Species diversity of polyporoid and corticioid fungi in northern ......Species diversity of...

Species diversity of polyporoid and corticioid fungi in northern hardwood forestswith differing management histories

Daniel L. Lindner1

Harold H. Burdsall Jr.Center for Forest Mycology Research, U.S.D.A. ForestService, Forest Products Laboratory, 1 Gifford PinchotDrive, Madison, Wisconsin 53726

Glen R. StanoszDepartment of Plant Pathology, 1630 Linden Drive,Russell Laboratories, University of Wisconsin atMadison, Madison, Wisconsin 53706

Abstract: Effects of forest management on fungaldiversity were investigated by sampling fruit bodies ofpolyporoid and corticioid fungi in forest stands thathave different management histories. Fruit bodieswere sampled in 15 northern hardwood stands innorthern Wisconsin and the upper peninsula ofMichigan. Sampling was conducted in five old-growthstands, five uneven-age stands, three even-age un-thinned stands and two even-age thinned stands. Plots100 m 3 60 m were established and 3000 m2 withineach plot was sampled during the summers of 1996and 1997. A total of 255 polyporoid and corticioidmorphological species were identified, 46 (<18%) ofwhich could not be assigned to a described species.Species accumulation curves for sites and manage-ment classes differed from straight lines, althoughvariability from year to year suggests that more than2 y of sampling are needed to characterize annualvariation. Mean species richness and diversity indexvalues did not vary significantly by management class,although mean richness on large diameter wood ($

15 cm diam) varied with moderate significance.Richness values on small diameter debris variedsignificantly by year, indicating that a large part ofyear-to-year variability in total species richness is dueto small diameter debris. Ten species had abundancelevels that varied by management class. Two of thesespecies, Cystostereum murraii and Rigidoporus crocatus,were most abundant in old-growth and might be goodindicators of stands with old-growth characteristics.Oxyporus populinus, an important pathogen of Acerspp., was most abundant in even-age stands. Re-gression analyses indicated that substrate quality(diameter and species), quantity and managementhistory of the stand were important in predicting thenumber of occurrences of the five most-abundant

species. Changes in the diversity and species compo-sition of the wood-inhabiting fungal communitycould have significant implications for the diversity,health and productivity of forest ecosystems.

Key words: Aphyllophorales, Basidiomycete,Corticiaceae, fungi, Polyporaceae, polypore, speciesdiversity

INTRODUCTION

Polyporoid and corticioid fungi are some of the mostcommon and important wood-inhabiting fungi inforests. These species can account for the majority offruit bodies found on woody debris (de Vries 1990),yet they often are overlooked in studies of fungaldiversity. When polypores and corticioid fungi aresampled, often only the largest or most conspicuousspecies are collected (e.g. Bader et al 1995, Ohlson etal 1997). Such sampling procedures ignore a largepercentage of the fungal community. In a studyconducted in an even-aged Picea abies stand in theNetherlands, de Vries (1990) found that 75% ofwood-inhabiting species had inconspicuous, tiny, thinor crustose fruit bodies and that such species made up44% of overall fungal species richness. Unfortunatelylittle is known currently about the ecological rolesplayed by many of these cryptic fungi. For somespecies with larger fruit bodies, such as Phellinusweirii, researchers have demonstrated significanteffects on the direction of forest succession, influ-ences on the composition and diversity of understoryvegetation and effects on microbial biomass anddecomposition rates in forests (Cromack et al 1991,Holah et al 1993, Holah et al 1997, Ingersoll et al1996). Due to the widespread nature and importanceof many polyporoid and corticioid species, it will bedifficult to implement ‘‘whole ecosystem’’ manage-ment (e.g. Pilz and Molina 1996) without firstunderstanding how different forest managementtechniques influence these fungi.

Although rarely collected, polyporoid and corti-cioid fruit bodies are ideally suited to large-scale,quantitative studies of fungal diversity. Fruit bodiesare often woody or rigid with low water content,making collection and transport easier in areas thatare difficult to access. Identification of these fungirarely depends on fresh characters, allowing largenumbers of fruit bodies to be collected, dried andstored during peak fruiting periods. It also has been

Accepted for publication 13 Jan 2006.1 Corresponding author. E-mail: [email protected]

Mycologia, 98(2), 2006, pp. 195–217.# 2006 by The Mycological Society of America, Lawrence, KS 66044-8897

195

proposed that the woody habit of their fruit bodiesmakes the occurrence of these fungi more regular onboth a seasonal and an annual basis (Renvall et al1991a, b; Wasterlund and Ingelog 1981) compared tomany fungal groups. Researchers who have studiedboth mycorrhizal and saprophytic fungi have con-cluded that saprophytic fungi fruit more regularlyfrom year to year (Villeneuve et al 1989). Wood-inhabiting species occupying unitary or discreteresources such as logs and depending solely on sporedispersal to travel from one resource to the next mustproduce fruit bodies to survive when resources aredepleted (Cooke and Rayner 1984). This biologicalstrategy, combined with the persistent nature of manypolyporoid and corticioid fruiting structures, suggeststhese fungi can be characterized with a relatively smallnumber of sampling periods in a given year and thatfruit body occurrence should be relatively indepen-dent of short-term changes in temperature ormoisture availability.

To date little quantitative sampling of polyporoidor corticioid fungi has occurred in North America. InEurope more sampling has taken place and numer-ous polyporoid and corticioid species are consideredsufficiently rare or threatened to warrant beingplaced on European red lists (Arnolds 1997, Rydinet al 1997, Stokland et al 1997). Work done innorthern Europe suggests that the harvesting of treesat various levels of intensity can affect the diversity ofwood-inhabiting fungi (Bader et al 1995, Høiland andBendiksen 1997, Lindblad 1998, Ohlson et al 1997,Wasterlund and Ingelog 1981). In a paper reviewingthe distribution of macrofungi in Sweden Rydin et al(1997) concluded that one of the main threats tomacrofungi in Sweden was modern forestry and that‘‘the high proportion of threatened macrofungi inspruce forests of Sweden indicates how strong theimpact of forestry and management has been on theSwedish landscape.’’ The effect of forest managementon fungal diversity also was noted in a paper byWasterlund (1989), who concluded that although thetotal production of fungi in Scandinavian coniferousforests was not necessarily decreased there werequalitative changes that usually resulted in a decreasein species diversity. The studies from northern Europeconcerning the effects of forest management on thediversity of wood-inhabiting fungi have shown thatspecies diversity of wood-inhabiting fungal fruit bodiescorrelates with the quality of woody substrates (asmeasured by a diversity of diameters and decay classes),as well as with total volume of wood at a site (Bader et al1995, Høiland and Bendiksen 1997, Lindblad 1998,Ohlson et al 1997, Wasterlund and Ingelog 1981).

To the best of our knowledge the only studies inNorth America that have addressed the question of

how forest management relates to the amount ofcoarse woody debris in a stand, and therefore to thediversity of fungi found in the stand, have beenconducted in the Pacific Northwest on hypogeousfungi (Amaranthus et al 1994, Colgan et al 1996).Amaranthus et al (1994) found that mature, naturallyestablished Pseudotsuga menziesii forest fragmentshad a greater percent frequency of occurrence oftruffles than plantations and that truffle number anddry weight also were greater in mature forests. Theyalso noted that coarse woody debris had a significanteffect on the numbers and biomass of truffles andconcluded that forest management practices ‘‘thatemphasize the retention of mature trees and coarsewoody debris promote the abundance and diversity oftruffles.’’

In the Midwestern United States old-growth forestsare rare. The majority of northern hardwood forestsin the upper Midwest were intensely clear-cut andswept by fires in the early 1900s. This periodproduced many even-age, second-growth forests.The trees in even-age stands are usually in the samesize and age classes, and such stands tend to lack treesor woody debris greater than 60 cm diam (Goodburnand Lorimer 1998). These forests are characterized bythe conspicuous absence of many of the structuralattributes of old-growth forests, including tree-fallmounds, snags, cavity trees, large diameter logs inmany stages of decay and a large volume of bothstanding and down coarse woody debris (Goodburnand Lorimer 1998). Even-age, unthinned standstherefore represent the lower extreme in terms ofthe quantities and diversities of woody substratesavailable for fungal colonization, while old-growthforests represent the upper extreme. In addition toold-growth and even-age forests, some northernhardwood stands have been selectively harvestedthroughout their entire management history, pro-ducing stands comprised of trees in many age classes.Selectively managed, uneven-age forests fall some-where between unmanaged even-age forests and old-growth forests in availability of woody substrates.

Two important questions regarding the manage-ment of northern hardwood forests in the upperMidwest are whether certain forest managementtechniques influence species diversity and whethersuch techniques produce forests capable of support-ing ‘‘old-growth dependent’’ species. To help answerthese questions with regard to fungi, we tested thenull hypothesis that there is no difference in thespecies richness or diversity of wood-inhabitingpolyporoid and corticioid fruit bodies among north-ern hardwood forest stands that have experienceddifferent management regimes. Due to obviousconstraints the individual forest stands were not

196 MYCOLOGIA

randomized to treatment (i.e. history of forestmanagement). Thus this study does not directlyinvestigate whether forest management causes differ-ences in the diversity of fungal fruit bodies but ratherwhether there are correlations between fungal di-versity and a stand’s management history.

MATERIALS AND METHODS

Site selection and sampling procedures.—Fifteen sites werelocated in mesic northern hardwood stands in northernWisconsin and the adjacent upper peninsula of Michi-gan (TABLE I). These stands are a subset of thosestudied by Goodburn and Lorimer (1998), who describethe area’s soils, climate and landscape classification.Although dominated by Acer saccharum (sugar maple),stands also often contained Betula alleghaniensis (yellowbirch), Tilia americana (basswood), Carya ovata (iron-wood) and Tsuga canadensis (eastern hemlock). Allstands were classified as the Acer-Tsuga-Dryopteris (ATD)habitat type (Coffman et al 1983, Kotar et al 1988),although a few were transitional between ATD and Acer-Viola-Osmorhiza (AViO) or Acer-Tsuga-Maianthemum(ATM) (Goodburn and Lorimer 1998).

Old-growth stands were greater than 20 ha and werelocated in the Sylvania Wilderness Area, Ottawa NationalForest, Michigan, which contains more than 6000 ha of old-growth forest (USDA. 1964). Only localized cutting hasbeen done for personal use in Sylvania, and tree ages range

up to approximately 350 y (Goodburn and Lorimer 1998).To qualify as old-growth, a stand had at least 34% of its basalarea (ba) in large trees with a diameter at breast height(dbh) . 46 cm, and at least 67% of its total ba in matureand large trees . 26 cm dbh. Uneven-age, selectivelymanaged sites were chosen based on ‘‘previous manage-ment by the selection system on a cutting cycle of 8–15 y,a minimum residual ba of 16.1 m2/ha (70 ft2/ha), anda maximum residual tree diameter . 45 cm dbh’’ (Good-burn and Lorimer 1998). All uneven-age stands have beenactively managed to fulfill forestry objectives but have notbeen cut within the previous 4 y. Such stands generally donot contain trees greater than 200 y of age (Cole andLorimer 1994). Even-age sites were located in second-growth stands that had naturally regenerated from clear-cutting. These stands were dominated by sugar maples 65–75 y of age (Goodburn and Lorimer 1998). Even-ageunthinned stands experienced no active management afterthe initial clear-cutting, while even-age thinned stands werethinned 9–14 y before sampling.

Plots 100 3 60 m were established at randomly locatedplot centers within each site in spring 1996 (FIG. 1). Eachplot ran east to west and was composed of three contiguoussubplots 20 3 100 m. Each subplot in turn was composed oftwo transects, each 10 3 100 m, and the entire plot wasdivided into 5 3 5 m quadrats. Because sampling isdestructive (specimens must be collected and all logs anddebris must be turned and examined), a design wasemployed whereby no area was resampled. All largediameter wood ($15 cm diam) was sampled in 3000 m2 of

TABLE I. Site description and location information for northern hardwood stands with different management histories

Stand Stand structure Max. tree age (y) Management history

Stand location

Latitude Longitude

#1 Old-growth <350 None 46u 139 18.8290 N 89u 179 59.5610 W#2 Old-growth <350 None 46u 129 27.6300 N 89u 159 50.6830 W#3 Old-growth <350 None 46u 129 05.4270 N 89u 169 56.4670 W#4 Old-growth <350 None 46u 119 36.9700 N 89u 159 38.3170 W#5 Old-growth <350 None 46u 119 11.2690 N 89u 159 53.9790 W#6 Uneven-aged <200 Selective cutting 45u 549 07.6030 N 89u 019 29.8470 W#7 Uneven-aged <200 Selective cutting 46u 119 30.9770 N 89u 059 49.1170 W#8 Uneven-aged <200 Selective cutting 46u 149 02.4150 N 89u 009 31.2790 W#9 Uneven-aged <200 Selective cutting 46u 189 15.6820 N 89u 149 11.3080 W#10 Uneven-aged <200 Selective cutting 46u 159 40.6060 N 89u 029 23.7050 W#11 Even-aged 65–70 Clear-cutting with natural

regeneration46u 119 26.6930 N 89u 049 36.0450 W

#12 Even-aged 65–70 Clear-cutting with naturalregeneration

46u 129 00.2250 N 88u 599 55.4130 W

#13a Even-aged 65–70 Clear-cutting with naturalregeneration

46u 199 17.4000 N 89u 139 43.6800 W

#14 Even-aged 65–70 Clear-cutting with naturalregeneration followed bythinning

45u 579 28.1950 N 88u 579 14.6040 W

#15 Even-aged 65–70 Clear-cutting with naturalregeneration followed bythinning

46u 159 40.7200 N 89u 039 29.8740 W

a Location data for Stand #13 are approximate.

LINDNER ET AL: FUNGAL DIVERSITY AND FOREST MANAGEMENT 197

the plot during summer 1996. (The authors sampled all6000 m2 of the plot during 1996, but the data obtainedfrom the first 3000 m2 were uninformative because sam-pling occurred in spring before most fruit bodies werefertile; therefore, only summer data were analyzed.) Areplicate plot was established and sampled contiguous tothe initial plot in 1997. Within each plot, the area sampledfor large diameter wood was determined by randomlyselecting either the northern or southern transect withineach subplot. Small diameter wood ($1 cm but ,15 cmdiam) was sampled in 1500 m2 of the plot by randomlychoosing to sample either the northern or southern 5 3

100 m strip of quadrats within each transect sampled forlarge woody debris. All sampling was done from groundlevel to a height of 2.5 m. Within each 5 3 5 m quadrat, allpolyporoid and corticioid species that could be identified bysight were recorded, while all unknown species werecollected. At least one voucher specimen was collected foreach species each year.

Substrate data were recorded for each piece of largediameter woody substrate in each 5 3 5 m quadrat.Information taken for large woody substrates ($15 cm)included substrate species, diameter at midpoint withineach 5 3 5 m quadrat, length within each 5 3 5 m quadrat,height off the ground (data taken only in 1997), form (e.g.log, tree, snag, stump, suspended log) and decay class.Decay classification was based on the procedures employedby Goodburn and Lorimer (1998). For small diameterdebris ($1 cm but ,15 cm), the diameter of each piecebearing a fruitbody was estimated as being either ,15 cm

but .10 cm, #10 cm but .5 cm, or #5 cm but $1 cm.During 1997 the amount of small, woody debris wasestimated on a scale of 1–5 within each 5 3 5 m quadrat,with 1 representing the smallest amount of debris (oftena skid trail) and 5 representing the largest amount (oftena treetop).

Sampling began 5 Aug 1996 and 9 Aug 1997, and oncesampling was initiated one stand was sampled ca each dayfor 15 consecutive days. For every three sites consecutivelysampled (hereafter referred to as a group of sites), samplingincluded one old-growth stand, one uneven-age stand andone even-age stand (either thinned or unthinned). Groupswere sampled in random order, and the order of sites withineach group was randomized. Even-age sites and uneven-agesites were paired based on geographic location, and theneach pair was randomly grouped with an old-growth site.Groups therefore were based on a combination of geo-graphic proximity and time of sampling. In 1997 new plotscontiguous to the 1996 plots were sampled as describedabove, again employing the same grouping scheme.

Species identification.—The families Polyporaceae s.l. andCorticiaceae s.l. are phylogenetically diverse (Gilbertsonand Ryvarden 1986, Gilbertson and Ryvarden 1987,Ginns and Lefebvre 1993, Hibbett and Donoghue 1995,Hibbett et al 1997) and contain species that producefruit bodies with either a more or less poroidhymenophore (see Gilbertson and Ryvarden 1986,1987) or fruit bodies with a flat to toothed (rarelyporoid) hymenophore (see Ginns and Lefebvre 1993,Parmasto 1997). These fungi will be referred to heresimply as ‘‘polyporoid and corticioid fungi.’’

Fruit bodies were dried within 24 h after collection andidentified to species with morphological characters. Micro-scopic observations were made with an Olympus BH-2compound microscope at 4003 and under oil immersion at10003. When specimens could not be matched to knownspecies descriptions, they were assigned to a genus andgiven a species number (e.g. Hyphodontia sp. No. 1).Voucher specimens were deposited in the herbarium ofthe Center for Forest Mycology Research (CFMR) at theUSDA Forest Service Forest Products Laboratory (Madison,Wisconsin).

Incomplete sampling dictated the exclusion of onecorticioid species, Aleurodiscus oakesii, from final analyses.Also excluded from final analyses were coralloid species(e.g. Clavicorona pyxidata, Ramaria stricta) and larger ‘‘jellyfungi’’ in the Tremellales, Auriculariales, Dacrymycetales,etc. (with the exception of one species, Pseudohydnumgelatinosum, for which complete data were available). Fruitbodies macroscopically undistinguishable from corticioid orpolyporoid fungi (e.g. Aporpium caryae, Heterochaetelladubia, Helicogloea farinacea, Basidiodendron spp. andTulasnella spp.) were included in analyses. All ecologicalguilds of wood-inhabiting fungi were analyzed, includingmycorrhizal species that consistently produce fruit bodieson woody substrates (e.g. Tomentella spp.).

Due to a lack of recent taxonomic work, some speciesconcepts were necessarily broad. This was true forHymenochaete fuliginosa, Hyphoderma sambuci, Hyphodontia

FIG. 1. Plot design for the sampling of polyporoid andcorticioid fruit bodies. Each 100 3 60 m plot had its longaxis running east to west. The ‘‘X’’ designates the pointwhere GPS data were taken. Each plot was divided into 5 3

5 m quadrats, although most data analyses used 10 3 10 mquadrats (outlined in upper left). During 1996 largediameter wood ($15 cm) was sampled in 3000 m2 of theplot and a replicate plot contiguous to the initial plot wassampled in 1997 in a similar fashion. The area sampled forlarge diameter wood was determined by randomly selectingeither the northern or southern transect within eachsubplot. Small diameter wood ($1 cm but ,15 cm diam)was sampled in 1500 m2 of the plot by randomly samplingeither the northern or southern 5 3 100 m strip of quadratswithin each transect sampled for large diameter debris.

198 MYCOLOGIA

rimosissima and some Botryobasidium species. Nomenclaturefor corticioid fungi generally followed Ginns and Lefebvre(1993), with the exception that Botryobasidium botryosumwas considered a synonym of Botryobasidium vagum and B.botryoideum is considered distinct from B. pruinatum. Forcorticioid species not found in Ginns and Lefebvre (1993)nomenclature followed Parmasto (1997). Polypore nomen-clature followed Gilbertson and Ryvarden (1986, 1987).

Data analysis.—The smallest sampling unit analyzedwithin our sites was the 5 3 5 m quadrat (FIG. 1).However only half of the 5 3 5 m quadrats wereexamined for both large and small diameter debris.Therefore the most basic unit that experienceda uniform sampling effort for large and small diameterdebris was a block 5 (east-west) 3 10 m (north-south)within each transect. Such a unit had all wood $ 15 cmdiam examined for fruit bodies, while all wood $ 1 cmbut , 15 cm diam has been examined in half the area(either the northern or southern 5 3 5 m quadrat). Foranalysis purposes species occurrence could be based onpresence or absence within these 5 3 10 m units orcould be based on larger groupings of these units. Apreliminary analysis based on different grouping sizesdemonstrated that the size of the sampling unit did notappreciably affect analyses. For example species accu-mulation curves generated with any of four differentquadrat sizes (25, 50, 100 or 500 m2) were almostindistinguishable (see Czederpiltz 2001). Accumulationcurve shapes were essentially independent of quadratsize, with the variation between sites being much greaterthan the variation caused by the size of the samplingunit. A 10 3 10 m quadrat therefore was chosen as thebasic unit used to quantify species abundance, primarilybecause this size yields a reasonable number of quadratsper site and is regular in shape.

Species accumulation curves were calculated with San-ders’ (1968) rarefaction equations as modified by Hurlbert(1971). These equations allow for the exact calculation ofthe mean species accumulation curve over all possiblepermutations of sampling order. The equation used toconstruct an exact mean species accumulation curve is givenby Smith et al (1979) as:

ss(m) ~XK

i ~ 1

1 {M { Li

m

�M

m

� �� ð1Þ

Where:s(m) 5 the expected number of species encountered

after sampling m unitsm 5 the number of ‘‘observed’’ sampling units, from 0 to MM 5 the total number of sampling unitsLi 5 the number of sampling units in which species i is

presentK 5 the total number of species

Equation 1 was used to calculate species accumulationcurves for each site (FIG. 2), as well as for each managementclass (FIGS. 3, 4). All Li values (abundance data) werecalculated with a quadrat size of 10 3 10 m (100 m2), thusgiving a total of 30 quadrats per site (M 5 30). Values for Li

were calculated for each treatment by summing abundancedata (the number of quadrats in which a species occurred)for each species across sites within a treatment (FIG. 3).Average species accumulation curves for each treatmentwere calculated by averaging s(m) values for each value of mfor all sites of a given treatment (FIG. 4).

Richness, diversity and evenness measures also werecalculated for each site (TABLE III). Multiple diversityindices were calculated because there is no generally agreedupon method of characterizing diversity (Magurran 1988).Richness was calculated as the total number of speciesobserved after 1 y of sampling at a site. In terms of speciesdensity, this is the number of species observed per 3000 m2

given our sampling effort, which included sampling onlyhalf the area for small diameter debris. For the calculationof diversity indices, species abundance was measured as thenumber of 10 3 10 m quadrats in which a species occurred(frequency). The equations for diversity measures wereadapted from Magurran (1988).

An analysis of variance (ANOVA) was conducted on allspecies richness and diversity calculations with the statisticalsoftware SAS 8.0 (#1999 SAS Institute Inc., Cary, NorthCarolina). Because this was an exploratory study and ourreplication number was relatively low, liberal a-levels wereemployed. P-values of 0.05–0.10 were considered ‘‘moder-ately significant,’’ while P-values less than 0.05 wereconsidered ‘‘significant.’’ The ANOVA analyses for speciesrichness and diversity took into account our groupingscheme, management history of the stands and the year ofsampling (1996 or 1997). The P-values associated withmanagement history, year of sampling and the managementhistory by year interaction are shown (TABLE III). Speciesrichness was calculated separately for large diameter($15 cm) wood and small diameter ($1 cm but ,15 cm)wood, and an ANOVA was run on these data in a similarfashion (TABLE IV). Trends in species abundance wereanalyzed by calculating the number of 10 3 10 m quadratsin which each species was observed at a site (TABLE V).Mean abundance for each species was compared amongmanagement classes with the same ANOVA used for thespecies richness analysis. An ANOVA for differences inabundance was run only for species that occurred at five ormore sites and that occupied more than 10 quadrats overall(73 of the 255 species fit these criteria).

The occurrence of woody substrates (including substrateswithout fruit bodies) also was characterized at each site (seeCzederpiltz 2001). Living trees, snags and stumps $15 cmdiam were counted in the 3000 m2 area that was sampled forfruit bodies. A stump was defined as being less than 1.5 mhigh, while snags were greater than 1.5 m high. Log volumewas calculated by diameter class as well as by decay class.Within each 5 3 5 m quadrat the circumference ordiameter of each log was taken at its midpoint, and thelength of the log within the quadrat was measured. Volumeoccupied within a quadrat was calculated for each log bymultiplying the radial area (pr2) by the length of the logwithin the quadrat. Log volumes were summed within eachquadrat and then across all quadrats at a site. Similarcalculations also were performed on suspended logs,defined as logs that maintained a distance of at least 5 cm


from the ground inside the boundaries of the 5 3 5 mquadrat. Small, woody debris ($1 cm but ,15 cm diam)was quantified in 60 5 3 5 m quadrats per site with a scale of1–5, where 1 represented the smallest amount of woodydebris (e.g. a skid trail) and 5 represented the largest (e.g.a fallen treetop). The mean of these values was calculatedfor each site and an ANOVA was performed on all woodydebris values, taking into account our grouping scheme andthe management history of the stands (data not shown).

An analysis was performed to determine whether therewas a relationship between fruitbody occurrence of selectedfungal species and the diameter classes and species of thesubstrates on which they were found (see Czederpiltz 2001).For these analyses each observation of a species on a distinct

piece of substrate within a 5 3 5 m quadrat was treated asa separate observation. The diameter and species prefer-ence analyses were limited to wood $ 15 cm diam. A linearregression was performed on species occurrence anddiameter class data, as well as on species occurrence andsubstrate species data. The response variable (y) represent-ed the number of fungal occurrences of a species at a site.For the substrate diameter analysis, fungal occurrences wererecorded by diameter class. Four diameter classes wererecognized: 15–30 cm, 31–45 cm, 46–60 cm and . 60 cm.Log volume data for each diameter class also werecalculated and were taken from the 1997 dataset; calcula-tions of log volume included all logs of a given diameterclass, including logs not colonized by fungi. The number of

FIG. 2. Species accumulation curves for polyporoid and corticioid fungi for individual northern hardwood stands. Curveswere calculated with rarefaction equations, thus permitting calculation of exact mean values over all possible permutations ofquadrat sampling order. Calculations were based on 30 10 3 10 m quadrats. Even-age thinned and unthinned stands arepresented on the same graph.

200 MYCOLOGIA

fungal occurrences and log volume data were calculated ona per diameter class per site basis. The regression on fungaloccurrence and substrate species was performed in a similarway. Six classes of substrate species were recognized: sugarmaple (Acer saccharum), basswood (Tilia americana), yellowbirch (Betula alleghaniensis), other hardwoods (all otherhardwood species pooled), hemlock (Tsuga canadensis)and other conifers (all other conifer species pooled). Logvolume data again were calculated, this time for eachsubstrate species (data were taken from the 1997 dataset);calculation of log volumes included logs not colonized withfungi. The number of fungal occurrences and log volumedata were calculated on a per substrate species per site basis.Based on an analysis of residuals all occurrence data weretransformed with the equation: y 5 log10 (occurrences + 1).These analyses were limited to the five species that occurredat least 70 times: Fomes fomentarius, Ganoderma applanatum,Oxyporus populinus, Stereum hirsutum and Trametes versico-lor.

In all cases the full regression model was considered firstand included the management class of the site, thediameter class or species class of the substrate, the volumeof logs in that diameter or species class, all two-wayinteractions and the three-way interaction. Thereafter termswere eliminated from the model with hierarchical backwardelimination with an a-value of 0.1. Effective R2 values then

were calculated for each model. This was done by firstcalculating the model with all significant fixed effects andthe random effects (the full model). An intermediate modelthen was calculated with only random effects (the randommodel) and a reduced model then was calculated withouteither fixed or random effects (the reduced model).Effective R2 values were calculated for the full and randommodels with:

effective R2full ~ 1 { (SSErrfull=SSErrreduced) ð2Þ

effective R2random ~ 1 { (SSErrrandom=SSErrreduced) ð3Þ

Where:SSErrfull 5 estimate of the sum of squares error for the

full modelSSErrrandom 5 estimate of the sum of squares error for the

random modelSSErrreduced 5 estimate of the sum of squares error for

the reduced model

RESULTS

Over the course of this study 255 polyporoid andcorticioid species were identified (TABLE II) and

FIG. 3. Species accumulation curves of polyporoid and corticioid fungi for each management class of northern hardwoodstands. Curves were calculated with rarefaction equations, and data were pooled for all stands of a given management class.Calculations were based on 30 10 3 10 m quadrats per stand. Five old-growth stands, five uneven-age stands, three even-ageunthinned stands, and two even-age thinned stands were sampled.


approximately 3000 specimens were collected. Fifteenspecies (<6%) varied sufficiently from their descrip-tions that the abbreviation cf. (‘‘compare’’) wasplaced between the genus and species name. Inaddition, appropriate descriptions could not befound for 46 species (<18%), so these species wereassigned to the genus they most resembled and givena species number.

Many little-known and seldom-reported specieswere collected, including: Boidinia furfuracea, Clavu-licium macounii, Cristinia helvetica, Cristinia mucida*,Heterochaetella dubia, Hyphodontia juniperi, Hypochni-cium detriticum, Lindtneria chordulata, Phlebia cana-densis*, Scytinostromella humifaciens, Sistotrema oblon-gispora*, Sistotrema sernanderi* and Trechisporastellulata*. Species with an asterisk have not beenreported previously from the United States (TA-

BLE II).All species accumulation curves differed from

straight lines on visual inspection (FIGS. 2, 3), whichindicates that sampling was at least moderatelycomplete at both the site and treatment level

(Magurran 1988). The slope of a line tangent to thetail end of a species accumulation curve is a goodindication of how completely a community has beensampled. A strongly increasing slope indicates thatmore sampling almost certainly would add newspecies, while a slope approaching zero indicates thatfew new species would be encountered with addition-al sampling (Colwell and Coddington 1994, Magurran1988).

Species accumulation curves had higher end pointsin 1997, indicating higher species richness during thesecond year of sampling (FIGS. 2–4). Of interest, theaverage number of species observed in 3000 m2

increased only by nine species for old growth in1996–1997, while it increased by an average of 17species for all other management classes (FIG. 4).

In both 1996 and 1997 the even-age unthinnedmanagement class displayed the lowest expectednumber of species per unit area (FIGS. 3, 4).Measurements of mean species richness, diversityand evenness were not significantly different (P .

0.10) among management classes (TABLE III). Mean

FIG. 4. Mean species accumulation curves of polyporoid and corticioid fungi for each management class. Mean curves wereconstructed by first calculating stand-level accumulation curves (as in FIG. 2) and then averaging the values at eachaccumulation step across all stands of a given treatment. The old-growth and uneven-age curves are the average of five stand-level curves, the even-age unthinned curve is the average of three stand-level curves and the even-age thinned curve is theaverage of two stand-level curves. Stand-level curves were calculated with rarefaction equations, and calculations were based on30 10 3 10 m quadrats.

202 MYCOLOGIA

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BL

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Spec

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TA

BL

EII

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on

tin

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riks

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Ryv

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urr

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rril

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—6

——

——

——

——

——

—6

Irpex

lact

eus

(Fr.

)F

r.2

—1

——

——

——

——

——

—3

Isch

nod

erm

are

sin

osu

m(S

chra

d.)

P.

Kar

st.

5—

——

——

——

——

——

——

5Ju

ngh

uhn

ian

itid

a(P

ers.

)R

yvar

den

11

——

——

——

1—

——

——

3Ju

ngh

uhn

iase

para

bili

ma

(Po

uza

r)R

yvar

den

e—

—2

——

——

——

——

——

—2

Kavin

iaalb

ovir

idis

(Mo

rgan

)G

ilb

.&

Bu

din

gto

n1

——

——

——

——

——

——

—1

Kavin

iahim

an

tia

(Sch

wei

n.)

J.E

riks

s.e

1—

——

——

——

——

——

——

1L

axi

text

um

cf.

bico

lor

(Per

s.)

Len

tz—

——

——

——

——

——

——

—0

Len

zite

sbe

tuli

na

(L.)

Fr.

——

——

——

——

——

——

——

0L

epto

spor

omyc

esfu

scos

tratu

s(B

urt

)H

jort

stam

——

——

——

——

1—

——

——

1L

epto

spor

omyc

escf

.m

un

du

s(H

.S.

Jack

s.&

Dea

rden

)Ju

lich

——

——

——

——

——

——

——

0L

epto

spor

omyc

essp

.#

1—

——

——

——

——

——

——

—0

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tosp

orom

yces

sp.

#2

1—

——

——

——

——

——

——

1L

epto

spor

omyc

essp

.#

31

——

——

——

——

——

——

—1

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dtn

eria

chor

du

lata

(D.P

.R

oge

rs)

Hjo

rtst

ame,f

——

1—

——

——

——

——

——

1L

ophari

aci

ner

asc

ens

(Sch

wei

n.)

G.

Cu

nn

.1

——

——

——

——

——

——

—1

Mer

ulo

psi

sco

riu

m(P

ers.

)G

inn

se—

——

——

——

——

——

——

—0

Mu

cron

ella

calv

a(A

lb.

&Sc

hw

ein

.)F

r.e

——

——

——

——

——

——

——

0M

ycoa

cia

au

rea

(Fr.

)J.

Eri

kss.

&R

yvar

den

f—

——

——

——

——

——

——

—0

Myc

oaci

afu

scoa

tra

(Fr.

)D

on

k—

—1

——

——

——

——

——

—1

Oli

gopor

us

caes

ius

(Sch

rad

.)G

ilb

.&

Ryv

ard

en—

——

——

—1

1—

——

—1

—3

Oli

gopor

us

tephro

leu

cus

(Fr.

)G

ilb

.&

Ryv

ard

ene

2—

——

——

—2

——

——

——

4O

ligo

por

us

un

dos

us

(Pec

k)G

ilb

.&

Ryv

ard

ene,f

2—

——

——

—1

——

——

—1

4O

xypor

us

cort

icol

a(F

r.)

Ryv

ard

ene,f

1—

——

2—

——

——

——

——

3O

xypor

us

pop

uli

nu

s(S

chu

mac

h.)

Do

nk

64—

—6

——

—1

——

——

——

71O

xypor

us

sp.

#1

——

1—

——

——

——

——

——

1P

enio

phor

acf

.in

carn

ata

(Per

s.)

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Kar

st.

——

——

——

——

——

——

——

0P

enio

phor

acf

.li

lace

aB

ou

rdo

t&

Gal

zin

——

——

——

——

——

——

——

0P

enio

phor

aci

ner

ea(P

ers.

)C

oo

ke—

——

——

——

——

——

——

—0

TA

BL

EII

.C

on

tin

ued

206 MYCOLOGIA

Spec

ies

of

fun

gi

Fu

nga

lo

ccu

rren

ced

ata

by

sub

stra

tesp

ecie

sa

To

tal

SMB

SYB

RM

QA

BC

IWU

HH

MF

RSP

WC

UC

UN

Pen

iophor

aru

fa(P

ers.

)B

oid

in—

——

——

——

——

——

——

—0

Pen

iophor

asp

.#

1—

—1

——

——

——

——

——

—1

Pen

iophor

asp

.#

2—

——

——

——

——

——

——

—0

Pen

iphor

avio

lace

oliv

ida

(So

mm

erf.

)M

asse

e—

——

——

——

——

——

——

—0

Per

enn

ipor

iam

edu

lla-p

an

is(J

acq

.)D

on

k5

—3

—4

——

31

——

——

117

Per

enn

ipor

iate

pei

ten

sis

(Mu

rril

l)R

yvar

den

e1

——

——

——

——

——

——

—1

Phan

eroc

haet

eaff

an

is(B

urt

)P

arm

asto

——

——

——

11

——

——

—1

3P

han

eroc

haet

eca

lotr

icha

(P.

Kar

st.)

J.E

riks

s.&

Ryv

ard

en1

——

——

——

——

——

——

—1

Phan

eroc

haet

eca

rnos

a(B

urt

)P

arm

asto

——

——

——

——

——

—1

——

1P

han

eroc

haet

efi

lam

ento

sa(B

erk.

&M

.A.

Cu

rtis

)B

urd

s.—

——

——

——

——

——

——

11

Phan

eroc

haet

eso

rdid

a(P

.K

arst

.)J.

Eri

kss.

&R

yvar

den

6—

——

——

—1

——

——

—1

8P

han

eroc

haet

evet

uli

na

(DC

.)P

arm

asto

1—

——

——

——

——

——

——

1P

hel

lin

us

cf.

mel

leop

oru

s(M

urr

ill)

Ryv

ard

en1

——

——

——

——

——

——

—1

Phel

lin

us

cf.

robu

stu

s(P

.K

arst

.)B

ou

rdo

t&

Gal

zin

——

1—

——

——

——

——

——

1P

hel

lin

us

chry

solo

ma

(Fr.

)D

on

k—

——

——

——

——

—1

——

—1

Phel

lin

us

ferr

eus

(Per

s.)

Bo

urd

ot

&G

alzi

ne

1—

——

——

——

——

——

——

1P

hel

lin

us

ferr

ugi

nos

us

(Sch

rad

.)P

at.e

,f6

—1

2—

—1

——

——

——

—10

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lin

us

gilv

us

(Sch

wei

n.)

Pat

.—

—1

——

——

——

——

——

—1

Phel

lin

us

ign

ari

us

(L.)

Qu

el.

——

16—

——

1—

——

——

——

17P

hel

lin

us

pu

nct

atu

s(F

r.)

Pil

at—

——

——

—2

——

——

——

—2

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lin

us

trem

ula

e(B

on

dar

tsev

)B

on

dar

tsev

&B

ori

sso

v—

——

—7

——

——

——

——

—7

Phle

bia

ace

rin

aP

eckc

6—

——

——

——

——

——

——

6P

hle

bia

cf.

inca

rnata

(Sch

wei

n.)

Nak

aso

ne

&B

urd

s.1

——

——

——

——

——

——

—1

Phle

bia

cen

trif

uga

P.

Kar

st.

——

2—

——

——

——

——

—1

3P

hle

bia

cocc

ineo

fulv

aSc

hw

ein

.—

—2

——

——

——

——

——

—2

Phle

bia

radia

taF

r.4

—1

——

——

——

——

——

—5

Phle

bia

can

aden

sis

W.B

.C

oo

ked

,g—

——

——

——

1—

——

——

—1

Phle

bia

trem

ulo

sus

(Sch

rad

.)N

akas

on

e&

Bu

rds.

2—

1—

2—

——

——

——

——

5P

hle

biel

lasp

.#

1—

——

——

——

——

——

——

—0

Phle

biel

lasp

.#

22

——

——

——

——

——

——

—2

Phle

biel

lasu

lphu

rea

(Per

s.)

Gin

ns

&M

.N.L

.L

efeb

vre

13—

——

1—

—1

1—

——

—1

17P

hys

olacr

iain

flata

(Sch

wei

n.

exF

r.)

Pec

k10

——

——

——

——

——

——

—10

Pip

topor

us

betu

lin

us

(Bu

ll.)

P.

Kar

st.

——

8—

——

——

——

——

——

8P

laty

gloe

apen

iophara

eB

ou

rdo

t&

Gal

zin

e—

——

——

——

1—

——

——

—1

Pli

catu

racr

ispa

(Per

s.)

Rea

——

82

——

—1

——

——

——

11P

olyp

oru

salv

eola

ris

(DC

.)B

on

dar

tsev

&Si

nge

r—

——

——

——

——

——

——

—0

Pol

ypor

us

badiu

s(P

ers.

)Sc

hw

ein

.6

11

——

——

2—

——

——

111

Pol

ypor

us

bru

mali

s(P

ers.

)F

r.—

——

——

——

——

——

——

—0

TA

BL

EII

.C

on

tin

ued


Spec

ies

of

fun

gi

Fu

nga

lo

ccu

rren

ced

ata

by

sub

stra

tesp

ecie

sa

To

tal

SMB

SYB

RM

QA

BC

IWU

HH

MF

RSP

WC

UC

UN

Pol

ypor

us

eleg

an

sB

ull

.1

——

——

——

——

——

——

—1

Por

othel

eum

fim

bria

tum

(Per

s.)

Fr.

17—

7—

1—

17

——

——

—4

37P

seu

doh

ydn

um

gela

tin

osu

m(S

cop

.)P

.K

arst

.—

——

——

——

—2

——

——

—2

Radu

lom

yces

con

flu

ens

(Fr.

)M

.P.

Ch

rist

.e—

——

——

——

——

——

——

—0

Ram

ari

ciu

malb

o-oc

hra

ceu

m(B

res.

)Ju

lich

f—

——

——

——

——

——

——

—0

Res

inic

ium

bico

lor

(Alb

.&

Sch

wei

n.)

Par

mas

to—

——

——

——

——

——

——

—0

Res

inic

ium

furf

ura

ceu

m(B

res.

)P

arm

asto

——

——

——

——

——

——

——

0R

esin

iciu

msp

.#

1—

—1

——

——

——

——

——

—1

Rig

idop

oru

scr

ocatu

s(P

at.)

Ryv

ard

ene

82

6—

——

——

—1

——

—1

18Sch

izop

hyl

lum

com

mu

ne

Fr.

f5

——

1—

——

1—

——

——

18

Sch

izop

ora

para

dox

a(S

chra

d.)

Do

nke

1—

——

——

——

——

——

——

1Sco

pu

loid

esri

mos

a(C

oo

ke)

Juli

ch1

——

—1

——

——

——

——

—2

Scy

tin

ostr

oma

gala

ctin

ium

(Fr.

)D

on

k—

——

——

——

—1

——

——

—1

Scy

tin

ostr

oma

pro

tru

sum

ssp

.se

pte

ntr

ion

ale

Nak

aso

ne

6—

2—

8—

—12

41

—4

21

40Scy

tin

ostr

omel

lahu

mif

aci

ens

(Bu

rt)

Fre

eman

&R

.H.

Pet

erse

ne,f

1—

——

1—

——

2—

——

——

4

Seb

aci

na

sp.

#1

——

——

——

——

——

——

——

0Seb

aci

na

sp.

#2

——

——

——

——

——

——

——

0Sis

totr

ema

bigg

siae

Hal

len

b.e

1—

——

——

——

——

——

——

1Sis

totr

ema

brin

kman

nii

(Bre

s.)

J.E

riks

s.f

3—

——

——

——

——

——

——

3Sis

totr

ema

oblo

ngi

spor

aM

.P.

Ch

rist

.&

Hau

ersl

evg

——

——

——

——

——

——

——

0Sis

totr

ema

radu

loid

es(P

.K

arst

.)D

on

k—

——

2—

——

——

——

——

—2

Sis

totr

ema

sern

an

der

i(L

itsc

h.)

Do

nkg

——

——

——

——

——

——

——

0Sis

totr

emast

rum

niv

eocr

emeu

m(H

oh

n&

Lit

sch

.)J.

Eri

kss.

e1

——

——

——

——

——

——

—1

Sis

totr

emast

rum

suec

icu

mL

itsc

h.

exJ.

Eri

kss.

e—

—1

——

——

——

——

——

—1

Ske

leto

cuti

sam

orpha

(Fr.

)K

otl

.&

Po

uza

re—

——

——

——

—1

——

——

—1

Ske

leto

cuti

sn

ivea

(Ju

ngh

.)Je

anK

elle

r—

——

——

——

——

——

——

—0

Ste

ccher

inu

mci

liol

atu

m(B

erk.

&M

.A.

Cu

rtis

)G

ilb

.&

Bu

din

gto

nf

——

——

——

——

——

——

——

0

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ccher

inu

mfi

mbr

iatu

m(P

ers.

)J.

Eri

kss.

1—

——

——

——

——

——

——

1Ste

ccher

inu

moc

hra

ceu

m(P

ers.

)G

ray

205

1—

1—

33

——

——

——

33Ste

ccher

inu

mor

eophil

um

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dse

y&

Gil

b.e

——

——

——

——

——

——

——

0Ste

ccher

inu

msu

bcri

nale

(Pec

k)R

yvar

den

e,f

——

——

——

——

——

——

——

0Ste

reu

mhir

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m(W

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.)P

ers.

147

39

4—

—3

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——

—1

118

1Su

buli

cyst

idiu

mlo

ngi

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um

(Pat

.)P

arm

asto

f4

1—

—1

——

——

——

——

—6

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buli

cyst

idiu

msp

.#

1—

——

——

——

——

——

——

—0

Tom

ente

lla

bryo

phil

a(P

ers.

)M

.J.

Lar

sen

1—

——

——

——

——

——

——

1

TA

BL

EII

.C

on

tin

ued

208 MYCOLOGIA

Spec

ies

of

fun

gi

Fu

nga

lo

ccu

rren

ced

ata

by

sub

stra

tesp

ecie

sa

To

tal

SMB

SYB

RM

QA

BC

IWU

HH

MF

RSP

WC

UC

UN

Tom

ente

lla

crin

ali

s(F

r.)

M.J

.L

arse

ne,f

——

——

——

—1

——

——

——

1T

omen

tell

aep

igaea

(Bu

rt)

M.J

.L

arse

ne

——

——

——

——

——

——

——

0T

omen

tell

afe

rru

gin

ea(P

ers.

)P

at.

——

——

——

——

——

——

——

0T

omen

tell

asp

.#

1—

——

——

——

——

——

——

—0

Tom

ente

lla

sp.

#2

1—

——

——

——

——

——

——

1T

omen

tell

asp

.#

3—

——

——

——

——

——

——

—0

Tom

ente

lla

sp.

#4

——

——

——

——

——

——

——

0T

omen

tell

asp

.#

5—

——

——

——

——

——

——

—0

Tom

ente

lla

sp.

#6

——

——

——

——

——

——

——

0T

omen

tell

ast

rum

badiu

m(L

ink)

M.J

.L

arse

nf

——

——

——

——

——

——

——

0T

omen

tell

opsi

scf

.pu

sill

aH

jort

stam

——

——

——

——

——

——

——

0T

ram

etes

hir

suta

(Wu

lfen

)P

ilat

——

——

——

——

——

——

——

0T

ram

etes

pu

besc

ens

(Sch

um

ach

.)P

ilat

7—

1—

——

—2

——

——

——

10T

ram

etes

sp.

#1

——

——

——

——

——

——

——

0T

ram

etes

ver

sico

lor

(L.)

Llo

yd13

1—

113

2—

—8

——

——

—1

156

Tre

chis

por

aaln

icol

a(B

ou

rdo

t&

Gal

zin

)L

iber

tae

1—

——

——

—2

——

——

—2

5T

rech

ispor

aco

haer

ens

(Sch

wei

n.)

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ch&

Stal

per

s1

——

——

——

——

——

——

—1

Tre

chis

por

afa

rin

ace

a(P

ers.

)L

iber

ta2

——

——

——

1—

——

——

—3

Tre

chis

por

am

ollu

sca

(Per

s.)

Lib

erta

e,f

1—

1—

——

—1

12

——

1—

7T

rech

ispor

ast

ellu

lata

(Bo

urd

ot

&G

alzi

n)

Lib

erta

g—

——

——

——

——

——

——

—0

Tri

chaptu

mabi

etin

um

(Dic

ks.)

Ryv

ard

en—

——

——

——

—9

11

—1

—12

Tri

chaptu

mbi

form

e(F

r.)

Ryv

ard

en10

—22

5—

——

——

——

——

—37

Tu

buli

crin

isgr

aci

llim

us

(D.P

.R

oge

rs&

H.S

.Ja

cks.

)G

.C

un

n.

——

——

——

——

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values did differ significantly (P ,0.05) by year, withthe exception of measurements from the Brillouinindex (TABLE III). The management history by yearinteraction was not significant (P .0.10).

When richness was calculated by diameter class ofsubstrate, a moderately significant difference (P 5

0.0839) was seen for mean species richness on largediameter woody substrates (TABLE IV). This differ-ence was not apparent for richness on small diameterwoody substrates (P 5 0.5359). However meanrichness values did differ significantly (P 5 0.0071)by year for small diameter wood, while they did notdiffer significantly by year (P 5 0.8623) for largediameter wood (TABLE IV). Ten species had meanabundance levels that differed (P ,0.10) by manage-ment class or by management class and year(TABLE V).

Woody debris volume and species composition alsovaried by management class (see Czederpiltz 2001).The average number of trees per site differedsignificantly among management classes (P ,0.05),and this difference was most pronounced for largetrees (.60 cm) or relatively small trees (15–30 cm).Small diameter snags (15–30 cm) were found to differby management class and were most common in even-age unthinned stands; in contrast large diametersnags (.60 cm) were most common in old-growthstands. The average number of stumps per site wasfound to differ significantly (P ,0.01) by manage-ment class, which was true for all four diameterclasses. Even-age thinned stands had the greatestnumber of stumps, followed by uneven-age stands,even-age unthinned stands and then old growth.Total log volume was highly significantly different (P,0.0001) among management classes, as was thevolume of suspended logs. For both total log volumeand suspended log volume, old growth had thelargest values, followed by uneven-age stands, even-age thinned stands and then even-age unthinnedstands. The volume of logs by decay class also wasfound to differ among management classes, with old-growth sites having larger volumes of all decay classes.Even-age unthinned and old-growth sites had thelargest amounts of smaller diameter ($1 cm but,15 cm) debris, although this difference was notfound to be significant (P .0.10). The volume ofsugar maple and yellow birch logs was found to differsignificantly (P ,0.05) among management classes, aswas the combined volume of all hardwood species.Log volume for sugar maple, yellow birch and thecombined volume of all hardwood species was great-est in old growth.

For all five fungal species that occurred at least 70times diameter class of substrate and the managementhistory of the stand were important in predicting the

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210 MYCOLOGIA

number of fungal occurrences at a site (see Czeder-piltz 2001). The volume of logs within each diameterclass did not help predict the occurrence of Fomesfomentarius or Oxyporus populinus but was importantfor Ganoderma applanatum, Stereum hirsutum andTrametes versicolor. The effective R 2 values indicatethat the full regression models for these fungiaccount for 78–91% of the variability associated withspecies occurrence, while 19–62% of the variabilitycan be explained by random effects alone. For thesecond regression analysis, the species of substrate,the management history of the stand and the volumeof logs by substrate species all were found to beimportant in predicting the number of fungaloccurrences at a site. The effective R 2 values indicatethat the full regression models for these fungiaccount for 69–94% of the variability associated withspecies occurrence, while approximately 12% of thevariability can be explained by random effects alone(Czederpiltz 2001).

DISCUSSION

Contrary to European findings (e.g. Bader et al 1995,Høiland and Bendiksen 1997, Lindblad 1998), speciesrichness values (i.e. the total number of speciesobserved per site per year) and measures of commu-nity diversity did not differ significantly (P . 0.10)among management classes (see TABLE III). A possi-ble explanation for this difference is that Europeanresearchers have tended to examine only largediameter woody debris (Bader et al 1995, Lindblad1998) or have restricted sampling to logs of a certainlength (Høiland and Bendiksen 1997). If our datasetis restricted to the species found on large diameterwood ($15 cm), differences in species richness aremoderately significant (P , 0.10) among manage-ment classes (see TABLE IV), and this effect did notvary significantly (P . 0.10) by year. In addition tofocusing on large diameter debris, most previousresearch has been conducted in boreal forests

TABLE III. Richness and diversity values for polyporoid and corticioid fungi in northern hardwood stands with differentmanagement histories

Old-growth

Species richnessShannon index

(H9)Simpson index

(1/D)Brillouin in-

dex (HB) Shannon evenness

1996 1997 1996 1997 1996 1997 1996 1997 1996 1997

Stand 1 34 38 3.04 3.19 14.13 15.96 116.0 076.1 0.86 0.88Stand 2 40 50 3.18 3.46 15.41 21.15 132.2 197.6 0.86 0.89Stand 3 47 53 3.36 3.57 18.74 23.96 126.3 081.4 0.87 0.90Stand 4 52 86 3.44 4.00 19.90 34.73 142.3 214.6 0.87 0.90Stand 5 61 52 3.53 3.45 19.48 20.81 162.7 154.0 0.86 0.87Old-growth mean: 46.8 55.8 3.31 3.53 17.53 23.32 135.9 144.7 0.86 0.89

Uneven-aged

Stand 6 49 54 3.38 3.55 19.18 23.57 141.2 144.9 0.87 0.89Stand 7 44 82 3.10 3.88 14.14 32.05 256.0 359.9 0.82 0.88Stand 8 44 81 3.31 3.91 19.02 31.59 181.2 246.9 0.88 0.89Stand 9 50 60 3.51 3.73 24.27 28.24 112.5 123.4 0.90 0.91Stand 10 41 55 3.35 3.60 19.50 24.41 066.1 120.6 0.90 0.90Uneven-aged mean: 45.6 66.4 3.33 3.73 19.22 27.97 151.4 199.1 0.87 0.89

Even-aged Unthinned

Stand 11 36 58 3.10 3.63 15.46 26.04 145.5 173.0 0.86 0.90Stand 12 46 52 3.30 3.60 16.98 26.52 107.3 162.1 0.86 0.91Stand 13 23 36 2.66 3.39 08.92 24.23 066.8 038.5 0.85 0.95Even-aged unthin. mean: 35.0 48.7 3.02 3.54 13.79 25.60 106.5 124.6 0.86 0.92

Even-aged Thinned

Stand 14 37 56 3.09 3.61 13.93 25.99 195.3 160.0 0.85 0.90Stand 15 58 72 3.67 3.88 27.01 32.64 114.2 137.6 0.91 0.91Even-aged thin. mean: 47.5 64.0 3.38 3.75 20.47 29.32 154.7 148.8 0.88 0.91

P-values

Management History: 0.3399 0.2647 0.3152 0.5962 0.5832Year: 0.0169 0.0077 0.0074 0.3055 0.0118Management History * Year: 0.6204 0.447 0.2182 0.4809 0.2075


dominated by conifers (e.g. Bader et al 1995, Høilandand Bendiksen 1997, Lindblad 1998, Ohlson et al1997, Wasterlund and Ingelog 1981), while this studywas conducted in the northern hardwood forests ofthe Midwest. These dissimilar forest types may re-spond to disturbance in different ways.

Regardless of forest type it is likely that some forestmanagement practices, including thinning and selec-tive harvesting, increase the amount of small diameterdebris at a site. This is significant for the many fungalspecies that preferentially colonize smaller substrateclasses. An increase in species richness on smalldiameter debris may offset a decrease in speciesrichness due to a loss of large diameter debris. Thusforest management may not change the overallnumber of wood-inhabiting species at a site, asreported in previous research. Rather forest manage-ment may favor species capable of colonizing smalldiameter debris.

Measuring the fungal community found on smalldiameter debris is difficult, however, because thesespecies may be more variable in occurrence comparedto species on large debris. In the present study thenumber of species found on small diameter woodvaried greatly in 1996–1997 (P , 0.01) and this mighthave masked differences among management classes.In addition it can be difficult in the field to carefullyexamine the numerous pieces of fine woody debristhat can be found in even a small area. In futurestudies researchers could choose to exclude a greatdeal of variability in species richness by sampling onlylarge diameter debris, although this will limit theconclusions that can be drawn with regard to overallspecies richness.

While particular fungi that inhabit large diameterdebris obviously will be affected by forest manage-ment practices that reduce the quantity and quality oflarge diameter logs (Bader et al 1995, Lindblad 1998,

TABLE IV. Species richness values by substrate diameter class for polyporoid and corticioid species in northern hardwoodstands with different management histories

Old-growth

Richness on largediameter wooda Richness on small diameter woodb

1996 1997 1996 1997

Stand 1 24 20 20 26Stand 2 24 20 28 40Stand 3 30 23 30 40Stand 4 30 33 37 69Stand 5 32 25 43 36Old-growth mean: 28.0 24.2 31.6 42.2

Uneven-aged

Stand 6 25 18 35 45Stand 7 26 32 33 66Stand 8 29 27 28 67Stand 9 36 29 29 45Stand 10 16 15 29 47Uneven-aged mean: 26.4 24.2 30.8 54.0

Even-aged Unthinned

Stand 11 16 22 27 45Stand 12 16 13 36 48Stand 13 09 16 17 29Even-aged unthin. mean: 13.7 17.0 26.7 40.7

Even-aged Thinned

Stand 14 15 24 29 43Stand 15 32 26 38 57Even-aged thin. mean: 23.5 25.0 33.5 50.0

P-values

Management History: 0.0839 0.5359Year: 0.8623 0.0071Management History * Year: 0.3812 0.421

a Large diameter wood was defined as $15 cm in diameter.b Small diameter wood was defined as $1 cm but ,15 cm in diameter.

212 MYCOLOGIA

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Ohlson 1997), some forest management practicesmay increase the overall species richness of polypor-oid and corticioid fungi at a site. Our uneven-age andeven-age thinned stands averaged approximatelyequal or greater species richness on small diameterdebris than old-growth stands. In addition overallrichness in uneven-age and even-age thinned standswas approximately equal to or greater than that ofold-growth stands, with this effect being most prom-inent in the 1997 data. This effect might be due tomanagement practices that increase the amount ofsmall diameter debris in stands and therefore thediversity of fungi found on this diameter class.Because small diameter substrates are common atthe landscape level, it is likely that an increase infungal diversity due to an increase in small diameterdebris will mean a site is occupied by many‘‘common’’ species. Therefore increases in speciesdiversity on small diameter debris may not be usefulfor the conservation of rare species. However, instands with a large volume of small diameter debris,changes in fungal diversity on smaller diameterclasses could greatly affect overall decay rates.

In Europe it has become widely accepted that some‘‘indicator’’ and ‘‘red-listed’’ fungi are endangeredbecause they are associated with particular habitats orforest management categories (Arnolds 1997, Rydinet al 1997, Stokland et al 1997). In this study a specieswas identified as being associated with a particularmanagement class using an a-level of 0.10 to judgesignificance (P-values , 0.10). A liberal a-level wasemployed because this was an exploratory study (i.e.a similar study of this nature had not been previouslyperformed) and only modest levels of site-levelreplication were possible. By considering P-values ,

0.10 but . 0.05 as ‘‘moderately significant,’’ it ispossible to identify species that may show significanttrends if more sites had been sampled. Although thisstrategy increases the risk of Type I statistical error, itmakes it possible to identify species that should besurveyed at a larger number of sites in future studies.

In our study 10 species tended to be associated withparticular management classes, displaying abundancelevels that differed (P , 0.10) among managementclasses (TABLE V). Some of these fungi, such asCystostereum murraii and Rigidoporus crocatus, weremost abundant in old-growth stands and thereforemight be useful indicators of stand with ‘‘old-growthcharacteristics.’’ Other species, in contrast, might beuseful indicators of managed stands; Oxyporuspopulinus and Vararia investiens were collected mostfrequently in managed forests and therefore may beuseful indicators of disturbance. Further experimen-tal work is needed to determine if even-age manage-ment promotes the occurrence of Oxyporus populinus

and whether this could negatively affect stand healthand productivity.

A problem with a study of this scope is that a certainnumber of species may vary significantly in abun-dance among management classes simply due torandom chance. In our analysis 73 of the 255 speciesoccurred frequently enough to warrant testing withANOVA. Using an a-level of 0.1 to judge statisticalsignificance, one would expect 7.3 species to varysignificantly due to chance alone. In our analysis 10species had P-values , 0.10, suggesting that 2–3species may have varied for reasons other than chancealone; for the other species there was not enoughstatistical power to indicate whether differences weredue to chance. If a Bonferroni correction is applied toaccount for multiple comparisons, none of thespecies examined had abundance levels that variedsignificantly. However it is likely that a full Bonferronicorrection is too conservative and that the relativelysmall number of species with significant P-values isdue to the small number of sites sampled, whichlimits statistical power, rather than due to a true lackof species that vary by management class. If all 73species are considered, the distribution of P-valuesassociated with abundance levels is significantly non-random (P 5 0.015), with 55% of the values being #

0.39. This suggests that further sampling wouldincrease the number of species with abundance levelsthat varied significantly. In the future directedsampling should be applied to the species (identifiedin TABLE V) to confirm that they are associated withforest stands of a particular management category.

It is likely that some species vary in abundanceamong management classes because substrate quan-tity and quality (species of substrate, diameter class,decay class, height off the ground, etc.) affect theabundance of particular species. For the five speciesexamined in depth in this study, substrate diameterand substrate species were found to be important inpredicting fungal abundance (see Czederpiltz 2001).Substrate quantity (log volume) at a site was found tobe important in all five cases, with the exception ofFomes fomentarius and Oxyporus populinus. Both ofthese fungi tend to occur on standing woody debris,which could explain why log volume is not predictiveof fungal occurrence. For all of the other speciestested, substrate quality, quantity and managementhistory of the stand were important in predicting thenumber of fungal occurrences at a site. Our models,although relatively simple, explained a large portionof the error associated with species occurrence.Effective R2 values for our models with fixed andrandom effects had a range of 0.78–0.94, whilemodels with random effects alone had R2 values thatranged from 0.12 to 0.62.

214 MYCOLOGIA

Analysis of species accumulation curves suggeststhat using larger quadrat sizes would not havesignificantly affected our analyses (Czederpiltz2001). Future researchers might find setting upa small number of larger quadrats less time consum-ing than installing a large number of smallerquadrats, as was done in this study. Large samplingunits however can over represent the abundance of‘‘small’’ species (Magurran 1988) or species thatoccur in small patches. There is therefore a trade-offbetween time needed to set up a field design andaccuracy of abundance measurements. In our casespecies accumulation curves would not have beenappreciably affected had we used sampling units aslarge as 50 3 10 m. However the accumulation curvesproduced with 50 3 10 m quadrats are shifted slightly‘‘downward.’’ This indicates that positive (clumping)autocorrelation is present within the data for certainspecies (Czederpiltz 2001). Further analyses areneeded to confirm the magnitude of this autocorre-lation and to determine which species contributed tothis effect.

Sampling 3000 m2/y/site worked well, producingsite-level species accumulation curves that differsignificantly from straight lines, yet do not flattenoff to the point where a great deal of effort was spentresampling common species. From a practical stand-point sampling larger areas within a stand might bedifficult and might not lead to flatter speciesaccumulation curves. We found it difficult to locatehomogenous northern hardwood stands able toaccommodate more than two 100 3 60 m plots.Sampling larger plots would necessitate the inclusionof more ‘‘nontarget’’ habitats (e.g. marshy areas,small ponds, hemlock stands, etc.), thus leading tocontinuously increasing species accumulation curves.It would be preferable to sample more sites withineach management class to offset the large intersitevariability observed within management classes (seeTABLE III and FIG. 2) and to represent the range ofstands within each management class more fully.

Sampling stands for more than 2 y also would bedesirable. Our species accumulation curves showconsiderable variation in the fungal community in1999–1997. With the sole exception of old-growthstand No. 5, the 1997 site-level species accumulationcurves had higher end-points than the 1996 speciesaccumulation curves. Although species accumulationcurves were larger in 1997, it is interesting to note thatold-growth sites did not show an average increase aslarge as other management classes. For old-growthsites, the average number of species observed in3000 m2 increased only by nine species in 1996–1997,while it increased by an average of approximately 17species for all other management classes. Two years of

data are too few to tell whether this disparity was dueto chance or some biological factor intrinsic to old-growth stands. One possible biological explanation isthat the old-growth fungal community is moreconsistent from year to year because a significantamount of wood at such sites is in large diameterclasses. Such pieces of substrate might providea relatively stable habitat in terms of moisture andtemperature conditions (Cooke 1948). On averagethe old-growth sites we sampled had more than 40%

of their total log volume in logs $45 cm diam, whilethe other management classes had from <2–17% oftheir log volume in logs of this size. If such anexplanation is correct it would indicate that old-growth sites typically exhibit a large percentage oftheir potential diversity at all times, while other standsdisplay a large percentage of their potential diversityonly during particularly favorable years.

Our sampling procedures were designed primarily tomeasure diversity of wood-inhabiting fungi in standsand thus were unlikely to detect species that are rare inlandscapes. Although changes in community diversitymight be of primary concern to researchers interestedin ecosystem processes, future investigations also needto address the issue of how to sample for individualspecies that occur infrequently in landscapes. Localextinction of some rare fungal species endemic toupper Midwest forests might have occurred before theirpresence could be documented by mycologists. Forexample, Neuman (1914) reported that ‘‘variouscollectors’’ had noted Fomitopsis officinalis (5 Fomesofficinalis) occurring on larch in northern Wisconsin.Although a few reports of this fungus have beenconfirmed from the Great Lakes region (Gilbertsonand Ryvarden 1986), F. officinalis no longer is consid-ered to occur in the Midwest. To the best of ourknowledge H.H. Burdsall Jr. made the most recentMidwest collection from an old-growth stand ofhemlock in the Huron Mountains of Michigan’s UpperPeninsula in 1974. Further work is needed to determinewhether this fungus still occurs in upper Midwestlandscapes. Future work also could document whetherother rare wood-inhabiting species, such as the old-growth hemlock inhabiting species Laetiporus huro-niensis (Burdsall and Banik 2001), have disappearedfrom significant portions of their former ranges.

Forest management, an important source of distur-bance in upper Midwest hardwood forests, appears tohave significant effects on wood-inhabiting fungi.Because modern forest management practices canradically alter the amounts and types of debris inforests, wood-inhabiting species might be of specialinterest for conservation efforts. Most polyporoid andcorticioid fungi are obligately dependent on woodydebris for growth and reproduction, and such species


will be directly affected by management regimes thataffect the quantities and qualities of woody debrisavailable for colonization. Fungi that rely on largediameter woody substrates might be at particular risk,as well as fungi that are specifically associated withtree species that decline in frequency after manage-ment, including basswood, hemlock and yellow birch.A reduction in overall species richness of wood-inhabiting fungi could have significant implicationsfor nutrient cycling and health in many forestecosystems.

ACKNOWLEDGMENTS

We thank Dr Karen Nakasone for kindly providing experttaxonomic advice and helping with the identification ofhundreds of corticioid specimens and Robert Mielke forhelping to collect the many specimens needed for thisproject. We also would like to thank Dr Jessie Micales forsupporting this work and Dr Karl-Henrik Larsson, Dr LeifRyvarden and Dr Gitta Langer for providing much neededtaxonomic advice. This project was supported by a NSFGraduate Fellowship, with additional financial supportprovided by the Wisconsin Department of Natural Re-sources, the Wisconsin Alumni Research Foundation andthe A.J. Riker Fund.

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