Sten Svantesson

Uppsats för avläggande av naturvetenskaplig magisterexamen i

Växtekologi 60 hp

Institutionen för biologi och miljövetenskap Göteborgs universitet

Ecological requirements of corticioid fungi

– a study on species richness and community

composition in south-eastern Norway

0

Contents

Abstract ................................................................................................. 1

Sammanfattning ................................................................................... 1

Introduction .......................................................................................... 2

Materials and methods ........................................................................ 6

Study sites .............................................................................................................................. 6

Field methods ......................................................................................................................... 7

Explanatory variables ............................................................................................................. 8

Studied taxa and species identifications ................................................................................. 9

Species categories ................................................................................................................ 10

Generalised linear mixed-effects models (GLMM) analysis ............................................... 11

Non-metric multidimensional scaling (NMDS) analysis ..................................................... 12

Results ................................................................................................. 14

General ................................................................................................................................. 14

Determinants of species richness ......................................................................................... 14

Ordination of species composition and correlated variables ................................................ 18

Discussion ........................................................................................... 21

Conclusions and implications for conservation .............................. 27

Acknowledgements ............................................................................ 27

References ........................................................................................... 28

Appendix 1 .......................................................................................... 33

Appendix 2 .......................................................................................... 37

1

Abstract

Although the corticioid fungi are a species rich and ecologically important group, with many

Red Listed species, knowledge about their ecological requirements is limited. Previous

ecological studies have often excluded them, due to their inconspicuous fruit bodies and time-

consuming identification. In the cases where corticioids have been included they have very

seldom been analysed separately from other fungal groups living in dead wood.

At six sites of old-growth spruce forest in south-eastern Norway, I made an inventory of 48

logs in total, equally divided between the decay classes 3, intermediately decomposed, and 4,

well decomposed. All fruit bodies of corticioid species were collected. In addition to decay

class 16 explanatory variables were analysed for correlations with species composition and

the richness of all, Red Listed, common, occasional and rare species. I also aimed to find the

best models explaining species richness and study whether the richness of Red Listed species

varied with the richness of non-Red Listed species.

I found on average 17.2 ± 4.5 (S.D.) species per log. The respective species richness of all,

common and occasional species were all positively correlated with log size. The total species

richness was also greater on logs created due to stem breakage than on uprooted logs, and

there were more occasional species on logs with more area of polypore hymenophores than on

logs with less polypore hymenophore area. The number of Red Listed species per log

increased with ground contact and the total number of non-Red Listed species, decreased with

bark coverage and was higher on broken logs and logs in decay class 4 than on uprooted logs

and logs in decay class 3. The best models explaining the species richness of the different

categories were quite consistent with the correlations the categories formed with the single

variables. The species composition on the studied logs was correlated with ground contact,

plant coverage, bark coverage, log type (uprooted or broken), polypore hymenophore area,

decay class and richness of Red Listed species.

I conclude that even within a single, narrowly defined kind of dead wood, a great variation

in microenvironments exists, and that many of these environments host differently

composited and species rich communities of corticioid fungi. The number of species found on

logs in this study was higher than what has previously been reported in any studies of similar

substrates, further underscoring the importance dead wood has for biodiversity. The rare

species needed many different kinds of logs, and hence a diversity of dead wood is required to

host them. Both the rare and the Red Listed species needed well decayed logs, a substrate that

today can almost only be found in the most natural forests.

Sammanfattning

Fastän skinnsvamparna är en artrik och ekologiskt viktig grupp med många rödlistade arter är

kunskapen om deras ekologiska krav mycket begränsad. Med anledning av sina oansenliga

fruktkroppar och tidskrävande artbestämning har de ofta blivit exkluderade ur tidigare

ekologiska studier. I de fall då de inkluderats har de mycket sällan separerats från andra

vedlevande svampar.

På sex lokaler med gammelskog av gran i sydöstra Norge genomförde jag en inventering

av sammanlagt 48 lågor, jämnt fördelade mellan nedbrytningsklasserna 3, intermediärt

nedbruten, och 4, väl nedbruten. Alla fruktkroppar samlades in. Utöver nedbrytningsklass

analyserade jag 16 olika variabler för att finna korrelationer mellan dessa och artrikedom och

artsammansättning av alla, rödlistade, vanliga, tillfälliga och sällsynta arter. Jag hade också

för avsikt att finna de modeller som bäst kunde förklara artrikedom samt ta reda på huruvida

rikedomen av rödlistade arter var korrelerad med rikedomen av icke rödlistade arter.

2

Jag fann i snitt 17.2 ± 4.5 (S.D) arter per låga. De respektive rikedomarna av alla, vanliga

och tillfälliga arter var samtliga positivt korrelerade med lågastorlek. Den totala artrikedomen

var också större på lågor som skapats till följd av stambrott än på rotvältor, och det fanns fler

tillfälliga arter på lågor med större fruktkroppsareal av tickor än på lågor med mindre

fruktkroppsareal. Antalet rödlistade arter per låga ökade med markkontakt och rikedom av

icke rödlistade arter, minskade med barktäckning och var högre på avbrutna lågor och lågor i

nedbrytningsklass 4 än på rotvältor och lågor i nedbrytningsklass 3. De modeller som bäst

kunde förklara artrikedomen i de undersökta kategorierna var tämligen lika de korrelationer

som förekom mellan kategorierna och de enskilda variablerna. Artsammansättningen på de

studerade lågorna korrelerade med variablerna: markkontakt, växttäckning, barktäckning,

lågatyp (avbruten eller rotvälta), fruktkroppsareal av tickor, nedbrytningsklass och rikhet av

rödlistade arter.

Jag drar slutsatsen att det även inom en enda, snävt definierad typ av död ved förekommer

en stor variation i mikromiljöer och att många av dessa miljöer hyser både olikt sammansatta

och artrika samfund av skinnsvampar. Antalet arter funna på stockarna i denna studie var

större än vad som tidigare rapporterats i någon studie av liknande substrat – ett resultat som

ytterligare understryker betydelsen död ved har för den biologiska mångfalden. De sällsynta

arterna behövde många olika sorters lågor och således krävs en mångfald av död ved för att

kunna härbärgera dem. Både de sällsynta och de rödlistade arterna behövde väl nedbrutna

lågor, ett substrat man idag nästan enbart finner i de mest naturliga skogarna.

Introduction

Wood-decaying fungi are in several senses very important organisms. Their action of decay

plays a crucial role in the nutrient recycling in the forests of the world (Harmon et al. 1986;

Lonsdale et al. 2008). Through constituting accessible nutrition and creating microhabitats

they make existence possible for, or start the food chains of, the many organisms living

directly in dead wood or who in turn are nutritionally dependent upon the ones that do

(Lonsdale et al. 2008; Gjerde et al. 2009). Siitonen (2001) found that in Finland there are

4000-5000 species of dead wood-dependent organisms, representing 20-25 % of the total

number of forest-inhabiting species.

Distinguished by their simple, often small and inconspicuous, crust-like fruit bodies that in

most cases need the aid of a microscope to be identified (Hjortstam et al. 1988), the corticioid

fungi constitute a major group of wood-inhabiting fungi; only in the Nordic countries there

are 555 species, accounting for approximately 27 % of all the wood-inhabiting fungal species

in the area (Stokland and Meyke 2008). The corticioids make up a highly polyphyletic group,

representing almost all major clades of the homobasidiomycetes (Larsson et al. 2004), and

though often referred to as wood decayers a smaller portion of them are in fact

ectomycorrhizal or litter and humus decayers (Stokland and Meyke 2008).

3

For 144 species, or 29 % of all the corticioid species present in Norway, the risk to go

nationally extinct is either 5 % or more, or is very hard to evaluate, and consequently they are

Red Listed (Hofton unpublished; Kålås et al. 2010). The reasons for this are that the

populations of some corticioid fungi seem to be very small and many are considered to be

declining (Hofton unpublished; Kålås et al. 2010). These conditions are in turn generally

regarded to be caused by the loss of natural forest (Kålås et al. 2010), but, even though such

information is badly needed in conservation, the relative importance of the factors involved

remains deficiently known, as knowledge about the ecological requirements of corticioid

fungi at the community level is very limited in available literature. The entire group has

received less attention than many other macrofungal groups, due to the often inconspicuous

fruit bodies of its members and the difficulties involved with their identification (Hjortstam et

al. 1988).

Knowledge about the ecological requirements of corticioid fungi at the community level is

mainly limited to what can be interpreted from studies addressing wood-inhabiting fungi in

general; there has been very little separate research on the ecology of corticioids. In studies on

species richness of wood-inhabiting fungi on Norway spruce Picea abies (L.) H. Karst in the

taiga region, providing a value for the mean number of species fruiting per log (Renvall 1995;

Lindblad 1998; Edman et al. 2004; Berglund et al. 2005; Juutilainen et al. 2011; Olsson et al.

2011; Stokland and Larsson 2011) this measure varies from 3.2 (Renvall 1995) to almost 11

(Lindblad 1998).

A positive relationship has been found to exist between the size of a dead wood substrate

and the number of wood-inhabiting fungi fruiting on it (Lonsdale et al. 2008; Bader et al.

1995; Heilmann-Clausen and Christensen 2004; Lindhe et al. 2004), consistent also in studies

with a large proportion of corticioid species included (Renvall 1995; Høiland and Bendiksen

1997; Lindblad 1998; Stokland and Larsson 2011). On the other hand corticioid fungi seem to

need less space than other basidiomycetic fungi; in a study "investigating the relative

importance of coarse (diameter >10 cm) and fine woody debris (1–10 cm) for fungi in

broadleaf forests in southern Sweden" Nordén et al. (2004) made nearly 80 % and more than

70 % of all their records of corticioid and stereoid fungi, respectively, on fine woody debris,

4

while the corresponding numbers for polypores and agarics were about 50 % and 40 %,

respectively. Renvall (1995) found that the species composition of wood-inhabiting fungi,

among them many corticioids, on spruce logs in Northern Finland, differs with substrate size -

a result confirmed by later studies; Stokland and Larsson (2011) found many corticioid

species to be specialised on logs larger or smaller than 30 cm in diameter, and Küffer et al.

(2008) and Juutilainen et al. (2011) found the communities of corticioid fungi occurring on

dead wood pieces with a diameter smaller than about one cm to be different from those on

larger pieces. Tikkanen et al. (2006) concluded that of all Red Listed (following Rassi et al.

2001), boreal, aphyllophoraceous fungi (also including species that do not live in dead wood)

in Finland, less than 5 % were specialised in dead wood with a diameter smaller than 10 cm,

more than 50 % in dead wood larger than 10 but smaller than 30 cm in diameter, and nearly

30 % in logs larger than 30 cm in diameter. About 10 % of the species were indifferent to the

diameter of dead wood.

Decay class has in several studies (Renvall 1995; Høiland and Bendiksen 1997; Lindblad

1998; Stokland and Larsson 2011) been found to be the quality-related variable with the

highest impact on species richness of wood-inhabiting fungi on coniferous trees in the Nordic

countries. Concerning spruce, all these studies found medium decayed logs to be the most rich

in species. Several studies also indicated the presence of species-specific differences in

relation to decay class (Renvall 1995; Høiland and Bendiksen 1997; Lindblad 1998; Küffer

and Senn-Irlet 2005; Tikkanen et al. 2006; Stokland and Larsson 2011).

Renvall (1995) found seven different successional pathways of wood-inhabiting fungi on

spruce in northern Finland; six pathways occurred on broken logs, while the species

community on uprooted logs constituted the seventh. He showed that the uprooted log

pathway had a mean species richness which was lower than any of the pathways of the broken

logs. He further argued that the type of death a tree suffers is linked to the identity of its

primary decayers, which start successional pathways with different species composition and

richness. Heilmann-Clausen and Christensen (2003) found a corresponding difference in

species richness of Red Listed wood-inhabiting fungi between uprooted and broken logs of

beech Fagus sylvatica L. in Denmark, for which they ascribed the same explanation. Lindblad

(1998) also included log type in her study but found no significant relation.

5

In a study by Mahmood et al. (2001) certain species of the corticioid, ectomycorrhizal

genus Piloderma Jülich turned out to colonise base rich ash granules experimentally placed in

soil, while other corticioid species did not, indicating that the calcium content of the ground

may affect the corticioid funga. Stokland and Kauserud (2004) also showed that soil

properties may indirectly have an impact on the community of wood-inhabiting fungi: the

strictly wood-decaying polypore, Phellinus nigrolimitatus (Romell) Bourdot & Galzin was

found to be more common in high productive than in medium and low productive forests.

Stokland and Larsson (2011) concluded that rare wood-inhabiting species on spruce were

reduced by forestry to a considerably higher degree than common species, thus implicating

that species of different occurrence frequency may have different ecological requirements.

Halme and Kotiaho (2012) showed that the outcome of any study on wood-inhabiting fungi

with annual fruit bodies is heavily dependent on the time of season, at which the data

collection is performed. I have taken both of these issues into account in this master's thesis, a

study focused on Norway spruce within the project Habitat fragmentation and Pathways to

Extinction in dead-wood dependent fungi (PATHEXT), led by Jenni Nordén and Karl-Henrik

Larsson at the University of Oslo. I have aimed to analyse the ecological requirements of

corticioid fungi at a community level through the following questions:

1. How is the composition and richness of corticioid fungi regarded as all species and

Red Listed species as well as rare, occasional and common species correlated with:

– the properties of the logs they inhabit?

– the properties of the surroundings of the logs they inhabit?

2. What model – i.e. what set of variables – best explains the richness of all, Red Listed,

common, occasional and rare corticioid species, respectively?

3. Does the richness of Red Listed corticioid species vary with the richness of:

– common, occasional and rare non-Red Listed species, respectively?

– all non-Red Listed species?

6

Tab

le 1

: B

ackg

rou

nd

in

form

ati

on

of

the s

tud

y s

ites. ”D

ista

nce t

o s

mall s

ite»

den

ote

s t

he d

ista

nce b

etw

een

th

e c

en

ter

co

ord

inate

s o

f th

e s

am

ple

plo

ts in

a s

ite p

air

(F

ig.

1).

Alt

itu

de is g

iven

as t

he r

an

ge b

etw

een

th

e lo

west

an

d t

he h

igh

est

po

sit

ion

ed

su

rvey

ed

lo

g. C

od

es u

sed

to

den

ote

veg

eta

tio

n s

ecti

on

s: O

C: in

dif

fere

nt;

O1: slig

htl

y

ocean

ic;

O2: m

ark

ed

ly o

cean

ic.

Materials and methods

Study sites

The fieldwork was performed at six 200 x

200 m sample plots of the PATHEXT

project, which held 60 preselected logs each.

The plots were positioned in sites (Table 1),

and the sites were situated in pairs; each pair

constituting one small and one large site.

The large sites were nature reserves and the

small sites comprised a woodland key

habitat (Sweden) and two nature type

localities (Norway; in forest, areas similar to

woodland key habitats; see further

Direktoratet for naturforvaltning 2007)

located to south-eastern Norway and south-

western Sweden (Fig. 1). The sites were

situated in the boreonemoral and middle

boreal vegetation zones (Dahl et al. 1986;

Gusfafsson and Ahlén 1996) and in the

indifferent (OC) to the markedly oceanic

(O2) vegetation sections (Moen 1999). The

altitude of the sample plots varied from 23

(Skee) to 569 m.a.s.l. (Rudskampen),

measured from the lowest to the highest

positioned log, respectively. All the sample

plots and great parts of the sites constituted

old, spruce-dominated forests with a lot of

dead wood (Norwegian Directorate for

Nature Management 2012; Swedish Forest

Agency 2012; personal observations). The

history of the sample plots and the sites was

varying, but all the sites have been left

OC

, O1

O2

O1

O1

O1

O2

Bo

reo

nem

ora

l

Mid

dle

bo

real

Mid

dle

bo

real

Bo

reo

nem

ora

l

Bo

reo

nem

ora

l

33

7-3

98

29

3-3

47

52

9-5

51

54

0-5

69

15

3-1

75

23

-53

17

.62

2

- 18

.16

2

- 44

.94

6

-

24

48

.1

13

.1

18

49

.9

8.2

39

2.4

5.1

Lar

ge

Sm

all

Lar

ge

Sm

all

Lar

ge

Sm

all

Ty

pe

Nat

ure

res

erv

e

Nat

ure

ty

pe

loca

lity

Nat

ure

res

erv

e

Nat

ure

ty

pe

loca

lity

Nat

ure

res

erv

e

Wo

od

lan

d k

ey h

abit

at

Sit

e (

na

me;

mu

nic

ipa

lity

; co

un

ty)

San

dal

slia

; D

ran

ged

al;

Tel

emar

k

Ru

dsk

amp

en;

Nan

nes

tad

; A

ker

shu

s

Tjø

stø

l; A

rem

ark

; Ø

stfo

ld

Sk

ee;

Str

öm

stad

; V

ästr

a G

öta

lan

d (

SE

)

Veg

eta

tion

secti

on

Veg

eta

tion

zon

e

Bo

reo

nem

ora

l, m

idd

le b

ore

al

Alt

itu

de

(m.a

.s.l

.)

Dis

tan

ce

to s

mall

sit

e (

km

)

Siz

e

(ha)

Siz

e

cla

ss

Mø

rkv

assj

uv

et;

No

me,

Dra

ng

edal

;

T

elem

ark

Sp

ålen

-Kat

no

sa;

Jev

nak

er, L

un

ner

,

R

ing

erik

e; B

usk

eru

d, O

pp

lan

d

7

Figure 1: The geographic position of the large (red) and small (blue) study sites.

untouched by the extensive cuttings of the 20th

century and in the sample plots there were few

or no visible signs of human impact (Norwegian Directorate for Nature Management 2012;

Swedish Forest Agency 2012; personal observations).

Field methods

At every sample plot I selected four logs each in the decay classes 3, intermediately

decomposed, or 4, well decomposed (for the decay classification, see Hottola and Siitonen

2008), with a diameter at breast height (DBH) in the range of 20 to 40 cm and a length of at

least eleven meters. The field work team measured the DBH and the total length of the logs.

We visually estimated the degree of bark and plant coverage and ground contact for each log

and noted whether they had fallen down due to breakage or uprooting. We also checked

8

Table 2: the

explanatory variables.

whether the tops had been broken or remained unbroken. Tops broken above the point where

the logs were five cm in diameter were still counted as unbroken. I identified the vegetation

types (see below) present within a distance of approximately two metres to each side of the

logs according to the older of the two standard systems used in Norway (Fremstad 1997). The

identification was made to the lowest level possible – in nearly all cases to subtype. We

carefully surveyed the logs for fruit bodies of corticioid fungi, collecting specimens of the

great majority that we could not identify in the field. We visually estimated the hymenophore

area of the polypores present, counting both living and dead fruit bodies. The field work was

conducted during October and early November 2011. We noted the survey date of each log to

account for seasonal effects (see further Halme and Kotiaho 2012).

Explanatory variables

In addition to the variables obtained directly in the field and site

size (Table 1), I chose to regard the effect of vegetation types in

four different ways and to calculate the volume and surface area

of the included logs, thence making the number of explanatory

variables 17 (Table 2).

By vegetation types I refer in this thesis to different types of

spruce-containing forests, swamps and fens separated by the

presence, absence and dominance of certain species of vascular

plants, bryophytes and lichens, in all vegetation layers (see further

Fremstad 1997). Halvorsen et al. (2009) showed that vegetation

types in forests are clearly defined by the combination of three

different ecoclines. I therefore choose to analyse the effect of

these rather than of the vegetation types per se. The calcium

content of the ground turned out to be the only ecocline with

enough variation to be analysed. I did so classifying it into three

different scales: a fine ordered scale (range 1-5), a coarse ordered

scale (range 1-3) and as presence/absence of richer vegetation

types. The rich vegetation types include: low herb woodland, tall

fern woodland, tall herb Norway spruce forest and rich swamp

woodland; the vegetation types belonging to the three highest

categories of Halvorsen’s et al. (2009) five-point scale of calcium

content. I also included the number of vegetation types as a variable.

Explanatory variables

DBH

Length

Volume

Surface area

Log type

Decay class

Bark coverage

Plant coverage

Ground contact

Calcium, fine scale

Calcium, coarse scale

Presence of richer vegetation types

Number of vegetation types

Top intactness

Polypore hymenophore area

Site size

Time of survey

9

To calculate the volume of the unbroken logs (the logs whose tops remained unbroken) I used

the formula of Laasasenaho (1982):

V = 0.022927·d1.91505

·0.99146d·h

2.82541·(h-1.3)

-1.53547/1000

where d is the DBH in cm, and h is the length in metres.

To calculate the volume of the broken logs I first used a linear model to see if the DBH of

the unbroken logs explained their length. I included all unbroken logs among the 60 logs

present at each sample plot in the PATHEXT data set and found unbroken log length to be

significantly explained by DBH at all plots but Mørkvassjuvet. Through linear regression I

could thus predict the length the broken logs had while they were still unbroken and

subsequently also calculate their unbroken volume, using the same formula as before. I

deduced the volume of the missing tops of the broken logs, assuming that they and the entire

logs, while unbroken, could be seen as parts of the same cone. I then subtracted the volume of

the top from the volume of the log. All these steps can be summarised as the formula:

V = (0.022927·d1.91505

·0.99146d·h

2.82541·(h-1.3)

-1.53547/1000)-(1/3·(d/200·(h-hbroken)/(h-

1.3))2·(h-hbroken)·π)

where V is the volume of the broken log, d is the DBH in cm, h is the predicted length of the

unbroken log and hbroken is the length of the broken log. The few broken logs that were longer

than their model-predicted full lengths were treated as unbroken logs, using their actual

lengths as full lengths.

To calculate the surface area of the logs I used the equation of the surface area of a cone,

subtracting the area of the top from the ones that were broken.

Studied taxa and species identifications

The study was mainly limited to corticioid fungi in a wide sense Appendix 1), i.e. apart from

species with effused, smooth to spiny fruit bodies, also including such whose fruit bodies

consist of separately growing spines (e.g. Mucronella Fr. spp. and Henningsomyces candidus

(Pers.) Kuntze). All specimens with spiny fruit bodies were thus included and identified to

species level. Specimens belonging to the genera Calocera (Fr.) Fr., Dacrymyces Nees,

Pseudotomentella Svrcek and Tomentella Pers. ex Pat. were included but only identified to

genus level (with the exception of the spiny T. fibrosa (Berk. & M.A. Curtis) Kõljalg).

10

Corticioid Heterobasidiomycetes were otherwise excluded, as were all Ascomycetes, with the

exception of Sebacina calcea (Pers.) Bres. and Camarops tubulina (Alb. & Schwein.) Shear –

two easily identified species. Due to doubts about the correctness of the stricter species

delimitations of Athelia epiphylla Pers., Litschauerella clematitis (Bourdot & Galzin) J.

Erikss. & Ryvarden, Thanatephorus fusisporus (J. Schröt.) Hauerslev & P. Roberts and

Tubulicrinis borealis J. Erikss., I chose to treat these species in broader concepts than what is

commonly recognised. I included specimens belonging to undescribed but clearly distinct

species in the analyses and assigned them names consisting of the genus name – if known –

and a number (e.g. Hyphodontia J. Erikss. sp. 1). The many specimens identified as

Botryobasidium candicans J. Erikss. most certainly belong to a separate, undescribed species,

as B. candicans is a species limited to deciduous wood (Eriksson and Ryvarden 1973), but

since they otherwise fit the species description well, and for want of a better name, I chose to

include them under B.candicans. I encountered specimens of Botryobasidium aureum

Parmasto or B. conspersum J. Erikss. or B. ellispsosporum Hol.-Jech. lacking their respective

anamorphs, and since these three species then cannot be separated I treated them as B. vagum

group indet.

I identified the specimens using a 300-1600x phase contrast microscope, a 50x stereo loupe

and by sight. The nomenclature follows the Species Nomenclature Database (Artsdatabanken

2012).

Species categories

To assign species to the frequency categories common, occasional and rare I directly used the

categories and classification provided in Stokland and Larsson (2011) for the major part of the

species. Their study covered roughly the same geographical area as mine, and their material is

the largest in Norway so far, with collections of polypores and corticioids from 1138 spruce

logs and 992 pine Pinus sylvestris L. logs from 90 managed and 34 natural or near natural

spruce or pine forests. Stokland and Larsson (2011) classified a species as rare if it was found

on 10 or fewer of their logs, occasional if it was found on 11-30 logs, and common if it was

found on more than 30 logs. With the exception of a few species that are deficiently known

(K.-H. Larsson, pers. comm.), I treated all officially described corticioid species absent from

their material as rare. I assigned the following taxa and groups of species to no category:

deficiently known species, Heterobasidiomycetes, Calocera spp., Mucronella spp.,

Henningsomyces candidus, Sphaerobolus stellatus Tode, Camarops tubulina and undescribed

species with the exception of Hyphoderma velatum K.-H. Larsson ad int (undescribed species

11

with an interimistic name) and Trechispora minuta K.-H. Larsson ad int. The last two were

instead considered rare (K.-H. Larsson, pers. comm.).

The Red List status follows The 2010 Norwegian Red List for Species (Kålås et al. 2010).

Generalised linear mixed-effects models (GLMM) analysis

To test the explanatory variables against each of the five species richness measures chosen as

response variables (see Introduction), and find the best models explaining these richnesses, I

used a generalised linear mixed-effects models (GLMM) analysis. It was also used to test the

richness of non-Red Listed common, occasional and rare species as well as the richness of all

non-Red Listed species against the richness of Red Listed species. As the response variables

were discrete, I applied a Poisson distribution. Site (n = 6) and site pair (n = 3) were included

as a nested random effect – i.e. site nested in site pair – to control for repeated measurements

within each site and site pair, and also to control for possible variation associated with each

site and site pair, respectively. Polypore hymenophore area was log10-transformed due to great

variation in the variable values.

To find the best models explaining the species richness groups studied, I tested every

possible combination of all explanatory variables against each response variable and ordered

the models according to their goodness of fit, as measured by the Akaike information criterion

(AIC). The models with the lowest AIC were considered to have the highest goodness of fit.

Applying a parsimonious approach (Burnham and Anderson 1998; 2002), I then added 2.0

AIC units to all models including more explanatory variables than one, and regarded the

model with the lowest AIC thus yielded as the best. In the cases where the best model

constituted a set of several explanatory variables, I also tested for all possible interactions of

those.

When performing the model selection tests I could not include correlated explanatory

variables. From a selection of such I therefore selected the one which resulted in the lowest

AIC-value together with the response variable. All log size measures – length, DBH, volume

and surface area – were correlated except for length and DBH, why either those two or

volume or surface area were included in the same test. All three calcium content variables

were also correlated.

Due to the moderate size of the data set and the big number of explanatory variables, some

variable combinations yielded singular convergence, and were considered unreliable. All taxa

12

that were clearly separate, at the level of individual logs, were included in the analysis

(Appendix 1).

For the GLMM, I utilised the function lmer(), in the R package lme4 (Bates et al. 2011).

Non-metric multidimensional scaling (NMDS) analysis

To analyse the relationship between the species composition – regarded as all and Red Listed

species as well as rare, occasional and common species – and the properties of the logs and

their surroundings, I performed a two-dimensional non-metric multidimensional scaling

(NMDS) analysis. NMDS is considered a good method to apply when analysing the effects of

many and possibly intercorrelated variables (Oksanen et al. 2012) on species communities. In

contrast to GLMM it takes the individual species identities into account.

I localised the logs in the ordination space according to their pairwise dissimilarities in

species composition. For this I used the Jaccard dissimilarity index (Oksanen et al. 2012), as it

was the one that performed the best when different indices were compared. I then plotted the

species on the average distance of their host trees' locations. I square-root transformed

polypore hymenophore area, due to great variation in the values of this variable. Subsequently

I fitted it and the other 16 explanatory variables (Table 2), site and site pair – in order to

control for possible variation they could be associated with – and the richness of Red Listed

and non-Red Listed species in the ordination space in the way that provided the maximum

correlation between each of them and the log scores (Oksanen et al. 2012). I then used a

permutation test with 1000 permutations to find out whether the fitted variables were non-

randomly distributed in the ordination space.

In the NMDS plot I decided to use isoclines to show the probability of a species being Red

Listed in different parts of the ordination space. As the real threat situation for species

belonging to the category DD is very unclear (they would with better knowledge be assigned

to any of the categories LC, NT, VU, EN or CR; Artsdatabanken 2009) I drew the isoclines

both including and excluding them from the Red Listed species.

All taxa that were clearly separate, at the level of the data set, were included (Appendix 1).

Hence the NMDS analysis spanned five species less than the GLMM.

For the NMDS, I utilised the R package vegan (Oksanen et al. 2012).

All statistical analyses and the volume calculations were performed in the statistical

programming environment R, version 2.15.0 (R Development Core Team 2012).

13

Estimate AIC

All species

Intercept 2.344 0.147 15.918 < 2e-16 48.54

Surface area 0.061 0.017 3.604 0.0003

Intercept 2.068 0.208 9.937 <2e-16 48.89

DBH 0.028 0.007 3.838 0.0001

Intercept 2.524 0.105 23.960 <2e-16 49.85

Volume 0.645 0.190 3.405 0.0007

Intercept 2.363 0.174 13.571 <2e-16 52.30

Length 0.031 0.011 2.945 0.0032

Intercept 2.890 0.054 53.760 <2e-16 55.74

Type (uprooted) -0.187 0.083 -2.240 0.0250

Intercept 2.772 0.066 41.820 <2e-16 57.85

Site size (small) 0.138 0.070 1.980 0.0477

Redlisted species

Intercept -1.302 0.621 -2.098 0.0359 59.49

Non-Red Listed species 0.081 0.035 2.341 0.0192

Intercept 0.288 0.200 1.441 0.1495 60.08

Decay class (3) -0.614 0.299 -2.056 0.0398

Common species

Intercept 2.128 0.170 12.522 <2e-16 49.17

Surface area 0.046 0.020 2.286 0.0223

Intercept 2.251 0.119 18.866 <2e-16 49.25

Volume 0.507 0.223 2.273 0.0230

Intercept 1.886 0.247 7.640 2.17e-14 57.87

DBH 0.022 0.009 2.549 0.0108

Occasional species

Intercept -1.421 0.513 -2.771 0.0056 61.65

Surface area 0.209 0.055 3.779 0.0002

Intercept -0.741 0.362 -2.046 0.0408 63.97

Volume 2.117 0.609 3.475 0.0005

Intercept -1.984 0.747 -2.654 0.0079 64.69

DBH 0.081 0.025 3.287 0.0010

Intercept -1.373 0.565 -2.430 0.0151 65.97

Length 0.109 0.033 3.316 0.0009

Intercept -0.250 0.341 -0.731 0.4647 71.13

Polypore hymenophore area 0.115 0.057 2.032 0.0422

Standard

errorZ value P value

Table 3: The variables significantly explaining the richness of all, Red Listed, common and occasional species.

The richness of rare species was not explained by any variable and is hence not included.

14

Results

General

The 48 logs of the survey yielded a total of 827 species occurrences (Appendix 1). These were

identified to146 different taxa; 136 officially described and ten undescribed species. The

species (or rather taxon) richness varied between 7 and 31 per log with a mean of 17.2 ± 4.5

(S.D). Twenty-two of the species were Red Listed, with a species richness varying between 0

and 4 per log and a mean of 1.1 ± 1.1 (S.D.). Red Listed species occurred only in the

frequency groups rare and occasional.

Determinants of species richness

The richness of all, common and occasional species were positively correlated with log size

(Table 3): they all increased with the size measures DBH, surface area and volume, and the

richness of all and occasional species also with length. The richness of all species was also

correlated with log type and site size, broken logs and logs at small sites having significantly

more species than uprooted logs and logs at large sites, respectively. The number of

occasional species increased with the area covered by polypore hymenophores.

There were more Red Listed species on logs in decay class 4 than on logs in decay class 3

(Table 3). The richness of Red Listed species also increased with the richness of non-Red

Listed species (Fig. 2).

6 8 10 12 14 16 18 20 22 24 26

0

1

2

3

4

Richness of non-Red Listed species

(species/log)

Ric

hn

ess

of

Re

d L

iste

d s

pe

cie

s

(sp

ecie

s/lo

g)

Figure 2: The richness of Red Listed species in relation to the richness of non-Red Listed

species. GLMM prediction (Table 3) shown as a line.

15

Estimate AIC

All species

Intercept 1.891 0.243 7.789 6.77e-15 43.07

Length 0.025 0.011 2.352 0.0187

DBH 0.022 0.007 2.989 0.0028

Type (uprooted) -0.192 0.084 -2.292 0.0219

Redlisted species

Intercept 0.562 0.192 2.930 0.0034 55.04

Bark -2.592 1.119 -2.316 0.0206

Decay class (3) -0.512 0.298 -1.721 0.0852

Standard

errorZ value P value

The most parsimonious models explaining the species richness in the different species

categories were quite consistent with the significance of the single variables (Tables 3 and 4);

the richness of all species was best predicted by length+DBH+log type (Fig. 3). It increased

with DBH and length, and broken logs had more species than uprooted logs. The most

parsimonious models for common (Fig. 4) and occasional (Fig. 5) species included only DBH

and surface area respectively. Albeit the number of Red Listed species present on a log could

not be significantly predicted by bark coverage alone –due to singular convergence (see

Materials and methods) – the combination bark coverage+decay class turned out to provide

the best model for it (Fig. 6). The model showed that the richness of Red Listed species

increased with decreasing bark coverage and that there were more species on logs in decay

class four than in decay class three.

The richness of rare species was not explained by any explanatory variable and the most

parsimonious model was non-significant. The ten most parsimonious models of each analysed

species group are listed in Appendix 2.

Table 4: The most parsimonious models predicting the richness of all and Red Listed species (Figs. 3 and 6

respectively). The richness of common and occasional species were most parsimoniously predicted by single

variables, and are thence presented in Table 3.

16

Figure 3: The most parsimonious model predicting the richness of all species

on broken (a) and uprooted (b) logs (Table 4).

17

20 22 24 26 28 30 32 34 36 38

0

5

10

15

20

25

DBH (cm)

Ric

hn

ess

of

co

mm

on

sp

ecie

s

(sp

ecie

s/lo

g)

4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5

0

1

2

3

4

Ric

hn

ess

of

occa

son

al

spe

cie

s

(sp

ecie

s/lo

g)

Figure 4: The most parsimonious model describing the richness of common species. GLMM

prediction (Table 3) shown as a line.

Figure 5: The most parsimonious model describing the richness of occasional species. GLMM

prediction (Table 3) shown as a line.

Surface area (m2)

18

Ordination of species composition and correlated variables

Six of the 17 explanatory variables tested (Table 2) and the richness of Red Listed species

correlated with the ordination configuration of the NMDS (Table 5; Fig. 7); ground contact

and plant coverage showed the strongest correlations, followed by richness of Red Listed

species, bark coverage, log type, polypore hymenophore area and decay class.

Logs in decay class 4 had more ground contact and plant coverage but less area covered by

polypore hymenophores and bark than logs in decay class 3 (Fig. 7). Uprooted logs had more

bark coverage than broken logs.

The number of Red Listed species per log was positively correlated with ground contact

and negatively correlated with bark coverage (Fig. 7). There were more Red Listed species

per log on broken than on uprooted logs.

The Red Listed species were concentrated to logs in decay class 4, with much ground

contact and plant coverage but little bark coverage and polypore hymenophore area (Fig. 7).

More precisely, the direction of increasing probability of a species being Red Listed as NT,

VU and EN was very similar to the direction of increasing plant coverage (Fig 7a). This

probability increased also with ground contact, decreasing polypore hymenophore area and

was higher for decay class 4 than 3. The species Red Listed as Data Deficient turned out to

respond differently than the other Red Listed species (Fig. 7b).

0 10 20 30 40 50 60 70 80 90

0

1

2

3

4

5

Bark coverage (%)

Ric

hn

ess

of

Re

d L

iste

d s

pe

cie

s

(sp

ecie

s/lo

g)

Figure 6: The most parsimonious model predicting the richness of Red Listed species on

logs in decay class 3 (blue) and 4 (green). GLMM predictions (Table 4) shown as lines.

19

Though distributed to a great variation of log characteristics, as measured by the significant

variables of the ordination, the rare species displayed the same pattern as the Red Listed

species (Fig. 7); they were concentrated to logs in decay class 4, with much ground contact

and plant coverage but little bark coverage and polypore hymenophore area. A number of rare

species also seemed to prefer logs with a high richness of Red Listed species.

Common species were overrepresented on logs with little ground contact and plant

coverage (Fig. 7) but much bark coverage and polypore hymenophore area, and on logs with

intermediate values on these variables.

Variable P(>r)

Ground contact 0.3957 <0.001

Plant coverage 0.3718 <0.001

Log type 0.1241 0.003

0.2153 0.006

Decay class 0.0899 0.011

Bark coverage 0.1563 0.017

0.1205 0.043

0.1060 0.071

Site size 0.0523 0.087

DBH 0.0663 0.229

Location 0.0535 0.291

0.0523 0.313

Site 0.1242 0.316

Calcium, coarse scale 0.0492 0.332

Volume 0.0288 0.518

Top intactness 0.0137 0.548

Surface area 0.0249 0.559

0.0113 0.587

Calcium, fine scale 0.0673 0.645

Time 0.0099 0.813

Length 0.0048 0.894

r2

Richness of Red Listed species

Polypore hymenophore area

Richness of non-Red Listed species

Number of vegetation types

Presence of richer vegetation types

Table 5: The coefficients of determination and

empirical p-values assessing the goodness of fit and

significance of the explanatory variables (Table 2), the variables

site and site pair and the richness of Red Listed and non-Red

Listed species fitted onto the NMDS ordination space (Fig. 7).

20

a) b)

Figure 7: Two-dimensional NMDS plots of the studied taxa, variables and logs significantly

correlating with the ordination configuration (Table 5). The symbols and their colors

indicate frequency and Red List status, respectively, of the taxa. Isoclines show how the

probability of a species being Red Listed in all categories, excluding (a) or including (b)

DD, changes in the ordination space. Explanatory variables and the richnesses of Red Listed

(RL) species are shown in black; continuous variables as vectors and categorical variables

by their centroids.

21

Discussion

Basing an inventory of corticioid fungi on fruit bodies collected during just one season may

be problematic. Halme and Kotiaho (2012) concluded that surveys of corticioid fungi should

preferably be performed at least at three consecutive occasions, in order to be give a good

representation of the funga of the studied logs. However, even with fruit body data from only

one season I think that some of the results are quite clear and worthy of discussion.

The mean number of species per log (17.2) was higher in my study than in previous studies

of wood-inhabiting fungi on Norway spruce in the taiga region (Renvall 1995; Lindblad 1998;

Edman et al. 2004; Berglund et al. 2005; Juutilainen et al. 2011; Olsson et al. 2011; Stokland

and Larsson 2011). The highest number occurring is that of Lindblad's study (1998), which

states a mean richness of less than 11species per log, on logs in decay class 2 in a natural

forest. In order to make a correct comparison of results two things may have to be taken into

account though: the delimitation of the surveyed decay classes and the size of the studied

logs; my study only included logs in decay class 3 and 4, 20-40 cm in DBH and at least 11 m

long. None of the studies initially mentioned in this paragraph have included logs with both

their lower limits for log size set as high as mine and few of them have presented separate

results for decay class 3 and 4. The most similar studies, however, seems to be Renvall (1995)

and Stokland and Larsson (2011). Renvall (1995) surveyed logs longer than 1.5 m, with a

base diameter exceeding 10 cm, and displayed separate results for decay class 3 and 4. He

found the logs of both classes to have a mean richness of slightly more than 4 species per log.

Stokland and Larsson (2011) surveyed logs longer than 60 cm, with a maximum diameter

larger than 10 cm and displayed separate results based on both decay class and diameter. In

decay class 3 their logs had a mean species richness of 7.9 and 9.1 species per log, for logs

with a maximum diameter between 20 and 30 cm, and more than 30 cm, respectively.

Corresponding values for logs in decay class 4 and 5 – which were combined – were 6.3 and

7.9 species per log. Whether the cause of the high mean species richness I acquired is the high

quality of the sample plots, the large size of the logs or the thorough survey remains unclear,

but it is evidently clear that coarse woody debris (CWD) can harbour more corticioid species

per log than what has previously been shown - a matter perhaps not very surprising given the

small amount of substrate many corticioid species seem to cope with (Nordén et al. 2004;

Juutilainen et al. 2011).

As expected, larger logs had a greater total species richness and held more common and

occasional species than smaller logs. Also Renvall (1995), Høiland and Bendiksen (1997),

22

Lindblad (1998) and Stokland and Larsson (2011) found a positive correlation between the

size of investigated resource units and the number of wood-inhabiting fungi fruiting on them.

More interestingly, broken logs had a greater total species richness, held more Red Listed

species and had a different species composition than uprooted logs. Renvall (1995), found

broken logs of spruce in northern Finland to hold more and different wood-inhabiting fungi

than uprooted logs, and Heilmann-Clausen and Christensen (2003), made the same result for

Red Listed wood-inhabiting fungi on beech in Denmark. Both studies ascribed their results to

Renvall's (1995) empirically supported theory of successional pathways (see Introduction),

i.e. believed the observed differences in species richness and composition between uprooted

and broken logs, to be caused by the outcome of separate and distinct chains of species

interactions, initially determined by a difference in the funga of primary decayers that

colonises the two log types. Mine and Heilmann-Clausen and Christensen's (2003) results

concerning log type could perhaps be of interest from a conservational point of view: if a

greater percentage of the trees in small areas, compared to large areas, become uprooted as a

result of heavier impact by strong wind on small areas, then these areas would suffer a greater

loss of Red Listed species – at least when measured as species per log.

Uprooted logs had more bark coverage than broken logs, and logs with more bark coverage

held less Red Listed species and had a different species composition than logs with less bark

coverage. The only earlier study which has found a relationship between any of these

variables made a result contradictory to mine: Lindblad (1998) discovered a positive

correlation between total species richness of wood-inhabiting fungi and bark coverage. A

theory to why the uprooted logs had more bark coverage than the broken logs is that the

former probably died and fell healthy, with all their bark still on, whereas the latter possibly

got broken because they were dead or dying and had parts of their bark removed already

while they were still standing.

Logs in decay class 4 held more Red Listed species and had a different species

composition than logs in decay class 3. A number of studies have shown that Red Listed

corticioid fungi exhibit species-specific differences in their use of decay classes (Renvall

1995; Høiland and Bendiksen 1997; Lindblad 1998; Stokland and Larsson 2011) but none

have shown this entire group of fungi to vary in species richness between decay classes.

Tikkanen et al. (2006), however, found boreal, Red Listed (following Rassi et al. 2001),

aphyllophoraceous fungi in Finland to most often utilise logs in decay class 3 as their primary

substrate, on a scale from 1 to 5, with addition of the class kelo (wood dry and hard, bark

lost). The second most employed logs used as primary substrate belonged to decay class 4,

23

followed by classes 2, kelo and 1. No species was found to primarily live on logs in decay

class 5. The difference between mine and Tikkanen's et al (2006) results could be caused by

many factors, but dissimilar inclusiveness of data (aphyllophoraceous fungi inhabiting logs of

boreal tree species vs. corticioid fungi inhabiting spruce logs), geographical areas or methods

(different decay classification) are probably the most likely candidates.

Logs at small sites held more species than logs at large sites (marginally significant). The

direction of this correlation is surprising but my data include only three site pairs and the

effect of chance or variables not included might therefore be considerable. According to

previous studies dead wood amount (Lonsdale et al. 2008; Junninen and Komonen 2011;

Stokland and Larsson 2011), continuity (Lonsdale et al. 2008; Junninen and Komonen 2011)

and connectivity (Jönsson et al. 2008; Lonsdale et al. 2008; Junninen and Komonen 2011;

Nordén et al. 2012; Norros et al. 2012) have all proved to affect species richness of wood-

inhabiting fungi positively. However, viewed more in general, small sites with more edges

(ecotones) can also be rich in species (Leopold 1933; Odum 1983; Walker et al. 2003; Senft

2009).

Logs with more polypore hymenophore area had a greater richness of occasional species

and a different species composition than logs with less polypore hymenophore area. A

possible explanation may be that these corticioid species are specialised in degrading wood

partially decomposed by certain polypores, or live of the living or dead polypore mycelia. For

example, some species of Sistotrema Fr., seem to favour growing on dead polypores (K.-H.

Larsson 2012, pers.comm.).

Logs with more ground contact held more Red Listed species and had a different species

composition than logs with less ground contact. Lindblad (1998) reported ground contact to

be the variable the strongest correlated with species richness of wood-inhabiting fungi on the

spruce logs of her study. Heilmann-Clausen and Christensen (2003) found also the species

richness of wood-inhabiting fungi on beech logs to increase with ground contact. Lindblad

(1998) argued that it was the increased moisture content of a log when it comes in contact

with ground, as found by Harmon et al. (1986), that caused the increase in species richness.

The moisture content of logs have indeed been found to be directly correlated with the wood-

inhabiting funga, but rather in the form of species composition (Boddy et al. 1989), not

richness. An alternative hypothesis was suggested by Heilmann-Clausen and Christensen

(2003): "Logs with a high degree of soil contact are likely to be buffered against fluctuations

in temperature and especially water content compared to logs with little soil contact". Yet

24

another idea might be to search for the cause in the characteristics of the species, not the log; a

number of corticioid species, such as several of those belonging to the genera Piloderma

(Eriksson et al. 1981), Tomentella (Kõljalg et al. 2000), Amphinema P. Karst. (Larsson et al.

2004) and Tylospora Donk (Larsson et al. 2004), have been proved to be ectomycorrhizal. For

their presence to impact on the species richness and composition of a log it must needs have

contact with ground, and for the same reason their numbers will most probably also increase

with the amount of contact – at least up to a certain point.

Logs with more plant coverage had a different species composition than logs with less

plant coverage. The only study which has discovered any aspects of the wood-inhabiting

funga to be correlated with this variable before seems to be that of Lindblad (1998), who

found plant coverage to be positively correlated with species richness on her spruce logs.

Heilmann-Clausen and Christensen (2003) did not find a correlation between moss coverage

and species richness of wood-inhabiting fungi on beech. These authors did not, however,

analyse species composition in this respect. Plant coverage was positively correlated to

ground contact, and perhaps it may be that it is correlated with the moisture content and

stability of the logs.

Logs with more Red Listed species held more non-Red Listed species and had a different

species composition than logs with less Red Listed species. Berglund and Jonsson (2003)

showed that corticioid fungi on spruce exhibit nestedness, i.e. species in species-poor sites

comprise a non-random subset of the species pool in richer ones. Rare species consequently

have a tendency to occur only at the most species rich sites. The species communities of

single logs do not seem to have been analysed for patterns of nestedness in any study, but as

18 of the 22 Red Listed species in my study were classified as rare (Appendix 1), the

existence of such a relationship among corticioids would probably explain my result. It would

then also be possible to identify indicator species of species rich logs or sites.

The most parsimonious models predicting the richness of common and occasional species

included DBH and surface area, respectively. No previous studies seem to have made separate

models for species of different frequency categories and it is difficult to compare also the

models of total species richness and richness of Red Listed species to those of others, since

the set of explanatory variables included in the analyses selecting a model varies considerably

between studies. I have found two comparable studies, both of which presented models that

were more similar to each other than to mine. The best model of Lindblad (1998) – which

explained the total richness of wood-inhabiting fungi on spruce logs – included ground

contact, decay class, log length and penetration value (as obtained from a penetrometer

25

measuring log hardness). The best model of Heilmann-Clausen and Christensen (2003) –

which explained the total richness of wood-inhabiting fungi on beech – comprised ground

contact, log age, log complexity (a measure of the number of bole forks and branches thicker

than 50 cm) and ground coverage of Anemone nemorosa L. The study of Heilmann-Clausen

and Christensen (2003) included a total of 19 explanatory variables and Lindblad's study

(1998) 17. Both studies had seven variables in common with mine, and in both cases these

comprised all but one of the variables included in my best models. Heilmann-Clausen and

Christensen's (2003) study also featured a model of the richness of Red Listed species but as it

included the variables log age, log complexity and log type, it did not have much in common

with mine. Worth noticing is that my measure of decay class is not entirely comparable to

those of the other studies mentioned, since it only includes two classes. With so big

differences in both our sets of explanatory and response variables – corticioid vs. wood

inhabiting fungi – it is hard to draw any further conclusions than that there is little consistency

in the current models of species richness of wood-inhabiting and corticioid fungi.

Logs in decay class 4 had more ground contact and plant coverage but less area covered by

polypore hymenophores and bark than logs in decay class 3. These correlations can be

explained by the physical changes taking place in a log when it passes from decay class 3 to

4; it loses more bark, the amount of area covered by polypore fruit bodies on it decreases and

a greater proportion of it settles to the ground, where it gets more moist (Harmon et al.1986)

and covered by plants.

Similarly, rare and Red Listed corticioid species were concentrated to logs in decay class 4

– with much ground contact and plant coverage but little bark coverage and polypore

hymenophore area. A possible explanation to why rare and Red Listed species were found to

prefer this substrate might be derived from studying the occurrence frequency of it. According

to Kruys's et al. (1999) study from northern Sweden, the amount of dead wood of spruce in

managed forests drastically decrease with decay, between each of the medium and late decay

classes. Including all pieces exceeding 5 cm in diameter and 30 cm in length, they found only

1.6 pieces of dead wood in late decay classes per ha, accounting for 3.4 % of all the pieces

found. In Norway managed forests are reported to cover 92-95 % of the total forest area

(Stokland et al. 2003) and hold on average 10 m3 dead wood per ha (Larsson and Hylen

2007), while mesic, spruce-dominated, old-growth forests in the southern and middle boreal

zones of Fennoscandia have been found to hold 90-120 m3 dead spruce wood per ha (Siitonen

2001). Perhaps a greater decline of wood in decay class 4 than in decay class 3 has caused the

26

species which prefer decay class 4 to become rare and Red Listed to a higher extent than the

species which prefer decay class 3.

The richness of common species only increased with different measures of log size and the

richness of occasional species increased with both log size and polypore hymenophore area,

while the richness Red Listed species did not change with any measure of log size but instead

correlated with four different explanatory variables related to the quality of the logs. Red

Listed corticioid species seem to be limited by the quality rather than the size of dead wood

substrates. This is interesting and has not been shown in any previous study.

The fact that the Data Deficient species behaved differently than the other Red-Listed

species in my NMDS-configuration is something I think is worth attention and which I hope

will make others be more cautionary about the juxtapositioned nature of these species (see

Materials and methods); if one aims to draw any conclusions about species threatened by

extinction, due to rareness, population decline or a combination thereof, I think one should

make very sure that that indeed is what one has been studying. I thoroughly regret the

inclusion of Data Deficient species in the Red-listed species in the GLMM analysis of my

study, as it surely added an unnecessary amount of error to it.

Further results could have been extracted from my analyses by comparing the occurrence

patterns of individual species or groups of species. This was not done merely due to lack of

time.

Lastly I would like to put words to a question, and the subsequent personal view derived

from it, awoken by the finding of species like Repetobasidium vestitum J. Erikss. & Hjortstam

(Appendix 1) – a corticioid whose known distribution in the world is limited to a few, widely

separated logs in northern Europe (Eriksson et al. 1981) – and developed through

conversations with my supervisor Karl-Henrik Larsson: how do the very rare corticioid

species, that occur only at one or a few logs in the small number of forest where they are

found, manage to survive? It strikes me as unreasonable that their occurrence could be traced

only to very specific demands on general environmental variables, such as the ones analysed

in this study; no niche can be that immensely rare, and even if it were: how would a sufficient

amount of spores be able to travel between across so widely separated substrates?

27

Conclusions and implications for conservation

This study has that even within a single, narrowly defined kind of dead wood, a great

variation in microenvironments exists, and that many of these environments host differently

composited and species rich communities of corticioid fungi. Numerous questions also remain

to be answered concerning species diversity and coexistence in dead wood. The number of

species found on logs in this study was clearly higher than what has previously been reported

in any studies of similar substrates, further underscoring the importance dead wood has for

biodiversity.

The rare species found in this study needed many different kinds of logs, and hence a

diversity of dead wood is required to host them. Both the rare and the Red Listed species

needed well decayed logs, a substrate that today can almost only be found in the most natural

forests. Large areas of natural spruce forest have to be protected to harbour also the rarest

corticioid species.

Acknowledgements

First and foremost I want to thank my three supervisors! Jenni Nordén, I am very grateful for

your enthusiasm, for our happy moments in the forest and by the microscope and for the great

amount of work you have laid in me and my project all along. Karl-Henrik Larsson, I am

most thankful for the support you have lent me to structure my work, for our conversations of

interesting things great and small and for being my mentor in the part I liked the best: the

understanding and identification of corticioid fungi. Björn Nordén, I thank you for your

optimism and your solutions, for our interesting discussions and generally for seeing me

through this. I also want to very much thank Ronny Steen: without your help I would have

spent considerably more hours with the statistics, a discipline you slowly made start to like

and begin to understand. I want to thank Johan Rydlöv, Vidar Brodin, Anna Norberg and

Anders Aas for help and support during my field work. I am grateful to Anders Grönberg for

his good grammatical aid and to my mother for the fine map. Last, but not least, I want to

thank my friend Olli Manninen, without whose initial inspiration this would not have come to

pass.

The subsistence allowance during the field work of my thesis was partly funded by the P.

A. Larsson foundation. For this I am very thankful.

28

References

Artsdatabanken. 2009. Norsk rødliste 2010 – Veileder til rødlistevurdering, versjon 2.1. Artsdatabanken,

Trondheim.

Artsdatabanken. 2012. Norwegian Species Nomenclature Database, version 180311 250412, accessed

30 April 2012.

Bader P., Jansson S. and Jonsson B. G. 1995.Wood-inhabiting fungi and substratum decline in selectively

logged boreal spruce forests. Biological Conservation. 72: 355-362.

Bates D., Maechler M. and Bolker B. 2011. lme4: Linear mixed-effects models using S4 classes, R package

version 0.999375-42.

Berglund H. and Jonsson B. G. 2003. Nested plant and fungal communities; the importance of area and habitat

quality in maximizing species capture in boreal old-growth forests. Biological Conservation. 112: 319-328.

Berglund H., Edman M., and Ericson L. 2005. Temporal variation of wood-fungi diversity in boreal old-growth

forests: implications for monitoring. Ecological Applications. 15: 970-982.

Boddy L., Owens E. M. and Chapela I.H. 1989. Small scale variation in decay rate within logs one year after

felling: Effect of fungal community structure and moisture content. FEMS Microbiology Letters. 62: 173-

183.

Burnham K. P. and Anderson D. R. 1998. Model selection and inference: a practical

information-theoretic approach. - Springer, New York. 353 pp.

Burnham K. P. and Anderson D. R. 2002. Model selection and multimodel inference: a practical information-

theoretic approach, 2nd edition. - Springer, New York. 488 pp.

Dahl E., Elven R., Moen A. and Skogen A. 1986. Vegetasjonskart over Norge 1 : l 500 000, Nasjonalatlas for

Norge. - Statens kartverk, Hønefoss.

Direktoratet for naturforvaltning. 2007. Kartlegging av naturtyper - Verdisetting av biologisk mangfold.

DN-håndbok 13 2. utgave. - Direktoratet for naturforvaltning, Trondheim. 254 pp.

Edman M., Kruys N. and Jonsson B. G. 2004. Local dispersal sources strongly affect colonization patterns of

wood-decaying fungi on spruce logs. Ecological Applications. 14: 893-901.

Eriksson J. and Ryvarden L. 1973.The Corticiaceae of North Europe, Vol. 2, Pp: 59-286. - Fungiflora, Oslo.

29

Eriksson J., Hjortstam K. and Ryvarden L. 1981. The Corticiaceae of North Europe, Vol. 6, Pp: 1048-1246. -

Fungiflora, Oslo.

Fremstad E. 1997. Vegetasjonstyper i Norge. NINA temahefte. 12: 1-279.

Gjerde I., Brandrud T. E., Ohlson M. and Ødegaard F. 2009. Skoglandskapet – Miljøforhold og påvirkninger på

rødlistearter. Artsdatabanken, Trondheim.

Gustafsson L., and Ahlén I. 1996. Sveriges Nationalatlas, växter och djur, vegetationszoner i Sverige. - Sveriges

Nationalatlas, Stockholm. 160 pp.

Halme P. and Kotiaho J. S. 2012. The importance of timing and number of surveys in fungal biodiversity

research. Biodiversity and Conservation. 21: 205-219.

Halvorsen R., Andersen T., Blom H. H., Elvebakk A., Elven R., Erikstad L., Gaarder G., Moen A., Mortensen

P. B., Norderhaug A., Nygaard K., Thorsnes T. and Ødegaard F. 2009. Naturtyper i Norge (NiN), version

1.0.0. - Artsdatabanken, Trondheim. , accessed 27 April 2012.

Harmon M. E., Franklin J. F., Swanson F. J., Sollins P., Gregory S. V., Lattin J. D., Anderson N. H., Cline S. P.,

Aumen N. G., Sedell J. R., Lienkaemper G. W., Jr. Cromack K. and Cummins K. W. 1986. Ecology of

coarse woody debris in temperate ecosystems. Advances in Ecological Research. 15:133-302.

Heilmann-Clausen J. and Christensen M. 2003. Fungal diversity on decaying beech logs – implications for

sustainable forestry. Biodiversity and Conservation. 12: 953-973.

Heilmann-Clausen J. and Christensen M. 2004. Does size matter? On the importance of various dead wood

fractions for fungal diversity in Danish beech forests.Forest Ecology and Management. 201: 105-117.

Hjortstam K., Larsson K.-H. and Ryvarden L. 1988. The Corticiaceae of North Europe, Vol. 1, pp. 1-58. -

Fungiflora,Oslo.

Høiland K. and Bendiksen E. 1997. Biodiversity of wood-inhabiting fungi in a boreal coniferous forest in Sør-

Trøndelag County, Central Norway. Nordic Journal of Botany. 16: 643-659.

Hofton T. H. 2010. Corticiaceae assessments for the 2010 Norwegian Red List, xl-sheet. BioFokus, Oslo.

Unpublished.

Hottola J., Siitonen J. 2008. Significance of woodland key habitats for polypore diversity and red-listed species

in boreal forests. Biodiversity and Conservation. 17: 2559-2577.

30

Jönsson M. T., Edman M. and Jonsson B. G. 2008.Colonization and extinction patterns of wood-decaying fungi

in a boreal old-growth Piceaabies forest. Journal of Ecology. 96: 1065-1075.

Junninen K. and Komonen A. 2011. Conservation ecology of boreal polypores: A review. Biological

Conservation. 144: 11-20.

Juutilainen K., Halme P., Kotiranta H. and Mönkkönen M. 2011. Size matters in studies of dead wood and wood-

inhabiting fungi. Fungal Ecology. 4: 342-349.

Kålås J.A., Viken Å., Henriksen S. and Skjelseth, S. (eds.). 2010. The 2010 Norwegian Red List for Species. -

Norwegian Biodiversity Information Centre, Norway. 480 pp.

Kõljalg U., Dahlberg A., Taylor A. F. S., Larsson E., Hallenberg N., Stenlid J., Larsson K. H., Fransson P. M.,

Kårén O. and Jonsson L. 2000. Diversity and abundance of resupinate thelephoroid fungi as ectomycorrhizal

symbionts in Swedish boreal forests. Molecular Ecology. 9: 1985-1996.

Küffer N., Gillet F., Senn-Irlet B., Aragno M. and Job D. 2008. Ecological determinants of fungal diversity on

dead wood in European forests.Fungal Diversity. 30: 83-95.

Küffer N. and Senn-Irlet B. 2005. Diversity and ecology of wood-inhabiting aphyllophoroid basidiomycetes on

fallen woody debris in various forest types in Switzerland. Mycological Progress. 4: 77-86.

Kruys N., Fries C., Jonsson B. G., Lämås T. and Ståhl G. 1999. Wood-inhabiting cryptogams on dead Norway

spruce (Picea abies) trees in managed Swedish boreal forests. Canadian Journal of Forest Research. 29: 178-

186.

Laasasenaho J. 1982. Taper curve and volume function for pine, spruce and birch. Communicationes Instituti

Forestalis Fenniae. 108: 1–74.

Larsson J. Y. and Hylen G. 2007. Skogen i Norge. Statistikk over skogforhold og skogressurser i Norge

registrert i perioden 2000 – 2004. Viten fra Skog og landskap. 1/07. - Norsk institutt for skog og landskap,

Ås. 91 pp.

Larsson K.-H., Larsson E. and Kõljalg U. 2004. High phylogenetic diversity among corticioid

homobasidiomycetes. Mycological Research. 108: 983-1002.

Leopold, A. 1933. Game management. - Charles Scribner’s Sons, New York. 481 pp.

31

Lindblad I. 1998. Wood-inhabiting fungi on fallen logs of Norway spruce: relations to forest management and

substrate quality. Nordic Journal of Botany. 18: 243-255.

Lindhe A., Asenblad N., Toresson H. G. 2004. Cut logs and high stumps of spruce, birch, aspen and oak – nine

years of saproxylic fungi succession. Biological Conservation. 119: 443-454.

Lonsdale D., Pautasso M. and Holdenrieder O. 2008. Wood-decaying fungi in the forest: conservation needs and

management options. European Journal of Forest Research. 127: 1-22.

Mahmood S., Finlay R. D., Erland S and Wallander H. 2001. Solubilisation and colonisation of wood ash by

ectomycorrhizal fungi isolated from a wood ash fertilised spruce forest. FEMS Microbiology Ecology. 35:

151-161.

Moen, A. 1999.National Atlas of Norway: Vegetation. - Norwegian Mapping Authority, Hønefoss. 200 pp.

Nordén B., Ryberg M., Götmark F., Olausson B. 2004. Relative importance of coarse and fine woody debris for

the diversity of wood-inhabiting fungi in temperate broadleaf forests. Biological Conservation.117: 1-10.

Nordén J., Penttilä R., Siitonen J., Tomppo E. and Ovaskainen O. 2012. Specialist species of wood-inhabiting

fungi struggle while generalists thrive in fragmented boreal forests. Submitted paper.

Norros V., Penttilä R., Suominen M. and Ovaskainen O. 2012. Dispersal may limit the occurrence of specialist

wood decay fungi already at small spatial scales. Oikos. 121: 961-974.

Norwegian Directorate for Nature Management. N.d. Naturbase. , accessed 14 March 2012.

Odum, E. P. 1983. Basic ecology. - Saunders College Publishing, Philadelphia. 613 pp.

Oksanen J., Blanchet F. G., Kindt R., Legendre P., Minchin P. R., O'Hara R. B., Simpson G. L., Solymos P.,

Stevens M. H. H. and Wagner H. 2012. vegan: Community Ecology Package, R package version 2.0-3.

Olsson J., Jonsson B. G., Hjältén J. and Ericson L. 2011. Addition of coarse woody debris – The early fungal

succession on Picea abies logs in managed forests and reserves. Biological Conservation. 144: 1100-1110.

R Development Core Team. 2012. R: A language and environment for statistical computing, version 2.15.0. R

Foundation for Statistical Computing, Vienna.ISBN 3-900051-07-0, http://www.R-project.org/.

Rassi P., Alanen A., Kanerva T. and Mannerkoski I. (eds.). 2001. The 2000 Red List of Finnish species. -

Ympäristöministeriö & Suomen ympäristökäskus, Helsinki. 432 pp. In Finnish with English summary

32

Renvall P. 1995. Community structure and dynamics of wood-rotting basidiomycetes on decomposing conifer

trunks in northern Finland. Karstenia. 35: 1-51.

Senft A. R. 2009. Species diversity patterns at ecotones. Master's thesis. University of North Carolina.

Siitonen J. 2001. Forest management, coarse woody debris and saproxylic organisms: Fennoscandian boreal

forests as an example. Ecological Bulletins. 49: 11-41.

Stokland J. N., Eriksen R., Tomter S. M., Korhonen K., Tomppo E., Rajaniemi S., Söderström U., Toet H. and

Riis-Nielsen T. 2003. Forest biodiversity indicators in the Nordic countries – status based on national forest

inventories. TemaNord 514: 1-106.

Stokland J. N. and Kauserud H. 2004. Phellinus nigrolimitatus – a wood-decomposing fungus highly influenced

by forestry. Forest Ecology and Management. 187: 333-343.

Stokland J. N. and Larsson K.-H. 2011. Legacies from natural forest dynamics: Different effects of forest

management on wood-inhabiting fungi in pine and spruce forests. Forest Ecology and Management. 261:

1707–1721.

Stokland J. N. and Meyke E. 2008. The saproxylic database: an emerging overview of the biological diversity in

dead wood. Revue d’Ecologie (Terre Vie). 63: 29-40.

Swedish Forest Agency. N.d. Skogens Pärlor. , accessed 14 March 2012.

Tikkanen O.-P., Martikainen P., Hyvärinen E., Junninen K. and Kouki J. 2006. Redlisted boreal forest species of

Finland: associations with forest structure, tree species, and decaying wood. Annales Zoologici Fennici.

43: 373-383.

33

Taxon Frequency Red List status

Amphinema byssoides Common LC 32

Amyloathelia crassiuscula Rare LC 1

Amylocorticium cebennense Common LC 7

Amylocorticium pedunculatum Rare DD 1

Amylocorticium subincarnatum Rare EN 1

Amyloxenasma allantospora Rare NE 1

Asterodon ferruginosus Occasional LC 2

Asterostroma laxum Rare NE 3

Athelia bombacina Occasional LC 2

Athelia decipiens Common LC 12

Athelia epiphylla s.l. Common LC 17

Athelia neuhoffii Rare LC 1

Athelia singularis Rare Not Red Listed 3

Athelia subovata Rare NE 1

Athelicium stridii Rare LC 1

Athelopsis glaucina Occasional LC 2

Athelopsis subinconspicua Common LC 9

Boidinia furfuracea Common LC 9

Botryobasidium botryosum Common LC 7

Botryobasidium candicans Rare LC 9

Botryobasidium intertextum Common LC 13

Botryobasidium medium Rare DD 1

Botryobasidium obtusisporum Common LC 11

Botryobasidium indet. No category Not Red Listed 7 x

No category LC/NE 2

Botryobasidium subcoronatum Common LC 9

Botryohypochnus isabellinus Common LC 1

Byssocorticium pulchrum Rare LC 5

Calocera spp. No category Not Red Listed 3

Camarops tubulina No category VU 2

Ceraceomyces eludens Common LC 22

Ceraceomyces sp. 1 No category Not Red Listed 1

Ceraceomyces tessulatus Occasional LC 3

Numbers of

occurences

(logs)

Absence

from NMDS

analyses (x)

Botryobasidium vagum group

indet.

Appendix 1

A list of all taxa present in the GLMM analyses, i.e. in the study. To the right those absent

from the NMDS analyses are indicated. Taxa assigned with the Red List status Not Red Listed

are either species that had not been found in Norway when the Red List was made,

undescribed species or taxa including several species, of which at least one is non-Red Listed.

34

Cerinomyces crustulinus Occasional LC 4

Coniophora olivacea Common LC 6

Corticiaceae sp. 1 No category Not Red Listed 2

Corticiaceae sp. 2 No category Not Red Listed 1

Corticiaceae sp. 3 No category Not Red Listed 1

Dacrymyces spp. Common Not Red Listed 12

Fibricium lapponicum Rare VU 2

Globulicium hiemale Common LC 16

Henningsomyces candidus No category LC 1

Hymenochaete fuliginosa Common LC 4

Hyphoderma argillaceum Common LC 11

Hyphoderma capitatum Rare NT 2

Hyphoderma definitum Common LC 12

Hyphoderma occidentale Rare LC 1

Hyphoderma sibiricum Occasional LC 3

Hyphoderma velatum in herb. Rare Not Red Listed 4

Hyphodontia abieticola Occasional LC 3

Hyphodontia alutacea Common LC 13

Hyphodontia alutaria Common LC 1

Hyphodontia curvispora Rare VU 1

Hyphodontia hastata Common LC 4

Hyphodontia pallidula Common LC 12

Hyphodontia subalutacea Common LC 4

Hypochnicium c.f. subrigescens Rare NE 1

Hypochnicium lundellii Rare LC 1

Hypochnicium punctulatum Rare LC 1

Jaapia ochroleuca Occasional LC 14

Laxitextum bicolor Rare LC 1

Leptosporomyces galzinii Common LC 2

Leucogyrophana romellii Common LC 1

Litschauerella clematidis s.l. Rare NE 1

Lobulicium occultum Rare LC 4

Luellia furcata Rare NE 1

Membranomyces delectabilis Rare LC 2

Mucronella bresadolae No category DD 4

Mucronella calva No category LC 17

Mucronella indet. No category Not Red Listed 2 x

Paullicorticium allantosporum Rare NT 1

Paullicorticium ansatum Occasional NT 13

Paullicorticium pearsonii Rare LC 2

Peniophorella pallida Rare LC 1

Peniophorella praetermissa Common LC 17

Phanerochaete laevis Common LC 1

Phanerochaete sanguinea Common LC 1

35

Phlebia centrifuga Rare NT 1

Phlebia lilascens Rare NE 2

Phlebia livida Occasional LC 3

Phlebia segregata Common LC 11

Phlebia subcretacea Occasional LC 9

Phlebia subulata Rare VU 8

Phlebiella borealis Rare LC 1

Phlebiella christiansenii Rare DD 4

Phlebiella subflavidogrisea Rare NT 1

Phlebiella tulasnelloidea Occasional LC 1

Phlebiella vaga Common LC 19

Physodontia lundellii Rare VU 1

Piloderma byssinum Common LC 28

Piloderma fallax Common LC 8

Piloderma olivaceum No category NE 1

Piloderma sp. 1 No category Not Red Listed 1

Piloderma sp. 2 No category Not Red Listed 1

Piloderma sp. 3 No category Not Red Listed 1

No category Not Red Listed 1 x

Piloderma sphaerosporum Common LC 19

Protodontia piceicola No category VU 2

Pseudohydnum gelatinosum No category LC 3

Pseudoxenasma verrucisporum Rare LC 6

Repetobasidium vestitum Rare DD 1

Resinicium bicolor Common LC 13

Resinicium furfuraceum Common LC 4

Scytinostroma odoratum Rare LC 1

Sebacina calcea No category LC 2

Serpula himantioides Rare LC 2

Sidera lunata Occasional LC 1

Sistotrema brinkmannii Common LC 1

Sistotrema c.f. alboluteum Rare NT 1

Sistotrema citriforme Rare VU 1

Sistotrema diademiferum Rare LC 1

Sistotrema muscicola Occasional LC 2

Sistotrema pistilliferum Rare DD 1

Sistotremastrum suecicum Common LC 1

Sistotremella perpusilla Rare LC 5

Sphaerobasidium minutum Common LC 11

Sphaerobolus stellatus No category LC 1

Stypella vermiformis No category NE 12

Thanatephorus fusisporus s.l. Occasional LC/NE 1

Thelephoraceae spp. Common Not Red Listed 21

Piloderma indet.; P. fallax or P.

olivaceum

36

Tomentella fibrosa Common NE 1

Tomentellopsis echinospora Common LC 13

Trechispora farinacea Common LC 13

Trechispora laevis Common LC 3

Trechispora minima Occasional LC 4

Trechispora minuta ad int Rare Not Red Listed 1

Trechispora indet. No category Not Red Listed 3 x

Trechispora subsphaerospora Common LC 10

Trechispora verruculosa Rare LC 1

Tretomyces lutescens Rare DD 1

Tubulicrinis accedens Common LC 12

Tubulicrinis borealis s.l. Common LC/NE 27

Tubulicrinis calothrix Common LC 1

Tubulicrinis chaetophorus Rare VU 1

Tubulicrinis globisporus Occasional LC 1

Tubulicrinis medius Common LC 1

Tubulicrinis propinquus Occasional LC 1

Tubulicrinis subulatus Common LC 10

Tylospora asterophora Common LC 7

Tylospora fibrillosa Common LC 38

Vesiculomyces citrinus Common LC 6

Xylodon asperus Common LC 16

Xylodon borealis Rare NE 2

Xylodon brevisetus Common LC 25

Xylodon indet. No category Not Red Listed 3 x

Xylodon sp. 1 No category Not Red Listed 2

37

Tim e Length DBH

All species

43.07 0 0 0 1 0 0 0 0 0 0 0 - - 1 1 -

44.00 0 0 0 1 0 0 0 1 0 0 0 - - 1 1 -

44.26 0 0 1 1 0 0 0 0 0 0 0 - - 1 1 -

44.32 0 0 0 1 0 0 1 0 0 0 0 - - 1 1 -

44.37 1 0 0 1 0 0 0 0 0 0 0 - - 1 1 -

45.39 1 0 0 0 0 0 0 0 0 0 0 - - 1 1 -

44.72 0 0 0 1 0 0 0 0 0 0 1 - - 1 1 -

44.87 0 1 0 1 0 0 0 0 0 0 0 - - 1 1 -

44.99 0 0 0 1 1 0 0 0 0 0 0 - - 1 1 -

45.01 0 0 0 1 0 0 0 0 1 0 0 - - 1 1 -

Red Listed species

55.04 1 0 0 0 1 0 0 0 0 0 - 0 - 0 0 -

55.01 1 0 0 0 1 0 0 0 0 0 - 0 - 1 0 -

56.15 1 0 0 0 0 0 0 0 0 0 - 0 - 0 0 - x

55.30 1 0 0 0 1 0 0 1 0 0 - 0 - 0 0 -

54.57 1 0 0 0 1 0 0 1 0 0 - 0 - 1 0 -

56.32 1 0 0 0 0 0 0 0 0 0 - 0 - 1 0 -

55.96 1 0 0 0 1 0 0 0 0 0 - 0 - 0 1 -

55.98 1 1 0 0 0 0 0 1 0 0 - 0 - 0 0 -

56.66 1 0 1 0 0 0 0 0 0 0 - 0 - 0 0 - x

56.12 1 0 0 1 1 0 0 0 0 0 - 0 - 0 0 -

AIC

value

Bark

coverage

Plant

coverage

Ground

contact

Log

type

Decay

class

Top

intactness

Log10

polypore

hym enophore

area

Area

size

Num ber of

vegetation

types

Calcium ,

fine

scale

Calcium ,

coarse

scale

Presence

of richer

vegetation

types

Surface

area

Singular

convergence

Appendix 2

A list of the ten most parsimonious models of each analysed species group. Presence/absence of variables in the models is indicated by 1/0, and

unreliable models due to singular convergence by an x.

38

Tim e Length DBH

Common species

46.81 0 0 0 0 0 0 0 0 0 0 1 - - 0 1 -

47.28 0 1 0 0 0 0 0 0 0 0 0 - - 0 1 -

47.87 0 0 0 0 0 0 0 0 0 0 0 - - 0 1 -

46.98 0 1 0 1 0 0 0 0 0 0 0 - - 0 1 -

47.76 0 0 0 1 0 0 0 0 0 0 0 - - 0 1 -

46.64 0 1 0 0 0 0 1 0 0 0 0 - - 1 1 -

47.49 0 1 0 0 0 0 1 0 0 0 0 - - 0 1 - x

47.65 0 1 0 0 0 0 0 0 0 0 1 - - 0 1 -

48.37 0 0 0 0 1 0 0 0 0 0 0 - - 0 1 -

47.80 0 0 0 0 1 0 0 0 0 0 1 - - 0 1 -

Occasional species

61.65 0 0 0 0 0 0 0 0 0 0 - - 0 - - 1

58.98 0 1 0 0 0 0 1 1 0 1 - - 0 - - 1

60.01 0 1 0 0 0 0 1 0 0 1 - - 0 - - 1

61.42 0 1 0 0 0 0 0 0 0 0 - - 0 - - 1

60.97 0 1 0 0 0 0 0 0 0 1 - - 0 - - 1

61.26 0 1 0 0 0 0 0 1 0 0 - - 0 - - 1

60.54 0 1 0 0 0 0 1 1 0 0 - - 0 - - 1

60.68 0 1 0 0 0 0 1 1 0 1 - - 0 - - 0

61.74 0 1 0 0 0 0 0 0 1 0 - - 0 - - 1

62.39 0 0 0 0 0 0 0 0 0 1 - - 0 - - 1

Rare species

55.26 0 0 1 0 0 0 0 0 0 0 - - 0 0 0 -

55.27 0 1 0 0 0 0 0 0 0 0 - - 0 0 0 -

55.35 0 0 0 0 1 0 0 0 0 0 - - 0 0 0 -

55.18 0 0 0 0 1 0 0 0 0 0 - - 0 1 0 -

56.12 0 0 0 0 0 0 0 0 0 0 - - 0 0 0 -

55.44 0 0 1 0 0 0 0 0 0 0 - - 0 1 0 -

AIC

value

Bark

coverage

Plant

coverage

Ground

contact

Log

type

Decay

class

Top

intactness

Log10

polypore

hym enophore

area

Area

size

Num ber of

vegetation

types

Calcium ,

fine

scale

Calcium ,

coarse

scale

Presence

of richer

vegetation

types

Surface

area

Singular

convergence

39

Time Length DBH

Rare species cont.

55.55 0 0 0 1 1 0 0 0 0 0 - - 0 0 0 -

55.69 0 0 0 0 1 1 0 0 0 0 - - 0 0 0 -

55.21 0 0 0 1 1 0 0 0 0 0 - - 1 0 0 -

55.99 0 1 0 0 1 0 0 0 0 0 - - 0 0 0 -

AIC

value

Bark

coverage

Plant

coverage

Ground

contact

Log

type

Decay

class

Top

intactness

Log10

polypore

hymenophore

area

Area

size

Number of

vegetation

types

Calcium,

fine

scale

Calcium,

coarse

scale

Presence

of richer

vegetation

types

Surface

area

Singular

convergence