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Structural Complexity Enhancement increasesfungal species richness in northern hardwoodforests
Nicholas C. DOVEa,1, William S. KEETONa,b,*aRubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT 05405, USAbGund Institute for Ecological Economics, University of Vermont, Burlington, VT 05405, USA
a r t i c l e i n f o
Article history:
Received 27 April 2014
Revision received 17 September 2014
Accepted 25 September 2014
Available online
Corresponding editor:
Jacob Heilmann-Clausen
Keywords:
Biodiversity
Coarse woody debris
Fungi
Northern hardwood forests
Structural Complexity
Enhancement
* Corresponding author. Rubenstein School oTel.: þ1 802 656 2518.
E-mail addresses: [email protected] Tel.: þ1 508 277 5039.
http://dx.doi.org/10.1016/j.funeco.2014.09.0091754-5048/ª 2014 Elsevier Ltd and The Britis
a b s t r a c t
Forest management practices directly influence microhabitat characteristics important to
the survival of fungi. Because fungal populations perform key ecological processes, there
is interest in forestry practices that minimize deleterious effects on their habitats. We
investigated the effects on fungal sporocarp diversity of modified uneven-aged forest
management practices in northern hardwood ecosystems, including a technique called
Structural Complexity Enhancement (SCE). SCE is designed to accelerate late-successional
stand development; it was compared against two conventional selection systems (single
tree and group) and unmanipulated controls. These were applied in a randomized block
design to a mature, multi-aged forest in Vermont, USA. Eight years after treatment, fungal
species richness was significantly greater in SCE plots compared to conventional selection
harvests and controls ( p < 0.001). Seven forest structure variables were tested for their
influence on fungal species richness using a Classification and Regression Tree. The
results suggested that dead tree and downed log recruitment, as well as maintenance of
high levels of aboveground biomass, under SCE had a particularly strong effect on fungal
diversity. Our findings show it is possible to increase fungal diversity using forestry
practices that enhance stand structural complexity and late-successional forest
characteristics.
ª 2014 Elsevier Ltd and The British Mycological Society. All rights reserved.
Introduction compaction (Ballard, 2000; Pilz and Molina, 2002), litter and
Forest management practices directly influence microhabitat
characteristics important to the survival of fungi (Bader et al.,
1995; Heilmann-Clausen and Christensen, 2003; Jones et al.,
2003; Martius et al., 2004; Kranabetter et al., 2005). Charac-
teristics important for fungal survival include soil
f Environment and Natu
(N.C. Dove), William.Keet
h Mycological Society. Al
downed coarse woody debris (DCWD) accumulation
(Siitonen, 2001; Nord�en et al., 2004; Lindner et al., 2006;
Lonsdale et al., 2008; M€uller and B€utler, 2010), changes in
soil chemistry (Keizer and Arnolds, 1994; Durall et al., 2006),
and canopy closure, which affects soil temperature, mois-
ture, and DCWD respiration (Ballard, 2000; Straatsma et al.,
ral Resources, University of Vermont, Burlington, VT 05405, USA.
[email protected] (W.S. Keeton).
l rights reserved.
mailto:[email protected]:[email protected]://crossmark.crossref.org/dialog/?doi=10.1016/j.funeco.2014.09.009&domain=pdfwww.sciencedirect.com/science/journal/17545048http://www.elsevier.com/locate/funecohttp://dx.doi.org/10.1016/j.funeco.2014.09.009http://dx.doi.org/10.1016/j.funeco.2014.09.009http://dx.doi.org/10.1016/j.funeco.2014.09.009
182 N.C. Dove, W.S. Keeton
2001; Jones et al., 2003; Martius et al., 2004; Forrester et al.,
2012; Walker et al., 2012). Fungi provide a variety of eco-
logical functions, and thus an understanding of how forest
management practices affect fungal populations and com-
munity composition, through the manipulation of micro-
habitat characteristics, is important for sustainable forestry
intended to maintain these functions.
Fungi participate in numerous ecological processes
important for forest ecosystem health and productivity. Sap-
robic fungi affect the decomposition of organic matter, mak-
ing nutrients available for uptake by plants (Ingham et al.,
1985; Kernaghan, 2005; van der Wal et al., 2013). Mycorrhizal
fungi form a mutualistic symbiosis with plants, thereby
enhancing uptake of nutrients and water by extending the
effective area of root systems in exchange for photosynthates
(Govindarajulu et al., 2005). Through the growth of mycor-
rhizal hyphae, fungi also improve soil aeration and porosity
(Miransari et al., 2008). Ectomycorrhizal fungi can improve
resistance to root pathogens, for instance through physical
shielding of root tips by fungal mantles and, in some cases,
release of anti-pathogenic chemicals (Duchesne et al., 1989).
Fungi contribute to the cycling of organic compounds through
themineralization of nitrogen and phosphorus (Ingham et al.,
1985; Govindarajulu et al., 2005). Furthermore, fungal diversity
is a major driver of plant diversity. For example, artificially
increased fungal diversity increased plant diversity in grass-
land ecosystems (van der Heijden et al., 1998), and artificially
reduced fungal diversity decreased plant diversity in British
meadows (Gange et al., 1993). Fungi also provide harvestable
mushrooms for human consumption (Pilz and Molina, 2002),
representing a culturally and economically high value non-
timber forest product in the northeastern U.S. (Robbins
et al., 2008) and other temperate regions globally (Pilz and
Molina, 2002; Christensen et al., 2008; Cai et al., 2011). We
propose that silvicultural practice targeted atmaintaining and
promoting fungal diversity, perhaps even enhancing pop-
ulations of beneficial (i.e. mycorrhizal) or harvestable fungi,
could be an important element of sustainable forest
management.
Researchers in the Vermont Forest Ecosystem Manage-
ment Demonstration Project (FEMDP) are studying a variety of
silvicultural treatments relating to sustainable forest man-
agement within the northeastern United States (McKenny
et al., 2006; Smith et al., 2008). They are testing an uneven-
aged harvest treatment that utilizes disturbance-based (see
Seymour et al., 2002; North and Keeton, 2008) forestry princi-
ples to accelerate the development of late-successional
structural characteristics, termed Structural Complexity
Enhancement (SCE) (Keeton, 2006).
Forest management practices that promote late-
successional structures are of particular interest because
northern hardwood forests have shifted from a historic pre-
dominance of late-successional forests to the currently pre-
dominant second growth, young to mature (40e80-year old)
forests (Lorimer and White, 2003). Riparian functions (Keeton
et al., 2007), habitat values (Keddy and Drummond, 1996;
McGee et al., 1999), and carbon storage (Harmon et al., 1990;
Houghton et al., 1999; Rhemtulla et al., 2009; Keeton et al.,
2011) associated with late-successional forests have declined
as a result. In the context of global climatic change, the
question of whether promoting late-successional structural
conditions might also contribute to ecosystem resilience has
taken on new significance.
As an experimental approach, SCE uses disturbance-based
practices, such as small group selection with structural
retention and variably-sized gaps (Franklin et al., 2007; Kern
et al., 2013), to accelerate development of structural hetero-
geneity and late-successional forest characteristics. Dis-
turbances decrease canopy closure, increase
microtopographic features, such as downed wood, and affect
soil conditions (e.g. compaction, temperature and moisture)
that collectively influence microhabitats for fungal species
(Ballard, 2000; Straatsma et al., 2001; Jones et al., 2003; Martius
et al., 2004; Walker et al., 2012). The central idea behind
disturbance-based forestry methods is that emulation of
natural processes, such as local disturbance regimes, is more
likely to perpetuate the evolutionary environment to which
organisms are adapted (North and Keeton, 2008), although
global change may shift these boundary conditions over time.
In the U.S. Northeast this would entail harvesting practices,
like those employed by SCE, that mimic the single-tree to
partial canopy mortality associated with low to intermediate
intensity disturbances (Seymour et al., 2002; Hanson and
Lorimer, 2007). Retention of legacy structures, such as resid-
ual live and dead trees, also helps approximate disturbance
effects while promoting late-successional structure (Choi
et al., 2007; Bauhus et al., 2009; Gustafsson et al., 2012). By
providing suitable substrata, such as standing live/dead trees,
downed logs, and cycling organic matter to the soil system,
they may help “lifeboat” fungi through the post-harvest
recovery period (Franklin et al., 2000; Outerbridge and
Trofymow, 2009).
SCE promotes the development of vertically differentiated
canopies, variable horizontal density (i.e. gappiness), reallo-
cation of basal area to large diameter classes, and elevated
downed log and large snag densities. These characteristics are
often poorly represented after conventional harvesting tech-
niques (Gore and Patterson 1986; McGee et al., 1999). This has
important implications for forest floor microhabitats, such as
spatial variations in moisture, temperature, and substratum
within the treatment (McKenny et al., 2006; Smith et al., 2008).
We studied fungal sporocarp diversity as an indicator for
forest ecosystem response to silvicultural treatment. We
predicted that practices, like SCE, which maintain canopy
cover and promote late-successional structure, will sustain
and possibly improve fungal diversity (species richness).
In the FEMPD, inwhich this fungal response study is nested,
the SCE treatment is compared against two conventional
uneven-aged harvest treatments, “Single Tree Selection” (STS)
and “Group Selection” (GS), modified to enhance post-harvest
structural retention and an untreated control (see Keeton,
2006). SCE has been shown to increase herbaceous species
richness post-harvest due to effects on forest structural het-
erogeneity (Smith et al., 2008), and it has been shown to
increase terrestrial salamander populations through an
increase in habitat availability, particularly enhanced large log
densities (McKenny et al., 2006). This study adds a third taxo-
nomic group as a potential indicator of biodiversity response.
Our first hypothesis was that fungal sporocarp diversity
would be greater in the SCE treatment than in the conventional
Structural complexity enhancement and fungi 183
treatments due to the differences in stand structure effects
reported previously (Keeton, 2006; McKenny et al., 2006; Smith
et al., 2008). Few previous studies have investigated fungal
responses to silvicultural practices specifically promoting late-
successional forest development as compared to more tradi-
tional harvesting techniques. However, a number of studies
have shown fungal community composition tobe influencedby
forest age and development. Examples include studies in
SpanishPinus pinaster forests (Fern�andez-Toir�anetal., 2006) and
Tsuga heterophylla forests of northwestern British Columbia
(Kranabetter et al., 2005). Conflicting evidencewas presentedby
Smith et al. (2002), who found no increase in fungal diversity
with stand age in Pseudotsuga menziesii forests in Oregon. Thus,
relationships may vary among forest types.
Our second hypothesis was that fungal sporocarp diversity
will be influenced by increased habitat availability, such as
downed woody debris substratum for saprobic fungi and
larger, more productive living trees for mycorrhizal species.
Support for this hypothesis comes from a range of studies that
have established a close relationship between wood-
inhabiting fungi with organic matter on the forest floor
(Siitonen, 2001; Nord�en et al., 2004; Lindner et al., 2006;
Lonsdale et al., 2008; M€uller and B€utler, 2010) and mycor-
rhizal fungi with larger, older trees (Visser, 1995). Our goals
were to: A) determinewhich stand structure responses are the
most predictive of fungal diversity; and B) inform sustainable
forest management practices, including those aimed at
maintaining and enhancing fungal habitats.
Fig 1 e Map of the FEMPD study area at the Mt. Mansfield State
units, soil series, skid trails, and soil disturbance caused by loggi
Complexity Enhancement; 4 and 5 are single-tree selection; an
horizons) was mapped with a high precision GPS the summer a
produced by J. Bradley Materick, University of Vermont.
Methods
Study area
The study was conducted in the Mt. Mansfield State Forest
(MMSF) in northern Vermont, USA located at 44�30023.0300
N; 72�50011.2400 W (Fig 1). The study area at MMSF is located onthe western slopes of the northern Green Mountain Range at
elevations ranging from 470 to 660 m. Soils are primarily Peru
extremely stony loams (Fig 1). The site is a mature (ca.
70e100 yr), multi-aged northern hardwood-conifer forest with
a documented history of timber management spanning much
of the 20th century. Theoverstory is dominatedby sugarmaple
(Acer saccharum), American beech (Fagus grandifolia), and yel-
low birch (Betula alleghaniensis) with aminor component of red
spruce (Picea rubens). Average temperature is 3.4 �C, and theaverage yearly precipitation is 586.8mm (Vermont Monitoring
Cooperative, Mt. Mansfield West Slope Station).
Silvicultural treatments
Detailed descriptions of the FEMDP’s silvicultural treatments,
summarized here, are provided in Keeton (2006). There were
four treatments: single-tree selection (STS), group-selection
(GS), Structural Complexity Enhancement (SCE), and an un-
harvested control. Treatments were randomly applied to
2 ha units and replicated twice within the MMSF study area.
Forest, VT, showing locations of the eight 2 ha treatment
ng activity. Units 1 and 8 are controls; 2 and 3 are Structural
d 6 and 7 are group selection. Soil disturbance (O and A
fter harvest following protocol in Rab (1999). Map and data
184 N.C. Dove, W.S. Keeton
Treatment units were separated by 50m (minimum) unlogged
buffers to minimize cross contamination of treatment effects.
All three treatments were designed to retain a high degree of
post-harvest forest stand structure. However, the treatments
differed in terms of spatial patterning, level of retention, and
the specific type of structural attributes retained (see Keeton,
2006; McKenney et al., 2006; Smith et al., 2008).
Logging was conducted on frozen ground in winter of 2003
tominimize soil compaction. Experimental units received one
of three manipulative treatments or were designated as
untreated controls. The conventional selection treatments
were modified to increase post-harvest structural retention.
Modifications were based on a target residual basal area of
18.4 m2 ha�1, maximum diameter of 60 cm, and a q-factor of1.3. The latter defines the slope of the curve in the diameter
distribution and, producing a curve flatter than the q-factors
more typical of the region (i.e. 1.5 or higher), allocated more
basal area to larger size classes, though less than the rotated
sigmoid described below (Keeton, 2006). The conventional
prescription was applied in a dispersed (single-tree selection)
or aggregated (group selection) spatial pattern. The approx-
imate size of individual group selection patches (0.05 ha) was
based on estimates of average fine-scale (0.05 ha) natural
disturbanceecaused canopy gap size in New England
(Seymour et al., 2002) and resulted in eight to nine groups per
2 ha experimental unit. There was no additional cutting in
matrix areas surrounding group selection openings.
SCE is designed to promote late-successional structural
characteristics, including vertically differentiated canopies,
elevated large snag and DCWD volumes and densities, varia-
ble horizontal density (including small canopy gaps), and
reallocation of basal area to larger size classes. FEMDP
researchers used several silvicultural methods to accelerate
development of these attributes, as described in Keeton (2006).
A target basal area (34 m2 ha�1) and maximum diameter atbreast height (90 cm), characteristic of old-growth structure,
were used to develop a target diameter distribution to which
stands were cut (Keeton, 2006). The diameter distribution was
also based on a rotated sigmoid form, which is typical of some
eastern old-growth forests, depending on disturbance history,
species composition, degree of understory suppression, and
other variables (Lorimer, 1980; Goodburn and Lorimer, 1999).
The sigmoidal distribution was applied as a non-constant q-
factor: 2.0 in the smallest size classes, 1.1 for medium sized
trees, and 1.3 in the largest size classes. Accelerated growth in
larger trees was promoted with full and partial crown release.
DCWD volumes were enhanced 140 % on average over pre-
harvest levels, compared to a 30 % increase following the
other selection treatments. In one of the SCE units, DCWD
enhancement involved uprooting trees to mimic the pit and
mound formation characteristic of wind throw in late-
successional northern hardwood-conifer forests (Dahir and
Lorimer, 1996; Curzon and Keeton, 2010).
Data collection
Although many recent papers use molecular techniques to
determine fungal diversity (Durall et al., 2006; Buee et al., 2007;
Dickie et al., 2009; Kebli et al., 2012; Walker et al., 2012), we,
like other studies, determined fungal diversity by recording
fungal sporocarps (Kranabetter et al., 2005; Fern�andez-Toir�an
et al., 2006; M€uller et al., 2007; Oria-de-Rueda, 2010; Juutilainen
et al., 2011; Olsson et al., 2011). Molecular methods allow
identification of belowground fruiting fungi as well as species
not fruiting during aboveground surveys. Also, they may be
the most accurate in terms of species identification; however,
they have drawbacks. Molecular analysis generally yields
many unknown species if a robust reference database is not
available, needs high sampling intensity to detect rare species
(Horton and Bruns, 2001) and has lower time efficiency per
unit of sampling area (Jonsson et al., 2000). Aboveground
sporocarp inventory methods, by comparison, have been
found to return robust results with reasonably high con-
fidence of accurate identification (Norden et al., 2004;
Fern�andez-Toir�an et al., 2006; Oria-de-Rueda, 2010). Because
of the demanding resource requirements (i.e. expensive
molecular analysis, and higher sampling intensity), we chose
morphological identification as our sampling method.
For the FEMDP, five randomly placed 0.1 ha permanent
sampling plots were established within each 2 ha treatment
unit, buffered by a 15 m minimum distance from the edge of
the unit. For the fungal survey, we randomly selected two of
the five plots in each unit in a nested sampling design,
resulting in a sample size of four per treatment (total
sample ¼ 16). This design matched our statistical populationof interest, which was forest patches within treatment types,
rather than treatment units. Keeton (2006) validated this
approach, using F tests to show that post-harvest stand
structure did not differ significantly between units when
sorted by treatment. Consequently, our experimental design
grouped plots by treatment rather than unit, and hence
showed that the practical implications of the inherent pseu-
doreplication are minimal.
Overstorey structural characteristics were inventoried by
permanently tagging all live and dead trees (>5 cm dbh and
>1.37 m tall) within the sampling plots, remeasured in 2008.
Species and diameter were recorded for each tagged tree. The
Northeast Decision Model (NED-2) (Twery et al., 2005) was
used to generate the variables: live/dead stem densities, can-
opy closure, live aboveground biomass, and quadratic mean
diameter. Aboveground biomasswas estimated using species-
group specific allometric equations from Jenkins et al. (2003),
with correction for stem height and decay stage in dead trees.
DCWD volumewas estimated using the line-interceptmethod
(two 31.62 m transects per plot). Downed log densities were
estimated using fixed-area sampling, and thus were inven-
toried across the 0.1 ha plots. Snag (1e9) and downed log (1e5)
decay classes followed Sollins (1982).
Within these 0.1 ha plots, one 10 � 10 m quadrat was sys-tematically placed in the centre of the plot, for a total area
surveyed of 1 600 m2. All aboveground sporocarps were
identified and counted within each quadrat. Sampling inclu-
ded sporocarps of macrofungi on all substrata including
mineral soil, organic matter, downed logs, and live and dead
stems. However, corticoid fungi (except Aleurodiscus and
Stereum) were not considered. Similarly, fungal tree pathogens
which produce small sporocarps and fruit manymeters above
the ground on standing trees (e.g. Neonectaria) may have been
missed. Fungi were identified to species level following Lincoff
(1981) using microscopy and were cross-referenced with
Structural complexity enhancement and fungi 185
online resources such as Mycobank (www.mycobank.org),
Species Fungorum (www.speciesfungorum.org), and Cata-
logue of Life (www.catalogueoflife.org). Non-destructive
methods were used to locate sporocarps under natural cover
objects and to search leaf litter and vegetation, including live
trees, stumps, and dead trees following the Spatial Sampling
Protocol described in Cannon (1997). Data were collected four
times (6/25/11, 7/30/11, 8/29/11, 9/25/11) during the summer of
2011, spaced approximately 1 month apart to capture
ephemeral sporocarp fruiting. This also avoided double
counting of non-woody fungal species, giving ample time for
specimens to decompose between collection periods.
Data analysis
Consistent with the methodologies adopted by previous
studies (Smith et al., 2002; Landis et al., 2004; Nord�en et al.,
2004; Fern�andez-Toir�an et al., 2006), we used species rich-
ness as our dependent variable in statistical analyses instead
of a diversity index. Sporocarp abundance per individual is
species-specific. Consequently, evenness of sporocarps
among species does not accurately predict community level
evenness. Furthermore, sporocarp numbers do not represent
the reproductive fitness of an organism because different
species’ fruit bodies can produce different amounts of spores
(Sanders, 2004).
Hypothesis 1. Analysis of treatment effects
A linear mixed effects model and post-hoc Bonferroni
multiple comparisons were used to determine if treatment
had a significant effect (a < 0.05) on fungal richness. A sig-
nificant treatment*time interaction was also tested for. Stat-
istical analyses were performed using SPSS (version 20, SPSS,
Chicago, Illinois).
Hypothesis 2. Analysis of stand structure and habitat effects
For our second analysis, we investigatedwhether the stand
structural characteristics (independent variables) associated
with the silvicultural treatments were predictive of overall
fungal richness (the dependent variable). Our sample size was
insufficient to run robust analyses separately for mycorrhizal
and saprobic groups. A multivariate analysis was warranted
because post-harvest stand structural characteristics varied
somewhatwithin treatments (Table 1) and, therefore, wewere
Table 1 e Total DCWD volumes and well-decayed DCWDvolumes (decay class 3e5) by treatment type. Standarderror is shown in parentheses. (Treatments: StructuralComplexity Enhancement (SCE), Single Tree Selection(STS), Group Selection (GS), and Control)
Treatment Mean total DCWDvolume (m3 ha�1)
Mean decayclass 3e5 DCWDvolume (m3 ha�1)
SCE 86.46 (9.59) 51.42 (11.28)
STS 81.30 (2.97) 47.37 (6.67)
GS 62.77 (8.05) 45.34 (6.04)
Control 33.44 (3.31) 32.79 (3.15)
interested in determining which specific treatment effects,
such as downed woody debris enhancement, were most
closely associated with variation in fungal responses. This
was performed using a Classification And Regression Tree
(CART) analysis conducted in S-Plus software (Statistical Sci-
ences 2002). CART is a robust, nonparametric, binary proce-
dure that partitions variance in a dependent variable through
a series of splits based on values of the independent variables
(De’ath and Fabricius, 2000). Cost-complexity pruning was
used to eliminate non-significant nodes (Keeton et al., 2007).
Also minimum node deviance was increased above the
default to 0.10 to increase output parsimony, resulting in a
tree containing only nodes explaining a significant amount of
variation in the dataset. CART provided a way to identify the
structural characteristics most strongly associated with fun-
gal diversity along a continuum of fungal species richness.
Seven independent variables were selected for describing
forest structure and habitat. They were: (1) Total DCWD Vol-
ume; (2) Well-Decayed DCWD (decay classes 3e5); (3) Dead
Stem Density; (4) Live Aboveground Biomass; (5) Canopy Clo-
sure, (6) Quadratic Mean Diameter; and (7) Live Stem Density.
DCWD volumes were included because they offer habitat to
many fungal species (Siitonen, 2001; Nord�en et al., 2004).Well-
Decayed DCWD was assessed specifically because it provides
a broader range of colonization niches (Siitonen, 2001;
Heilmann-Clausen and Christensen, 2003). Dead Stem Den-
sity was studied because it, too, can offer habitat for fungi
(Siitonen, 2001). Characteristics 4e7 provide indicators of
forest structure that may affect fungal habitat availability. For
example, canopy closure influences soil temperature and
moisture regimes (Ballard, 2000; Oria-de-Rueda et al., 2010).
Results
Treatment effects
There were 537 occurrences of 88 different species of fungi in
the plots sampled (Table 2). Of the species found, 41 species
were in at least two different treatment units. For the
remaining 47 species, 35 were found only in SCE treatment
units. Additionally, more mycorrhizal fungal species were
found in SCE treatment units. Of the 20 mycorrhizal species
found, 17 of these were in SCE units compared to five in STS,
three in GS, and five in the control units (Table 2).
Analysis of species richness trends over time using the
linear mixed effects model showed a significant treatment
type effect on fungal richness ( p ¼
Table 2 e Species of fungi found in this study. Treatmentunits in which the species were found are noted
Species Treatment units Occurrences
SCE STS GS control
Aleurodiscus oakesii x x x 4
Amanita flavoconia* x x 2
Amanita spreta* x x x x 7
Amanita virosa* x 1
Bisporella citrina x x x x 14
Cheimonophyllum
candidissimus
x x 3
Chlorociboria
aeruginascens
x 2
Clavariadelphus ligula x 1
Clavicorona pyxidata x x x 3
Clavulinopsis fusiformis x 1
Clitocybula familia x x x x 11
Collybia alkalivirens x 6
Collybia confluens x x x x 7
Conocybe tenera x 1
Coprinus radians x 1
Cordyceps capitata x 2
Crepidotus applanatus x 1
Crepidotus mollis x x 2
Cryptoporus volvatus x x 2
Dacrymyces palmatus x 1
Daedaleopsis confragosa x x x x 11
Dendrocollybia racemosa x x 4
Entoloma murrayi x 1
Entoloma salmoneum x 1
Entoloma strictius x 1
Flammulina velutipes x 1
Fomes fomentarius x x x x 10
Fomitopsis pinicola x 2
Ganoderma applanatum x x x 7
Gomphus floccosus* x 1
Hapalopilus nidulans x x 2
Hohenbuehelia petaloides x 2
Hygrocybe flavescens x x x 3
Hygrocybe psittacina x 1
Hygrophorus
cantharellus*
x x x x 5
Hygrophorus
olivaceoalbus*
x 1
Inocybe geophylla* x 1
Lactarius corrugis* x x 7
Lactarius luteolus* x 1
Lactarius volemus* x 1
Lenzites betulina x x 8
Leotia lubrica x x x 6
Lycoperdon perlatum x x x x 8
Lycoperdon pyriforme x 2
Marasmius rotula x x x x 15
Marasmius siccus x 1
Marasmius strictipes x 2
Microglossum rufum x 1
Mycena galericulata x x x x 16
Mycena haematopus x x x x 12
Mycena leaiana x x x 7
Mycena rosella x 1
Naematoloma
sublateritium
x x 4
Omphalotus olearius x 1
Panaeolus
campanulatus
x x 2
Panellus stipticus x x x 4
Table 2 e (continued )
Species Treatment units Occurrences
SCE STS GS control
Phellinus nigricans x x 3
Pholiota squarrosoides x x 2
Phyllotopsis nidulans x 10
Physalacria inflata x 1
Piptoporus betulinus x 1
Pleurotus dryinus x 2
Pleurotus ostreatus x 1
Polyporus varius x x 3
Psathyrella conissans x x 2
Psathyrella hydrophila x 1
Psathyrella velutina x 1
Psilocybe caerulipes x 1
Pycnoporus
cinnabarinus
x 1
Ramaria stricta* x x 3
Russula claroflava* x 1
Russula compacta* x 1
Russula fragilis* x x x 6
Russula krombholzii* x x 5
Russula xerampelina* x 1
Stereum ostrea x 1
Suillus americanus x 1
Trametes versicolor x x x x 15
Tremellodendron
pallidum
x 1
Trichaptum biforme x 3
Tricholomopsis
platyphylla
x x x x 10
Tubaria furfuracea x x x 9
Tylopilus ballouii* x 1
Tylopilus chromapes* x 1
Tyromyces chioneus x x x x 12
Xeromphalina
campanella
x 1
Xerula radicata x x x x 9
Xylaria polymorpha x x x x 16
Occurrence is defined by finding at least one fruiting body in a plot
at any time (Maximum ¼ 16).* indicates mycorrhizal species. (Treatment Units: Structural
Complexity Enhancement (SCE), Single Tree Selection (STS), Group
Selection (GS), and control).
186 N.C. Dove, W.S. Keeton
Influence of stand structure on fungi abundance
The CART results supported our second hypothesis that sil-
vicultural treatments increasing fungal microhabitat avail-
ability, such as Well-decayed DCWD, Total DCWD, Dead Stem
Density, and Live Aboveground Biomass, will positively cor-
relate with overall fungal richness (Fig 3). This is
Table 3 e Linear mixed effects model. Bold p-valuerepresents significant effect on fungal species richness
Numeratordf
Denominatordf
f p
Treatment 3 12 19.846
Table 4 e Fungal species richness, standard error, andconfidence intervals for each of the treatments: StructuralComplexity Enhancement (SCE), Single Tree Selection(STS), Group Selection (GS), and control
Treatment Meanrichness
std.error
df 95 % Confidenceinterval
Lowerbound
Upperbound
SCE 12.9 1.65 12 11.0 14.9
STS 4.9 0.58 12 3.0 6.8
GS 4.9 0.44 12 3.0 6.9
Control 5.4 0.52 12 3.5 7.4
Fig 3 e Classification and regression tree, showing
independent variables selected, split values, and
partitioned mean values (bottom) of the dependent
variable (fungal species richness). CWD refers here to
downed logs only. The figure ranks variables by predictive
strength (top to bottom) and in sequential order of
importance as richness increases (left to right). The length
of each vertical line is proportional to the amount of
deviance explained. Independent variables were selected
from an initial set of seven structural variables. Minimum
observations required for each split [ 5; minimum
deviance [ 0.10.
Structural complexity enhancement and fungi 187
demonstrated by the sequential ranking of the variables by
predictive strength for increasing richness. Of the seven
independent variables fed apriori into the CART modeling,
only four were selected by the final CART output, and thus
deemedmost predictive of fungal abundance.While limited in
not distinguishing group-specific (i.e. mycorrhizal vs. sap-
robic) responses, the CART analysis provided evidence of
overall fungal community response to treatment. However,
the individual predictor variables (see Discussion) selected by
the CART (Fig 3) are consistent with responses likely asso-
ciated with different fungal groups.
Well-decayed DCWD Volume was the most important
predictor of fungal richness. Beyond the threshold of
71.42 m3 ha�1, richness was comparatively high at 12 species.Below this threshold, richness was most highly correlated
with a decreasing influence of secondary and tertiary factors.
Using the threshold of 28.85 trees ha�1, Dead Stem Densityvalues partitioned two subsets of tertiary factors, Live
Aboveground Biomass and Total DCWD Volume, below and
above the threshold, respectively. Although the influence of
Live Aboveground Biomass explained only a small amount of
variance (proportionate to the vertical lines in Fig 3) at the
tertiary node, higher Live Aboveground Biomass positively
influenced richness of fungi for a subset of plots, increasing
the richness by 134%when the aboveground biomass reached
at least 69.25 Mg ha�1. Richness also increased more
0
2
4
6
8
10
12
14
16
18
6/25/11 7/30/11 8/29/11 9/25/11
Fung
al S
peci
es R
ichn
ess
Collec on Date
SCE
STS
GS
Control
Fig 2 e Fungal species richness for the four treatments. The
SCE treatment resulted in significantly greater richness at
each collection data. The other treatments and the control
were not significantly different from each other at any
time. Error bars represent ± one standard error of the
mean.
dramatically with Total DCWD Volume (a 257 % increase
above the threshold of 68.59m3 ha�1). AlthoughWell-DecayedDCWD was the single greatest factor contributing to high
richness, the highest richness was achieved with a combina-
tion of high Dead Stem Density and high Total DCWD Volume
(Fig 3).
Discussion
Treatment effects on fungal diversity
The hypothesis that Structural Complexity Enhancement
promotes higher fungal species richness compared to con-
ventional selection systems was strongly supported by the
results. SCE plots had higher fungal species richness com-
pared to controls and other treatments, and thus we can infer
that SCE maintains habitat conditions (e.g. high canopy clo-
sure, wood debris availability, etc.) allowing both mycorrhizal
and saprotrophic fungi to persist and establish post-
disturbance. Furthermore, while the control units and the
other two treatments had many of the same species, SCE had
many species not found in any other treatments (Table 2). This
indicates that SCE created microhabitats not available or
found to a lesser degree following conventional forestry
treatments or in mid-successional forests like our controls.
These findings are important because they suggest that
SCE, and similar forest management treatments encouraging
late-successional structure, can increase fungal diversity and
related ecological functions (e.g. higher plant diversity, aer-
ated soils, etc.). Enhanced availability of structural elements
188 N.C. Dove, W.S. Keeton
related to fungal habitats were clearly linked to the effects of
silvicultural treatment in our dataset, showing the greatest
increase in response to treatments promoting late-
successional structure. This implies that it is possible to
accelerate stand development processes by creating micro-
habitats benefiting fungal diversity. This conclusion is in
agreement with previous research examining other taxa.
Disturbance-based forestry practices, such as microhabitat
creation, have been shown to increase plant diversity (Smith
et al., 2008) and salamander abundance (McKenny et al.,
2006) at our study sites.
We found that single tree selection and group selection
plots did not significantly reduce fungal species richness
compared to control plots. This implies that these harvesting
methods, which left residual DCWD, if conducted using best
management practices that minimize site impacts (as was the
case for the FEMDP), do not significantly impair fungal hab-
itats. Habitat creation, in the form of both standing and
downed CWD, may compensate for disturbances caused by
forest management in group selection (Dickie et al., 2009) and
single tree selection harvesting (Kranabetter and Kroger, 2001;
Kebli et al., 2012; Blaser et al., 2013). However, with similar
harvesting methods, if residual downed wood is removed,
fungal populations and diversity can be negatively affected
(Bader et al., 1995).
Influence on mycorrhizal fungi
Mycorrhizal fungi influence ecosystem functioning by
extending the effective area of root systems, increasing soil
porosity, and increasing resistance to soil pathogens. There-
fore, minimizing impacts on mycorrhizal fungi is of great
importance for sustainable forestmanagement. The relatively
low numbers of mycorrhizal fungi in our dataset did not
permit a statistical assessment of their responses to the
treatments. However, the data suggested a possible associa-
tion of mycorrhizal species richness with SCE treatment units
worthy of further investigation.
SCE units had the greatest overall richness of mycorrhizal
fungi (Table 2). There are several possible explanations for
variable responses to harvesting based on previous research
(Kernaghan, 2005), including interactions, both positive and
negative, with other soil organisms (Garbaye, 1994), competi-
tion with saprobic fungi (Shaw et al., 1995), browsing by
aboveground and belowground organisms (Set€al€a et al., 1995;
North et al., 1997), and positive and negative effects related to
tree species composition (Molina and Trapp, 1982). However,
because vegetation type, geographical position, productivity
and disturbance history are similar among the treatment
units, it is unlikely that these factors (e.g. fungi/soil organism
competition and tree species composition) varied between the
treatments examined in our study. Therefore, it is likely the
differences in mycorrhizal diversity can be attributed to
treatment effects on stand structure.
Fungi in the genera Russula andAmanita, themost common
genera of mycorrhizal fungi found in our study (Table 2), are
considered to be late-stage fungi, and would, therefore, be
associated with older, larger trees, promoted by SCE (Keizer
and Arnold, 1994; Durall et al., 2006). Furthermore, retention
of large overstorey trees helps maintain a consistent soil
environment, promoting a favourable habitat for mycorrhizal
fungi (Jones et al., 2003).
While SCE is likely to have also influenced soil charac-
teristics (e.g. temperature, moisture, pH, and nutrients)
deemed important for the mycorrhizal community (Jones
et al., 2003; Kranabetter et al., 2005; Dickie et al., 2009), we
have only preliminary data to support such a conclusion. For
example, SCE resulted in significantly lower near-term nitri-
fication rates (Keeton, Tobi, and McKenny unpublished) and
less disturbance (see Rab, 1999) of the O and A soil horizons
compared to the other treatments (Keeton and Materick
unpublished data, see Fig 1). However, none of the treat-
ments significantly affected soil bulk density as a measure of
compaction (Keeton and Materick unpublished data). Smith
et al. (2008) found some relationships, both positive and
negative, between understorey plant responses and post-
harvest soil nutrient changes at the FEMDP sites, but fur-
ther research will be needed to establish a direct connection
with fungal responses.
Relationships with stand structure
Our CART suggested that, of the variables we tested, and in
order of significance, overall fungal species richness under
disturbance-based forestry practices responds most strongly
to changes in well-decayed DCWD volume, snag abundance,
total DCWD volume, and live aboveground biomass. All of
these variables relate to the availability of potential sub-
stratum for fungal mycelia (the first three for saprobic fungi
and the latter for mycorrhizal species). Therefore, the struc-
tural characteristics most predictive of fungal richness were
those directly related to suitable fungal substratum. Colo-
nization by wood-inhabiting fungi is influenced by sub-
stratum structure, moisture, and nutrient content, with
different fungi colonizing at different stages of decay
(Siitonen, 2001; Lonsdale et al., 2008; Bunnell and Houde,
2010; Junninen and Komonen, 2011; Juutilainen, 2011;
Walker et al., 2012). Likewise, mycorrhizal fungi are
strongly associated with living tree substrata (i.e. Live
Aboveground Biomass).
We found a larger or more detectable response in saprobic
fungi as compared to mycorrhizal fungi (Table 2). This was
consistent with the very dramatic change in downed coarse
woody debris substrata produced by the SCE treatment (140%
initial increase over pre-treatment volumes, Keeton, 2006).
We might infer that mycorrhizal fungal richness was higher
under SCE due to the higher levels of retained aboveground
biomass, a tertiary variable selected in the CART, although our
evidence is not definitive in this respect. Our finding that
DCWD availability, especially well-decayed DCWD, is an
important, and possibly predominant, driver of overall fungal
diversity under variants of uneven-aged forestry was largely
in agreement with previous research. Many studies have
found that DCWD availability directly influences fungal
diversity (Fern�andez-Toir�an et al., 2006; Bunnell and Houde,
2010; Juutilainen, 2011; Markkanen and Halme 2012; Walker
et al., 2012), as does DCWD quality (Siitonen, 2001; Norden
et al., 2004; Lonsdale et al., 2008; Junninen and Komonen,
2011). Well-decayed DCWD supports a higher diversity of
fungal species because of greater numbers of niches in more
Structural complexity enhancement and fungi 189
decayed DCWD (Heilmann-Clausen and Christensen, 2003)
and because of the greater time over which fungal colo-
nisation could have occurred (Bader et al., 1995).
Additionally, like our study, artificial enhancement of
DCWD has been shown to increase fungal biodiversity in P.
abies forests in southern Finland (Berglund et al., 2011). How-
ever, different fungal assemblages were found in artificial
DCWD compared with natural residues (Berglund et al., 2011),
and artificial DCWD likely does not fully mimic natural DCWD
(Komonen et al., 2014). This likely explains differences in
fungal communities in our control versus SCE treatments
(Table 2).
While aboveground structural complexity may explain less
of the variance in richness than direct substratum additions,
our results suggest aboveground biomass contributes indirectly
to fungal diversity by adding to the supply of biomass for ecto-
mycorrhizal growth (Jones et al., 2003; Kranabetter et al., 2005;
Durall et al., 2006; Peter et al., 2008).
Management implications
Sustainable forest management practices aim to maintain
biodiversity and ecosystem functioning while providing
services upon which human communities depend, including
timber revenue. Experimental research at the scale of indi-
vidual forest stands provides an opportunity to assess the
contribution of these practices to broader, landscape-scale
conservation efforts. Assessments of how fungi respond to
silvicultural treatments inform our understanding of forest
ecosystem health as a whole. The results of our study will be
useful in this context.
We found that disturbance-based forest management
practices, such as Structural Complexity Enhancement and
modified selection systems, maintain fungal diversity. As
has been proposed for retention forestry more broadly
(Gustafsson et al., 2012; Lindenmayer et al., 2012),
disturbance-based forestry systems can be designed to retain
larger amounts of dead and live stems used by fungi as refugia
during the recovery period post-harvest.
Additionally, we found that disturbance-based variants of
uneven-aged forestry practices, like Structural Complexity
Enhancement, are effective in promoting fungal species
richness, possibly mycorrhizal species richness, and poten-
tially, by inference, related ecosystem functions. These pos-
itive effects are due in part to the maintenance and
enhancement of fungal substrata such as dead stems and
DCWD, particularly well-decayed DCWD, which was created
through decomposition over the 8 yr since experimental
harvest. It is also likely that maintaining a high degree of
overall forest complexity (i.e. aboveground biomass, spatial
variability in canopy closure, etc.) also promotes fungal
growth (Jones et al., 2003; Fern�andez-Toir�an et al., 2006; Oria-
de-Rueda et al., 2010). Incorporating management for DCWD
promotion and maintenance, dead stem retention, and
structural complexity into forestry practices is essential to
provide habitats for fungi and their associated ecological
functions, such as nutrient cycling (Ingham et al., 1985;
Govindarajulu et al., 2005), mycorrhizal symbiosis
(Govindarajulu et al., 2005), and root rot resistance (Duchesne
et al., 1989; Whipps, 2004).
Limitations of the study
There are limitations in our studymethods that are important
to acknowledge. For example, the number of sampling visits
was limited due to time constraints, which may have left
some species undetected. Halme and Kotiaho (2012) found
that some sites only plateau in species richness after twenty-
four visits (we made four). However, this limitation was
compensated by high sampling intensity per visit, combined
with strongly significant test results (Table 2). Together these
render our resultsmeaningful evenwith sampling limitations.
Within the design of the experiment there are several
important limitations to discuss. The first is that the spatial
scope of the treatments is limited. Although the botanical
composition and climate of the study site is typical of north-
ern hardwood forests, the whole experiment is within a 1 km2
area. Therefore, caution is needed when extrapolating our
results into other forest types or variants of northern hard-
wood forests. Given the promising results of our study, we
recommend this as an important area for future research in
order to better inform forest management.
Acknowledgments
This research was supported by grants from the USDA CSREES
National Research Initiative, the Vermont Monitoring Coopera-
tive, the Northeastern States Research Cooperative, and the
USDA McIntire-Stennis Forest Research Program. We are
grateful to Susan Moegenburg and Gary Hawley for their com-
mentsonanearlierdraft.Wewould liketoextendspecial thanks
to Clare Ginger and Marla Emery for literature suggestions.
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Structural Complexity Enhancement increases fungal species richness in northern hardwood forestsIntroductionMethodsStudy areaSilvicultural treatmentsData collectionData analysis
ResultsTreatment effectsInfluence of stand structure on fungi abundance
DiscussionTreatment effects on fungal diversityInfluence on mycorrhizal fungiRelationships with stand structureManagement implicationsLimitations of the study
AcknowledgmentsReferences