fungal ecology 13 (2015) 181e192

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Structural Complexity Enhancement increases fungal richness in northern hardwood forests

Nicholas C. DOVEa,1, William S. KEETONa,b,* aRubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT 05405, USA bGund Institute for Ecological Economics, University of Vermont, Burlington, VT 05405, USA article info abstract

Article history: Forest management practices directly influence microhabitat characteristics important to Received 27 April 2014 the survival of fungi. Because fungal populations perform key ecological processes, there Revision received 17 September 2014 is interest in forestry practices that minimize deleterious effects on their habitats. We Accepted 25 September 2014 investigated the effects on fungal diversity of modified uneven-aged forest Available online management practices in northern hardwood ecosystems, including a technique called Corresponding editor: Structural Complexity Enhancement (SCE). SCE is designed to accelerate late-successional Jacob Heilmann-Clausen stand development; it was compared against two conventional selection systems (single tree and group) and unmanipulated controls. These were applied in a randomized block Keywords: design to a mature, multi-aged forest in Vermont, USA. Eight years after treatment, fungal Biodiversity species richness was significantly greater in SCE plots compared to conventional selection Coarse woody debris harvests and controls ( p < 0.001). Seven forest structure variables were tested for their Fungi influence on fungal species richness using a Classification and Regression Tree. The Northern hardwood forests results suggested that dead tree and downed log recruitment, as well as maintenance of Structural Complexity high levels of aboveground biomass, under SCE had a particularly strong effect on fungal Enhancement 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 downed coarse woody debris (DCWD) accumulation Forest management practices directly influence microhabitat (Siitonen, 2001; Norden et al., 2004; Lindner et al., 2006; characteristics important to the survival of fungi (Bader et al., Lonsdale et al., 2008; Muller€ and Butler,€ 2010), changes in 1995; Heilmann-Clausen and Christensen, 2003; Jones et al., soil chemistry (Keizer and Arnolds, 1994; Durall et al., 2006), 2003; Martius et al., 2004; Kranabetter et al., 2005). Charac- and canopy closure, which affects soil temperature, mois- teristics important for fungal survival include soil ture, and DCWD respiration (Ballard, 2000; Straatsma et al.,

* Corresponding author. Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT 05405, USA. Tel.: þ1 802 656 2518. E-mail addresses: [email protected] (N.C. Dove), [email protected] (W.S. Keeton). 1 Tel.: þ1 508 277 5039. http://dx.doi.org/10.1016/j.funeco.2014.09.009 1754-5048/ª 2014 Elsevier Ltd and The British Mycological Society. All rights reserved. 182 N.C. Dove, W.S. Keeton

2001; Jones et al., 2003; Martius et al., 2004; Forrester et al., question of whether promoting late-successional structural 2012; Walker et al., 2012). Fungi provide a variety of eco- conditions might also contribute to ecosystem resilience has logical functions, and thus an understanding of how forest taken on new significance. management practices affect fungal populations and com- As an experimental approach, SCE uses disturbance-based munity composition, through the manipulation of micro- practices, such as small group selection with structural habitat characteristics, is important for sustainable forestry retention and variably-sized gaps (Franklin et al., 2007; Kern intended to maintain these functions. et al., 2013), to accelerate development of structural hetero- Fungi participate in numerous ecological processes geneity and late-successional forest characteristics. Dis- important for forest ecosystem health and productivity. Sap- turbances decrease canopy closure, increase robic fungi affect the decomposition of organic matter, mak- microtopographic features, such as downed wood, and affect ing nutrients available for uptake by plants (Ingham et al., soil conditions (e.g. compaction, temperature and moisture) 1985; Kernaghan, 2005; van der Wal et al., 2013). Mycorrhizal that collectively influence microhabitats for fungal species fungi form a mutualistic symbiosis with plants, thereby (Ballard, 2000; Straatsma et al., 2001; Jones et al., 2003; Martius enhancing uptake of nutrients and water by extending the et al., 2004; Walker et al., 2012). The central idea behind effective area of root systems in exchange for photosynthates disturbance-based forestry methods is that emulation of (Govindarajulu et al., 2005). Through the growth of mycor- natural processes, such as local disturbance regimes, is more rhizal hyphae, fungi also improve soil aeration and porosity likely to perpetuate the evolutionary environment to which (Miransari et al., 2008). Ectomycorrhizal fungi can improve organisms are adapted (North and Keeton, 2008), although resistance to root pathogens, for instance through physical global change may shift these boundary conditions over time. shielding of root tips by fungal mantles and, in some cases, In the U.S. Northeast this would entail harvesting practices, release of anti-pathogenic chemicals (Duchesne et al., 1989). like those employed by SCE, that mimic the single-tree to Fungi contribute to the cycling of organic compounds through partial canopy mortality associated with low to intermediate the mineralization of nitrogen and phosphorus (Ingham et al., intensity disturbances (Seymour et al., 2002; Hanson and 1985; Govindarajulu et al., 2005). Furthermore, fungal diversity Lorimer, 2007). Retention of legacy structures, such as resid- is a major driver of plant diversity. For example, artificially ual live and dead trees, also helps approximate disturbance increased fungal diversity increased plant diversity in grass- effects while promoting late-successional structure (Choi land ecosystems (van der Heijden et al., 1998), and artificially et al., 2007; Bauhus et al., 2009; Gustafsson et al., 2012). By reduced fungal diversity decreased plant diversity in British providing suitable substrata, such as standing live/dead trees, meadows (Gange et al., 1993). Fungi also provide harvestable downed logs, and cycling organic matter to the soil system, mushrooms for human consumption (Pilz and Molina, 2002), they may help “lifeboat” fungi through the post-harvest representing a culturally and economically high value non- recovery period (Franklin et al., 2000; Outerbridge and timber forest product in the northeastern U.S. (Robbins Trofymow, 2009). et al., 2008) and other temperate regions globally (Pilz and SCE promotes the development of vertically differentiated Molina, 2002; Christensen et al., 2008; Cai et al., 2011). We canopies, variable horizontal density (i.e. gappiness), reallo- propose that silvicultural practice targeted at maintaining and cation of basal area to large diameter classes, and elevated promoting fungal diversity, perhaps even enhancing pop- downed log and large snag densities. These characteristics are ulations of beneficial (i.e. mycorrhizal) or harvestable fungi, often poorly represented after conventional harvesting tech- could be an important element of sustainable forest niques (Gore and Patterson 1986; McGee et al., 1999). This has management. important implications for forest floor microhabitats, such as Researchers in the Vermont Forest Ecosystem Manage- spatial variations in moisture, temperature, and substratum ment Demonstration Project (FEMDP) are studying a variety of within the treatment (McKenny et al., 2006; Smith et al., 2008). silvicultural treatments relating to sustainable forest man- We studied fungal sporocarp diversity as an indicator for agement within the northeastern United States (McKenny forest ecosystem response to silvicultural treatment. We et al., 2006; Smith et al., 2008). They are testing an uneven- predicted that practices, like SCE, which maintain canopy aged harvest treatment that utilizes disturbance-based (see cover and promote late-successional structure, will sustain Seymour et al., 2002; North and Keeton, 2008) forestry princi- and possibly improve fungal diversity (species richness). ples to accelerate the development of late-successional In the FEMPD, in which this fungal response study is nested, structural characteristics, termed Structural Complexity the SCE treatment is compared against two conventional Enhancement (SCE) (Keeton, 2006). uneven-aged harvest treatments, “Single Tree Selection” (STS) Forest management practices that promote late- and “Group Selection” (GS), modified to enhance post-harvest successional structures are of particular interest because structural retention and an untreated control (see Keeton, northern hardwood forests have shifted from a historic pre- 2006). SCE has been shown to increase herbaceous species dominance of late-successional forests to the currently pre- richness post-harvest due to effects on forest structural het- dominant second growth, young to mature (40e80-year old) erogeneity (Smith et al., 2008), and it has been shown to forests (Lorimer and White, 2003). Riparian functions (Keeton increase terrestrial salamander populations through an et al., 2007), habitat values (Keddy and Drummond, 1996; increase in habitat availability, particularly enhanced large log McGee et al., 1999), and carbon storage (Harmon et al., 1990; densities (McKenny et al., 2006). This study adds a third taxo- Houghton et al., 1999; Rhemtulla et al., 2009; Keeton et al., nomic group as a potential indicator of biodiversity response. 2011) associated with late-successional forests have declined Our first hypothesis was that fungal sporocarp diversity as a result. In the context of global climatic change, the 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 Methods et al., 2008). Few previous studies have investigated fungal responses to silvicultural practices specifically promoting late- Study area successional forest development as compared to more tradi- tional harvesting techniques. However, a number of studies The study was conducted in the Mt. Mansfield State Forest 0 00 have shown fungal community composition to be influenced by (MMSF) in northern Vermont, USA located at 44 30 23.03 0 00 forest age and development. Examples include studies in N; 72 50 11.24 W(Fig 1). The study area at MMSF is located on Spanish Pinus pinaster forests (Fernandez-Toir an et al., 2006)and the western slopes of the northern Green Mountain Range at Tsuga heterophylla forests of northwestern British Columbia elevations ranging from 470 to 660 m. Soils are primarily Peru (Kranabetter et al., 2005). Conflicting evidence was presented by extremely stony loams (Fig 1). The site is a mature (ca. e Smith et al. (2002), who found no increase in fungal diversity 70 100 yr), multi-aged northern hardwood- forest with with stand age in Pseudotsuga menziesii forests in Oregon. Thus, a documented history of timber management spanning much relationships may vary among forest types. of the 20th century. The overstory is dominated by sugar maple Our second hypothesis was that fungal sporocarp diversity (Acer saccharum), American (Fagus grandifolia), and yel- will be influenced by increased habitat availability, such as low birch (Betula alleghaniensis) with a minor component of red downed woody debris substratum for saprobic fungi and spruce (Picea rubens). Average temperature is 3.4 C, and the larger, more productive living trees for mycorrhizal species. average yearly precipitation is 586.8 mm (Vermont Monitoring Support for this hypothesis comes from a range of studies that Cooperative, Mt. Mansfield West Slope Station). have established a close relationship between wood- inhabiting fungi with organic matter on the forest floor Silvicultural treatments (Siitonen, 2001; Norden et al., 2004; Lindner et al., 2006; Lonsdale et al., 2008; Muller€ and Butler,€ 2010) and mycor- Detailed descriptions of the FEMDP’s silvicultural treatments, rhizal fungi with larger, older trees (Visser, 1995). Our goals summarized here, are provided in Keeton (2006). There were were to: A) determine which stand structure responses are the four treatments: single-tree selection (STS), group-selection most predictive of fungal diversity; and B) inform sustainable (GS), Structural Complexity Enhancement (SCE), and an un- forest management practices, including those aimed at harvested control. Treatments were randomly applied to maintaining and enhancing fungal habitats. 2 ha units and replicated twice within the MMSF study area.

Fig 1 e Map of the FEMPD study area at the Mt. Mansfield State Forest, VT, showing locations of the eight 2 ha treatment units, soil series, skid trails, and soil disturbance caused by logging activity. Units 1 and 8 are controls; 2 and 3 are Structural Complexity Enhancement; 4 and 5 are single-tree selection; and 6 and 7 are group selection. Soil disturbance (O and A horizons) was mapped with a high precision GPS the summer after harvest following protocol in Rab (1999). Map and data produced by J. Bradley Materick, University of Vermont. 184 N.C. Dove, W.S. Keeton

Treatment units were separated by 50 m (minimum) unlogged fungal sporocarps (Kranabetter et al., 2005; Fernandez-Toir an buffers to minimize cross contamination of treatment effects. et al., 2006; Muller€ et al., 2007; Oria-de-Rueda, 2010; Juutilainen All three treatments were designed to retain a high degree of et al., 2011; Olsson et al., 2011). Molecular methods allow post-harvest forest stand structure. However, the treatments identification of belowground fruiting fungi as well as species differed in terms of spatial patterning, level of retention, and not fruiting during aboveground surveys. Also, they may be the specific type of structural attributes retained (see Keeton, the most accurate in terms of species identification; however, 2006; McKenney et al., 2006; Smith et al., 2008). they have drawbacks. Molecular analysis generally yields Logging was conducted on frozen ground in winter of 2003 many unknown species if a robust reference database is not to minimize soil compaction. Experimental units received one available, needs high sampling intensity to detect rare species of three manipulative treatments or were designated as (Horton and Bruns, 2001) and has lower time efficiency per untreated controls. The conventional selection treatments unit of sampling area (Jonsson et al., 2000). Aboveground were modified to increase post-harvest structural retention. sporocarp inventory methods, by comparison, have been Modifications were based on a target residual basal area of found to return robust results with reasonably high con- 18.4 m2 ha 1, maximum diameter of 60 cm, and a q-factor of fidence of accurate identification (Norden et al., 2004; 1.3. The latter defines the slope of the curve in the diameter Fernandez-Toir an et al., 2006; Oria-de-Rueda, 2010). Because distribution and, producing a curve flatter than the q-factors of the demanding resource requirements (i.e. expensive more typical of the region (i.e. 1.5 or higher), allocated more molecular analysis, and higher sampling intensity), we chose basal area to larger size classes, though less than the rotated morphological identification as our sampling method. sigmoid described below (Keeton, 2006). The conventional For the FEMDP, five randomly placed 0.1 ha permanent prescription was applied in a dispersed (single-tree selection) sampling plots were established within each 2 ha treatment or aggregated (group selection) spatial pattern. The approx- unit, buffered by a 15 m minimum distance from the edge of imate size of individual group selection patches (0.05 ha) was the unit. For the fungal survey, we randomly selected two of based on estimates of average fine-scale (0.05 ha) natural the five plots in each unit in a nested sampling design, disturbanceecaused canopy gap size in New England resulting in a sample size of four per treatment (total (Seymour et al., 2002) and resulted in eight to nine groups per sample ¼ 16). This design matched our statistical population 2 ha experimental unit. There was no additional cutting in of interest, which was forest patches within treatment types, matrix areas surrounding group selection openings. rather than treatment units. Keeton (2006) validated this SCE is designed to promote late-successional structural approach, using F tests to show that post-harvest stand characteristics, including vertically differentiated canopies, structure did not differ significantly between units when elevated large snag and DCWD volumes and densities, varia- sorted by treatment. Consequently, our experimental design ble horizontal density (including small canopy gaps), and grouped plots by treatment rather than unit, and hence reallocation of basal area to larger size classes. FEMDP showed that the practical implications of the inherent pseu- researchers used several silvicultural methods to accelerate doreplication are minimal. development of these attributes, as described in Keeton (2006). Overstorey structural characteristics were inventoried by A target basal area (34 m2 ha 1) and maximum diameter at permanently tagging all live and dead trees (>5 cm dbh and breast height (90 cm), characteristic of old-growth structure, >1.37 m tall) within the sampling plots, remeasured in 2008. were used to develop a target diameter distribution to which Species and diameter were recorded for each tagged tree. The stands were cut (Keeton, 2006). The diameter distribution was Northeast Decision Model (NED-2) (Twery et al., 2005) was also based on a rotated sigmoid form, which is typical of some used to generate the variables: live/dead stem densities, can- eastern old-growth forests, depending on disturbance history, opy closure, live aboveground biomass, and quadratic mean species composition, degree of understory suppression, and diameter. Aboveground biomass was estimated using species- other variables (Lorimer, 1980; Goodburn and Lorimer, 1999). group specific allometric equations from Jenkins et al. (2003), The sigmoidal distribution was applied as a non-constant q- with correction for stem height and decay stage in dead trees. factor: 2.0 in the smallest size classes, 1.1 for medium sized DCWD volume was estimated using the line-intercept method trees, and 1.3 in the largest size classes. Accelerated growth in (two 31.62 m transects per plot). Downed log densities were larger trees was promoted with full and partial crown release. estimated using fixed-area sampling, and thus were inven- DCWD volumes were enhanced 140 % on average over pre- toried across the 0.1 ha plots. Snag (1e9) and downed log (1e5) harvest levels, compared to a 30 % increase following the decay classes followed Sollins (1982). other selection treatments. In one of the SCE units, DCWD Within these 0.1 ha plots, one 10 10 m quadrat was sys- enhancement involved uprooting trees to mimic the pit and tematically placed in the centre of the plot, for a total area mound formation characteristic of wind throw in late- surveyed of 1 600 m2. All aboveground sporocarps were successional northern hardwood-conifer forests (Dahir and identified and counted within each quadrat. Sampling inclu- Lorimer, 1996; Curzon and Keeton, 2010). ded sporocarps of macrofungi on all substrata including mineral soil, organic matter, downed logs, and live and dead Data collection stems. However, corticoid fungi (except Aleurodiscus and Stereum) were not considered. Similarly, fungal tree pathogens Although many recent papers use molecular techniques to which produce small sporocarps and fruit many meters above determine fungal diversity (Durall et al., 2006; Buee et al., 2007; the ground on standing trees (e.g. Neonectaria) may have been Dickie et al., 2009; Kebli et al., 2012; Walker et al., 2012), we, missed. Fungi were identified to species level following Lincoff like other studies, determined fungal diversity by recording (1981) using microscopy and were cross-referenced with Structural complexity enhancement and fungi 185

online resources such as Mycobank (www.mycobank.org), interested in determining which specific treatment effects, Species Fungorum (www.speciesfungorum.org), and Cata- such as downed woody debris enhancement, were most logue of Life (www.catalogueoflife.org). Non-destructive closely associated with variation in fungal responses. This methods were used to locate sporocarps under natural cover was performed using a Classification And Regression Tree objects and to search leaf litter and vegetation, including live (CART) analysis conducted in S-Plus software (Statistical Sci- trees, stumps, and dead trees following the Spatial Sampling ences 2002). CART is a robust, nonparametric, binary proce- Protocol described in Cannon (1997). Data were collected four dure that partitions variance in a dependent variable through times (6/25/11, 7/30/11, 8/29/11, 9/25/11) during the summer of a series of splits based on values of the independent variables 2011, spaced approximately 1 month apart to capture (De’ath and Fabricius, 2000). Cost-complexity pruning was ephemeral sporocarp fruiting. This also avoided double used to eliminate non-significant nodes (Keeton et al., 2007). counting of non-woody fungal species, giving ample time for Also minimum node deviance was increased above the specimens to decompose between collection periods. 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 Data analysis structural characteristics most strongly associated with fun- gal diversity along a continuum of fungal species richness. Consistent with the methodologies adopted by previous Seven independent variables were selected for describing studies (Smith et al., 2002; Landis et al., 2004; Norden et al., forest structure and habitat. They were: (1) Total DCWD Vol- 2004; Fernandez-Toir an et al., 2006), we used species rich- ume; (2) Well-Decayed DCWD (decay classes 3e5); (3) Dead ness as our dependent variable in statistical analyses instead Stem Density; (4) Live Aboveground Biomass; (5) Canopy Clo- of a diversity index. Sporocarp abundance per individual is sure, (6) Quadratic Mean Diameter; and (7) Live Stem Density. species-specific. Consequently, evenness of sporocarps DCWD volumes were included because they offer habitat to among species does not accurately predict community level many fungal species (Siitonen, 2001; Norden et al., 2004). Well- evenness. Furthermore, sporocarp numbers do not represent Decayed DCWD was assessed specifically because it provides the reproductive fitness of an organism because different a broader range of colonization niches (Siitonen, 2001; species’ fruit bodies can produce different amounts of Heilmann-Clausen and Christensen, 2003). Dead Stem Den- (Sanders, 2004). sity was studied because it, too, can offer habitat for fungi (Siitonen, 2001). Characteristics 4e7 provide indicators of Hypothesis 1. Analysis of treatment effects forest structure that may affect fungal habitat availability. For A linear mixed effects model and post-hoc Bonferroni example, canopy closure influences soil temperature and multiple comparisons were used to determine if treatment moisture regimes (Ballard, 2000; Oria-de-Rueda et al., 2010). 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). Results

Hypothesis 2 Analysis of stand structure and habitat effects . Treatment effects For our second analysis, we investigated whether the stand structural characteristics (independent variables) associated There were 537 occurrences of 88 different species of fungi in with the silvicultural treatments were predictive of overall the plots sampled (Table 2). Of the species found, 41 species fungal richness (the dependent variable). Our sample size was were in at least two different treatment units. For the insufficient to run robust analyses separately for mycorrhizal remaining 47 species, 35 were found only in SCE treatment and saprobic groups. A multivariate analysis was warranted units. Additionally, more mycorrhizal fungal species were because post-harvest stand structural characteristics varied found in SCE treatment units. Of the 20 mycorrhizal species somewhat within treatments (Table 1) and, therefore, we were 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 Table 1 e Total DCWD volumes and well-decayed DCWD type effect on fungal richness ( p ¼ <0.001). However, treat- volumes (decay class 3e5) by treatment type. Standard ment*time interaction was insignificant ( p ¼ 0.781) (Table 3). error is shown in parentheses. (Treatments: Structural Overall, the SCE treatment had a higher fungal species rich- Complexity Enhancement (SCE), Single Tree Selection ness, 8 yr after logging, than other treatment types (Table 4). (STS), Group Selection (GS), and Control) The highest richness of 18 species was found in an SCE plot, Treatment Mean total DCWD Mean decay while the lowest richness of one was found in an STS plot. volume (m3 ha 1) class 3e5 DCWD Furthermore, at all collection times the mean fungal richness volume (m3 ha 1) was highest in SCE treatments compared to other treatment SCE 86.46 (9.59) 51.42 (11.28) types and the controls (Fig 2). The conventional treatments, STS 81.30 (2.97) 47.37 (6.67) STS and GS, and the control were not significantly different in GS 62.77 (8.05) 45.34 (6.04) fungal species richness. This held true for all temporal sam- Control 33.44 (3.31) 32.79 (3.15) pling points (Fig 2). 186 N.C. Dove, W.S. Keeton

Table 2 e Species of fungi found in this study. Treatment Table 2 e (continued) units in which the species were found are noted Species Treatment units Occurrences Species Treatment units Occurrences SCE STS GS control SCE STS GS control Phellinus nigricans xx 3 Aleurodiscus oakesii xxx 4 Pholiota squarrosoides xx 2 Amanita flavoconia* xx 2 Phyllotopsis nidulans x10 Amanita spreta* xxx x 7 Physalacria inflata x1 Amanita virosa* x1Piptoporus betulinus x1 citrina xxx x 14 Pleurotus dryinus x2 Cheimonophyllum xx3Pleurotus ostreatus x1 candidissimus Polyporus varius xx 3 Chlorociboria x2Psathyrella conissans xx 2 aeruginascens Psathyrella hydrophila x1 ligula x1Psathyrella velutina x1 Clavicorona pyxidata xx x 3 Psilocybe caerulipes x1 Clavulinopsis fusiformis x1Pycnoporus x1 Clitocybula familia xxx x 11 cinnabarinus Collybia alkalivirens x6 Ramaria stricta* xx 3 Collybia confluens xxx x 7 Russula claroflava* x1 Conocybe tenera x1Russula compacta* x1 Coprinus radians x1Russula fragilis* xxx6 Cordyceps capitata x2Russula krombholzii* xx5 Crepidotus applanatus x1Russula xerampelina* x1 Crepidotus mollis xx 2 Stereum ostrea x1 Cryptoporus volvatus xx2Suillus americanus x1 Dacrymyces palmatus x1 Trametes versicolor xxx x 15 xxx x 11 Tremellodendron x1 Dendrocollybia racemosa xx 4 pallidum Entoloma murrayi x1Trichaptum biforme x3 Entoloma salmoneum x1Tricholomopsis xxx x 10 Entoloma strictius x1platyphylla Flammulina velutipes x1Tubaria furfuracea xxx9 Fomes fomentarius xxx x 10 Tylopilus ballouii* x1 Fomitopsis pinicola x2Tylopilus chromapes* x1 Ganoderma applanatum xxx 7 Tyromyces chioneus xxx x 12 Gomphus floccosus* x1Xeromphalina x1 Hapalopilus nidulans xx 2 campanella Hohenbuehelia petaloides x2Xerula radicata xxx x 9 Hygrocybe flavescens xxx 3 Xylaria polymorpha xxx x 16 Hygrocybe psittacina x1 Hygrophorus xxx x 5 Occurrence is defined by finding at least one fruiting body in a plot ¼ cantharellus* at any time (Maximum 16). Hygrophorus x1* indicates mycorrhizal species. (Treatment Units: Structural olivaceoalbus* Complexity Enhancement (SCE), Single Tree Selection (STS), Group Inocybe geophylla* x1Selection (GS), and control). Lactarius corrugis* xx 7 Lactarius luteolus* x1 Lactarius volemus* x1Influence of stand structure on fungi abundance Lenzites betulina xx 8 Leotia lubrica xxx6 The CART results supported our second hypothesis that sil- Lycoperdon perlatum xxx x 8 vicultural treatments increasing fungal microhabitat avail- Lycoperdon pyriforme x2ability, such as Well-decayed DCWD, Total DCWD, Dead Stem Marasmius rotula xxx x 15 Density, and Live Aboveground Biomass, will positively cor- Marasmius siccus x1 Marasmius strictipes x2relate with overall fungal richness (Fig 3). This is Microglossum rufum x1 Mycena galericulata xxx x 16 Mycena haematopus xxx x 12 Mycena leaiana xx x 7 Table 3 e Linear mixed effects model. Bold p-value Mycena rosella x1represents significant effect on fungal species richness Naematoloma xx 4 Numerator Denominator fp sublateritium df df Omphalotus olearius x1 Panaeolus xx 2 Treatment 3 12 19.846 <0.001 campanulatus Treatment 9 36 0.609 0.781 Panellus stipticus xx x 4 time Structural complexity enhancement and fungi 187

Table 4 e Fungal species richness, standard error, and confidence intervals for each of the treatments: Structural Complexity Enhancement (SCE), Single Tree Selection (STS), Group Selection (GS), and control Treatment Mean std. df 95 % Confidence richness error interval

Lower Upper bound bound

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 demonstrated by the sequential ranking of the variables by independent variables selected, split values, and predictive strength for increasing richness. Of the seven partitioned mean values (bottom) of the dependent independent variables fed apriori into the CART modeling, variable (fungal species richness). CWD refers here to only four were selected by the final CART output, and thus downed logs only. The figure ranks variables by predictive deemed most predictive of fungal abundance. While limited in strength (top to bottom) and in sequential order of not distinguishing group-specific (i.e. mycorrhizal vs. sap- importance as richness increases (left to right). The length robic) responses, the CART analysis provided evidence of of each vertical line is proportional to the amount of overall fungal community response to treatment. However, deviance explained. Independent variables were selected the individual predictor variables (see Discussion) selected by from an initial set of seven structural variables. Minimum the CART (Fig 3) are consistent with responses likely asso- observations required for each split [ 5; minimum ciated with different fungal groups. deviance [ 0.10. 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 dramatically with Total DCWD Volume (a 257 % increase with a decreasing influence of secondary and tertiary factors. above the threshold of 68.59 m3 ha 1). Although Well-Decayed 1 Using the threshold of 28.85 trees ha , Dead Stem Density DCWD was the single greatest factor contributing to high values partitioned two subsets of tertiary factors, Live richness, the highest richness was achieved with a combina- Aboveground Biomass and Total DCWD Volume, below and tion of high Dead Stem Density and high Total DCWD Volume above the threshold, respectively. Although the influence of (Fig 3). 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 Discussion influenced richness of fungi for a subset of plots, increasing the richness by 134 % when the aboveground biomass reached Treatment effects on fungal diversity at least 69.25 Mg ha 1. Richness also increased more The hypothesis that Structural Complexity Enhancement 18 promotes higher fungal species richness compared to con- 16 ventional selection systems was strongly supported by the 14 results. SCE plots had higher fungal species richness com- 12 SCE pared to controls and other treatments, and thus we can infer 10 that SCE maintains habitat conditions (e.g. high canopy clo- 8 STS sure, wood debris availability, etc.) allowing both mycorrhizal 6 GS and saprotrophic fungi to persist and establish post- 4 Control disturbance. Furthermore, while the control units and the 2 Fungal Species Richness Fungal other two treatments had many of the same species, SCE had 0 6/25/11 7/30/11 8/29/11 9/25/11 many species not found in any other treatments (Table 2). This CollecƟon Date indicates that SCE created microhabitats not available or found to a lesser degree following conventional forestry Fig 2 e Fungal species richness for the four treatments. The treatments or in mid-successional forests like our controls. SCE treatment resulted in significantly greater richness at These findings are important because they suggest that each collection data. The other treatments and the control SCE, and similar forest management treatments encouraging were not significantly different from each other at any late-successional structure, can increase fungal diversity and time. Error bars represent ± one standard error of the related ecological functions (e.g. higher plant diversity, aer- mean. 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 environment, promoting a favourable habitat for mycorrhizal silvicultural treatment in our dataset, showing the greatest fungi (Jones et al., 2003). increase in response to treatments promoting late- While SCE is likely to have also influenced soil charac- successional structure. This implies that it is possible to teristics (e.g. temperature, moisture, pH, and nutrients) accelerate stand development processes by creating micro- deemed important for the mycorrhizal community (Jones habitats benefiting fungal diversity. This conclusion is in et al., 2003; Kranabetter et al., 2005; Dickie et al., 2009), we agreement with previous research examining other taxa. have only preliminary data to support such a conclusion. For Disturbance-based forestry practices, such as microhabitat example, SCE resulted in significantly lower near-term nitri- creation, have been shown to increase plant diversity (Smith fication rates (Keeton, Tobi, and McKenny unpublished) and et al., 2008) and salamander abundance (McKenny et al., less disturbance (see Rab, 1999) of the O and A soil horizons 2006) at our study sites. compared to the other treatments (Keeton and Materick We found that single tree selection and group selection unpublished data, see Fig 1). However, none of the treat- plots did not significantly reduce fungal species richness ments significantly affected soil bulk density as a measure of compared to control plots. This implies that these harvesting compaction (Keeton and Materick unpublished data). Smith methods, which left residual DCWD, if conducted using best et al. (2008) found some relationships, both positive and management practices that minimize site impacts (as was the negative, between understorey plant responses and post- case for the FEMDP), do not significantly impair fungal hab- harvest soil nutrient changes at the FEMDP sites, but fur- itats. Habitat creation, in the form of both standing and ther research will be needed to establish a direct connection downed CWD, may compensate for disturbances caused by with fungal responses. forest management in group selection (Dickie et al., 2009) and single tree selection harvesting (Kranabetter and Kroger, 2001; Relationships with stand structure Kebli et al., 2012; Blaser et al., 2013). However, with similar harvesting methods, if residual downed wood is removed, Our CART suggested that, of the variables we tested, and in fungal populations and diversity can be negatively affected order of significance, overall fungal species richness under (Bader et al., 1995). disturbance-based forestry practices responds most strongly to changes in well-decayed DCWD volume, snag abundance, Influence on mycorrhizal fungi total DCWD volume, and live aboveground biomass. All of these variables relate to the availability of potential sub- Mycorrhizal fungi influence ecosystem functioning by stratum for fungal mycelia (the first three for saprobic fungi extending the effective area of root systems, increasing soil and the latter for mycorrhizal species). Therefore, the struc- porosity, and increasing resistance to soil pathogens. There- tural characteristics most predictive of fungal richness were fore, minimizing impacts on mycorrhizal fungi is of great those directly related to suitable fungal substratum. Colo- importance for sustainable forest management. The relatively nization by wood-inhabiting fungi is influenced by sub- low numbers of mycorrhizal fungi in our dataset did not stratum structure, moisture, and nutrient content, with permit a statistical assessment of their responses to the different fungi colonizing at different stages of decay treatments. However, the data suggested a possible associa- (Siitonen, 2001; Lonsdale et al., 2008; Bunnell and Houde, tion of mycorrhizal species richness with SCE treatment units 2010; Junninen and Komonen, 2011; Juutilainen, 2011; worthy of further investigation. Walker et al., 2012). Likewise, mycorrhizal fungi are SCE units had the greatest overall richness of mycorrhizal strongly associated with living tree substrata (i.e. Live fungi (Table 2). There are several possible explanations for Aboveground Biomass). variable responses to harvesting based on previous research We found a larger or more detectable response in saprobic (Kernaghan, 2005), including interactions, both positive and fungi as compared to mycorrhizal fungi (Table 2). This was negative, with other soil organisms (Garbaye, 1994), competi- consistent with the very dramatic change in downed coarse tion with saprobic fungi (Shaw et al., 1995), browsing by woody debris substrata produced by the SCE treatment (140% aboveground and belowground organisms (Setal€ a€ et al., 1995; initial increase over pre-treatment volumes, Keeton, 2006). North et al., 1997), and positive and negative effects related to We might infer that mycorrhizal fungal richness was higher tree species composition (Molina and Trapp, 1982). However, under SCE due to the higher levels of retained aboveground because vegetation type, geographical position, productivity biomass, a tertiary variable selected in the CART, although our and disturbance history are similar among the treatment evidence is not definitive in this respect. Our finding that units, it is unlikely that these factors (e.g. fungi/soil organism DCWD availability, especially well-decayed DCWD, is an competition and tree species composition) varied between the important, and possibly predominant, driver of overall fungal treatments examined in our study. Therefore, it is likely the diversity under variants of uneven-aged forestry was largely differences in mycorrhizal diversity can be attributed to in agreement with previous research. Many studies have treatment effects on stand structure. found that DCWD availability directly influences fungal Fungi in the genera Russula and Amanita, the most common diversity (Fernandez-Toir an et al., 2006; Bunnell and Houde, genera of mycorrhizal fungi found in our study (Table 2), are 2010; Juutilainen, 2011; Markkanen and Halme 2012; Walker considered to be late-stage fungi, and would, therefore, be et al., 2012), as does DCWD quality (Siitonen, 2001; Norden associated with older, larger trees, promoted by SCE (Keizer et al., 2004; Lonsdale et al., 2008; Junninen and Komonen, and Arnold, 1994; Durall et al., 2006). Furthermore, retention 2011). Well-decayed DCWD supports a higher diversity of of large overstorey trees helps maintain a consistent soil fungal species because of greater numbers of niches in more Structural complexity enhancement and fungi 189

decayed DCWD (Heilmann-Clausen and Christensen, 2003) Limitations of the study and because of the greater time over which fungal colo- nisation could have occurred (Bader et al., 1995). There are limitations in our study methods that are important Additionally, like our study, artificial enhancement of to acknowledge. For example, the number of sampling visits DCWD has been shown to increase fungal biodiversity in P. was limited due to time constraints, which may have left abies forests in southern Finland (Berglund et al., 2011). How- some species undetected. Halme and Kotiaho (2012) found ever, different fungal assemblages were found in artificial that some sites only plateau in species richness after twenty- DCWD compared with natural residues (Berglund et al., 2011), four visits (we made four). However, this limitation was and artificial DCWD likely does not fully mimic natural DCWD compensated by high sampling intensity per visit, combined (Komonen et al., 2014). This likely explains differences in with strongly significant test results (Table 2). Together these fungal communities in our control versus SCE treatments render our results meaningful even with sampling limitations. (Table 2). Within the design of the experiment there are several While aboveground structural complexity may explain less important limitations to discuss. The first is that the spatial of the variance in richness than direct substratum additions, scope of the treatments is limited. Although the botanical our results suggest aboveground biomass contributes indirectly composition and climate of the study site is typical of north- to fungal diversity by adding to the supply of biomass for ecto- ern hardwood forests, the whole experiment is within a 1 km2 mycorrhizal growth (Jones et al., 2003; Kranabetter et al., 2005; area. Therefore, caution is needed when extrapolating our Durall et al., 2006; Peter et al., 2008). results into other forest types or variants of northern hard- wood forests. Given the promising results of our study, we Management implications recommend this as an important area for future research in order to better inform forest management. Sustainable forest management practices aim to maintain biodiversity and ecosystem functioning while providing services upon which human communities depend, including Acknowledgments timber revenue. Experimental research at the scale of indi- vidual forest stands provides an opportunity to assess the This research was supported by grants from the USDA CSREES contribution of these practices to broader, landscape-scale National Research Initiative, the Vermont Monitoring Coopera- conservation efforts. Assessments of how fungi respond to tive, the Northeastern States Research Cooperative, and the silvicultural treatments inform our understanding of forest USDA McIntire-Stennis Forest Research Program. We are ecosystem health as a whole. The results of our study will be grateful to Susan Moegenburg and Gary Hawley for their com- useful in this context. mentsonanearlier draft. We would liketo extend special thanks We found that disturbance-based forest management to Clare Ginger and Marla Emery for literature suggestions. practices, such as Structural Complexity Enhancement and modified selection systems, maintain fungal diversity. As has been proposed for retention forestry more broadly references (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 Bader, P., Jansson, S., Jonsson, B.G., 1995. 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