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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: ndove@ucmerced.edu1 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.

on@uvm.edu (W.S. Keeton).

l rights reserved.

mailto:ndove@ucmerced.edumailto:William.Keeton@uvm.eduhttp://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