Wildfires negatively impact conifer regeneration and growth  
ABSTRACT 
Ectomycorrhizal fungi (ECM) and soil nutrients are important for conifer growth and 
regeneration. ECM engage in a symbiotic relationship with conifers, with the fungus conferring 
nutrients such as nitrogen and phosphorous to their host tree in exchange for carbohydrates they 
receive from the tree. This project examined the effects of wildfires on ECM and soil nutrient 
content and observed how these changes may affect conifer regeneration post-fire. In 2012, the 
Arapaho fire spread through the Rogers Research Site located in the Northern Laramie 
Mountains of Wyoming. The fire altered ECM and soil nutrient levels and killed a large number 
of conifers. In order to examine the fire's effects and different restoration techniques, a block 
design was implemented at the field site, and soil was collected from both high-severity (burned) 
and low-severity burned (control) plots five years post wildfire. Pine seeds were planted in both 
types of soil in microcosms and were incubated for a period of fourteen months in growth 
chambers. In February, the seedlings were harvested and analyzed based on their length and 
biomass. It was observed that trees grown in soil from the control sites grew more on average 
and exhibited a higher seedling biomass than trees grown in soil from the burned sites, indicating 
that changes implemented by wildfires do negatively impact conifer regeneration. In order to 
combat the negative effects of wildfires, both ECM and nutrients may need to be introduced to 
these areas post-fire. This can be accomplished either by inoculating burned areas with soil from 
non-burned sites, or by transplanting seedlings grown in the lab in non-burned soil to burned 
areas. 
 
 
INTRODUCTION 
The soil microbial community has many roles in tree growth and regeneration. The 
microbial community helps with decomposition, nutrient cycling, and maintaining soil structure 
(Hart et. al 2005). Wildfires often have negative impacts on the soil microbial community by 
altering the soil chemical properties and through heat-induced mortality (Covington and DeBano 
1990; DeBano 1991). Research has shown that fungi are generally more affected than bacteria by 
fire (Dooley and Treseder 2012; Pressler et al. 2019). This means that ectomycorrhizal fungi 
(ECM) have a higher mortality rate and take longer to regenerate in the soil. This is detrimental 
because ECM are important for the growth and maintenance of various plant species, as ECM 
engage in symbiotic relationships with plants, specifically coniferous trees (Balestrini et al. 
2014).  
After wildfires, it is hard for ponderosa pines in mid-elevation forest ecosystems to 
germinate and regenerate. This is due to several factors, with the main factor being reduced seed 
source and reduced access to nutrient as the organic soil horizon is often completely burned by 
the fire. But even with restoration efforts by planting or seeding pines, other factors play a role in 
creating dry soil conditions, including high wind speeds combined with high temperatures, low 
precipitation, and high soil radiation. These harsh conditions can be overcome through symbiotic 
relationships with ectomycorrhizal fungi (ECM), which increase water and nutrient availability 
to conifer species. In exchange, they receive carbohydrates from their host (Balestrini et al. 
2014). 
There is an abundance of ECM taxa. While all of these taxa are beneficial in some way to 
conifer trees, previous research has shown that a select few are actually able to survive post-
wildfire (Cowan et al. 2016). Rhizopogon salebrosus, Rhizopogon ochraceorubens, Wilcoxina 
rehmii, and Suillus pseudobrevipes were all relatively abundant in unburned, low, and high 
intensity burned soil (Cowan et al. 2016). However, while some ECM taxa may not be as 
equipped to survive wildfires, they are beneficial in different ways. For example, a study 
examining ECM abundance after a mountain pine beetle epidemic found that Chroogomphus 
ochraceus, Hydnum repandum, Hygrophorus piceae, and Lactarius rufus, among others, were 
able to withstand pine beetle attacks on conifers (Treu et al. 2014). It is interesting to note that 
none of the ECM taxa that survived post-wildfire and the taxa that survived a mountain beetle 
epidemic are the same. Therefore, it stands to reason that there is a variety of ECM taxa that 
contributes to the health of conifer trees in different ways. Research has also shown that 
ectomycorrhizal fungi (ECM) can regenerate after one growing season (Cowan et al. 2016). 
However, no one knows for certain how long it takes ECM to return to normal levels in the soil 
post-fire.  
During wildfires, many soil physical and chemical properties are altered. An example of 
this is changes in nitrogen (N) and phosphorous (P) in the soil. Organic N is lost initially when 
the organic soil horizon is burned by the wildfire. However, inorganic N, such as ammonium 
(NH4+) and nitrate (NO3-), increased initially along with fire intensity (Mroz et al. 2980; Weston 
and Attiwill 1990). The combustion of forest vegetation during wildfires converts organic P to 
inorganic P, or orthophosphate. Eventually, however, NH4+ is adsorbed and NO3- is leached from 
the soil post-fire. Orthophosphate eventually binds to calcium, aluminum, and iron in the soil 
(Certini 2005). As a result, inorganic levels of N and P begin to return to normal about five years 
post-fire (Hauer et al., 1998) 
While it was observed that wildfires can reduce the soil microbial community, these 
studies were only short-term. Long-term effects of wildfires haven’t been studied yet (Hart et al. 
2005). A long-term study looking at ponderosa pine regeneration and mycorrhizal fungi 
regeneration in the soil would be beneficial in better understanding wildfire effects on ponderosa 
pine forest regeneration. My project therefore will be looking at ECM regeneration and effect on 
conifer growth post-wildfire using a growth chamber experiment, in which we will measure 
ECM abundance and specific taxa, along with the health and growth of conifer seedlings. This 
experiment will allow us to answer the following questions: 
Do ECM have the same potential to return to normal levels post-wildfire? I propose that, 
if N and P return to normal levels in the soil post-fire, then ECM will be able to as well. But how 
long will this take? Is it dependent on the health of their host species? Research has shown that 
ECM are sensitive to the health and abundance of their host species (Cowan et al. 2016). If 
mycorrhizal fungi are beneficial to tree growth and development, a lack of mycorrhizal fungi 
could lead to a decline in the number of trees found post-wildfire. Additionally, it has been 
proven that a variety of ECM taxa benefit conifers in different ways (Treu et al. 2014, Cowan et 
al. 2016). Some taxa are able to withstand high temperatures, and thus are able to survive 
wildfires. However, are these taxa as beneficial to conifers as others? Based off of previous 
studies, I propose that the ECM taxa that survive wildfires are not necessarily as beneficial to 
conifer trees as taxa that did not survive. Additionally, will ECM and soil nutrient content have 
an effect on tree growth and biomass? I hypothesize that trees growing in soil from the control 
site will grow more based on stem length and diameter and will have a higher seedling biomass 
than trees that were grown in soil from the burn sites. This is due to the fact that ECM and soil 
nutrient content are diminished post wildfire. As a result, trees in the burn site will not have these 
advantages and will grow less on average. 
 
METHODS 
Site Description 
 To understand the consequences of wildfires on vegetation, I conducted a growth 
chamber experiment. There were two types of soil used in the experiment: low-severity burned 
(Control), and high-severity burned (Burned) soil. Soil was gathered On June 5th, 2018 from the 
Rogers Research Site (RRS), located in the north Laramie mountains in Wyoming. RRS is 
managed by the Wyoming Agricultural Experiment Station and is owned by the University of 
Wyoming (UW). The elevation at RRS ranges from 2,000-2,200 m and is about 129.5 ha high. 
The mountains have moderate to steep slopes. A weather station is located at the site that 
continuously measures precipitation and air and soil temperature. Based on the data gathered 
from the station, the average annual precipitation is 37.6 cm, and the average annual temperature 
varies, ranging anywhere from 0-14 C. Vegetation found at RRS included shrubs, primarily 
those in the Rosaceae family. Forbs included fringed sagewort (Artemisia frigida), wild 
geraniums (Geranium spp.), and cinquefoil, (Potentilla spp.), while dominant grasses and sedges 
included Idaho fescue (Festuca idahoenis), elk sedge (Carex geyeri), and prairie grass 
(Koeleria macrantha) (Howard 2003). 
 Soil located at RRS is usually coarse and deep (50-100 cm) on hillsides and ridges. 
However, a high water table results in soil that is thick, dark, and fine. The soil pH can range 
from 7.2 (above 10 cm) to 6.4 (below 10 cm) (Williams and Waggener 2017, Wilkin et al. 2019). 
RRS soils are classified as moderately developed Alfisols and Entisols. These soils have low 
fertility, and low water-holding capacity. Generally, these types of soils are fine-loamy, mixed, 
superactive, and frigid. 
In 2012, the Arapaho fire occurred, with reported temperatures ranging from 200-500 C. 
This was determined based on black soil and white ash found at the soil surface. This also 
indicated the complete consumption of organic matter in some of these areas (Wilkins et al., 
2019). Pre-fire, ponderosa pines covered approximately 80% of RRS (Seymour et al. 2017). 
However, only about 5% of ponderosas survived post-fire. As a result, the forested landscape 
was converted to a shrub and herbaceous dominated landscape. 
 
Restoration Experimental Design at RRS 
A block design was implemented in 2015 at RRS for restoration efforts. Four blocks were 
established. Each block was made up of 18 plots 50 m x 50 m in size, accumulating in a total of 
72 plots. Every plot was exposed to different combinations of post-fire management treatments, 
which included three different pine introduction treatments, three different logging treatments, 
and two erosion treatments (Herget et al. 2018). These treatments were distributed randomly 
throughout each block. One plot in each block was left untreated, in order to serve as a control 
plot. 
Soil was also collected at three low-severity burned plots with living ponderosa pine trees 
and an intact duff layer. From the high-severity burned plots, the control plot from each of the 
four blocks was selected, which had no cutting, planting, or erosion treatment applied. This 
resulted in a total of 7 plots (3 Control and 4 Burned).  
Experimental Design Seedling Microcosms 
Ponderosa pine seeds (collected from the Laramie 
Mountains prior to the wildfire by the USDA Forest Service) 
were soaked in 70% ethanol for approximately 30 minutes, then 
rinsed five times with sterile water. Three ponderosa pine seeds 
were planted per microcosm in 40 grams of dry soil, which are 
contained in 50 ml Falcon tubes wrapped in aluminum foil in 
order to exclude light (Fig. 1). Seeds were planted in a total of 
105 tubes (15 replicates per plot). The seeds were pushed down 
Figure 1. Freshly planted seedlings (picture using a clean glass rod. Tubes were placed in a growth chamber 
taken by Dr. van Diepen) 
and incubated at 42% relative humidity, and 364 ppm CO2, and maintained at 30% gravimetric 
soil moisture by watering with DI water twice a week (Fig. 2). A 
photoperiod of 18 hours of light and 6 hours of darkness was 
implemented, at 21 and 18 degrees Celsius, respectively. Tubes 
were monitored daily for emergence, and growth was measured 
after 2 months. Any seeds that emerged from the soil were 
pushed back down into the soil to ensure proper seeding.  
Figure 2. Watering setup (picture taken by Alexys 
McGuire) 
Seedlings began emerging on December 20, 
2018, with the last recorded emergence occurring 
on May 28, 2019 (Fig. 3). After the end of the 
incubation period (~14 months), trees were 
harvested (Fig. 4). Samples were analyzed based on 
a seedling harvest protocol. First, any moss, algae, 
or fungi was identified and discarded. Next, the 
seedling and soil were removed from the falcon Figure 3. a Seedling emergence (picture taken by Dr. van Diepen). b 
Seedlings 3-7 weeks after planting (picture taken by Dr. van Diepen). 
tube and placed in an ethanol sterilized sieve 
bottom. An ethanol sterilized spatula was used to clean any remaining soil from the tube and 
separate the soil from the seedling roots in the sieve. The 
seedling was then placed in another container. The soil was 
homogenized, and subsamples of the soil were taken. Then, 
the seedling itself was measured and weighed. Measurements 
taken included stem length from the cotyledon scar (cm), stem 
diameter, (mm), stem length with the needle attachment (cm), 
and fresh and dry stem weight (g). These measurements were Figure 4. Harvesting setup (picture taken by Alexys 
McGuire) 
all recorded (Fig. 4). Data was analyzed, and the average and standard error of each plot were 
calculated. A one-way ANOVA was performed as statistical analysis to compare treatment 
averages. Due to the recent onset of COVID-19, three trees labeled C1-5, C1-14, and C1-15 were 
harvested three weeks later than they were originally intended to be harvested. Consequently, 
this data may skew the overall results. In addition, no mycorrhizal or soil nutrient measurements 
were done, which were originally planned for this project. 
 
RESULTS 
Stem Length 
Overall, the trees in the control sites (n = 15) were taller than trees in the burn sites (n = 
21) based on the length of the stem with the needle attachment (Fig. 5a). Trees grown in the 
control site grew to an average length of 1.42 cm  0.17 cm, while trees grown in the burn site 
grew to an average length of 1.02 cm  0.07 cm. This is consistent with a previous pilot study 
testing trees from the same burn and control sites that grew for 7 months in pots in the 
greenhouse. Similarly, trees within the control site plots all grew more on average than trees 
within the burn site plots based on the length of the stem with the needle attachment (Fig. 5b).  
1.8
A p-value = 0.02
1.6
1.4
1.2 B 
1
0.8
0.6
0.4
0.2
1.42 1.019047619
0
Control Burn
2.5
AB 
2
1.5 AB 
A 
AB 
B B 
1 AB 
0.5
0
C1 C2 C3 B1 B2 B3 B4
 
 Figure 5. Comparison of stem length from the needle attachment between control and burn sites. a (top) Bar graph comparing 
average stem length from the needle attachment between control and burn sites. b (bottom) Bar graphs comparing average 
stem length from the needle attachment between plots in control and burn sites. Error bars represent 1 standard error. Control 
site n = 15 trees; Burn site n = 21 trees. Different letters above bars indicate significant differences between plots at  = 0.1 as 
determined by ANOVA. 
Trees in the control sites (n = 15) were also taller than trees in the burn sites (n = 21) 
based on the total height of the stem (measured from the cotyledon scar, Fig. 6a), though not 
significantly (p-value = 0.11). Trees grown in the control site grew to an average height of 3.17 
cm  0.18 cm, while trees grown in the burn site grew to an average length of 2.76 cm   0.17 
Length of Stem with Needle 
Stem Length (cm)
Attachment (cm)
cm. Similarly, trees within the control site plots grew more on average than trees within the burn 
site plots based on the length of the stem from the cotyledon scar, with the exception of Plots B3 
and C3 (Fig. 6b). In this plot, B3 grew to a length of 3.32 cm  0.33 cm, while C3 grew to a 
length of 2.6 cm  0.18 cm. 
4
p-value = 0.11
3.5
3
2.5
2
1.5
1
0.5
3.173333333 2.757619048
0
Control Burn
 
4.5
A 
4
A AB
3.5
B 
3 B B
B 
2.5
2
1.5
1
0.5
0
C1 C2 C3 B1 B2 B3 B4
 
Figure 6. Comparison of stem length from cotelydon scar between control and burn sites. a (top) Bar graph comparing average 
stem length from cotelydon scar between control and burn site. b (bottom) Bar graphs comparing average stem length from 
cotelydon scar between plots in control and burn sites. Error bars represent 1 standard error. Control site n = 15 trees; Burn site 
n = 21 trees. Different letters above bars indicate significant differences between plots at  = 0.1 as determined by ANOVA. 
Stem Length (cm) Length of Stem from Cotelydon 
Scar (cm)
 Interestingly, trees in the burn site (n = 21) grew slightly more on average than trees in 
the control site (n = 15) based on tree height after two months, but not significantly (p-value = 
0.53) Fig. 7a). Trees in the burn site grew to an average height of 2.52 cm  0.11 cm, while trees 
in the control site grew to an average height of 2.50 cm  0.13 cm. In contrast, the average height 
of trees within both the burn and control site plots varied, with no one site growing more on 
average than the other (Fig. 7b), and thus there was no difference in growth yet after two months. 
2.65
2.6
2.55
2.5
2.45
2.4
2.35
2.3
2.25
2.49333333 2.51904762
2.2
Control Burned
 
Stem Height (cm)
3.5
3
2.5
2
1.5
1
0.5
0
C1 C2 C3 B1 B2 B3 B4
 
Figure 7. Comparison of stem length between control and burn site after two months incubations. a Bar graph comparing 
average stem length between control and burn site. b Bar graphs comparing average stem length between plots in control and 
burn sites. Error bars add 1 standard error. Control site n = 15 trees; Burned site n = 21 trees. Different letters above bars 
indicate significant differences between plots at  = 0.1 as determined by ANOVA. 
Stem Diameter 
 Interestingly, trees in the burn site (n = 21) grew more on average than trees in the control 
site (n = 15) based on stem diameter, but this was not significant (p-value = 0.70, Fig. 8a). Trees 
grown in the burn site grew to an average diameter of 1.5 mm  0.16 mm, while trees in the 
control site grew to an average diameter of 1.42 mm  0.07 mm. Additionally, trees within the 
control site plots grew less on average than trees within the burn site plots based on stem 
diameter, with the exception of Plot 3 (Fig. 8b). In this plot, B3 grew to an average diameter of 
1.93 mm  0.55 mm, while C3 grew to an average of 1.37 mm  0.11 mm.  
Stem Height (cm)
1.8
p-value = 0.70
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
1.419333333 1.497142857
0
Control Burn
 
3
2.5
2
1.5
1
0.5
0
C1 C2 C3 B1 B2 B3 B4
 
Figure 8. Comparison of stem width between control and burn sites. a Bar graph comparing average stem diameter between 
control and burn site. b Bar graphs comparing average stem diameter between plots in control and burn sites. Error bars added 
1 standard error.  Control site n = 15 trees; Burn site n = 21 trees. Different letters above bars indicate significant differences 
between plots at  = 0.1 as determined by ANOVA. 
Seedling Biomass 
 It was observed that trees in the control site (n = 15) had a higher biomass based on fresh 
stem weight on average than trees in the burned site (n = 21) (Fig. 9a). Trees in the control site 
had an average weight of 0.52 g  0.03 g, while trees in the burn site had an average weight of 
Diameter of Stem (mm)
Stem Diameter (mm)
0.38 g  0.02 g. Similarly, trees within the control site plots grew more on average than trees in 
the burn site plots (Fig. 9b). The same is true for dry stem weight. Trees in the control site had a 
higher dry stem weight on average than trees in the burn site. Trees within the control site plots 
had a higher dry stem weight on average than trees in the burn site plots as well. 
  
 
0.6
A  p-value = 0.0004
0.5
B 
0.4
0.3
0.2
0.1
0.524 0.383809524
0
Control Burn
 
0.7
0.6 AB AB AB
0.5 AB AB 
AB
0.4
B
0.3
0.2
0.1
0
C1 C2 C3 B1 B2 B3 B4
 
Figure 9. Comparison of seedling biomass between control and burn sites. a Bar graph comparing average fresh stem weight 
between control and burn site. b Bar graphs comparing average fresh stem weight between plots in control and burn sites. 
Error bars added 1 standard error. Control site n = 15 trees; Burn site n = 21 trees. Different letters above bars indicate 
significant differences between plots at  = 0.1 as determined by ANOVA. 
 
DISCUSSION 
 Overall, the results were consistent with our hypotheses. Trees grown in soil from the 
control site seemed to grow more and exhibit a higher seedling biomass than trees in the burn 
Stem Weight (g) Stem Weight (g)
site, both on average and within the different plots. This is consistent with previous studies, 
where it was found that conifers grown in burned soil grew at a lesser density compared to 
conifers that grew in unburned soil (Chambers et al., 2016). There was very little variability 
between the control and burn sites, as well as within the different plots. This finding of lower 
growth of seedlings in burned plots could be due to the alteration of physical and chemical 
properties in the soil. For example, when a fire burns through a forest, it burns the top organic 
layer which contain nutrients like N and P. As a result, N is converted to NH4+ and NO3- and P is 
converted to orthophosphate. Initially, this is beneficial to conifer regeneration. However, the 
soil used in this experiment was collected approximately five years after the fire took place, so 
these initial advantages were lost. As a result, the soil was not as nutrient rich as it would be pre-
wildfire. 
 Additionally, these results could be due to a decreased amount of ECM in the soil. 
Wildfires kill beneficial taxa via heat-induced mortality. Fungi have a higher sensitivity to heat 
than bacteria and are easily lost during a wildfire as a result (Add reference here that you also 
included in the introduction on this topic). Because of this, ECM have a higher mortality rate 
than the rest of the microbial community and take longer to regenerate in the soil. As a result, 
ECM are not able to help conifers grow post-wildfire. And while some studies have shown that 
ECM can begin to regenerate post-wildfire after one growing season, no one knows how long it 
takes ECM to return to normal levels in the soil. It is possible that some different species of 
ECM could have regenerated post-wildfire. However, based on the growth patterns of trees in the 
control and burn sites, it stands to reason that these ECM did not confer the same benefits as the 
original species did. It is important to note that, due to COVID-19, the labs were shut down and I 
was not able to gather any results based on ECM regeneration, so all of this is simply speculation 
based on previous studies. 
 The similar heights of trees within the burn and control sites after two months was 
unexpected. I thought that trees in the burn sites would be significantly smaller. However, these 
results indicated that wildfires do not have as great an effect on pine regeneration in the early 
stages of growth. This could mean that ECM is only beneficial towards the later stages of 
growth. This could also be due to the fact that younger trees do not need as many nutrients or as 
much water as older trees. As a result, their growth is not as impaired. 
Interestingly, the biomass of plot B2 was significantly less than the biomass of the other 
plots in either the control or burn sites. This result is consistent with previous findings. Plot 
samples were taken from different locations varying in slope and aspect, so it makes sense that 
one plot could be different than the others. Plot B2 could have been more heavily affected by the 
wildfire than the other plots, and so ECM could be taking longer to regenerate than in the other 
plots. However, because I could not examine ECM in the lab, it is impossible to say for sure. 
Additionally, soil nutrients like N and P could be taking longer than normal to go back to regular 
levels in the soil. 
 The larger stem diameter of trees in the burn sites was an unexpected result. 
Traditionally, stem diameter has been used as an indicator of seedling health. It has been used in 
the field to assess field survival and growth. Stem diameter can be used to indicate root system 
size and stem volume. Additionally, a height:diameter ratio is used as a way to measure seedling 
sturdiness (Haase, 2008). Therefore, it was surprising that trees grown in the burn site had a 
higher stem diameter on average. However, this higher overall average was due to an anomaly in 
Plot 3 (Fig. 6). The tree labeled B3-11 had a stem diameter of 4.67 mm, much higher than the 
diameters of any other stems in both the control and burn sites. The standard error of the B3 plot 
was also much greater than the other plots, which suggests greater variability among the trees 
within this plot. This anomaly could be due to operator error or could be a typo in the results. 
However, with the exception of Plot 3, all of the trees within the burn site plots had a smaller 
stem diameter on average than trees in the control site plots and were fairly consistent. 
 Eventually, I hope to return to the lab to measure both ECM and soil nutrient content in 
order to determine if these factors really did play a role in inhibiting conifer regeneration. If 
future research proves that ECM and soil nutrient content really did impact conifer regeneration, 
then we can use this information to help aid in conifer growth after wildfires. For example, soil 
in burned areas could be inoculated with soil from non-burned areas. Seedlings grown in labs 
with sufficient levels of ECM, nitrogen, and phosphorous can also be transplanted into burned 
plots. While it is hard for conifers to regenerate post-wildfire, this experiment looks into the 
long-term effects of wildfires on conifer growth and what could impact it. Using this 
information, long-term plans could be developed to help aid in conifer regeneration in forests 
post-wildfire.  
 
ACKNOWLEDGEMENTS 
Funding for this project was provided by The Wyoming Agricultural Experiment Station 
and U.S. Department of Agriculture, National Institute of Food and Agriculture McIntire-Stennis 
award. I would also like to thank Dr. van Diepen for being my mentor and giving me guidance 
throughout the course of this experiment, and Zachary Ahrndt and Elizabeth Traver for their help 
in the lab.  
REFERENCES 
Balestrini, Raffaella & Lumini, Erica & Borriello, Roberto & Bianciotto, Valeria., 2014. Plant-
Soil Biota Interactions. 10.1016/B978-0-12-415955-6.00011-6. 
Certini, G., 2005. Effects of fire on properties of forest soils: A review. Oecologia. 143,1–10. 
https://doi.org/10.1007/s00442-004-1788-8. 
Covington, W.W., DeBano, L.F., 1990. Effects of fire on pinyon-juniper soils. General Technical 
Report - US Department of Agriculure, Forest Service 191, 78–86. 
Chambers, M.E., Fornwalt, P.J., Malone, S.L., Battaglia, M.A., 2016. Patterns of conifer 
regeneration following high severity wildfire in ponderosa pine – dominated forests of the 
Colorado Front Range. For. Ecol. Manage. 378, 57-56. 
https://doi.org/10.1016/j.foreco.2016.07.001. 
DeBano, L.F., 1991. The effect of fire on soil properties. Proc. Manag. Product. West. For. soils 
(April 1990 - Missoula, MT) GTR-INT-280 151–156. 
Dooley, S.R., and Treseder, K.K. 2012. The effect of fire on microbial biomass: a meta-analysis 
of field studies. Biogeochemistry 109:49-61. 
Haase, D. 2008, Understanding forest seedling quality: measurements and interpretation. Tree 
Planter’s Notes, 52(2):24-30 
Hauer, F. R., Spencer, C.N. 1998, Phosphorous and Nitrogen Dynamics in Streams Associated 
with Wildfire: a Study of Immediate and Longterm Effects. International Journal of 
Wildland Fire 8(4), 183-198 
Herget, M. E., Williams, S. E., van Diepen, L. T.A., Winters, S. M., and waggener, R. W., 2018, 
Restoration of ponderosa pine following high-intensity fire, Rogers Resesarch Site, north 
Laramie Mountains, Wyoming: University of Wyoming, Wyoming Agricultural Experiment 
Station, Rogers Research Site Bulletin 5, iv + 72 p. 
Mroz, G.D., Jurgensen, M.F., Harvey, A.E., and Larsen, M.., 1980. Effects of fire on nitrogen in 
forest floor horizons 1. Soil Sci. Soc. Am. 44, 395–400. 
Pressler, Y., Moore, J. C., & Cotrufo, M. F., 2018. Belowground community responses to fire: 
meta-analysis reveals constrasting responses of soil microorganisms and mesofauna. Oikos, 
128(3), 309-327. https://doi.org/10.1111/oik.05738.  
Seymour, M., Driese, K. L., and Waggener, R. W., 2017, Vegetation mapping of Rogers 
Research Site, north Laramie Mountains, Wyoming, using high spatial resolution 
photography and heads up digitizing: University of Wyoming, Wyoming Agricultural 
Experiment Station, Rogers Research Site Bulletin 4, iv + 50 p. 
Reazin, C., Morris, S., Smith, J.E., Cowan, A.D., Jumpponen, A., 2016. Fires of differing  
intensities rapidly select distinct soil fungal communities in a Northwest US ponderosa pine  
forest ecosystem. For. Ecol. Manage. 377. https://doi.org/10.1016/j.foreco.2016.07.002. 
Treu, R., Karst, J., Randall, M., Pec, G.J., Cigan, P.W., Simard, S.W., Cooke, J.E.K., Erbilgin, 
N., Cahill, J.F., 2014. Decline of ectomycorrhizal fungi following a mountain pine beetle 
epidemic. Ecology 95, 1096–1103. https://doi.org/10.1890/13-1233.1 
Villareal-Ruiz, L., Neri-Luna, C., 2018. Testing sampling effort and relative abundance 
descriptors of belowground ectomycorrhizal fungi in a UK planted scots pine woodland. 
Mycology 9(2), 106-115. https://dx.doi.org/10.1080%2F21501203.2017.1394393 
Weston, C.J., Attiwill, P.M., 1990. Effects of fire and harvesting on nitrogen transformations and 
ionic mobility in soils of Eucalyptus regnans forests of south-eastern Australia. Oecologia 
83, 20–26. https://doi.org/10.1007/BF00324628. 
Wilkins, C.D., Williams, S. E., Van Diepen, L.T.A., Urynowicz, M.A., Munn, L.C., Waggener, 
R.W., 2019. Soil amendment additiona and microbial community recovery following high-
severity fire at UW Rogers Research Site. Wyoming. Rogers Res. Site Bull. 8.