f u n g a l e c o l o g y 6 ( 2 0 1 3 ) 1 9 2 e2 0 4
available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/funeco
Stand-replacing wildfires alter the community structure of
wood-inhabiting fungi in southwestern ponderosa pine
forests of the USA
a, Stephen C. HARTc,
Valerie J. KURTHa,*, Nicholas FRANSIOLIb, Peter Z. FULE
d
Catherine A. GEHRING
a
School of Forestry, Northern Arizona University, Flagstaff, AZ 86011, USA
School of Earth Sciences and Environmental Sustainability, Northern Arizona University, Flagstaff, AZ 86011, USA
c
Life & Environmental Sciences and Sierra Nevada Research Institute, University of California, Merced, CA 95343, USA
d
Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ 86011, USA
b
article info
abstract
Article history:
Increases in stand-replacing wildfires in the western USA have widespread implications for
Received 19 July 2012
ecosystem carbon (C) cycling, in part because the decomposition of trees killed by fire can
Revision received 31 December 2012
be a long-term source of CO2 to the atmosphere. Knowledge of the composition and
Accepted 7 January 2013
function of decay fungi communities may be important to understanding how wildfire
Available online 7 March 2013
alters C cycles. We assessed the effects of stand-replacing wildfires on the community
Corresponding editor:
structure of wood-inhabiting fungi along a 32-yr wildfire chronosequence. Fire was asso-
Jacob Heilmann-Clausen
ciated with low species richness for up to 4 yr and altered species composition relative to
unburned forest for the length of the chronosequence. A laboratory incubation demon-
Keywords:
strated that species varied in their capacity to decompose wood; Hypocrea lixii, an indicator
C cycling
of the most recent burn, caused the lowest decomposition rate. Our results show that
Fungal diversity
stand-replacing wildfires have long-term effects on fungal communities, which may have
Fungal species richness
consequences for wood decomposition and C cycling.
ITS1FeITS4
ª 2013 Elsevier Ltd and The British Mycological Society. All rights reserved.
Molecular methods
Mycelial isolation
Wood decomposition
Introduction
Tree mortality in the western USA has increased in recent
years due to a combination of drought, insect outbreaks, and
wildfire (Westerling et al. 2006; Van Mantgem et al. 2009; Allen
et al. 2010), changes that have profound implications for carbon (C) storage and cycling. In particular, large, high-severity
wildfires often kill trees but do not consume all the tree
biomass, and the C flux from decomposition of these dead
trees can exceed net primary production for years to decades
(Kashian et al. 2006). The length of time that recently burned
forests function as net sources of atmospheric CO2 is uncertain; high-severity burns in mixed conifer and ponderosa pine
forests of the Inland Northwest remain net atmospheric
sources for 4e5 yr (Meigs et al. 2009), whereas similar burns in
southwestern ponderosa pine forests are likely to remain a
* Corresponding author. Present address: Department of Forest Resources, University of Minnesota, 1530 Cleveland Avenue North,
St. Paul, MN 55108, USA. Tel.: þ1 612 624 3639; fax: þ1 612 625 5212.
E-mail address: vjkurth@umn.edu (V.J. Kurth).
1754-5048/$ e see front matter ª 2013 Elsevier Ltd and The British Mycological Society. All rights reserved.
http://dx.doi.org/10.1016/j.funeco.2013.01.006
Community structure of wood-inhabiting fungi in southwestern ponderosa pine forests
net source for decades (Dore et al. 2008; Hurteau & Brooks
2011). A stronger mechanistic knowledge of the controls on
wood decomposition would enhance our understanding of
ecosystem C dynamics following high-severity wildfires.
Wildfire alters many of the factors that limit the decomposition of wood, including colonization by saprotrophic
organisms, substratum quality and quantity, and abiotic
environmental characteristics (Harmon et al. 1986). Wildfire
reduces fungal inoculum sources due to heat-induced mortality (Raison 1979; Choromanska & DeLuca 2002; Korb et al.
2004). The maximum ground temperatures reached in forest
fires range from 200 C to 300 C (Neary et al. 1999), while
fungal mortality occurs at temperatures <100 C (Dunn et al.
1985). The distance and arrangement of the fungal inoculum
source are key factors controlling fungal recolonization following wildfire; further, reductions of soil-based fungal inoculum by fire make dispersal ability, either via airborne spores,
animal transport, or migration of mycelium in the soil, critical
to fungal establishment in wood (Kirby et al. 1990). Fungi that
overcome dispersal limitations in a recently burned area are
likely to encounter abundant wood substratum, but it will
probably be at least partially charred. Charring decreases
decomposition rates (Cornwell et al. 2009), but may provide
greater habitat opportunities for microbes because of
€inen et al. 2000). Post-fire
increased surface area (Pietika
changes in abiotic variables, such as microclimate and
nutrient availability, may also alter the responses of woodinhabiting fungi. Wildfires generally cause an increase in
land surface temperatures, and they alter chemical characteristics of soil, notably by increasing the pH and nutrient
availability (Hart et al. 2005); the interactions of all these variables may affect the establishment of fungi.
Although the environmental changes resulting from fire
are likely to affect the diversity and community composition
of decay fungi, few studies have examined the short- and
long-term effects of fire on fungi (Cairney & Basitas 2007).
Prescribed burning favors certain species of fungi, causing
moderate to strong alterations in community composition
(Olsson & Jonsson 2010; Berglund et al. 2011). Species richness
declines in the first year following prescribed burning (Olsson
& Jonsson 2010), but also recovers within 1e5 yr (Berglund
et al. 2011). Even fewer studies have focused on the effects of
wildfire on wood-inhabiting fungi. In a study in a boreal forest,
wildfire disturbance had a short-term effect on species richness, but little effect on community composition (Lumley et al.
2001). However, similar studies on wildfire or prescribed
burning have not been conducted in more arid environments
where fire frequency and intensity have increased in recent
years.
An understanding of how the composition of fungal communities changes over time is of particular relevance to climate change science because species composition may affect
wood decomposition rates, and thus have consequences for C
cycling. Although fungal community structure is thought to be
more important in the later stages of decay, when the majority
of the C remaining is in the form of lignin (McGuire & Treseder
2010), species assemblage history may be important in
determining these later-stage communities (Fukami et al.
2010). Several laboratory experiments have confirmed that
complex species interactions, including a legacy of exudates
193
in the wood, promote the growth of certain secondary fungal
€ et al. 1995; Holmer
colonizers while inhibiting others (Niemela
et al. 1997; Heilmann-Clausen & Boddy 2005). As a result, the
initial colonizers of a wood substratum can be instrumental in
determining subsequent species succession, and this can
have associated impacts on rates of wood decomposition
(Fukami et al. 2010; Lindner et al. 2011).
Southwestern ponderosa pine (Pinus ponderosa var. scopulorum) forests are an excellent model ecosystem to examine
wildfire effects on wood decomposition and C cycling for
numerous reasons. First, ponderosa pine forests are widespread in the southwestern USA, comprising almost half of the
commercial forest land in Arizona, Utah, New Mexico and
Colorado (Schubert 1974). Also, the Southwest is predicted to
suffer large-scale tree mortality due to its high sensitivity to
drought, predicted increases in ambient temperatures, and
related increases in wildfire frequency and bark beetle outbreaks (Williams et al. 2010). The land-use history in these
forests also has strongly influenced the risk of wildfire and
associated tree mortality. Historic forests underwent frequent,
low-intensity surface fires, but the introduction of land management practices (cattle grazing, timber harvest, and active
fire suppression) by Euro-American settlers led to a shift to
infrequent, stand-replacing wildfires (Covington & Moore
1994b). Research on southwestern ponderosa pine ecosystems is relevant to other forests in the western USA because
these land management practices are widely applied across
the region, and climate models predict widespread drought
conditions and warmer temperatures (Easterling et al. 2000;
Hoerling & Kumar 2003). Finally, the relatively common incidence of stand-replacing wildfires in the last w40 yr (Stephens
2005; Littell et al. 2009) has provided the opportunity to construct a chronosequence of burned sites for examining the
post-fire legacy of decaying wood.
In this study, we compared the diversity and species
composition of wood-inhabiting fungi associated with wildfires of varying ages to that of adjacent unburned stands of
ponderosa pine in northern Arizona. Relatively little is known
about the fungi responsible for wood decay in these forests
beyond sporocarp surveys (Gilbertson 1974), but evidence
suggests that sporocarps may not be representative of the
r et al. 2006; Lindner
entire fungal community in wood (Allme
et al. 2011). Therefore, we used a combination of mycelial
isolation and molecular techniques to identify woodinhabiting fungi community structure. We also measured
the decomposition potential of a subset of the species in the
laboratory. We hypothesized that wildfire would initially
affect the fungal community structure by decreasing fungal
species richness and diversity (due to heat-induced mortality
and dispersal limitations) and altering species composition (as
a result of changes in environmental conditions); however,
these effects would diminish with time since fire as the
environmental impacts of fire lessen and substratum quality
becomes a stronger driver of community structure than dispersal abilities. We also hypothesized that the species of fungi
we isolated would vary in their ability to decompose the same
substratum. Specifically, we hypothesized that the early colonizers characteristic of recent fires would be less able to
decompose wood than species characteristic of unburned
forests, because of a trade-off between rapid colonization
194
V.J. Kurth et al.
ability and the enzymatic capacity to process complex substrates such as lignin.
Materials and methods
Study sites and field design
We selected five sites in ponderosa pine forest covering a
region approximately 615 km2 within the Coconino and Kaibab National Forests of northern Arizona, USA (Table 1, Fig 1).
Site selection was based on knowledge of the fire history and
our ability to visually delineate the burn perimeter in the field.
Forest Service records confirmed each site had experienced
one stand-replacing crown fire (>95 % tree mortality) within
the last 60 yr (burn years ranged from 1977 to 2005). At each
site, burned areas were paired with an adjacent unburned
area to better isolate the effects of fire and reduce other
sources of environmental and temporal variation (Johnson &
Miyanishi 2008). All sites were located on basaltic parent
material. Woody vegetation in the unburned areas consisted
of ponderosa pine with some Gambel oak (Quercus gambelii) at
lower elevations, and Douglas-fir (Pseudotsuga menziesii) and
southwestern white pine (Pinus strobiformis) at higher elevations. Woody vegetation was limited to small shrubs at all
the burned areas except at the 1984 burn site, where there
were some small ponderosa pine trees.
The precise land-use history of each site is not known, but
the general forest history of the region has been welldocumented. Historically, ponderosa pine stands were comprised of uneven-aged groups that experienced frequent,
low-intensity surface fires (Cooper 1960; White 1985; Swetnam
& Baisin 1996). In the 1880s, Euro-American settlers introduced livestock grazing, which diminished surface fuels and
reduced fire frequency. This, coupled with active fire
Fig 1 e Map of study area for evaluating the impact of
wildfires on the community structure of wood-inhabiting
fungi in southwestern ponderosa pine forests, USA.
Wildfires (hashed) are labeled with the year of burn. Inset
shows the area of Arizona represented by the main map.
Table 1 e Characteristics and mean decay class and log volume of burned (B) and adjacent unburned (UB) areas in
southwestern ponderosa pine forests, USA. Organic horizon and woody debris carbon (C) data are from Ross et al. (2012),
and are the mean of nine plots (±1 standard error). Decay class (Maser et al. 1979) and log volume are the means of six logs
(±1 SE). Decay classes range from 1 to 5; 1 is the least and 5 is the most decayed.
Sitea
Treatment
2005
Bc
UBc
2000
Log decay
class
Log volume
(m3)
Organic horizon C
(g C m2)b
Woody debris C
(g C m2)b
28
e
1.0 0
1.7 0.3
6.751 0.775
25.004 8.148
405.1 151.8
3 851.4 756.7
1 672.3 445.30
78.1 21.49
B
UB
268
e
2.0 0.3
1.8 0.2
4.121 0.731
4.202 1.062
558.5 118.2
3 819.9 493.59
4 889.0 818.3
372.3 98.31
1996
B
UB
3 495
e
3.0 0.3
3.0 0.3
5.491 2.442
0.981 0.384
153.1 38.8
2 906.1 376.8
584.0 144.48
495.2 137.40
1984
B
UB
1 258
e
4.5 0.2
4.3 0.2
3.462 0.474
5.231 2.703
410.7 147.1
4 199.8 1 111
762.4 261.97
446.7 153.16
1977
B
UB
1 858
e
4.3 0.2
19.559 3.146
8.044 2.814
598.6 142.3
2 192.5 625.0
382.9 157.97
837.9 406.32
Area burned
(ha)
a Site refers to the year the wildfire occurred.
b From Ross et al. (2012). Organic horizon was sampled from two 30 30 cm quadrats per plot and did not include woody debris on the surface
(classified as fine wood debris). Coarse woody debris was classified as those wood pieces >7.62 cm in diameter. Both organic horizon and coarse
woody debris were converted to g C m2.
c B refers to burned and UB refers to unburned.
Community structure of wood-inhabiting fungi in southwestern ponderosa pine forests
suppression, led to an extended period of fire exclusion until
the initiation of stand-replacing fires in recent decades
(Covington & Moore 1994b). Favorable climate conditions and
seed production around 1919 led to high levels of natural
regeneration (Schubert 1974), and, since many large trees
were lost to logging, it is common to find dense, homogenous
stands of suppressed, small diameter trees (Covington &
Moore 1994a; Allen et al. 2002). Although precise logging
records were not kept, we did not notice evidence of past
logging (large stumps) in the unburned forests.
Of all our study sites, only the 1984 fire site was subjected
to salvage logging following the wildfire. At this site, we
sampled from a slope that we are confident was not logged
based on the lack of stumps and the prevalence of charred
logs. Therefore, we are certain that the only major disturbance
that the sites experienced was their respective standreplacing wildfire. More detailed information on site characteristics can be found in Ross et al. (2012). Although their
measurements were taken at different locations within the
sites than those sampled in our current study, all of our
samples were taken within 1 km of their plots.
The month we sampled (Apr. 2009) was drier than average,
but air temperatures were similar to 30-yr averages (Western
Regional Climate Center, Fort Valley Station; www.wrcc.dri.
edu). The region received 1.7 cm precipitation in Apr. of 2009,
and the cumulative total for that spring (Jan.eApr.) was
8.3 cm; the 30-yr (1971e2000) mean precipitation for Apr. is
3.2 cm, and 21.4 cm for the spring. The mean air temperature
during Apr. 2009 was 4.2 C, while the 30-yr mean Apr. temperature was 4.5 C. The mean spring air temperature for
Jan.eApr., 2009, was 0.9 C, and the 30-yr spring mean temperature was 0.5 C.
Field sampling and laboratory analyses
At each site, we selected areas to sample where the fire
perimeter could be visually delineated. We then established a
sampling swath inside the burned area between 100 and
300 m from the fire perimeter and approximately 200 m in
length. Within this swath, we selected six dead and downed
tree boles (hereafter referred to as “logs”) to sample. Logs were
chosen based on several criteria. They were representative of
all the logs in the burned area in terms of size (length and
diameter) and state of decay (Maser et al. 1979; Table 1). They
were also spatially distinct in that the logs were sampled
throughout the entire sampling swath (approximately
200 200 m). Four cuboid samples, each approximately
6 8 1.5 cm, were removed from each log using a chainsaw
or bow saw and a hatchet. One sample was taken at each end
of the log, and the other two samples were evenly spaced
across the length of the log. All samples were removed from
the highest point along the side of the log. We recorded log
length, diameter (both ends and middle), aspect, and decay
class. We calculated log volume using the formula for the
volume of a frustum of a cone (V ¼ (p * l/3) * (r12 þ r22 þ r1r2)),
where “l” is the length of the log and “r” equaled the radius at
either end. Decay class was determined following Maser et al.
(1979) wherein logs were assigned a value of one to five, one
being the least decayed and five being the most decayed.
Given that state of decay and time since fire co-vary, it was not
195
possible to sample logs from the same decay class at all
chronosequence sites; however, the approximate decay class
was kept consistent within each site (Table 1). Also, by sampling at the edge of the burns, rather than the center, we
intended to minimize the potential impacts of variation in
burn size on fungal communities. In this way, we kept the
distance to sources of fungal inoculum (unburned forest), as
well as any edge effects, consistent across all sites.
We followed a similar procedure in each of the adjacent
unburned areas in that we established a swath between 100 m
and 300 m from the burn perimeter, but this time the swath
was established outside of the burned area. Unburned areas
had no evidence of recent fire in that no charred wood was
observed and substantial soil organic horizons were present.
To reduce variation in fungal community composition that
might be associated with log size and age, we sampled logs
that were similar in size and state of decay to those we sampled in the adjacent burned area. Again, the chronosequence
design precluded us from sampling logs of the same decay
class at all sites; however, all the logs sampled at a given site
(burned/unburned pair) were approximately the same decay
class (from one to five; Table 1). All wood pieces were placed in
polyethylene bags and transported on ice back to the laboratory and stored at 4 C until processing (<10 d).
To isolate fungi from within the wood samples, a small
piece (5 5 10 mm) was removed from each sample using a
coping saw. The wood sample was surface sterilized by briefly
dipping in 70 % ethanol and then burning off the ethanol with
a flame. The wood segments were immediately placed on a
r et al. 2006)
sterile Petri dish containing Hagem agar (Allme
and monitored for hyphal growth. Emerging hyphae were
placed in pure culture on Hagem agar and grouped by their
hyphal and conidial morphology as assessed using a microscope. All plating and subculturing were performed inside a
laminar flow hood to prevent contamination. A total of 240
wood pieces from 60 logs were plated (6 logs per site).
Multiple representatives from each culture morphotype
were selected for DNA extraction. A small portion of hyphae
was scraped off the culture using a sterile knife (approximately 0.5 g) and placed in a 96-well DNA extraction plate.
The DNA was then extracted using Qiagen DNEasy (Qiagen,
Valencia, CA, USA) plant kits according to the manufacturer’s
instructions, using the modification recommended for fungal
tissue. The internal transcribed spacer (ITS) region of the
ribosomal DNA was amplified using polymerase chain reaction (PCR) with the forward ITS1F (50 -CTTGGTCATTTAGAGG
AAGTAA-30 ) and reverse ITS4 (50 -TCCTCCGCTTATTGATAT
GC-30 ) primer pair (Gardes & Bruns 1993). Restriction fragment
length polymorphism (RFLP) data were obtained following the
methods of Gehring et al. (1998) using restriction enzyme
digestion with HinfI and MboI, and morphological groupings
were confirmed using the distinctive RFLP band patterns (RFLP
types).
The ITS region from no fewer than two representatives of
each RFLP type was sequenced. Forward and reverse
sequencing was performed on an ABI 3730 Genetic Analyzer
(Applied Biosystems, Foster City, CA, USA) at the Environmental Genetics and Genomics Laboratory (Northern Arizona
University, Flagstaff, AZ, USA). Sequences were aligned and
edited using Geneious Pro software (Drummond et al. 2010).
196
Basic Local Alignment Search Tool (BLAST) searches were also
performed using Geneious and the GenBank database (http://
www.ncbi.nlm.nih.gov). Sequences with similar BLAST
results were aligned using Geneious Consensus alignment to
verify their similarity (>97.0 % pairwise identity). Final morphological groupings were determined using the sequence
results, and samples that did not amplify or did not yield a
high quality sequence were grouped based on a combination
of RFLP pattern and culture morphology. For consistency,
teleomorphic species identifications were used; anamorph
names were converted to the appropriate teleomorph when
necessary.
Fungal community composition was analyzed using PCORD software (McCune & Mefford 2006) with each log considered a plot. Fungal species were collapsed into genera
because it reduced the number of singletons and minimized
potential ambiguities in the species identification to ensure a
more conservative analysis. This change only affected five
genera (Chaetomium spp., Hypocrea spp., Penicillium spp., Rhinocladiella spp., Umbelopsis spp.), all of which contained only
two species except for Hypocrea spp. (four) and Penicillium spp.
(nine), and all of which contained taxa that could not be
identified to the species level. Furthermore, collapsing samples into genera had no effect on the general conclusions
drawn in other studies of wood-inhabiting fungi (e.g.,
Crawford et al. 1990). We used a two-way factorial PerMANOVA (Anderson 2001) on relative abundance data, with
presence of fire (burned or unburned area) and year of fire
(hereafter referred to as “year”) as the main effects. If we
encountered a significant fire year interaction as hypothesized, the effects of fire and year were analyzed separately
using a multi-response permutation procedure (MRPP). An
indicator species analysis (Dufrene & Legendre 1997) was
conducted based on the year and fire (burned or unburned),
and a threshold indicator value of >25 was used (Dufrene &
Legendre 1997). Because of the high diversity in the fungal
communities, in addition to the inherent spatial variability, we
set the a priori alpha level at 0.10 for the community analyses.
Species richness and Shannon’s diversity index (H0 ; computed in PC-ORD) were computed for each log (as a plot), and
these data were analyzed using a two-way analysis of variance (ANOVA) in JMP statistical software (SAS Institute; Version 5.0.1.2). Fire (burned or unburned) and year were the
main effects. If we encountered a significant treatment year
interaction, we examined each burn/unburned pair separately
for each year. Temporal changes in the burned areas were
assessed for both species richness and H0 using a one-way
ANOVA with year as the main effect; when significant,
means were separated using Tukey’s HSD. Species richness
and H0 were also computed for each burned and unburned
area, and these values were used to compare the overall effect
of fire using a paired t-test. Sampling effort was assessed by
producing sample-based rarefaction curves of species accumulation for the burned and unburned treatments using
EstimateS (Colwell 2009).
The relationships between the environmental variables
(log length, volume, aspect, organic horizon C and woody
debris C) and fungal community composition were assessed
using a non-metric multidimensional scaling (NMDS) ordination in PC-ORD. A matrix of species composition was
V.J. Kurth et al.
correlated to an environmental matrix, and the relationships
were displayed as a joint plot ordination with secondary
overlays for each environmental variable (Murray et al. 2010).
Joint plots portray relationships between environmental variables and the species composition ordination scores as a
diagram with lines radiating from the centroid of the ordination scores. The direction and angle of a line depicts the
direction and strength of the relationship (McCune & Grace
2002). Separate ordinations were performed for the burned
areas, the unburned areas, and the combined dataset for the
burned and unburned areas of all sites. Correlation coefficients (r) for each environmental variable were considered
significant at alpha ¼ 0.10 if r 0.805 (burned or unburned
areas separately, df ¼ 3) or r 0.549 (burned and unburned
areas together, df ¼ 8) based on a table of critical values (Zar
1999).
Laboratory incubation to assess the decomposition
capabilities of fungal isolates
To further explore the role of individual species of fungi in
wood decomposition, we measured experimentally the
decomposition of standard pine dowels following inoculation
with common isolates of individual species used in our field
study. We selected eight species that were dominant in a
range of burn ages. The species used were: Byssochlamys nivea
(common throughout); Chaetomium sp. (predominantly 2005
unburned); Coniochaeta ligniaria (predominantly 2000 and 1996
burned and unburned); Hypocrea lixii (Hypocrea spp. was common throughout, but dominated 2005 burned); Neurospora terricola (1996 burn); Neosartorya sp. (2000, 1996, and 1984 burned);
Penicillium corylophilum (Penicillium spp. was common throughout); and Rhinocladiella atrovirens (common throughout).
We placed five (5 5 5 mm) cubes of hyphal-rich agar
from our subcultures onto nutrient-poor media (Bacto-Agar).
We obtained manufactured pine dowels (8 mm diameter;
www.dowelsondemand.com), cut them into 8 mm lengths
(initial mass was approximately 1.0 g), and sterilized them in
an autoclave. Five pieces of wood were weighed together and
then placed in the media so that each wood piece was situated
next to one of the five agar cubes. In total, 10 plates were made
for each of the 8 species (n ¼ 10).
After 26 weeks, the wood pieces were harvested. The wood
pieces contained in each plate were removed from the Petri
dish and placed together in a small envelope. The wood pieces
were dried at 60 C for 48 hr, and then any fungal hyphae
remaining on the wood were gently scraped off using a sterile
knife. The wood pieces were weighed to the nearest 0.01 g, and
decomposition was expressed as the percentage of initial
mass lost. A one-way ANOVA on arcsine transformed data
was used to compare the percentage mass loss across the
species with the a priori alpha level set at 0.05, and Tukey’s
HSD was used to separate differences when the model was
significant.
Results
We isolated fungi from 222 of the 240 wood samples (92.5 %);
109 of them were from burned areas and 114 were from
Community structure of wood-inhabiting fungi in southwestern ponderosa pine forests
unburned areas. Only one species of fungus (singleton) was
isolated from most of the wood samples (62 %); of the
remaining samples, 27 % had two species isolated, 10 % had
three, and 1 % had four. We sequenced the DNA of a total of
197
280 samples, and almost three-quarters (203) of them yielded
high quality sequences. We observed 68 distinct fungal taxa;
43 were identified to species, five were identified to genus, one
was identified to family, and the remaining taxa (20), mostly
Table 2 e The wood-inhabiting fungi identified from burned and adjacent unburned southwestern ponderosa pine forests,
USA. Species identification was made using the Basic Local Alignment Search Tool (BLAST) matched to the sequence of the
internal transcribed spacer region (ITS) of the ribosomal DNA.
Species
GenBank accession #
Sequence length
Percentage similaritya
Bit scoreb
Ascomycota
Arthrographis cuboidea
Aspergillus sp.
Aureobasidium pullulans
Biscogniauxia mediterranea
Byssochlamys nivea
Chaetomium sp.
Chromelosporium carneum
Coniochaeta ligniaria
Cytospora pruinosa
Cytospora austromontana
Didymella fabae
Fimetariella rabenhorstii
Geopyxis carbonaria
Gyromitra infula
Hypocrea lixii
Hypocrea lutea
Hypocrea schweinitzii (Anamorph:
Trichoderma citrinoviride)
Nemania serpens
Neosartorya hiratsukae
Neurospora terricola
Oidiodendron griseum
Ophiostoma sapniodorum
Ophiostoma deltoideosporum
Penicillium spinulosum
Penicillium canescens
Penicillium citreonigrum
Penicillium corylophilum
Penicillium decumbens
Penicillium fellutanum
Penicillium janithellum
Penicillium purpurogenum
Phaeomoniella sp.
Phialophora alba
Podospora miniglutinans
Preussia sp.
Pyronema domesticum
Rhinocladiella atrovirens
Rhinocladiella sp.
Sydowia polyspora
Thielavia arenaria
Myxotrichaceae sp.
AB213444
FJ770067
AF121282
EF026134
AY265223
HM222951
FJ872075
AY198390
EU552121
EU552118
GQ305306
HM036593
Z96986
AJ698480
AF443917
AB027384
EU280098
455
647
509
570
928
566
596
577
627
642
544
524
553
773
602
596
638
99.3
83.8
99.8
99.6
94.6
99.1
99.3
99.7
96.0
99.1
100.0
99.6
99.8
90.2
99.8
100.0
100.0
825
564
934
1 043
1 454
1 015
1 078
1 054
1 013
1 151
1 005
955
1 014
961
1 105
1 101
1 179
EF155504
GQ461906
AY681176
AF062796
HM031507
EU879121
GU566247
FJ439586
EU497959
GU566277
AY373909
AY373913
AB293968
GU566210
GQ153128
HM116755
AY515362
FJ210518
HQ115722
AB091215
GU067765
GQ412722
GU966511
FJ475803
596
629
583
514
544
545
597
579
586
599
568
559
627
594
540
608
528
537
592
606
536
578
555
563
99.5
98.7
99.8
99.8
90.6
88.4
99.6
100.0
100.0
100.0
99.6
99.1
99.2
99.5
99.4
100.0
100.0
99.5
98.8
99.0
99.4
99.8
99.6
99.6
1 083
1 116
1 072
945
701
623
1 088
1 070
1 083
1 107
1 037
1 002
1 127
1 079
981
1 123
976
976
1 053
1 085
972
1 062
1 015
1 029
Basidiomycota
Cerinosterus luteoalbus
Coniophora prasinoides
Dichomitus squalens
Gloeophyllum sepiarium
Rhodotorula lamellibrachiae
AY618667
GU187519
AM988622
AY089732
AB263122
450
677
638
592
605
98.7
98.5
98.7
99.8
95.9
799
1 198
1 129
1 088
967
Mucoromycotina
Umbelopsis sp.
Umbelopsis ramanniana
GQ241270
EU715662
603
629
99.5
98.7
1 096
1 111
a Percent similarity of query to published reference sequence.
b Bit score is an evaluation of the query and reference sequence alignment based on their lengths and the number of gaps and substitutions
between the two. Bit scores are normalized, thus they can be compared across search results.
198
V.J. Kurth et al.
singletons, remained unidentified (Table 2). Of the 68 identified taxa, 42 were Ascomycota, five were Basidiomycota, and
two were members of the Mucormycotina.
Effects of fire on fungal community structure
There was no consistent effect of burning on fungal species
richness or diversity across all of the sites. However, some
burned areas differed significantly from their paired unburned
areas, providing some support for our first hypothesis. Overall, mean species richness did not differ between burned (18.4
species) and unburned (16.4 species) areas ( p ¼ 0.604). Similarly, Shannon’s diversity did not vary between the burned
(1.144) and unburned (1.201) areas ( p ¼ 0.803). Species accumulation curves for the burned and unburned areas did not
differ, and both exhibited asymptotic trends (data not shown).
At the site level, differences between burned and unburned
areas were analyzed separately for each year because of significant fire year interactions ( p ¼ 0.009 for species richness,
p ¼ 0.021 for H0 ). Mean species richness was lower in the
burned than the unburned area at the 2005 site ( p ¼ 0.070),
and it was higher at the burned than the unburned area at the
1984 site ( p ¼ 0.0006). At the sites for the other three burn
years, species richness did not differ between the burned and
unburned areas (Fig 2). Shannon’s diversity index at the site
level was higher in the burned areas than unburned areas for
2000 and 1984 (Table 3).
Compared to richness and diversity, community composition showed more consistent differences between burned
and unburned areas, supporting our first hypothesis. Based on
the PerMANOVA, there were significant main effects of fire
(F ¼ 2.0237, p ¼ 0.023), year (F ¼ 2.9284, p ¼ 0.002) and their
interaction (F ¼ 2.6482, p ¼ 0.0 002) on fungal community
composition. Subsequent MRPP analyses on individual sites
showed that species composition differed significantly
between four of the five burned and unburned pairs (all except
1996; Fig 3). The indicator species analysis revealed several
species as indicators for particular burn and year combinations. Aspergillus spp., Phialophora spp., and Hypocrea spp.,
were indicators of the 1984, 1996, and 2005 burns, respectively.
In the unburned sites, Biscogniauxia mediterranea was an indicator of 1996, while both B. nivea and Pezizomycotina spp. were
indicators of 1984 (Table 4).
Effects of time since fire on fungal community structure
Consistent with our hypothesis that time since fire would
affect fungal communities, mean species richness and H0
varied by year among the burned areas ( p ¼ 0.002 and 0.007,
respectively). Richness (Fig 2) and H0 were significantly lower
at the 2005 burn (the most recent) than the 2000, 1996, and
1984 burns, but the 2005 burn did not differ from the 1977
burn. Richness and H0 at the 1977 burn did not differ from the
other burned areas. Within the unburned areas, species
richness and H0 did not differ, except the 1996 unburned area
had higher richness than the 1984 unburned area.
Community composition at the burned areas varied with
time since fire (A ¼ 0.13625, p < 0.0 001; Fig 4A). Pairwise
comparisons showed that community composition differed
among all burns except for 1984 and 1977, the two oldest
Fig 2 e Species richness of wood-inhabiting fungi at
burned (closed bars) and adjacent unburned (open bars)
areas at each of the five ponderosa pine chronosequence
sites. Asterisk indicates differences between the burned
and unburned areas at each year (a [ 0.10). Each bar
represents the mean of six logs sampled per area, and
error bars represent ±1 standard error.
burns. The unburned areas also were distinct from each other
in community composition (A ¼ 0.13464, p < 0.0 001; Fig 4B)
except at the two most recent burnings, 2005 and 2000. The
joint plot analysis (using data from the burned, unburned, or
burned and unburned sites combined) showed that none of
the environmental variables measured (log length, log diameter, log volume, aspect, organic horizon C and woody debris
C) were significantly correlated with the species composition
ordination scores (data not shown).
Experimental assessment of wood decay potential
All of the fungal species tested were able to decompose the
wood substratum, but the percent of initial mass lost ranged
Table 3 e Mean Shannon’s diversity index (H0 ) for woodinhabiting fungi found in logs from burned and adjacent
unburned areas of southwestern ponderosa pine forests,
USA. Values represent the mean of six logs (plots) ±1 SE,
and p-values are the results of a one-way ANOVA at each
site with fire (burned or unburned) as the main effect.
Significant differences between burned and unburned
areas for each year are denoted by a boldface p-value
(a [ 0.10).
Burn year
Shannon’s diversity index
Burned
2005
2000
1996
1984
1977
0.685
1.505
1.506
1.660
1.234
(0.24)
(0.10)
(0.23)
(0.07)
(0.21)
p-Value
Unburned
1.045
1.165
1.622
0.847
1.230
(0.26)
(0.13)
(0.15)
(0.13)
(0.18)
0.330
0.069
0.676
0.003
0.988
Community structure of wood-inhabiting fungi in southwestern ponderosa pine forests
A
B
C
D
199
E
Fig 3 e Percent relative abundance of all of the fungal species at burned (closed bars) and adjacent unburned (open bars)
areas, at each of the five ponderosa pine chronosequence sites (AeE; 2005, 2000, 1996, 1984, 1977, respectively). Relative
abundance was computed for each unburned and burned area separately, and only common species are shown (those with
relative abundance >5 % in either the burned or unburned area). Species are arranged in order of decreasing relative
abundance beginning with the burned areas. Statistics shown (A and p values) are the results of MRPP analysis comparing
the community structure at each burned and unburned area.
from 1.7 % to 11.1 % and varied by species ( p < 0.0 001).
The wood colonized by H. lixii was less decomposed than
that colonized by six of the other species tested; wood
decomposition rate by the other seven species did not differ
(Fig 5).
Discussion
Our study is the first to assess the community structure of
wood-inhabiting fungi in southwestern ponderosa pine
200
V.J. Kurth et al.
Table 4 e Results of indicator species analysis of wood-inhabiting fungal communities in burned and adjacent unburned
areas of southwestern ponderosa pine forests, USA. The maximum indicator value possible is 100, and high indicator
values indicate that a species occurred in an area frequently and with high abundance.
Species
Aspergillus spp.
Hypocrea spp.
Phialophora spp.
Biscogniauxia mediterranea
Byssochlamys nivea
Coniochaeta ligniaria
Pezizomycotina spp.
Indicator value
p-Value
Indicator year
Indicator treatment
26.7
47.2
33.3
33.3
34.4
36.6
31.3
0.018
0.001
0.082
0.056
0.002
0.004
0.005
1984
2005
1996
1996
1984
1996
1984
Burned
Burned
Burned
Unburned
Unburned
Unburned
Unburned
forests using mycelial isolation followed by molecular analysis. Ascomycetes dominated in our study, a finding that is
comparable to boreal forests when similar survey techniques
were used (Lindner et al. 2011). We observed only a handful of
species of basidiomycetes and likely undersampled their
diversity with the mycelial isolation approach we employed.
Gilbertson (1974) observed 200 species of basidiomycetes in
association with ponderosa pine in his extensive survey of
basidiomycete sporocarps. Only two species (Dichomitus
squalens and Gloeophyllum sepiarium) and one genus (Coniophora) observed in our study overlap with those identified by
Gilbertson (1974). These findings emphasize the importance of
utilizing multiple methods to characterize wood-inhabiting
fungal communities, especially in semi-arid climates where
direct sporocarp observations are inconsistent (Hart et al.
2006).
Wildfire had both short- and long-term effects on fungal
community structure. In the short-term, we observed that
fungal species richness was reduced in the most recent burn
(2005, 4 yr post-fire) compared to the paired unburned area, a
finding that contrasts with observations from other ecosystems. For instance, Olsson & Jonsson (2010) found that species
richness was similar in burned and unburned plots 4 yr
postburn in a boreal forest. Junninen et al. (2008), also studying
boreal forests, did not observe differences in species richness
even 1 yr following fire. However, these studies examined the
impact of prescribed burning, a much weaker disturbance
than a high-severity wildfire. Also, fire may have a smaller
impact in boreal forest compared to semi-arid regions because
ample precipitation in boreal forests may favor fungal fruiting
and subsequent dispersal of propagules to burned areas.
Fungal species richness in the burned areas recovered to
levels comparable to paired unburned areas by 9 yr post-fire
(beginning at the 2000 burn). However, species richness at the
1984 burn was higher than the paired unburned area. This
could be due to the high relative abundance of one species in
the unburned area, B. nivea (Fig 3D). Although we observed this
species at nearly all of the sites (burned and unburned), suggesting that it does not have a consistent response to fire, it is
known to have heat-resistant spores (Bayne & Michener 1979);
this trait may allow B. nivea to survive fires and proliferate in a
range of habitats. Furthermore, we cannot explain why
B. nivea had such a high abundance at the 1984 unburned area,
but this species is also a known inhibitor of some fungal
pathogens (Hoff et al. 2004), which may provide it with a
competitive advantage over other wood-inhabiting fungi.
Fungal community composition differed between paired
burned and unburned areas in four of the five burn years,
suggesting that stand-replacing wildfire has a long-term effect
on the species composition in these southwestern ponderosa
pine forests. Other studies in forests have found that prescribed burning caused alterations in fungal species composition up to 4 or 5 yr post-fire (Junninen et al. 2008; Olsson &
Jonsson 2010; Berglund et al. 2011), but our study is the first
to suggest that community composition may be altered up to
32 yr after a stand-replacing wildfire. Interestingly, although
fire consistently altered species composition, we did not
detect a consistent post-fire fungal community, potentially
because of the covariation in state of decay with time since
fire. However, the presence of this pattern, despite the close
proximity of paired burned and unburned sites (w200e600 m),
suggests that post-fire fungal colonization is somewhat idiosyncratic. Importantly, if these idiosyncrasies also apply to
the relative rates of wood decay, patterns of C release in
burned forests may be less predictable than patterns in
unburned forests.
The one exception to the general pattern that fire altered
fungal community composition was the 1996 site, which had a
relatively high number of species with low abundances. This
pattern could be due to more available niches and greater
€ nsson et al.
species co-existence at this site, similar to what Jo
(2008) observed at intermediate stages of wood decay in a
boreal forest. In support of this hypothesis, the majority of the
logs sampled at the 1996 site were of the intermediate decay
class 3.
The relative scarcity of soil organic material on the forest
floor in burned areas of southwestern ponderosa pine forests
may limit post-fire fungal colonization and influence longterm community composition. Lower levels of organic material were consistently observed at the burned areas compared
to the unburned areas even 30 yr after fire (Ross et al. 2012;
Table 1). Furthermore, because pine regeneration is extremely
slow following stand-replacing wildfires in these forests
(Heidmann 2008), it is likely that levels of organic material will
remain low for an extended period. Given that the forest floor
may be an underestimated source of fungal inoculum for
r et al. 2009), the relatively
wood-inhabiting species (Allme
slow post-fire development of the organic horizon in these
forests may explain the long-term alterations in species
composition we observed in the burned areas. Similarly, the
slow rate of organic horizon redevelopment following wildfire
in these dry forests might also explain why the fungal
Community structure of wood-inhabiting fungi in southwestern ponderosa pine forests
201
A
B
Fig 4 e Non-metric multidimensional scaling of woodinhabiting fungal community composition in burned (A)
and adjacent unburned (B) areas of the five ponderosa pine
chronosequence sites. The final stress after 250 runs with
real data was 25.5 (burned) and 25.6 (unburned) for a 2dimensional solution. Each centroid is the mean of a site in
ordination space, and bars represent ±1 standard error. Six
logs were sampled per area and four wood pieces were
taken from each log (n [ 6). Results of MRPP analysis
confirmed that the 2005, 2000, and 1996 burns differed
compositionally from each other, and the 1984 and 1977
burns were similar in composition. MRPP analysis on the
unburned areas confirmed that the 2000 and 2005 sites
were similar compositionally, while the 1996, 1984, and
1977 were distinct (a [ 0.10 for both burned and unburned
area analyses).
Fig 5 e Mean mass loss of pine dowels incubated in the
laboratory with individual wood-inhabiting fungal species.
Fungal species were cultured from logs of burned and
unburned areas of southwestern ponderosa pine forests,
USA, and were incubated with sterile pine dowels for 26
weeks. Each incubation was replicated ten times (n [ 10)
and bars represent ±1 standard error. Data were arcsine
transformed prior to analysis. Hypocrea lixii had a lower
decomposition rate than six of the other species, which
were all similar. Unique letters represent differences
among the fungal species (a [ 0.05).
community responses differ from those in other, more mesic
forests.
We observed a high degree of heterogeneity in the species
composition among the unburned areas, which could be a
result of environmental variation or differences in state of
wood decay. To facilitate comparisons with burned areas, we
sampled logs of similar decay classes within a year (burned
and unburned); however, the use of the chronosequence
approach meant that decay class varied among burned areas,
which could have influenced the fungal community. Also, to
encompass a range of times since fire, we had to sample from
a relatively large spatial extent (615 km2), and it is unlikely
that environmental conditions were constant across this
entire area. In particular, stand structural differences, such as
variation in organic horizon depth and number of large
diameter trees, may have resulted in a higher degree of heterogeneity in the unburned areas than we expected.
The length of our chronosequence (32 yr) was sufficient to
capture changes in fungal community composition over time
since fire. Fungal species composition was distinct at the three
recent burns (2005, 2000 and 1996), but similar at the two older
burns (1984 and 1977; Fig 4A), providing support for our
hypothesis that fungal communities would become more
similar over time since fire. These results are consistent with
Rajala et al. (2010, 2011) who observed a succession of fungal
species over the course of log decay in spruce forests, with the
more decomposed logs having similar community compositions. The authors suggest that airborne colonization by
202
fungal spores is maximized after a certain point in wood
decomposition, resulting in a more stable community structure at later stages of decay (Rajala et al. 2010). Likewise, given
the similarities in the mean decay class for the 1984 and 1977
burns (4.5 and 4.3, respectively; Table 1), we speculate that
composition had stabilized in the more decayed logs in these
areas.
We found some evidence in support of our second
hypothesis that fungi would vary in their capacities to process
wood. For example, H. lixii decomposed wood about five times
slower than most of the other species of fungi we tested.
Members of the genus Hypocrea are cosmopolitan soilborne fungi that are commonly found on decaying wood
(Druzhinina & Kubicek 2005). However, members of the order
Hypocreales, to which the Hypocrea belongs, do not possess
strong abilities to decompose wood (Worrall et al. 1997). In
addition, the anamorph of H. lixii, Trichoderma harzianum, is a
well-known mycoparasite (Chet & Inbar 1994), which may also
explain its limited ability to decompose wood. These findings
suggest that members of the genus Hypocrea are rapid colonizers and generalists, but may lack the enzymatic capacity to
process complex C, such as lignin. Furthermore, we observed
that members of the Hypocrea were an indicator for the most
recent burn (2005) and had a high relative abundance there
(Fig 3A, Table 4). Consistent with our findings, members of the
anamorph of this genus, Trichoderma, are abundant in soil
following prescribed burning (Froelich et al. 1978). High
abundance following wildfire of a species with poor decomposition abilities, like H. lixii, could substantially alter the
amount of C respired from burned areas.
The remaining species decomposed the wood to a similar
degree, suggesting some functional redundancy among the
species tested. Similar studies have found more variability in
decomposition abilities among fungal species. In a microcosm
experiment, Clinton et al. (2009) found that fungal species
varied widely in their abilities to decay Nothofagus wood.
Worrall et al. (1997) tested a variety of fungi from different
taxonomic orders and also observed varying degrees of ability
to decay pine wood. Notably, they found an intermediate
ability in members of the order Sordariales, of which three of
the species we tested are members (Chaetomium sp., C. ligniaria
and N. terricola). In particular, we expected C. ligniaria to
demonstrate strong decomposition abilities because it is an
ascomycete that produces some of the enzymes required for
lignin decomposition (Lopez et al. 2007). We also expected
P. corylophilum to exhibit high decomposition based on its
strong ability to decay spruce wood (Allison et al. 2009).
However, the limited number of species tested in these
experiments (Allison et al. 2009 tested five; we tested eight), as
well as methodological differences (e.g., length of incubation),
makes it difficult to compare relative rates of decay.
The inferences we can draw from our single species incubations are somewhat limited because we did not include
interactions among species of decay fungi, which are complex
and usually antagonistic (Boddy 2000). Although the precise
mechanism is not entirely understood, it is thought that the
production of secondary metabolites by earlier colonizing
fungi can have either inhibitory or stimulatory effects on the
growth of successive colonizers in wood (Heilmann-Clausen &
Boddy 2005). This predecessor legacy effect can influence
V.J. Kurth et al.
fungal community structure and associated decomposition
rates in wood (Fukami et al. 2010).
The observational portion of our study is limited in several
ways. We sampled four pieces of wood from relatively few
logs over a large area, so it is possible that we undersampled
the fungal community. Indeed, only a few of the species
accumulation curves generated for individual burned or
unburned areas had strong asymptotes (data not shown), but
asymptotic trends for burned and unburned areas as a whole
suggest that we adequately sampled for the effect of fire. Also,
because fungal distribution differs between the interior and
r et al. 2006), we
exterior portions of woody debris (Allme
limited our samples to the external portion of the wood,
biasing our survey toward species that tend to inhabit those
regions. Finally, we were only able to identify those fungal
species that grew in culture. Therefore, our analysis missed
species that were not able to exploit the media. Extraction of
fungal DNA directly from wood followed by one of several
molecular techniques (cloning and sequencing, Terminal
RFLP, DGGE, next generation sequencing) would potentially
identify more species (Lindner et al. 2011), though mycelial
isolation remains a powerful method for identifying woodr et al. 2006; Lindner
inhabiting fungal communities (Allme
et al. 2011). Higher resolution identification of wood-inhabiting fungal communities, in conjunction with more information on functional responses, represents an important next
step in studies of wildfire effects on decay fungi and wood
decomposition.
Conclusions
Our results suggest stand-replacing wildfire disturbance in
semi-arid areas may have substantial, long-term impacts
on wood-inhabiting fungal communities. Species richness
recovered relatively slowly compared to studies of prescribed
burning in boreal forests. Also, the species composition in the
burned areas was consistently different from comparable
unburned areas, up to 32 yr after a wildfire. These findings
may have widespread implications for post-wildfire C cycling
because differences in fungal species composition may alter
rates of wood decomposition (Fukami et al. 2010), as our
experimental findings demonstrated. Furthermore, the
increasing frequency and size of stand-replacing wildfires in
the western USA suggests that greater amounts of C will also
be stored in decaying wood in the future. Given these results
and trends, our research suggests a deficiency in current
decomposition models that could be ameliorated with more
information on the community structure of wood-inhabiting
fungi (McGuire & Treseder 2010).
Acknowledgments
This work was supported by funding from the USDA NRICG
program (Award number 2005-35101-16179) and an NSF Doctoral Dissertation Improvement Grant (Award number DEB1011415). We thank Samuel Harworth and the staff at the
Ecological Research Institute (Northern Arizona University)
Community structure of wood-inhabiting fungi in southwestern ponderosa pine forests
for field assistance and Todd Wojtowicz for suggesting the
incubation experiment. Karen Haubensak, the Associate Editor, and two anonymous reviewers provided useful comments
on the manuscript.
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