Berg-habitat Preferences of Red Listed Fungiin Woodlnd Key h

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Biodiversity and Conservation 11: 14791503, 2002.

2002 Kluwer Academic Publishers. Printed in the Netherlands.

1

Habitat preferences of red-listed fungi and

bryophytes in woodland key habitats in southern

Sweden analyses of data from a national survey

1,2, 3 3 *AKE BERG , ULF GARDENFORS , TOMAS HALLINGBACK and4MIKAEL NOREN

1 2

Department of Conservation Biology, SLU, Box 7002, S-750 07 Uppsala, Sweden; The Swedish3

Biodiversity Centre, SLU, Box 7007, S-75007 Uppsala, Sweden; Swedish Threatened Species Unit,4

SLU, Box7007, S-75007 Uppsala, Sweden; National Board of Forestry, S-55183 Jonkoping, Sweden;*Author for correspondence (e-mail: [email protected]; fax:146-18-673537)

Received 20 February 2001; accepted in revised form 30 August 2001

Key words: Bryophytes, Forest stand composition, Fungi, Geographical location, Ground conditions,

Historical land-use, Lichens, pCCA, Woody substrates

Abstract. The aim of this study was to identify habitat preferences of red-listed epiphytic and epixylic

bryophyte, lichenized and non-lichenized fungi species in woodland key habitats (WKHS) (areas less

than 10 ha, where forest structures indicate occurrence of red-listed species) in southern Sweden. The

relative importance of different groups of environmental factors was assessed with partial canonical

correspondence analysis techniques and a cross-validation approach using data from 7196 selected

WKHs. Different woody substrates (old trees, logs and snags) made up the most important variable group

for occurrence of red-listed species (30% unique explainable variation). Species associated with Fagus

sylvatica and Picea abies habitats, but also species associated with Quercus spp. and Populus tremula

habitats showed distinct habitat preferences. The second most important variable group (16% unique

explainable variation) was geographical location. A westeast gradient was identified, and species

concentrated to Baltic islands in the east were separated from other species. This gradient, and anidentified southnorth gradient, probably reflect differences in temperatures and rainfall between

different regions. Among the remaining variable groups, historical land-use, ground conditions and forest

stand composition were of similar importance (57% unique explainable variation). Traditional manage-

ment regimes resulting in semi-open forest habitats (leaf harvesting, forest grazing and selective cutting)

were associated with the occurrence of many species, probably due to differences in microclimate

between sites of different openness. Furthermore, a ground moisture gradient extending from species

associated with dry sites (mainly lichenized fungi) to species associated with wet sites (mainly

bryophytes), and a nutrient gradient from species associated with nutrient-poor sites to species occurring

at nutrient-rich sites, were identified. Thus, conservation measures are needed in a broad spectrum of

habitats with different substrates. Also sites with similar substrates, but situated in different regions (and

climates), or with different ground moisture and nutrient conditions are needed to cover the full spectrum

of habitat conditions suitable for different red-listed bryophytes and fungi.

Introduction

Swedish forests have been used by forestry for centuries, and especially the forests

1Includes both lichenized fungi (lichens) and non-lichenized fungi.


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in southern and central Sweden have been heavily influenced by forestry operations(Tenow 1974; Bernes 1994; Hansson 1997). Modern forestry with clearcutting,cleaning and thinning started during the 1950s and has changed the forest landscape

drastically (Esseen et al. 1992), and today only 3% of the productive forestland has

escaped intensive harvesting (Ostlund et al. 1997). As a consequence, plant andanimal communities have changed considerably and many threatened species are

restricted to remnants of natural forests or substrates typical of old forests. Earlier

studies of threatened species in Swedish forests have suggested that lack of old treesand dead wood, low proportion of deciduous trees (Berg et al. 1994, 1995; Rydin et

al. 1997; Jonsell et al. 1998) and restricted areas of old forests on soils with high

fertility (Gustafsson 1994; Rydin et al. 1997) are major causes of the decline andrarity of many populations of forest plants and animals. Modern forestry, with short

rotation periods and planting of monocultures of coniferous trees, has been sug-

gested to be a major cause of the decline of many red-listed species (e.g. Berg et al.1994, 1995), i.e. species in risk of extinction.

Swedish forestry policy changed markedly in 1993 when the earlier productiongoal and a new environmental goal were given equal importance, wherebybiodiversity was to be secured and ecosystems conserved. These new goals were

mainly to be achieved by the forestry sector itself. Therefore, a countrywide survey5 2

of woodland key habitats (WKHs) on privately owned forestland (1.2 3 10 km orhalf of all forestland in Sweden) was initiated in Sweden in 1993 (Nitare and Noren

1992) by the government and was completed in 1998. A WKH is defined as an area

(mostly less than 10 ha) where one or more nationally red-listed species occur, orwhere forest structures indicate a strong likelihood for such an occurrence (Nitare

and Noren 1992). Red-listed species in Sweden encompass threatened species as

well as a category of lower threat denoted Near Threatened (earlier termed CareDemanding), including ca. one-third of the red-listed species (Aronsson et al. 1995;

Gardenfors 2000).

The main task of the WKH survey was to localise, delimit and describe WKHs onall privately owned forestland. During the survey, 40071 WKHs with a total area of

21187 km were investigated, which makes it one of the largest nature conservation

surveys in the world (National Board of Forestry 1998). A later validation surveyrevealed that a majority of the sites (71%) harboured red-listed bryophyte or

lichenized fungi species (Gustafsson et al. 1999). On the other hand, red-listed

vascular plants seemed to be as rare in WKHs as in surrounding production forests(Gustafsson 1999). However, identified WKHs seem to fulfill their initial goal, i.e.,

to harbour red-listed species. Species with habitat selection on a larger landscape

scale (e.g. many mammals and birds) could not, on the other hand, be expected to be

restricted to small habitat fragments such as the studied WKHs.5

A large number of observations (a magnitude of 10 observations) of red-listed

species (according to the classification in Aronsson et al. 1995), mainly bryophytes,lichenized and non-lichenized fungi, have been made (National Board of Forestry

1998). This offers unique possibilities to analyse patterns of occurrence, quantify

the importance of different environmental factors, and to identify conservationmeasures and priorities for Swedish forest landscapes. Earlier studies of habitat


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requirements of red-listed species have largely been based on opinions of experts on

different taxa (see e.g. Berg et al. 1994, 1995; Gustafsson 1994; Berg and Tjernberg1996; Rydin et al. 1997; Jonsell et al. 1998), or species-by-species presentations of

habitat associations and threats to red-listed species (Larsson 1997; Hallingback

1998; Thor and Arvidsson 1999).The aim of the present study was to identify broad-scale habitat preferences of

easily identified red-listed epiphytic and epixylic bryophytes, lichenized and non-

lichenized fungi in WKHs in southern Sweden. The relative importance of differentvariable groups (geographical location, stand composition, forest structure, woody

substrates, ground conditions and present and historical land-use) was assessed with

ordination techniques and a cross-validation approach. Based on the present andsimilar studies, a desirable goal in forest conservation efforts is to be able to predict

occurrences of cryptic or hard to identify red-listed species from easily available

environmental data. Implications of the findings for forest management and con-servation are discussed, and future investigations in WKHs are suggested.

Methods

The survey of woodland key habitats

The main aim of the WKH survey was to identify and describe the WKHs. Twomain criteria for identification of WKHs were occurrence of (1) structural elements,

such as old trees, logs and snags a priori classified as suitable for red-listed species,

and (2) presence of indicator species (including not red-listed species), mainlybryophytes, fungi and vascular plants with habitat demands at stand level (Noren et

al. 1995). The personnel doing the fieldwork were qualified silviculturists who had

been especially trained in the identification of threatened habitats and red-listedspecies. Halfway through the survey an evaluating assessment was made in order to

control and secure uniform quality of the fieldwork. Complete censuses of different

taxa were not made, but all observations of red-listed species and indicator species[easily identified, relatively common species indicating high conservation values

(see National Board of Forestry 1998)] were noted. Large numbers of observations

of red-listed species were made and red-listed species were observed in 41% of theWKHs in southern Sweden (National Board of Forestry 1998). A large proportion

of these were bryophytes, lichenized and non-lichenized fungi (ca. 95%) and

therefore sites with observations of red-listed bryophytes and fungi were selected for

the present study. We chose to delimit the study to southern Sweden (see Figure 1).One important reason for this delimitation is that forest companies and the Swedish

state, whose censuses were not included in the WKH survey, own large tracts offorest (ca. 60%) in northern Sweden (Statistics Sweden 1999). Furthermore, large

continuous areas of WKHs, and occurrence of different habitat types (e.g. mountain

forests) in northern Sweden made it more difficult to apply standardised WKHcensus methods there.


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Figure 1. Map of the counties of southern Sweden that were included in the study (dark grey). The dark

line delimits the two major regions (Gotaland and Svealand) in southern Sweden.

Databases and analyses

Initially, nearly 8600 WKHs with observations of red-listed bryophytes, lichenized

and non-lichenized fungi, located on privately owned small-scale forestry areas in

southern Sweden, were selected from databases (a total of ca. 21000 WKHs insouthern Sweden) at the National Board of Forestry. This reduction of the database

was performed in order to delimit the study to sites that were shown to harbour

red-listed species at present. Further reductions of the database due to lack of datafor the selected habitat variables (see below), as well as exclusion of sites where

only very rare red-listed species (found at , 30 sites) were observed, resulted in a

final set of 7196 WKHs. The original databases included over 250 habitat variables(see National Board of Forestry 1998). However, many of these measured similar

attributes and others were considered to be of restricted interest for the present

study. Therefore, many variables were combined or omitted, and a final set of 130

habitat variables was selected for exploratory analyses (Table 1). These variableswere grouped into six main categories (geographical location, stand composition,

forest structure, woody substrates, ground conditions, and present and historicalland-use).

The restricted censuses do not make usage of standard statistical methods

feasible, since non-occurrences in reality could be occurrences. However, thecanonical correspondence analyses (CCAs) emphasise occurrences and this tech-


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Table 1. Description of habitat variables in the six main variable groups.Variables in parentheses were

excluded from further analyses due to correlations with other habitat variables or weak associations to the

identified axes in the exploratory analyses.

Habitat variables and variable categories Description

Geographic location (including transformations)

Longitude (X) Swedish national grid coordinates forLatitude (Y) the centre of each site with 10 m precision

X2, X3, Y2, Y3, X2Y, XY2 Square and cubic transformations

Woody substrates

Abundance of logs of: Ordinal scale 13

Populus, Picea, Pinus (all coniferous trees), all Ordinal scale 13a

other deciduous trees and all southern deciduousb

trees(Snags) Ordinal scale 13(Tall stumps) Ordinal scale 13Woody debris Ordinal scale 13(Standing dead trees) Ordinal scale 13Number of old trees of:

Ulmus glabra, Ul. minor, Fraxinus, Populus, Ordinal scale 13Fagus, Corylus, Tilia, Acer, Quercus, (Pinus), (allconiferous trees) and all deciduous treesTrees with wood mould (decayed inner parts of the Presence/ absencestem)Slow growing Quercus Presence/absence(Slow growing trees of all species) Presence/absenceLarge trees of:Ulmus, (Fraxinus), (Populus), (Fagus), (Quercus), Number of large trees above species specific size(Picea), (Alnus), (Acer), Tilia, (Salix), (Pinus), limit (National Board of Forestry 1998)

Betula, (all deciduous trees)

Historical and present land-use

Earlier selectively cut forest Presence / absence

Earlier forest grazing Presence / absenceEarlier leaf harvesting Presence /absenceUndisturbed natural forest Presence / absence(Earlier mowing for hay) Presence /absence(Earlier clearcutting) Presence /absencePresent grazing Presence / absenceOccurrence of pollarded trees Presence / absence(Fire refugia) Presence / absenceForest fire area Presence / absence

Ground conditions

Spring Presence / absenceMoisture Ordinal scale 14 of dominating moisture classHeterogeneity in moisture Difference in moisture classes within a site (0 3)Proportions of different ground vegetation types:

Lichen dominated, (lichen rich), (poor dwarf- Ordinal scale ( 10% cover of different groundshrub dominated), (EmpetrumCalluna domi- vegetation types)nated), (Vaccinium vitisidaea dominated), Vac-cinium myrtillus dominated, Equisetum-Carexdominated, dominated by broadleaf grasses, domi-nated by narrowleaf grasses, bare ground domi-nated, low herb dominated, tall herb dominatedand rich herb dominated


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Table 1. (continued)

Habitat variables and variable categories Description

Boulder-rich ground Presence / absenceOutcrop of bedrock Presence / absence

Slopes Presence / absence(Moving groundwater) Presence /absence(Flooded forest) Presence / absenceCalcareous ground Presence / absence

Forest structure and composition3 3

Tree volume (of all species) Ordinal scale (m forest in 50 m intervals / ha)Tree density Ordinal scale 14Forest stratification Stratification or not of tree strataForest age Age class (10 years) of dominating treesProportion of different tree species:

Fraxinus, Populus, (Carpinus), Fagus, Quercus, Ordinal scale (10% cover classes) of differentBetula, Picea, Corylus, Alnus, Acer, (Tilia), dominating tree species(Pinus), (Ul. glabra) Ul. minor, and (all deciduous

trees combined)(Tree species diversity) Simpson indexStand composition

WKH area Area (ha)Proportion of main habitats:Production forest, (mire), rocks, patches of arable Proportion of main habitats (10% classes)land within forest, (water) and (other habitats)

aAll other deciduous trees includes deciduous trees found over most of Sweden (Populus tremula, Betula

bspp., Alnus glutinosa, Sorbus spp., Salix spp. etc.). All southern deciduous trees includes speciesrestricted to southern Sweden (Fagus sylvatica, Quercus robur, Quercus petraea, Ulmus spp., Tiliacordata, Fraxinus excelsior, Acer platanoides etc.).

nique has earlier been used on incomplete species data (Hallgren et al. 1999).

Results from the latter study and exploratory analyses of the WKH data set (see

below) suggest that meaningful evaluations of species in relation to habitat gradientscan be made using such data. However, evaluations of site scores are not meaning-

ful, since complete species data does not exist for all sites. Noisy environmental data

have been shown to have a strong influence on CCA, i.e., non-existing gradientsare easily identified (McCune 1997). Therefore, we used CCAs on a divided data set

with explanatory analyses and hypothesis formation (by using an explanatory data

set) and hypothesis testing (by using a confirmatory data set, cf. Hallgren et al.1999). The final evaluation of gradients was made on the full database (i.e. both

halves) in order to include as much information as possible in the analyses (as

suggested for predictive models by Fielding and Bell 1997). The analyses were

conducted with CANOCO 4 (ter Braak and Smilauer 1998). However, limitationsset by the software made it impossible to use all 7196 sites in a few of the final

partial canonical correspondence analyses (pCCA) on the full database, and amaximum of 2300 sites had to be randomly excluded in a few analyses.

Stepwise CCA (ter Braak 1987; ter Braak and Smilauer 1998), which selects the

most significant variables one at a time, was used to identify important habitatvariables and the order in which the main variable groups entered the models. CCA


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was used to investigate if one group of variables could explain any variation in

species composition not explained by another set of habitat variables (Legendre andLegendre 1998; ter Braak and Smilauer 1998).

The different categories of habitat variables (Table 1) to some degree contain the

same information, e.g. geographical location and tree species composition (i.e.different species dominate in different parts of southern Sweden). In order to

quantify the unique contribution of each class of variables in explaining species

composition, we performed variation partitioning on the models for the entire dataset (kland and Eilertsen 1994; Roche et al. 1998). We partitioned the variation

explained by the explanatory variables, since total inertia has been suggested to be

an inadequate measure of the total variation in the data set (kland 1999). However,only selected combinations of variable groups that were of special interest were

included in the variation partitioning due to the relatively large number of variable

groups.In the initial CCAs we found that species scores were most easily interpreted

when the presence/absence of species was used, and therefore available abundanceestimates of different species were not used. Furthermore, species scores andenvironmental axes were most easily interpreted when variables that were recorded

as proportions (e.g. abundance of tree species, ground vegetation types) were arcsine

transformed (Legendre and Legendre 1998).The exploratory analyses also showed that two variables (logs of coniferous trees

and old coniferous trees) were highly correlated with other habitat variables and

these variables were therefore omitted from the analyses. Furthermore, a relativelylarge number of variables (33) that were weakly associated with the identified axes

(t-value, 2.1) could not be expected to contribute to the fit of the species data (terBraak and Smilauer 1998) and were therefore excluded from the following analyses

(excluded variables, see Table 1). Still, a large number (70) of habitat variables

remained, but the number of sites was much larger (limitations in variable numbers,

see ter Braak and Smilauer 1998), and associations with variables were mainlyrelated to categories of several habitat variables (Table 1).

Results

Exploratory analyses

In order to identify the importance of the different groups of habitat variables, we

performed a series of stepwise CCAs. Generally, several variables from the same

variable category entered the models before those from other categories. Thevariable groups entered the models in the following order: woody substrates,

geographical location, historical and present land-use, ground conditions, forestcomposition, and finally stand composition. We therefore performed a series of

sequential analyses where the importance of these categories was tested in sequence

after removing effects of other variable groups regarded as more important (Table2). These analyses suggested that the occurrence of bryophytes, lichenized and


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non-lichenized fungi in the censused WKHs was strongly associated with the

occurrence of suitable woody substrates and geographical location. There were alsosubstantial effects of ground conditions, forest structure, and present and historical

land-use, while the effect of stand composition was weak. These hypotheses (Table

2) were subsequently tested on the confirmatory data set (see below).

Confirmatory analyses

The proposed hypotheses were tested with Monte Carlo simulations (199 permuta-

tions) of all canonical axes using Holms adjustment (Legendre and Legendre 1998)

for multiple comparisons on the confirmatory data set. All statistical tests resulted inacceptance of the proposed hypotheses (see Table 2), so we conclude that the

included habitat categories all were related to the occurrence of the studied red-

listed species in WKHs in southern Sweden.

Combined analyses based on the entire database

Occurrence of different woody substrates was the most important group of factors

for occurrence of the studied epixylic and epiphytic species. The first axis of the

Table 2. Hypotheses and results from statistical tests on all canonical axes on the confirmatory data set.

The sequential analyses and hypotheses had been proposed by the exploratory analyses. Holms

adjustments consider six comparisons (see Legendre and Legendre 1998).

Hypothesis Environmental Covariables Calculated Holms

variables P-value adjusted

P-value

Red-listed species Woody substrates 0.005 0.03

are associated with

amount of different

woody substrates

Red-listed species Latitude and longitude Woody substrates 0.005 0.03

are associated with

latitude and longitude

Red-listed species Land-use Latitude and longitude 0.005 0.03

are associated with Woody substrates

historical and present land-use

Red-listed species Ground conditions Latitude and longitude 0 .005 0.03


ground conditions Land-use

Red-listed species Forest composition Latitude and longitude 0 .005 0.03

are associated with Woody substratesforest stand composition Land-use

Ground conditions

Red-listed species Stand composition Latitude and longitude 0 .005 0.03


stand composition Land-use

Ground conditions

Forest composition


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CCAs, with abundance of different woody substrates as environmental variables,

consisted of a gradient from species associated with old oaks Quercus robur/petraea (including many lichenized fungi, e.g., Cliostomum corrugatum, Ramalina

baltica, and Sclerophora coniophaea) and beech Fagus sylvatica (e.g. the bryophyte

Neckera pumila and the lichen Biatora sphaeroides) to species associated withNorway spruce Picea abies and aspen Populus tremula, which mainly included

bryophytes and non-lichenized fungi (Figure 2). The groups associated with aspen

and spruce were also separated along the first ordination axis, i.e., species associatedwith spruce (e.g. the bryophyte Lophozia ascendens and the fungus Phellinusnigrolimitatus) were found together in the biplot, while species associated with

aspen logs (e.g. Clavicorona pyxidata and Antrodia pulvinascens) and old aspen(e.g. P. populicola and N. pennata) were found in the lower right quartile of the

biplot (Figure 2). Species associated with beech were clearly separated from the

other species along the second axis (Figure 2). Species associated with otherdeciduous trees and Scots pine Pinus sylvestris were found at the centre of the

biplot, suggesting that these species were not so strongly associated with specificwoody substrates.Geographical location was the most important group of factors for occurrence of

different red-listed species after covarying out occurrence of different woody

substrates (i.e. using woody substrates as covariables in pCCA). The first axis of thepCCA with geographical coordinates as environmental variables was interpreted as

a westeast gradient and the second axis as a southnorth gradient (Figure 3). Most

northern species (e.g. Bryoria bicolor, Collema subnigrescens and Chaenotheca

gracillima) occurred together in the upper left quartile of the biplot. Eastern species

(e.g. Dicranum tauricum, Inonotus hispidus and Gyalecta truncigena) occurred at

the right part of the biplot, while south-western species (e.g. Holwaya mucida,Normandina pulchella and Opegrapha vermicellifera) occurred in the lower left

part of the biplot (Figure 3). There were no apparent differences in the distribution

patterns of the three main taxa (bryophytes, lichenized and non-lichenized fungi).Historical land-use was classified as the most important factor for occurrence of

different red-listed species, after covarying out geographical location and occur-

rence of different woody substrates. The first axis of the pCCA with different formsof land-use as environmental variables separated species associated with leaf

harvesting and occurrence of pollarded trees (mainly lichenized fungi, e.g., S.

farinacea, G. ulmi and Biatorella monasteriensis) from the other species (Figure 4).The second ordination axis seemed to consist of a gradient extending from relatively

open sites (at the top of the biplot), including the mentioned species associated with

leaf harvesting, but also species associated with earlier-grazed forests or sites used

for selective cutting (e.g. Microcalicium ahlneri, Catillaria sphaeroides and An-trodia pulvinascens) to species found in dense forests (e.g. Pertusaria multipuncta,

Dicranodontium denudatum and O. viridis) at the bottom of the biplot (Figure 4). Ingeneral, several lichenized and non-lichenized fungi species were associated with

sites having these types of old management regimes, while most bryophytes were

found at sites where these management regimes have not been practised.Ground conditions were classified as the most important variable group for


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Figure 2. Ordination biplot (CCA) with woody substrates as environmental variables. The locations of

species have in some cases been shifted slightly for clarity. Slowoak abundance of slow-growing oaks.

Five environmental variables with weak associations to the identified axes are excluded from the biplot.

7SPEC indicates approximate position of Pac tube, Bue viol, Ope ille, Cha pae, Cli corr, Fis hepa and Cal

luci. 4SPEC indicates position for Eve diva, Mic ahln, Bry bico and Cal parv. For full species names see

Appendix.


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Figure 3. Ordination biplot (pCCA) with geographical location as environmental variables and woody

substrates as covariables. The locations of species have in some cases been shifted slightly for clarity.

7SPEC indicates approximate position of Ant pulv, Phe ferf, Ram balt, Cla pyxi, Cal quer, Scli coni and

Cli corr. For full species names see Appendix. For explanation of environmental variables, see Table 1.


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Figure 4. Ordination biplot (pCCA) with historical land-use as environmental variables and woody

substrates and geographical location as covariables. PollTrees occurrence of pollarded trees, Leafhar-

vest signs of earlier leaf harvesting, Grazing earlier grazed forests, Undisturbed not cut forest and

SelectCut selectively cut forest. The locations of species have in some cases been shifted slightly for

clarity. 6SPEC position for Pyr nita, Bue viol, Fis hepa Sch peri, Cli corr and Phe ferf. 3SPEC

position for Cla pyxi, Ram balt and pac tube. 3 SPECIES position for Phe nigl, Lca glab and Scl coni.

For full species names see Appendix.


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occurrence of different red-listed species when using coordinates, woody substrates

and land-use as covariables in pCCA. The first axis of the pCCA with groundcondition as environmental variable consisted of a moisture gradient extending from

species associated with relatively dry sites (e.g. Cladonia parasitica, Evernia

divaricata and B. bicolor) to species associated with moist or wet sites (e.g.Trichocolea tomentella, Menegazzia terebrata and Arthonia spadicea). In general,

bryophytes were associated with moist sites, while lichenized fungi were associated

with relatively dry sites (Figure 5). The second axis was interpreted as a nutrientgradient from species associated with nutrient-poor sites (e.g. M. terebrata and C.

gracillima) to species occurring at nutrient-rich sites (e.g. T. tomentella, S. peronella

and Skeletocutis nivea), see Figure 5.Forest stand composition was classified as the most important factor for occur-

rence of different red-listed species when using coordinates, woody substrates,

land-use and ground conditions as covariables in pCCA. The first axis of the pCCAwith forest stand composition as environmental variable consisted of a forest age

gradient, extending from species associated with moderately old forests (ca. 100years) with a high volume of trees (e.g. Usnea florida, Sk. nivea and B. bicolor) tospecies associated with very old forests (ca. 180 years) with a smaller volume of

trees (e.g. Cl. corrugatum, C. phaeocephala and R. baltica), see Figure 6. In general,

lichenized fungi were associated with older forests than bryophytes and non-lichenized fungi. The second axis was difficult to interpret.

Stand composition was significantly associated with the occurrence of different

red-listed species when using all other variable groups as covariables in pCCA. Thefirst axis of the pCCA with stand composition as environmental variable consisted

of a topographic gradient, from species associated with occurrence of rocks

(including both species using rocks as a substrate and species associated with treesin rocky areas, e.g. B. bicolor, O. illecebrosa and Cla. parasitica) to species

occurring in plain forests (e.g. Bi. sphaeroides, U. florida and Sk. nivea), see

Figure 7. The second axis consisted of a gradient extending from species associatedwith sites with patches of arable land within forests (e.g. G. truncigena, M. terebrata

and Inonotus hispidus) to species associated with continuous forest (E. divaricata

and Bia. monasteriensis). There were no apparent differences in the effects of standcomposition on the three main taxa.

Variation partitioning

Variation partitioning was only performed for certain combinations of variable

groups due to the relatively large number of groups. First, the unique variation

explained by each variable group (i.e. when effects of all other variable groups wereremoved) was calculated for all six variable groups. These calculations suggested

that woody substrate variables accounted for the largest amount of unique explain-able variation (30.2%). Geographical location also accounted for a large proportion

of unique variation (15.8%), while the unique variation explained by ground

conditions (6.6%), stand composition (6.0%) and land-use (4.8%) was smaller. Thevariation accounted for by stand composition (1.1%) was small. Thus, 64.5% of the


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Figure 5. Ordination biplot (pCCA) with ground conditions as environmental variables and woody

substrates, geographical location and historical land-use as covariables. Vshortherb proportion with

short herb vegetation,Vtallherb proportion with tall herb vegetation,VrichHerb proportion with rich

herb vegetation,Vvaccin proportion with Vaccinium myrtillus dominated vegetation and VcarexEquis

proportion with Carex or Equisetum dominated vegetation. The locations of species have in some cases

been shifted slightly for clarity. 4SPEC position for Tric tom, Dicr den, Buxb vir and Ope ochr. For full

species names see Appendix.


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Figure 6. Ordination biplot (pCCA) with forest composition as environmental variable and woody

substrates, geographical location, historical land-use and ground conditions as covariables. The locations

of species have in some cases been shifted slightly for clarity. 4 SPEC Ope viri, Phe fere, Pyr nita and

Lca glab. For full species names see Appendix.


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Figure 7. Ordination biplot (pCCA) with stand composition as environmental variable and woody

substrates, geographical location, historical land-use, ground conditions and forest composition as

covariables. The locations of species have in some cases been shifted slightly for clarity. 4SPEC

position for Per mult, Cha phae, Ram balt and Cal quer. 3SPEC position for Ope sore, Anas hel and

Buxb vir. For full species names see Appendix.


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Table 3. Partitioning of the explained variation among the four different habitat composition variablea

groups (woody substrates, ground conditions, forest composition and stand composition) .

A B Percentage of explained variation

AuB A>B BuA AB is the variation jointly described by A and B. BuAis the variation explained by B but not by A. A


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compilations of expert knowledge (e.g. Berg et al. 1994, 1995; Rydin et al. 1997),

suggesting that occurrence of suitable substrates is the major factor restricting theoccurrence of red-listed Swedish forest species. It must, however, be stressed that

some taxa (e.g. mycorrhizal fungi) with different habitat preferences than the

studied species were not included in our study. The present study suggests thatred-listed species associated with beech, oak, spruce or aspen substrates have

distinct habitat preferences and rarely occur at sites dominated by other substrates.

Species associated with other tree species were not strongly associated with anysingle tree species, but other factors (e.g. earlier land-use and soil conditions) were

more important. Thus, five major WKH types (dominated by beech, oak, spruce,

aspen and a group with the remaining tree species, respectively) with complemen-tary flora of red-listed species could be identified, although the transition between

some groups is gradual. Of these WKHs, habitats dominated by beech, oak or other

broad-leaved deciduous trees harboured the largest number of red-listed species,while fewer species were associated with spruce- or aspen-dominated habitats

(Figure 2).The second most important factor (16% unique explainable variation) for theoccurrence of red-listed bryophytes and fungi in the studied WKHs was geographi-

cal location, which partly consisted of a westeast gradient (Figure 3). An analysis

of the total number of red-listed species in different regions of Sweden (data fromGardenfors 2000) reveals that the eastern islands Oland and Gotland also have very

large numbers of red-listed species unique for each province [and also large

numbers of red-listed species in total (including species occurring in other regions)],in relation to their small areas. Similar results have also been found for vascular

plants (Rosen and Borgegard 1999).

Geographical location also included a southnorth gradient. There are consider-able climatological differences between different regions in southern Sweden (e.g.

precipitation, mean temperature), which result in differences of up to 30 days in the

length of the growing season, but also in climatological differences between coastalareas and continental interior areas (Raab and Vedin 1995). Furthermore, the

southnorth gradient seems to reflect differences in tree species composition

between regions. For instance, many of the species restricted to southernmostSweden (Skane, Halland and Blekinge) were associated with different southern

deciduous trees that are much more abundant in these regions than in central

Sweden (Statistics Sweden 1999). Similarly, species associated with aspen weremore abundant in central than in southernmost Sweden, probably because aspen is

twice as abundant in the boreonemoral zone (a mean of 2.3% of the tree volume, butup to 6% of the tree volume in some regions around Lake Malaren) as in the nemoral

zone (1.2% of the tree volume, see Statistics Sweden 1999). However, the dis-tribution of several species associated with spruce and oak did not reflect the present

abundance of these trees in different regions. Species associated with spruce weremore common in central Sweden than in southern Sweden, despite the fact that

spruce is as common in the nemoral as in the boreonemoral zone (ca. 50% of the tree

volume (see Statistics Sweden 1999)). However, spruce has recently colonised (and


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been planted in) southernmost Sweden (Berglund et al. 1996) and a probable reason

for the restricted occurrence of red-listed species associated with spruce in theseregions is the lack of old forests with continuous supply of suitable spruce

substrates. Similarly, species associated with oak were more abundant in central

than in southernmost Sweden, despite the fact that oak trees were more abundant insouthernmost (ca. 3.2% of the tree volume) than in central Sweden (ca. 1.5% of the

tree volume (see Statistics Sweden 1999). Possible reasons are the higher levels of

air pollution in southernmost Sweden (e.g. Thor 1998), differences in standcomposition or differences in continuity of occurrence of suitable substrates

between regions.

Historical land-use, ground conditions and forest composition were all of similarimportance (57% unique explainable variation) for the bryophyte and fungi flora.

First, species associated with leaf harvesting were separated from the other species.

The pollarded trees with red-listed species were mainly trees such as ash Fraxinus

excelsior, lime Tilia cordata and elm Ulmus glabra. Historically, leaf harvesting

was a widespread agricultural practice throughout most of Sweden (Slotte 2000).Today, trees that earlier were pollarded, often situated in landscapes that no longerare managed in a traditional way, are refuges for several threatened species (see also

Moe and Botnen 1997). Historically, these species were found in extensive areas of

natural meadows and pastures that are now overgrown, planted with forest or usedas arable fields (Slotte 2000). Pollarded trees are often very old, with decaying wood

and cavities, and offer a complex substrate with large variation in microclimate and

nutrient conditions within the same tree. Such trees are of great conservation valuealso for taxa such as insects (Berg et al. 1994). Leaf harvesting of deciduous trees,

and grazing or mowing of natural grasslands in semi-open agricultural landscapes, is

therefore of great conservation value also for forest species and must be promotedby subsidies since there is no economic incitement for such a practice.

Additionally, associations with historical land-use consisted of a gradient extend-

ing from relatively open sites, including the species associated with leaf harvestingas well as species associated with earlier grazed forests or selectively cut sites, to

species in closed forests (Figure 4). Generally, most non-lichenized fungi were

associated with grazed or cut forests, while most bryophytes were found in dense,earlier not managed forests. These differences between taxa probably depend on

biological differences regarding preferences for climatic factors, such as humidity

and sun exposure, that differ between open and dense forests. Earlier studies havealso suggested that threatened macrofungi are often found in mesic to dry habitats

(Rydin et al. 1997), whereas lichenized fungi in general have been found to be most

frequent in open dry sites (Gustafsson and Eriksson 1995). Many red-listed

invertebrates in Sweden have also been shown to prefer open forest and trees inexposed positions, for instance species associated with oak, while species associated

with beech frequently prefer shaded forests (Gardenfors and Baranowski 1992;Jonsell et al. 1998). Most bryophytes, on the other hand, are favoured by humid

conditions found in dense or protected sites (see also Gustafsson et al. 1992;

Gustafsson and Eriksson 1995). Thus, the openness of the habitat and the microcli-


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mate is important for many forest organisms. Management regimes resulting in

semi-open forest habitats (leaf harvesting, forest grazing and selective cutting) arenot used in modern forestry and farming.

Among ground condition variables there was a moisture gradient from species

associated with relatively dry sites to species associated with moist or wet sites(Figure 5). Again, bryophytes were mainly associated with moist sites, while

lichenized fungi were associated with relatively dry sites. Furthermore, there was a

nutrient gradient extending from species associated with nutrient-poor sites tospecies occurring at nutrient-rich sites. This suggests that there is an effect of

nutrient conditions on the occurrence of red-listed bryophytes, lichenized and

non-lichenized fungi which is not associated with occurrence of suitable substratesor large regional differences. Most of the studied species are found on dead wood or

old trees, suggesting that bark chemistry is affected by nutrient conditions in the

ground, possibly by transport of nutrients from the roots to the bark. This has beensuggested in a study of the epiphytic flora on aspens (Gustafsson and Eriksson

1995), in which field vegetation type correlated strongly with chemical properties ofthe bark and in the ground. Thus, sites with similar substrate types, but on differentsoils, harbour different rare bryophytes, lichenized and non-lichenized fungi, e.g.

Lobaria pulmonaria and U. longissima have been shown to be associated with bark

of trees situated in different soil conditions (Gauslaa 1995; Gauslaa et al. 1998).Protection and management of a spectrum of sites with similar substrates but

different soil conditions are therefore needed.

Among forest composition variables there was a forest age gradient, extendingfrom species associated with forests aged 100 years to species associated with

forests aged 180 years (Figure 6). However, all WKHs consist of more or less old

forests (or forests with some old trees) compared with Swedish forests in general(88% of the forests in southern Sweden are younger than 100 years; see Statistics

Sweden 1999). When comparing organism groups, lichenized fungi were more

associated with older forests than bryophytes and non-lichenized fungi. Preferencesof lichenized fungi for very old (and mostly large) trees have been suggested to be

due to continuity in substrate occurrence, which is important due to the slow growth

and poor dispersal abilities of many species (Moe and Botnen 1997; Peck andMcCune 1997). However, also the development of specific bark structures on old

trees has been suggested to be a major reason for the preference for very old trees by

several lichenized fungi (Gauslaa 1995). Many of these species have been assumedto be restricted to large trees situated in sunny sites in farmland landscapes.

However, several of the species included in the present WKH study have also been

found on relatively small, but very old oaks, on steep forested slopes (Ek et al.

1995). This suggests that these species might originally have been found also innatural sites and emphasises the importance of substrate continuity in relation to

substrate quality.Of the studied factors, stand composition was classified as the least important (1%

unique explainable variation) for occurrence of red-listed species. Thus, local habitat

composition (see above) seemed to be much more important than stand compositionfor the occurrence of forest-associated red-listed bryophytes and fungi. In contrast,


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studies of birds and mammals in southern Sweden have suggested that adjacent

habitats and stand composition on a relatively large scale are important for theiroccurrence (e.g. Hansson et al. 1995; Hansson 1997). Most WKHs are too small to

harbour viable populations of many mammals and birds and management on a larger

landscape scale is important for these organism groups. However, the WKH surveywas not designed to investigate effects of surrounding landscapes on bryophytes and

fungi (i.e. no large-scale landscape level variables were included). Thus, stand

composition might be important also for these taxa, and furthermore, the long-termsurvival of red-listed species in WKHs might depend on the structure of surrounding

areas.

In conclusion, the present study quantifies results from earlier studies suggestingthat availability of high quality substrates (mainly old living trees and logs) is very

important for the occurrence of red-listed bryophytes, lichenized and non-lichenized

fungi (e.g. Berg et al. 1994; Rydin et al. 1997). Suitable substrates were generallyfound in old forests, but red-listed lichenized fungi were found in older forests than

the other taxa. Sites with similar substrates, but situated in different regions (andclimates), or with different ground moisture and nutrient conditions are needed tocover the full spectrum of habitat conditions suitable for different red-listed

bryophytes and fungi. Most protected forest areas in Sweden consist of old forests,

but the present forest nature reserve system covers 1.2% of the total forest area insouthern Sweden, i.e. Gotaland and Svealand (Statistics Sweden 1999). Thus, many

forest types (e.g. deciduous forests and spruce forests on nutrient-rich soils) are

sparsely represented among nature reserves and national parks in Sweden (see alsoNilsson and Gotmark 1992; Fridman 2000). Retention of trees and dead wood in the

managed forest is of major importance for many red-listed species, since a sufficient

increase in the protected area is unlikely. The relatively large amounts of dead woodin mature forests (Fridman and Walheim 2000) suggest that retention of snags and

logs in silviculture is a possible measure in many areas, although active generation

of dead wood might be needed in southern Sweden where dead wood decays faster(Fridman and Walheim 2000). Conservation measures in the relatively small

WKHs, often surrounded by a matrix of managed forest, will therefore be of large

importance. Nonetheless, monitoring of a selected number of WKHs, and in-vestigations of reproduction and dispersal abilities of a spectrum of bryophyte and

fungi species, would be desirable in order to evaluate the long-term quality of

WKHs, not the least because the surroundings might affect survival and reproduc-tion of red-listed species.

Acknowledgements

Thanks to Lena Gustafsson, Nick Hodgetts and Per Milberg for comments on earlier

versions of the paper. This study was funded by the Swedish University of

Agricultural Sciences by the programme field Analyses and Prediction ofBiodiversity (to U.G.).


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Appendix

Number of occurrences at model sites, test sites and total number of occurrences for the 43 lichenized

fungi species, nine bryophyte species and 11 non-lichenized fungi species included in the study.

Abbreviated names are used in all figures. For more information on distribution, ecology and threats to

individual species see Larsson 1997; Hallingback 1998; Thor 1998.

Species Model sites Test sites Total

Lichenized fungi

ART SPAD Arthonia spadicea 123 136 259

BAC ROSE Bacidiarosella 95 74 169

BIA MONA Biatoridium monasteriense 21 17 38

BIA SPHA Biatora sphaeroides 94 90 184

BRY BICO Bryoria bicolor 17 16 33

BUE VIOL Buellia violaceofusca 77 98 175

CAL LUCI Caloplaca lucifuga 107 103 210

CAL PARV Calicum parvum 20 22 42CAL QUER Ca. quercinum 42 41 83

CAT LAUR Catinaria laureri 33 39 72

CHA CHLO Chaenotheca chlorella 96 102 198

CHA GRAC C. gracillima 35 44 79

CHA PHAE C. phaeocephala 427 480 907

CLA PARA Cladonia parasitica 20 22 42

CLI CORR Cliostomum corrugatum 307 287 594

COL SUBN Collema subnigrescens 78 96 174

EVE DIVA Evernia divaricata 32 39 71

GYA FLOT G. flotowii 52 51 103

GYA TRUN G. truncigena 47 51 98

GYA ULMI G. ulmi 283 295 578

LCA GLAB Lecanora glabrata 229 217 446

LCI EPIZ B. chrysanta 25 26 51

MEG GROS Megalaria grossa 207 195 402

MEN TERE Menegazzia terebrata 71 49 120

MIC AHLN Microcalicium ahlneri 35 34 69

NEP LAEV Nephroma laevigatum 53 64 117

NOR PULC Normandina pulchella 72 84 156

OPE ILLE Lecanographa amylacea 103 100 203

OPE OCHR Opegrapha ochrocheila 25 29 54

OPE SORE O. sorediifera 31 36 67

OPE VERM O. vermicellifera 31 24 55

OPE VIRI O. viridis 48 55 103

PER MULT Pertusaria multipuncta 44 46 90

PHL AGEL Phlyctis agelaea 114 111 225

PYR NITA Pyrenula nitida 470 462 932RAM BALT Ramalina baltica 71 66 137

SCH DECO Schismatomma decolorans 191 186 377

SCH PERI Sc. pericleum 212 222 434

SCL CONI Sclerophora coniophaea 77 93 170

SCL FARI Sc. farinacea 24 42 66

SCL PERO Sc. peronella 34 30 64a

SPH TURB Sphinctrina turbinata 39 34 73


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Appendix (continued)

Species Model sites Test sites Total

USN FLOR Usnea florida 57 57 114

BryophytesANAS HEL Anastrophyllum hellerianum 246 254 500

BUXB VIR Buxbaumia viridis 240 245 485

DICR DEN Dicranodontium denudatum 22 18 40

DICR TAU Dicranum tauricum 22 27 49

LOPH ASC Lophozia ascendens 18 15 33

NECK PEN N. pennata 38 31 69

NECK PUM Neckera pumila 139 144 283

ORTH GYM Orthotrichum gymnostomum 43 52 95

TRIC TOM Trichocolea tomentella 135 140 275

Non-lichenized fungi

ANT PULV Antrodia pulvinascens 44 53 97

CLA PYXI Clavicorona pyxidata 114 102 216

FIS HEPA Fistulina hepatica 113 81 194

HOL MUCI Holwaya mucida 52 41 93INO HISP Inonotus hispidus 45 55 100

PAC TUBE Pachykytospora tuberculosa 59 64 123

PHE FERE Phellinus ferrugineus 28 15 43

PHE FERF P. ferrugineofuscus 341 343 684

PHE NIGL P. nigrolimitatus 52 60 112

PHE POPU P. populicola 53 49 102

SKE NIVE Skeletocutis nivea 23 27 50

aNot lichenized but grows on the lichenized fungus Pertusaria pertusa.

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