of non-native species predicted for each grid cell.

2\. Number of different non-native species predicted for each grid cell.

t.. Distributor: Mohammed A. Kalkhan

Address: 1499 Campus Delivery, Colorado State University, Fort Collins,
CO 80523

Telephone: (970) 491-5262

Email: <mohammed@nrel.colostate.edu>

C.4.2 *Hi Meadow*

a.. Project: See entry for Section C.4.1.

b.. Dataset: : Invasion Predictions - Hi Meadow, CO.

c.. Publication Date: See entry for Section C.4.1.

d.. Originator: See entry for Section C.4.1.

e.. Abstract of the data set: : The data show predicted cover and
richness of non-native species in the post-fire landscape of the 2000 Hi
Meadow wildfire on the Pike National Forest. The predictions were
derived from spatial statistical models based on field sampling
augmented by full-coverage remotely sensed data layers. Sample plots
were distributed randomly across the landscape within strata determined
by fire severity level, aspect, vegetation type, and management
activity.

f.. Purpose: See entry for Section C.4.1.

g.. Format: See entry for Section C.4.1.

h.. Date collected: Summer 2002.

i.. Location: Central Colorado between UTM Eastings 464,590 and 474,749
and between UTM Northings 4,356,363 and 4,366,131 (Zone 13S, NAD27).

j.. Access Constraints: See entry for Section C.4.1.

k.. Use Constraints: See entry for Section C.4.1.

l.. Keywords: Non-native, exotic, invasive, spatial statistics, trend
surface, variogram, kriging, Colorado, Pike National Forest, ponderosa
pine, Douglas-fir.

m.. Contact: See entry for Section C.4.1.

n.. Publications: In preparation.

o.. Data quality and accuracy: See entry for Section C.4.1.

p.. Completeness: See entry for Section C.4.1.

q.. Methodology: See Section 2 of this report.

r.. Spatial Data: See entry for Section C.4.1.

s.. Attributes: See entry for Section C.4.1.

t.. Distributor: See entry for Section C.4.1.

C.3.3 *Hayman*

a.. Project: See entry for Section C.4.1.

b.. Dataset: : Invasion Predictions - Hayman, CO.

c.. Publication Date: See entry for Section C.4.1.

d.. Originator: See entry for Section C.4.1.

e.. Abstract of the data set: The data show predicted cover and richness
of non-native species in the post-fire landscape of the 2002 Hayman
wildfire on the Pike National Forest. The predictions were derived from
spatial statistical models based on field sampling augmented by
full-coverage remotely sensed data layers. Sample plots were distributed
randomly across the landscape within strata determined by fire severity
level, aspect, vegetation type, and management activity.

f.. Purpose: See entry for Section C.4.1.

g.. Format: See entry for Section C.4.1.

h.. Date collected: Summer 2003.

i.. Location: Central Colorado between UTM Eastings 461,452 and 491,453
and between UTM Northings 4,314,388 and 4,352,888 (Zone 13S, NAD27).

j.. Access Constraints: See entry for Section C.4.1.

k.. Use Constraints: See entry for Section C.4.1.

l.. Keywords: Non-native, exotic, invasive, spatial statistics, trend
surface, variogram, kriging, Colorado, Pike National Forest, ponderosa
pine, Douglas-fir.

m.. Contact: See entry for Section C.4.1.

n.. Publications: See entry for Section C.4.1.

o.. Data quality and accuracy: See entry for Section C.4.1.

p.. Completeness: See entry for Section C.4.1.

q.. Methodology: See Section 2 of this report.

r.. Spatial Data: See entry for Section C.4.1.

s.. Attributes: See entry for Section C.4.1.

t.. Distributor: See entry for Section C.4.1.

**Appendix D: Outline for Potential Workshop**

[Format:]{.underline} Special Conference Session.

[Venue:]{.underline} Third International Fire Ecology and Management
Congress, 13-17 November 2006, San Diego, CA.

[Title:]{.underline} Fire Management and Invasive Species: Fuel
Treatments Versus Wildfires.

[Abstract:]{.underline} Fuel treatments have expanded substantially in
recent years to mitigate the threat of extreme wildfire. However,
widespread disturbances created by treatments may exacerbate threats
posed by invasion of non-native species. While the effect of fuel
treatments on invasive species may be less exacerbating than that of
wildfires, there is little published evidence to support this
hypothesis. However, in recent years, the Joint Fire Science Program has
funded at least 10 research projects to address the relationship between
fire management and invasive species. This Special Session will provide
a forum to compare and contrast recent findings on the relative effects
of fuel treatments and wildfires on non-native plant invasions.

  ---------------------------------- ------------------------------------
  [Topic:]{.underline}               [Candidate Speaker:]{.underline}

  Rocky Mountains                    

  Wildfires                          Molly Hunter, Colorado State
                                     University

  Fuel Treatments                    Merrill Kauffman, USFS

  Cascade Mountains                  

  Wildfires                          To be determined

  Fuel Treatments                    Carl Fiedler, University of Montana

  Sierra-Nevada Mountains            

  Wildfires                          Jon Keeley, USGS

  Fuel Treatments                    Jan Beyers, USFS

  Great Basin                        

  Wildfires                          Lisa Floyd-Hanna, Prescott College

  Fuel Treatments                    Jeanne Chambers, USFS

  Southern Piedmont                  

  Wildfires                          To be determined

  Fuel Treatments                    Ross Phillips, USFS

  Mid-western Grasslands             

  Wildfires                          To be determined

  Fuel Treatments                    Fred Giese, USFWS

  Hawaiian Islands                   

  Wildfires                          Rhonda Loh, NPS

  Fuel Treatments                    Michael Castillo, USFWS

  Scaling Predictions from Plots to  Mohammed Kalkhan, Colorado State
  Landscapes                         University

  Summary and Implications           Philip Omi, Colorado State
                                     University
  ---------------------------------- ------------------------------------