Forest Ecology and Management 310 (2013) 523–530

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Forest Ecology and Management

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Temporal variation of polypore diversity based on modelled dead wooddynamics in managed and natural Norway spruce forests

0378-1127/$ - see front matter � 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.foreco.2013.08.053

⇑ Corresponding author. Tel.: +358 40 801 5329.E-mail address: mikko.peltoniemi@metla.fi (M. Peltoniemi).

Mikko Peltoniemi ⇑, Reijo Penttilä, Raisa MäkipääFinnish Forest Research Institute (METLA), Jokiniemenkuja 1, 01301 Vantaa, Finland

a r t i c l e i n f o

Article history:Received 14 June 2013Received in revised form 19 August 2013Accepted 24 August 2013Available online 24 September 2013

Keywords:BiodiversityConservationDecompositionEcosystem serviceForest managementSustainable forest management

a b s t r a c t

Decline of dead wood in managed boreal forests has variously affected polypore species that use it astheir substrate, some now being listed as threatened while some are still thriving. Management of poly-pore diversity requires species-specific information about their occurrence probabilities, which partiallydepend on stand dead wood availability and other properties.

We implemented an ensemble of polypore habitat models to simulations of stand and dead wood avail-ability estimated with a decomposition model we fitted to data. We asked how management of a borealNorway spruce stand influences the dead wood availability and polypore occurrence probabilities andtheir diversity.

Simulations of multiple polypore species with stand management scenarios provided insight to deadwood dynamics and polypore species management. In a managed stand, diversity thrived after final har-vesting, but declined to low level by mid-rotation. Harvest residues and stumps, although low qualitysubstrate for many species, were important for diversity in young managed stand due to their high quan-tities. High mixtures of naturalness in stand management were required to elevate diversity of managedspruce stands from the mid-rotation lows to levels in natural-like stands.

Our study suggests that dead-wood supply of managed stands could be optimised to lift lowest speciesexpectations towards levels in natural-like forests, but it seems that reaching these levels requires dead-wood quantities much higher than provided by conventional management. Management of stand diver-sity can presumably be facilitated with wise landscape planning but more research is needed in order toincorporate polypores to spatio-temporal management simulation context.

� 2013 Elsevier B.V. All rights reserved.

1. Introduction

Timber harvesting and decreased amount of decomposing woodin managed forests is a major driver of the loss of speciesdiversity in boreal forests (Gärdenfors, 2010; Harmon et al., 1986;Siitonen et al., 2000). As a consequence of loggings there isconsiderably less standing dead and down wood material in man-aged forests (4–10 m3/ha) than there typically is in natural forests(70–100 m3/ha)(Jonsson et al., 2005; Siitonen, 2001; Siitonen et al.,2000), which has led to decline in the diversity of species that aredependent on decaying wood. For instance, in polypore, which area well-known group of wood-inhabiting basidiomycetes, over onethird of the species in Finland and Sweden are recorded asred-listed (Gärdenfors, 2010; Kotiranta et al., 2010). Since thepolypores are directly dependent on suitable dead wood habitats,their survival can be enhanced by altered forest management prac-tices and by habitat conservation. Discontinuous supply of suitablehabitat in space, but also in time can influence species existence in

a forest stand or in larger area. One way of evaluating the impor-tance of temporal variation of dead wood availability to polyporediversity in forests is to conduct model analyses of dead woodhabitat change. Stand dynamics are typically simulated with standmanagement models. Traditionally, these models have focused ongrowth and economic return, but they are increasingly used forplanning sustainable forest management, and developed furtherto support these goals. Sustainable forest management planningand maintaining biodiversity would greatly benefit if alternativemanagement chains were designed with tools predicting thequality continuum of dead wood and the presence of potentialspecies inhabiting forests.

Current knowledge of habitat requirements of dead wooddependent species, such as polypores, is sufficient for quantitativeanalyses of species occurrences. Polypore species have varyingrequirements for e.g. tree species, size, resource type (standing ordown), and decay class (Bader et al., 1995; Berglund et al.,2011a; Norden et al., 2013; Renvall, 1995) and they host a richgroup of associated species (Komonen et al., 2000; Stoklandet al., 2012). Stand properties such as density and naturalness havealso been reported to influence species presence in a stand, as well

524 M. Peltoniemi et al. / Forest Ecology and Management 310 (2013) 523–530

as factors related to spatial connectivity (Berglund et al., 2011a;Komonen et al., 2000; Norden et al., 2013; Penttilä et al., 2004;Sippola et al., 2001).

The significant role polypores play in boreal biodiversity and inwood decay calls upon including them in stand and landscape sim-ulation models. The incorporation of polypores into these modelsrequires accurate prediction of the temporal development of theirmain resource, dead wood. Recently, applicable models have beendeveloped for representing mortality of trees in natural and semi-natural boreal Spruce forest (Peltoniemi and Mäkipää, 2011), fortree fall (Aakala, 2011; Mäkinen et al., 2006), and dead wooddecomposition (Kruys et al., 2002; Ranius et al., 2003; Aakala,2011; Mäkinen et al., 2006), which are useful for these purposes,and may be used to extend applicability of the stand simulationmodels from conventionally managed to more natural stands.

Acknowledging the importance of dead wood for sustainableforest management, some studies have already investigated effec-tive ways of managing dead-wood stocks in boreal forests ofNorthern Europe (Ranius et al., 2003; Tikkanen et al., 2012), andin North America where focus has been on fire return interval(Brown et al., 2003; Tinker and Knight, 2001). Some of the earliermodel studies have analyzed sustainable harvest scenarios byincluding total number of polypore species as a criteria (as ex-plained by the total volume of dead wood)(Hynynen et al., 2005),or by making cost-effective selection of conservation areas includ-ing measured polypore diversity of stands as one of the criteria(Juutinen et al., 2004). Further development in this direction re-quires incorporation of a large ensemble of species-specific occur-rence models, as it is unlikely that management of few species, oreven species groups, is enough to maintain the whole biodiversityeffectively. Applying a wide set of polypore models also allowsposing life-strategy or threat-status specific questions about spe-cies vulnerability management, and replying to questions such ashow much dead wood should be introduced to managed forestsin order to maintain a specified group of target species.

In order to take further steps in the development of methods forintegrating polypore diversity into the models of stand manage-ment, we incorporated species-level probability models of poly-pore occurrence into model scenarios of stand management. Asthe dead wood resource and its quality are crucial for these models,we first developed models of decomposition of dead wood materialbased on repeated measurements of dead logs. New polypore mod-els apply widely used classification based on hardness of the logassessed with a knife (Mäkinen et al., 2006; Renvall, 1995). Earlierdecomposition models of logs, which we found in the literaturewere not applicable for the polypore habitat models (Nordenet al., 2013), because decomposition of logs was presented oncontinuous scale (Mäkinen et al., 2006), log decomposition wasmerged with dynamics of soil organic matter (Tuomi et al.,2011), or classification of decaying logs was made relying on visualfeatures of the logs (Aakala, 2011; Kruys et al., 2002).

The objective of our study was to investigate (i) how standmanagement influences the continuum of dead wood habitatsand the diversity of polypore species inhabiting dead wood, (ii)how model-predicted polypore diversity differ in managed andnatural-like stands, and (iii) how much natural dynamics mustbe introduced to a typical management scenario to achievepolypore diversity leveling that of natural-like forests.

2. Materials and methods

2.1. Predicting polypore presence in forest stands

We applied recently published models of occurrence probabilityof polypore species in boreal forests of Finland (Norden et al.,

2013). As the models have been depicted in detail earlier (Nordenet al., 2013), we provide only brief summary of them here, and de-scribe the way we applied them. The models predict the occur-rence probability of individual polypore species on individualdead wood units. Models need resource-unit (i.e. dead wood)specific data about tree species, decay class, diameter, and typeof the dead-wood (snag, log, stump, man-made, etc.). Besides thisresource-unit specific information, the models apply also otherdata related to site and its spatial connectivity. Canopy cover isexpressed using the basal area of the stand, naturalness as the ba-sal area of cut stumps. Connectivity measures are provided at threescales: (i) the total amount of suitable habitat in the stand, which isestimated by applying resource-unit specific model part to theother dead wood units in the stand (dead wood havingd > 15 cm), (ii) weighted mean age of the surrounding forests,and iii) geographical location within Finland. All stand level-vari-ables and connectivity measures in the polypore occurrence prob-ability models are standardized to zero and unit standarddeviation. When applying the models we assumed that the originalrange of standardized variables was similar to our data and predic-tions made with the stand simulator (see Section 2.3).

In order to estimate the probabilities for the polypore presencein the whole stand, we summed up the probabilities of polyporepresences on unit logs to get an expectation for probability ofoccurrence of species on site, p ¼ 1�

QCk¼1ð1� pkÞ, where k runs

through all log categories (1 . . .C), which were characterized by re-source type, diameter and decay class. The probability of polyporespecies being present on a category of similar logs waspk ¼ 1� ð1� plÞ

nk , where nk is the number of similar logs in thecategory k, and pl is the probability of species being present on asingle log. Probability pl was estimated with models of Nordenet al. (2013). Sum of the species-specific occurrence probabilitieson site gives the expected number of species in the stand. Modelpredictions correspond to the presence of species in a plot of sim-ilar size as used in the original material (Norden et al., 2013). Mostof the used plots were 0.2 ha (the whole range was 0.2–4 ha)(Norden et al., 2013).

2.2. Models to simulate stand and, dead wood production anddecomposition

In order to predict the availability of the amount and quality ofsuitable dead wood for polypore species, we estimated tree mortal-ity and quantity of harvest residues (tree tops and stumps) in aNorway spruce stand using a stand simulation model. We used astand simulator (Motti), which simulates growth and mortalityby tree diameter class. Motti stand simulator is based on a largeempirical material representative to the study region (Hynynenet al., 2005). In Motti, tree growth and mortality are influencedby tree’s own and competitors’ sizes, and described with sets ofempirical competition functions. Motti implements also a stand le-vel self-thinning threshold,which starts to thin trees when there isno space in the canopy, but it is not usually reached in managedstands due to thinning of the stand.

In order to compare the amount of dead wood in managedstands to that in natural stands, we estimated the average mortal-ity rate in a material collected from unmanaged Norway spruceforests in Southern Finland (Peltoniemi and Mäkipää, 2011). In thisstudy, natural forests were assumed to be in steady-state, e.g. mor-tality and regeneration are balanced by small-scale gap dynamics(Janisch and Harmon, 2002), and dead wood stocks have stabilizedto levels corresponding to dead wood formation.

We allocated dead trees to four size classes according to theirdiameter at breast height (d) in order to reduce the computationalburden when applying the decomposition and polypore models.Three size classes of naturally created dead wood were the small

M. Peltoniemi et al. / Forest Ecology and Management 310 (2013) 523–530 525

(<15 cm, mean 8 cm), medium (15–30 cm, mean 22 cm), and large(>30 cm, mean 38 cm) logs. For each dead and fallen tree, a stumpof corresponding diameter was created. We assumed that harvestresidues had a mean diameter of 5 cm, and a stump belonging toone of the former three categories. Stumps were classified as nat-ural and man-made as required by polypore models.

The resource type of dead wood is an important factor in pre-dicting polypore presence. Some of the species prefer standingdead wood (snags), some down wood (e.g. logs). We estimatedthe stock of snags, down wood and stumps to provide input forthe polypore models. To do this, we kept record on stock of snags,and estimated expected rate of snag fall for each year of the simu-lation in the above-mentioned three size-categories. Expected rateof snag fall was estimated using the model for the probability of asnag remaining standing (Mäkinen et al., 2006). The model(Mäkinen et al., 2006) gives a probability <1 for year zero, indicat-ing that some of the trees die while or soon after falling. We ap-plied the same models in both natural and managed stands.

Down wood decomposes according to an empirical stage-basedmodel we developed and fitted as described in Appendix A. Themodel assumes that the decomposition of Norway spruce startswhen the snag falls to ground. Data used in model parameteriza-tion showed little decomposition of Norway spruces while theyare standing, which is reasonable as conifer trees tend to dry outwhile standing. Initially, fallen logs belong to decay class 1 (unde-composed or little decomposed). Thereafter, the model predictsprobabilities for a log belonging to decay classes between 1 and5 (thoroughly decomposed, but can still be identified as a log) asa function of time. We assumed that stumps decompose at thesame rate as logs.

The data showed little dependency between decay class of a logand its diameter. We thus assumed that larger logs have longerdecomposition times only because their expected time to remainin the snag mode (when they do not decompose) is longer. How-ever, if small logs decomposed quicker than large logs (whichwas not supported by our data), not only the stocks of small deadwood would be smaller, but also species dependent on small logswould be exposed to higher temporal variability, especially inmanaged forests where small wood input is dominated by harvestresidues. Smaller logs also tend to be lost from polypores earlierthan larger logs because they are covered earlier by ground vege-tation, but we had no data to assess this effect. However, this effectpotentially lessens the role of small logs to the polypore diversity,by decreasing the time frame and quantities of early decay smalldead wood and by decreasing the quanties of late decay small deadwood available for polypore species.

In Appendix A, we fit the log decomposition model separatelyfor Norway spruce (Picea abies), Scots pine (Pinus sylvestris) andbirch species (Betula sp.), although here in the main section weconsider spruces, and polypore species inhabiting spruce.

2.3. Cases simulated

We simulated the growth and mortality of trees in a typicalNorway spruce stand on mesic site in Central Finland. Stand man-agement followed current silvicultural scenarios (TAPIO, 2007).Thinnings were made by removing trees from the lower canopy,and the stand was clear-cut at the age of 66 years when it had amean diameter 29.1 cm (at 1.3 m height, and weighted by trees’basal area). Preliminary analyses showed that there is little differ-ence in quantities in tree mortality (they are low anyway) betweendifferent thinning scenarios so we settled with the most typicalscenario.

In order to compare the managed forests to unmanaged forests,we also made scenarios for unmanaged Norway spruce stands, byassuming that the forest is in steady state, i.e. it produces dead

wood at a constant rate. The rates were estimated from experimen-tal data (Peltoniemi and Mäkipää, 2011).

We also generated intermediate scenarios between natural andmanaged forests, where we combined mortality information oftrees from managed and natural scenarios in varying proportionsof area (location within stand not specified). This was conductedby allocating, for example, a semi-managed scenario 50% of man-aged forest dead wood input and 50% of natural forest dead woodinput. We applied the corresponding proportions to harvestresidues and stumps. Other two intermediate scenarios were3=4-managed and 1/10-managed corresponding to selective har-vesting. Canopy openness and number of stumps were estimatedas weighted averages according to these ratios.

When applying the polypore occurrence probability models, weestimated the basal area of the managed stand with the model andscaled it to interval from �2 to 1.5 by using the highest basal areawe found in the material from natural forests (60 m2) as the upper97.5% value. Natural forest stand had the standardized basal area ofzero, because old natural forests usually contain a complex struc-ture with openings and complete canopy closures. Standardizedbasal area of cut stumps was set to zero for managed forest and�2 for natural forest. Surrounding forest ages were set to 0 in allcases, which means that we assumed that the surrounding forestages correspond to mean in data the species models were basedon (Norden et al., 2013). Simulations were made for Central Fin-land, so the long-range connectivity terms was set to 0, whichmeans that our location also corresponds to the mean of speciesmodel data.

We simulated stand for three consecutive rotation periods, eachrepeating the same harvest regime. The first rotation started withan empty dead wood stock. Deadwood stocks stabilized withinone rotation period close to their final steady state. When we plot-ted predictions related to polypores, they refer to predictions madefor the third cycle.

Stand simulations and prediction of mortality were made withMotti stand simulator (Hynynen et al., 2005), all other calculationswere implemented in R (R Development Core Team, 2011).

3. Results

3.1. Dead wood dynamics

Harvests created large pulses of small diameter harvest residues(Fig. 1). Natural mortality of small trees leveled to that of naturalforest but only for a short period after which first thinning de-creased this rate to negligible. Managed stand produced marginallylarge dead wood, and some medium-sized dead wood. Expectedsnag fall rates lagged behind the actual mortality events so that alarge proportion of snags remained at the site at the time ofclear-cutting.

Deadwood stocks of different decay class and dead wood sizesdistributed unevenly during the rotation period (Fig. 2). Densityof small snags peaked 20 years earlier than that of medium-sized.Medium-sized and large snags were nearly absent at the middle ofthe rotation period. Fine down wood stocks faced their rotationminimums approximately at the same time as medium-sized snagstock. Medium-sized down wood provided some continuity tosmall dead wood across decay classes, but e.g. stocks of late decayclass down wood were still at low level in both groups at the end ofthe rotation.

The maximum stock of snags in managed forest was less than 1=4

of the stock estimate in natural forest (Fig. 3). This was reflected inconsequent down wood stocks of natural origin (Fig. 4). Largenumber of harvest residues compensated some of these differences(Fig. 1, top panel).

CW

D g

ener

ated

in h

arve

sts,

(a h

a)−1

0 10

0 3

00

500

Tree topsSmall stumpsMedium stumpsLarge stumps

Nat

ural

mor

talit

y, a

−1

S

M

L

0 1

2

3 4

5

6

7 Small treesMediumLarge

Snag

fall

rate

, a−1

L

0.0

0.5

1

.0

1.5

Years from final harvest0 20 40 60 20 40 60 20 40 60

Fig. 1. Dead wood production and snag fall rates in managed forest during threerepeated (and similar) rotation periods. Average natural mortality rates inunmanaged forests (Peltoniemi and Mäkipää, 2011) are presented on right forsmall (S), medium (M) and large (L) dead wood. Snag fall rates are in equilibriumwith these production rates, meaning that the snags fall at the corresponding rates.

Expe

cted

no.

of s

nags

, ha−1

010

2030

4050

Expe

cted

num

ber o

f do

wnw

ood

logs

, ha−

1

02

46

810

1214

0 20 40 60 20 40 60 20 40 60

Decay class12345

Log sizeSmallMediumLarge

Years from final harvest

Fig. 2. Continuums of dead wood stocks of different resource type, size (line width)and decay classes (gray scale) in managed stand.

NaturalRotation

startStart

+ 25aStart

+ 50a

Large, 29−Medium, 16−29 cmSmall, < 16 cm

Expe

cted

num

ber o

f sna

gs, h

a−1

050

100

150

200

Fig. 3. Comparison of snag stocks in natural and managed stand at different timeduring the rotation.

No.

of s

mal

l dow

nwoo

d, h

a−1

0

10

20

30

40

5

0

60

No.

of m

ediu

m d

ownw

ood,

ha−1

0

5

10

15

1 2 3 4 5

NaturalManaged, rot. startManaged, 25 yearsManaged, 50 years

No.

of l

arge

dow

nwoo

d, h

a−1

0

2

4

6

8

10

Decay class

Fig. 4. Comparison of down wood stocks in natural and managed forest at differenttimes during the rotation.

526 M. Peltoniemi et al. / Forest Ecology and Management 310 (2013) 523–530

Fine down wood material that had not decomposed yet wasaccumulated to the end of the rotation, some of it remaining afterthe regeneration of a stand, whereas down wood at late decay clas-ses decreased to less than half from its peak at young stand(30 year) by the age 60 years at the end of the rotation (Fig. 2).

3.2. Probability of species occurrences

Expected number of polypore species in managed forest washighly variable during the rotation period peaking soon after

clear-cutting and being lowest in middle-aged (30–40 years)stands (Fig. 5, see also Appendix B for species-specific predictionsand functional groupings). We predicted the peak species numberleveling to that in natural forests in recently clear-cut stand withextremely high number of harvest residues, legacy dead wood

No.

of s

peci

es, p

lot−1

NaturalManaged3/4−managed, 1/4 natural dynamics1/2−managed, 1/2 natural dynamicsMildly managed, 9/10 natural dynamics0

510

1520

25

0 20 40 60 20 40 60 20 40 60

Fig. 5. Expected number of polypore species on managed and natural plots. Naturalforest scenario started from an empty dead wood stock while managed scenariostarted from the estimated stock of harvest residues. Two management cycles werethen used to run the dead wood stock and species expectations to steady state, andthird cycle provides fully comparable results between the scenarios.

amylap

antserantsin

fomros

glosep

iscben

pospty

pheferphenigposcaepostep

0.0

0.2

0.4

0.6

0.8

1.0

0 15 30 45 60

NaturalManaged

Prob

abilit

y of

occ

urre

nce

Time elapsed from clear−cut, years

antsin

glosep

postep

iscben

pospty

pheferphenig

antser

poscae

amylap

fomros

Fig. 7. Species-specific probability curves for occurrence of a species in a managedstand and mean levels in a natural-like stand. Species appearing in figure areAntrodia serialis (antser), Postia caesia (poscae), Phellinus ferrugineofuscus (phefer),Phellinus nigrolimitatus (phenig), Antrodia sinuosa (antsin), Postia tephroleuca(postep), Gloeophyllum sepiarium (glosep), Ischnoderma benzoinum (iscben), Postiaptychogaster (pospty), Fomitopsis rosea (fomros)�, Amylocystis lapponica (amylap)�.Different line-types are used to group species responding differentially tomanagement. �Threatened species.

M. Peltoniemi et al. / Forest Ecology and Management 310 (2013) 523–530 527

from the previous rotation, and an open canopy. Leaving some ofthe stand unmanaged increases the number of species more thancould be expected solely based on ratio of area of managed andunmanaged fractions (Fig. 5). Expected number of species thatare classified as vulnerable or near-threatened was low in all man-agement-scenarios and they were relatively more influenced bymanagement than other species (Fig. 6).

Our models that predicted probability of occurrences of thetarget species showed that some species, e.g. Antrodia serialis andPostia caesia, can survive in managed forests although their proba-bility of occurrence declined in the middle aged stands (Fig. 7, andApp. Fig. B1). On the other hand, species like Fomitopsis rosea andAmylocystis lapponica that had intermediate probabilities of occur-rence in unmanaged forest, were predicted to be almost absent inthe managed stands (Fig. 7, and App. Fig. B1). Species that hadhigher probability of occurrence in the unmanaged stands (e.g.Ischnoderma benzoinum, Postia ptychogaster) may also survive inthe managed forests at the beginning of the rotation, but afterthe harvest residues and stumps had decomposed, their probabili-ties greatly declined. This was also a case with Antrodia sinuosa,Gloeophyllum sepiarium, Postia tephroleuca, which had high proba-bilities in natural forest. Finally, species like Phellinus ferrugineofus-cus and Phellinus nigrolimitatus had clearly lower probabilities ofoccurrence in managed forests of all stand development phasesthan in natural forests.

No.

of s

peci

es (V

U a

nd N

T), p

lot−1

0.0

0.5

1.0

1.5

0 20 40 60 20 40 60 20 40 60

Fig. 6. Predicted number of polypore species (top panel) and rare polypore species(bottom) in regime mixtures. VU means vulnerable species, and NT near-threatenedspecies (Kotiranta et al., 2010).

4. Discussion

We integrated an ensemble of polypore species into forest man-agement scenarios in order to evaluate the biodiversity implica-tions of forest management. We predicted that managed Norwayspruce stand hosts approximately 50% of species in natural-likeforest, on average over the rotation. This proportion was clearlyhigher at the start, and somewhat higher at the end of the rotationthan in the middle of the rotation. At mature managed forest, ourpredictions compare well with earlier studies, which have found72–100% more species in mature natural than in mature managedforests (Junninen and Komonen, 2011).

Our study and earlier studies analyzing biodiversity orientedforest management (Ranius and Kindvall, 2004; Ranius et al.,2003) demonstrate high temporal variability in forest dead woodstocks. Our study predicted that high variability of availability ofdead wood of diffent qualities (and variability of other stand prop-erties) generated high variation to the species-specific polyporeoccurrence probabilities. We predicted that the number of speciesbenefitting from the harvest residues and stumps early in the rota-tion was exceptionally high. This is consistent with the observationthat great majority of polypore species are be able to utilize man-made substrates besides natural substrates (Norden et al., 2013),and earlier observation that fine woody debris is important forpolypore diversity in managed Norway spruce forests (Kruys andJonsson, 1999). In addition to the large body of harvest residues,the polypore diversity peaks because of increase of species prefer-ring open habitats and species, which generally grow on deciduoushosts but in clear-cut areas may inhabit coniferous hosts (Berglundet al., 2011b). In natural forests, the early stage of succession hasalso been found to support high diversity of polypores (Junninenet al., 2006; Ylisirniö et al., 2012). Our simulations could be further

528 M. Peltoniemi et al. / Forest Ecology and Management 310 (2013) 523–530

extended to the quantification of the effects of biomass removal forbioenergy. Based on current simulations it seems evident that re-moval of tree tops, branches and stumps leads to decline of specieshabiting harvest residues early at rotation. This kind of results havealso been obtained in an empirical study, which measured smallernumber of polypore species and occurrences in sites where bio-mass was collected than in control clear-cuts (Toivanen et al.,2012).

Our simulations predicted a clear mid-rotation depression ofspecies diversity in managed forests. A great majority of the spe-cies, which had as high occurrence probability after clearcut as innatural forest (or even higher) declined steeply by mid-rotationwhen dead wood pools we low. To increase the polypore diversityat this stage requires creation of down and standing dead woodduring clear-cutting and thinnings, or as in our scenarios, by leav-ing a considerable proportion of a stand unmanaged. These mea-sures are also suitable for the more demanding species likePhellinus ferrugineofuscus and P. nigrolimitatus (Berglund et al.,2011a; Stokland and Kauserud, 2004), which prefer large naturallogs but can also utilize man-made logs. Even some of the mostdemanding red-listed species, like Amylocystis lapponica and Fomit-opsis rosea, may benefit from a large proportion of natural dynam-ics. However, polypore models also indicated that the majority ofthe red-listed species are so rare even in natural forests that man-agement actions in managed forests would have little influence.This was also conclusion by Ranius and Fahrig (2006) who showedthat the most demanding species require amounts of dead woodthat are impossible to maintain in managed forests. For these spe-cies a functional network of forest conservation areas followingnatural dynamics seems to be the best way to ensure viablepopulations.

Simulated average dead wood stock was somewhat higher thanis measured in national forest inventory, but the estimates are notcomparable. According to national forest inventory, the dead wooddensity is 3.5 m3 ha�1 in central parts of Finland (Finnish StatisticalYearbook of Forestry, 2012), which includes all forest types and ex-ludes small diameter dead wood and harvest residues by recordingdeadwood with diameter >10 cm. We simulated that natural origindead wood stock was on average 7.3 m3 ha�1 during the rotation(6.6–8.7 m3 ha�1), which included dead wood of all sizes. Differ-ences of dead wood stocks may also be caused by the tendencyof smaller dead wood to escape inventories because it is more eas-ily covered by ground vegation, and by the fact that dead wood isoften collected by forest owners. These effects were not included inthe simulations, but must have been also recorded to some extentin the data the species models were based on. Inclusions of theseeffects would decrease the role of small dead wood on speciesdiversity in simulations.

We assumed that natural forest provides a continuous selectionof various dead wood substrates for polypore (excluding man-made residues). It can be questioned how continuous such deadwood supply is in natural forests as natural forests can follow var-ious disturbance dynamics. Aakala (2010) studied this with modelanalyses on two natural Norway spruce stands with contrastingpatterns of tree mortality, one dominated by stable mortality pat-tern, whereas the other dominated by episodic mortality. Bothsites still maintained considerable dead wood stock, especially inintermediate-to-late stages of decomposition. Furthermore, themajority of the natural boreal Norway spruce forests tend to be dri-ven by small scale gap dynamics (Kuuluvainen and Aakala, 2011),which generates steady background mortality. For example, in thematerial used here to estimate mean mortality rate in natural-likeforests (Peltoniemi and Mäkipää, 2011), large episodic mortalityevents were absent, although the data consisted altogether 57 sites(900–2500 m2), which were monitories for 7–15 years. Data col-lected in natural Norway spruce stands in Sweden also showed

that there is a high and continuous supply of all coarse woody deb-ris qualities (Jonsson, 2000). Another study from Sweden, appliedsimilar steady-state assumption and modelling approach andobtained a mean stock of coarse woody debris 78 m3 ha�1 for sixold-growth spruce forests (Ranius et al., 2004). Based on our mor-tality data, we simulated a steady state total dead wood stock 91m3 ha�1.

One could be presume that the largest uncertainties of deadwood simulations are generated by dead wood decompositionrates, because these rates are generally based on much smallerdatasets than stand growth and mortality estimates. It has beenmeasured earlier that it takes on average 40 year in Mid-NorthernSweden (s.d.14 years) before dead logs are soft and have crevicesand pieces are lost (Kruys et al., 2002). Our model predicted thatmean sized logs reached decay class 4 (that roughly correspondsto previous log description) in 39 years (Appendix A), given thatthey first stay as undecomposed snags for 18 years, which presentsthe highest fall probability according to Mäkinen et al. (2006).

Individual polypore species predictions should be taken asindicative, and analyses concentrate on groups of species. Polyporemodels had been fitted to data, which had varying number ofobservations for individual species (Norden et al., 2013), so theymay not describe realistic environmental response in every case.Models were neither fine-tuned for individual species, rather thesame model form was used for all species. This may explain vari-able number of insignificant parameters in the models. Severalprocesses related to polypore life-cycle remain currently unquanti-fied in models, too. It is well known that there exists communitysuccession among polypore species, some species tending to followeach other in a decay continuum (Niemelä et al., 1995; Renvall,1995). The succession pathways may partially follow from similar-ities in species preferences and dispersal strategies, and partiallyfrom co-evolutionary aspects not explained by the successionalchange of substrate and environment (Ovaskainen et al., 2010),which could result either in higher or smaller number of speciesin real forests than we predicted based with current polypore mod-els. Limited information exists currently to include relationshipsbetween polypore or other wood-inhabiting fungi (antagonistic,symbiotic, mutualistic, and facilitating), or even within-speciestemporal correlations of presences to models. More realistic pre-diction of polypore succession may require identification of differ-ent decay pathways and rates, which may be partially driven bydisturbance regime (frequency, variability and extent), consequentdecay wood continuum, and species dispersal and colonizationstrategies on new substrate. None of these processes and relation-ships can be incorporated into models at present, and we are con-fined to analyzing how average forests response, rather thananalyzing individual cases.

The long history of intensive forest management has changedthe living environment of most forest-dwelling species, with a con-sequence that many polypore species will decline further and somewill disappear from the region. Polypore occurrence models, on theother hand, were based on data that records a snap-shot of thepolypore species occurrences at the time of sampling. Given thereunderlies an extinction debt threathing several specialist species,the models based on that data will predict too optimistic futurefor these species.

A natural way forward with predicting polypore diversity seemsto be incorporation of spatial aspects in more detail to models.Many studies have shown that the structure of the surroundinglandscape influences the occurrence of polypore species. For exam-ple, many red-listed species seem to be highly dependent on thespatial and temporal connectivity of suitable substrates (oftenlarge fallen logs) both at local and at landscape scale (Edmanet al., 2004; Norden et al., 2013; Penttilä et al., 2006). Thus, theinclusion of the characteristics and spatiotemporal availability of

M. Peltoniemi et al. / Forest Ecology and Management 310 (2013) 523–530 529

suitable substrates and habitats both at local and landscape scaleare important components when improving the models to predictthe polypore diversity. In addition, information on the dispersaland colonization abilities of species may be highly important be-cause in spite of the huge amounts of spores produced the dis-persal and especially the colonization ability of wood-inhabitingfungi seems to be limited (Edman et al., 2004; Norros et al.,2012). Landscape models (Scheller and Mladenoff, 2007) providea natural framework for evaluating persistence of polypores inlandscape when information about polypore migration grows,and how it is influenced by mechanical wind dispersal and releaseand deposition of basidiospores (Kuparinen, 2006). Futurelandscape models are also likely to include more detail related totemporal dynamics of stand and its management. While moremechanistic models based on dispersal-colonization dynamicsare missing, landscape models could make use of informationabout spatial connectivity in current empirical models of polyporeoccurrence probabilities by simulating forests in more detail. Thiskind of analyses would benefit from more detailed within standspatial correlations of polypore occurrences (e.g. Edman and Jons-son, 2001; Jönsson et al., 2008).

Forest management has a clear and species-specific influenceon polypore species occurrence, as it directly influences the stocksof resource these species are utilizing. Our study suggests that wisetemporal management of dead wood supply could support thepresence of several species with different habitat requirementsthat are not frequently found in managed forests. Even higherexpectations could be met with wise landscape managementbecause uniform conservation measures may not be the most effi-cient way of maintaining high polypore diversity over a region ofinterest. Incorporation of information about mean occurrence ofprobabilities of species based on resource dynamics should be use-ful also in landscape context but further improvements requireinformation of dispersal–colonization dynamics of individualspecies.

Acknowledgements

Study was funded by the Academy of Finland (Project Number140766). We thank Jouni Siipilehto for preparing Motti manage-ment scenario, Jogeir Stokland for comments on the manuscript,and Otso Ovaskainen for providing original model parameters forpolypore models.

Appendix A. Supplementary appendices

Supplementary data associated with this article can be found,in the online version, at http://dx.doi.org/10.1016/j.foreco.2013.08.053.

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