Response of habitat quality to mixed severity disturbance regime in Norway spruce forests

Abstract

Natural disturbances change forest habitat quality for many species. As the extent and intensity of natural disturbances may increase under climate change, it is unclear how this increase can affect habitat quality on different spatial scales. To support management tools and policies aiming to prevent habitat loss, we studied how habitat quality develops in the long run depending on the disturbance severity using a space‐for‐time substitution approach.
We explored the effects of time since disturbance (0–250 years) and disturbance severity (20%–100% canopy removal) on structure‐based habitat quality indicators in European primary Norway spruce Picea abies forests using 1000 m² circular plots in hierarchical design (a total of 407 plots in 35 stands). Disturbance history was reconstructed from tree cores. Habitat quality indicators were modelled as a function of the severity of the most severe disturbance and the time since this disturbance. We hypothesised that high within‐stand habitat heterogeneity is formed by different successional stages after disturbances of various intensities.
The results showed a U‐shaped response of habitat quality to post‐disturbance habitat succession on the plot scale. The decline deepened with disturbance severity. The U‐shape response occurred in: large tree occurrence, amount of standing and lying deadwood, diversity of understory and understory openness. The spatial diversity in disturbance parameters increased spatial diversity of habitat quality on a stand level as expected. This high within‐stand habitat heterogeneity also decreased with increasing age of the most recent disturbance. This suggests that the absence of young successional stages results in the absence of some important elements for biodiversity, for example sun‐exposed snags.
Synthesis and applications. Our results demonstrate that currently intensifying natural disturbance regime can consequently result in a lower habitat heterogeneity. In managed spruce forests after natural disturbances, we recommend at least the partial retention of biological legacies to preserve habitat heterogeneity and to avoid uniform and dense plantations resulting in a greater homogenisation. To emulate the natural disturbances pattern, spruce forests should be managed with a wide range of harvested patches of the size limited by a local natural disturbance regime creating spatial heterogeneity.

Full text

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J Appl Ecol. 2023;60:1352–1363.wileyonlinelibrary.com/journal/jpe
Received: 19 Februar y 2022
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Accepted: 17 March 2023
DOI : 10.1111/136 5-2664.144 09
RESEARCH ARTICLE
Response of habitat quality to mixed severity disturbance
regime in Norway spruce forests
Radek Bace1 | Jenyk Hofmeister1 | Lucie Vitkova1 | Marek Brabec2 |
Kresimir Begovic1 | Vojtech Cada1 | Pavel Janda1 | Daniel Kozak1 |
Martin Mikolas1,3 | Thomas A. Nagel1,4 | Jakob Pavlin1 | Ruffy Rodrigo1,5 |
Ondrej Vostarek1 | Miroslav Svoboda1
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2023 The Authors. Journal of Applied Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.
1Department of Forest Ecology, Faculty
of Forestry and Wood Sciences, Czech
University of Life Sciences, Praha, Czech
Republic
2Academy of Sciences of the Czech
Republic, Department of Statistical
Modeling, Institute of Computer Science,
Prague 8 , Czech Republic
3Prales, Rosina, Slovakia
4Department of Forestry and Renewable
Forest Resources, Biotechnical Facult y,
University of Ljubljana, Ljubljana, Slovenia
5Department of Forest Science, Biliran
Province State Universit y, Biliran,
Philippines
Correspondence
Radek Bace
Email: bace@fld.czu.cz
Funding information
Czech Science Foundation, Grant/Award
Number: 21- 27454S; TRANSFER MSMT
LTT2 0 01 6
Handling Editor: Pieter De Frenne
Abstract
1. Natural disturbances change forest habitat quality for many species. As the ex-
tent and intensity of natural disturbances may increase under climate change, it is
unclear how this increase can affect habitat quality on different spatial scales. To
support management tools and policies aiming to prevent habitat loss, we studied
how habitat quality develops in the long run depending on the disturbance sever-
ity using a space- for- time substitution approach.
2. We explored the effects of time since disturbance (0– 250 years) and disturbance
severity (20%– 100% canopy removal) on structure- based habitat quality indica-
tors in European primary Norway spruce Picea abies forests using 1000 m2 circular
plots in hierarchical design (a total of 407 plots in 35 stands). Disturbance history
was reconstructed from tree cores. Habitat quality indicators were modelled as a
function of the severity of the most severe disturbance and the time since this dis-
turbance. We hypothesised that high within- stand habitat heterogeneity is formed
by different successional stages after disturbances of various intensities.
3. The results showed a U- shaped response of habitat quality to post- disturbance
habitat succession on the plot scale. The decline deepened with disturbance
severity. The U- shape response occurred in: large tree occurrence, amount of
standing and lying deadwood, diversity of understory and understory openness.
The spatial diversity in disturbance parameters increased spatial diversity of habi-
tat quality on a stand level as expected. This high within- stand habitat hetero-
geneity also decreased with increasing age of the most recent disturbance. This
suggests that the absence of young successional stages results in the absence of
some important elements for biodiversity, for example sun- exposed snags.
4. Synthesis and applications. Our results demonstrate that currently intensifying
natural disturbance regime can consequently result in a lower habitat heteroge-
neity. In managed spruce forests after natural disturbances, we recommend at

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1 | INTRODUC TION
Disturbance regimes in temperate forests mainly shift to more in-
tensive, frequent and large disturbance events due to ongoing cli-
mate change, especially through drought- induced insect outbreaks
and wildfires (Johnstone et al., 2016; Thom et al., 2017). Altered
disturbance regimes therefore increase the uncertainty about fu-
ture forest stand dynamics. More intensive disturbance regimes can
lead to the loss of forest diversity as well as to the loss of native
species (Socolar et al., 2016). Understanding the natural changes in
species diversity across disturbance severities and post- disturbance
successional gradients is an essential prerequisite for the evaluation
of potential impacts of global climate change resulting in the inten-
sification of disturbance regimes and reliable predictions of future
biodiversity dynamics (Thom et al., 2017).
Structural forest components in the form of large trees, snags,
lying deadwood, open space and diverse understory increase local
heterogeneity and consequently provide a richer diversity of hab-
itats and species; these structural features are thus considered to
be useful biodiversity indicators (McElhinny et al., 20 05). The con-
cept of using structure- based indicators as a proxy for biodiversity
has not yet been subjected to more robust testing (Gao et al., 2015;
Zeller et al., 2022). Nevertheless, deadwood volume and diversity,
higher tree age and increasing openness of the canopy significantly
increase the diversity of key taxonomic groups (Gao et al., 2015;
Hofmeister et al., 2015). An additional important attribute is the
diversity of undergrowth formed by plants and natural regenera-
tion (McElhinny et al., 2005). These habitat characteristics make up
habitat heterogeneity or ‘habitat quality’. We use the term ‘habitat
quality’ to describe the potential of biodiversity that may or may not
be met according to the lo cal heterogeneity in fores t struc tur al attri-
butes. Assessing the potential of biodiversity through habitat quality
has the advantage that, compared to directly measured species rich-
ness, it is not affected by circumstances such as population size or
population isolation (Pardini, 2017 ) and constrained by immigration
credit and extinction debt (Jackson & Sax, 2009).
The complex forest structure that is rich in high quality habitats
is traditionally associated with late- successional stages in temperate
forests (Franklin et al., 2002) but also with stages that follow shortly
after disturbances (Swanson et al., 2011). Severe disturbances often
dramatically increase the amount of standing dead trees and lying
woody debris and post- disturbance early seral open forest provide
a range of habitats for a number of conservation- dependent spe-
cies (Swanson et al., 20 11; Winter et al., 2015). However, over time,
deadwood deteriorates, live trees recruit and grow and thus pre-
vent the penetration of light and heat into the understory (Hilmers
et al., 2018). Post- disturbance successional trajectories vary de-
pending on the process of natural regeneration (Donato et al., 2012).
If the site is reforested by a vertically homogeneous cohort of nat-
ural regeneration that quickly and completely fills the space (Bače
et al., 2015), a decrease in habitat quality can be expected. If, on the
contrary, the process of natural regeneration is spatially and tem-
porally heterogeneous with long- lasting tree- less patches, then the
forest can still preser ve the stand complexity and the habitat quality
decline may not occur (Donato et al., 2012).
Long- term studies of the impact of natural disturbances on bio-
diversity are rare for many reasons such as the absence of long- term
non- intervention after natural disturbance in European forests or
the shortness of time series (Christensen, 2014). This is one of the
reasons why often conflicting results were reported (e.g. Monk, 1967
found the increase of diversity with the time since disturbance; while
Habeck, 1968 found the opposite trend). The relationship between
disturbances and biodiversity can develop into a range of forms de-
pending on type, intensity, size and timing of the disturbance (Miller
et al., 2011). However, the study of these characteristics is lacking
in some natural ecosystems. Therefore, we are unable to apply the
models into practice and we are currently unaware whether the fre-
quently observed increase in biodiversity after disturbance is fol-
lowed by its decline or not. There are only very few studies dealing
with long- term biodiversity response (exceeding 100 years) to nat-
ural disturbances; in addition, they only deal with the biodiversity
responses after fires in boreal forests, while providing conflicting
results (see appendix S2 in Thom & Seidl, 2016).
It has been hypothesised that a variety of possible forest
structures and developmental stages in space would maximise
the gamma diversity at the stand scale (Roberts & Gilliam, 19 95).
For example, if only disturbances with low variability in size, fre-
quency or intensity occurred, a large scale- level diversity would
likely be reduced since the species dependent on the extremes of
disturbance regime would be eliminated (Roberts & Gilliam, 1995).
The premise that the habitat heterogeneity of the landscape
(equivalent of the gamma- level biodiversity) is related to the
number of distinct patch types formed by diversified (in time and
space) disturbance regime is obvious; nonetheless, it has not yet
least the partial retention of biological legacies to preserve habitat heterogeneity
and to avoid uniform and dense plantations resulting in a greater homogenisation.
To emulate the natural disturbances pattern, spruce forests should be managed
with a wide range of harvested patches of the size limited by a local natural dis-
turbance regime creating spatial heterogeneity.
KEYWORDS
biodiversity, canopy openness, disturbance regime, forest ecology, forest structure, habitat
quality, Picea abies (L.) Karst, spatial heterogeneity

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been tested in a real ecosystem (Willig & Presley, 2017). European
Nor way spruce (Picea abies (L.) H.Karst.) forests represent an ideal
ecosystem to test this premise since they have been influenced
by only two types of natural disturbances (i.e. windthrow and
bark beetle— Ips typographus L.) leaving similar legacies follow-
ing a disturbance (Čada et al., 2020). Both disturbance drivers kill
mainly adult trees and leave advanced regeneration intact (Bače
et al., 2015). In this study, we quantify the disturbance severity
as a percentage of the canopy removed as an evaluation of the
impact of disturbance drivers (Graham et al., 2021).
In this paper, we specifically want to improve knowledge on suc-
cessional sequence (~200 years) of habitat quality following natural
disturbance in its entire range of intensities. We focused on the
remote remains of Europe's primary forests that have been unaf-
fected directly by humans and evaluated the current habitat quality
levels and tracked disturbance history. We used dendrochronolog-
ical reconstruction for time substitution using a large dataset. This
dataset enabled us to use a dendrochronological proxy that covers
the entire disturbance severity gradient and to extend outlook for
the future development of recently heavily disturbed sites. This is
to enable forest management to truly mimic nature and not to use
only selected phases of forest stand development (e.g. old- growth)
as a standard (Roberts & Gilliam, 1995). It should be stressed that
here we study the influence of the historical disturbance regime on
the current species habitat quality at the plot level (1000 m2) as well
as the stand level (hundreds of hectares). We study the “biodiver-
sity potential”, which may or may not be fulfilled. We are fully aware
that real biodiversity is also influenced by the landscape context (e.g.
proportion of open areas among stands).
Our aim was to explain how the past disturbances drive the
current habitat quality. We hypothesised that forest habitat qual-
ity changes over time and exhibits a U- shaped response follow-
ing a disturbance and that the disturbance severity increases the
magnitude of the effect (Donato et al., 2012). We expect habitat
quality to be high in the beginning because of the high amount of
deadwood and open patches; low in the middle because of large
deadwood decomposition, low light and heat in the understory;
and high at the end of the cur ve because of the high complexity of
late- successional stages. We further hypothesised that, on the stand
scale, the stands affected by most diversified disturbance history
are expected to have the highest within- stand habitat heterogeneity
(Roberts & Gilliam, 1995) and consequently have higher species pool
since different species use different successional stages (Hilmers
et al., 2018).
2 | MATERIALS AND METHODS
2.1 | Study area and design
The study areas were selected in the remnants of European pri-
mary mountain forest that span between north- western (Germany)
to south- eastern (Romania) Europe. The altitude of the selected
study sites ranges between 1000 and 1700 m a.s.l. in Germany and
Romania, respectively. The average annual precipitation is between
1000 and 2000 mm with the annual temperatures ranging between
2 and 4°C (Janda et al., 2 019).
Study polygons (henceforth stands) were delineated using pre-
vious inventory of primary forest (Mikoláš et al., 20 19). The primary
forests inventory contains national maps of protected areas, recent
satellite images and field- based surveys. The original aim of the in-
ventory was to decide for each stand if it fulfilled the criteria fora
primary forest (e.g. absence of stumps; Mikoláš et al., 2019). All
study plots are included in primary forest dataset of the REMOTE
project (Remote Primary Forest, 2019). Each stand included in
this study contains 5– 22 circular plots with the area of 1000 m2
(Appendix 2: Table S1). Plots were placed randomly within a regu-
lar grid (141.4 × 141.4 m) at a restricted random position (the inner
0.49 ha core in each 2ha cell). The total number of 407 plots was
generated in the overall 35 stands.
Tree species composition in the study area was dominated by
Norway spruce, which accounted for 99.6% of the trees whose di-
ameter at the breast height (hereafter DBH) was ≥10 cm. The re-
maining minor proportion of tree species composition is formed by
rowan (Sorbus aucuparia L.) and stone pine (Pinus cembra L.) along
with several other species.
2.2 | Data collection
The DBH of all live and dead trees with the DBH ≥10 cm and their
species were recorded. The crown projection in four cardinal di-
rections of five randomly selected live trees per plot was meas-
ured. The decay stage of all standing and lying dead trees was
assessed according to Sippola and Renvall (1999). All deadwood
lying within the plot was recorded and its volume was calculated
by line intersect sampling (Van Wagner, 1968) using five 20 m long
lines directed outwards from the plot centre with the angle of 0°,
72°, 144°, 216° and 288°. Diameters (≥10 cm) at the point of inter-
section of the lyin g logs were re cor ded . One cano py he mis pherica l
photo was taken at the plot centre and five hemispherical photos
were taken on lines 12.1 m from the plot centre. Circular fisheye
lens (Sigma 4.5 mm F2.8 EX DC) was used to take the hemispheri-
cal photos 1.3 m above the ground. The coverage of six basic un-
derstory vegetation types (i.e. bilberry, grasses, ferns, mosses,
bare lit te r, and tre e regeneration) was estimated to the ne arest 5%
on the entire plot.
In order to complete dendrochronological analysis, 25 non-
suppressed live spruce trees (DBH ≥10 cm) were selected in each
plot. All live trees were cored on recently disturbed plots with a
smaller number of live trees. All tree cores were dried, cut by a core
microtome and measured following the standard dendrochronologi-
cal methods (Gärtner & Nievergelt, 2010).
Permission for the fieldwork was given by local authorities with
the help of our partners, listed on the website of the REMOTE proj-
ect (https://www.remot efore sts.org/partn ers.php).

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2.3 | Data analysis
2.3.1 | Dendrochronological analysis— Time since
disturbance and severity detection
Coordinated increase in ring increment sizes among non- suppressed
trees indicating changes in canopy cover was used to reconstruct
estimates of the time since disturbance. Two models of recruitment
to the forest canopy were used: (1) open canopy recruitment— trees
that initiated growth under a relatively open canopy shortly after
a canopy- opening disturbance event as indicated by a rapid initial
growth, or (2) release— trees that established in the shaded under-
story and recruited to the canopy through a gap forming later as
demonstrated by a slow initial growth followed by an abrupt growth
release (Lorimer & Frelich, 1989). A tree was classified as open can-
opy recruitment if the mean ring width of the fifth to fifteenth ring
from the pith exceeded the early growth rate threshold, as previ-
ously defined by comparing early growth rates in young trees sam-
pled in gaps versus those growing under a forest canopy (Janda
et al., 20 19; Appendix 3: Table S2). The year when the gap origin
tree was recruited to coring height (1 m) was used as proxy evidence
of a disturbance event. The year of a release from suppression was
determined to be proxy evidence of a disturbance event using the
absolute increase method (Fraver & White, 2005). The release was
a year in the growth- series when the difference between the fol-
lowing 10- year mean and the preceding 10- year mean peaked and
exceeded the threshold value that was defined as 1.25 times the
standard deviation using the growth differences (absolute increase
values) in the whole data set. We excluded growth increases that
lasted less than 7 years by comparison of lagged and leaded 10- year
means with the original preceding and following 10- year means. We
considered only releases that happened more than 20-years after
the potential rapid early growth rate. In addition, we considered only
releases before a tree reached the threshold diameter. The threshold
diameter was based on comparisons of tree diameters of currently
suppressed versus released trees (Čada et al., 2020). All the thresh-
olds used were region specific (Appendix 3: Table S2). Multiple
canopy accessions were allowed in deriving the disturbance chro-
nologies, because more than one disturbance may be needed for
Norway spruce to reach the canopy (Lorimer & Frelich, 1989).
The years of the disturbance proxy evidence were smoothed at
each plot using a running kernel density function. All the disturbance
proxy evidences were weighted by current tree crown areas; crown
area of a tree was divided by the sum of crown areas of all trees cored
on a plot. Crown area was modelled using regression between mea-
sured crown area and DBH (Appendix 3: Table S2). For the moving
30- year window, we calculated the density with a smoothing band-
width equal to five. From the resulting curve, we estimated the years
of individual disturbance events by extracting peak years before
which the curve was increasing for at least 5 years. We also required
that the distance between the two peaks was more than 10 years.
The severity of an individual disturbance event was estimated as
the relative canopy area disturbed on the plot, as determined by
summing the relative current crown areas of trees whose distur-
bance proxy evidence occurred within the 11- year window around
the peak (see Čada et al., 2020 for further details). This calculation
relies on the conventional assumption that most trees respond to
disturbances within a decade of the event and that the sum of the
relative current crown areas of trees that indicate past disturbance
is representative of the proportion of the plot previously disturbed
(Lorimer & Frelich, 1989). The maximum severity and its year were
used for further analysis. If the current canopy gaps on plots (caused
by recent disturbances) exceeded a dendrochronologically detected
maximum disturbance severity, the current openness was used as a
severity of the main disturbance.
2.3.2 | Habitat quality index
The habitat quality index construction follows the rules of McElhinny
et al. (2005). We selected a comprehensive set of five basic structural
attributes: (i) living trees dimensions diversity highlighting the old and
big trees, (ii) standing deadwood and its dimensional and decay di-
versity, (iii) lying deadwood and its dimensional and decay diversity,
(iv) diversity of the main understory vegetation types, and (v) under-
story light availability and its variability. These attributes were consid-
ered as strong predictors of biodiversity by several studies (e.g. Gao
et al., 2015). Habitat quality index was calculated on the basis of the
sum (for additive index) or multiplication (multiplicative index) of the
individual basic attributes (Figure 1). The additive biodiversity index
assumes that each biodiversity attribute can partially compensate
each other. Integrative (multiplicative) index more severely penalises
the plots with one or several attributes close to zero. Integrative ap-
proach is therefore more suitable for species that need the simultane-
ous occurrence of multiple attributes. Values of the basic attributes
were determined by the sum of the basic components (see Figure 1).
All five basic at tributes and all compone nt s wer e sc aled below one (di-
vided by max) to achieve the same weight for all attributes. Therefore,
all five attributes have the same weight in the summary index. The
first fifth (i.e. 20%) of the weight is represented by the standing dead
trees (wood volume and diversity). The standing deadwood is, for in-
stance, suitable habitat for a species- rich group of saproxylic beetles
(Jonsell & Weslien, 2003). The second fifth is formed by the lying
logs supporting saproxylic fungi (Pouska et al., 2011). Third fifth com-
prise s thick and old trees that are ty pi cally associated with lichens and
mosses (Zemanová et al., 2017). Fourth fifth represents diversity of
the main understory vegetation types (Halpern & Spies, 1995 ). And
finally, the last fifth is formed by light demanding species support-
ing, for example true bugs (Müller et al., 2008). The commonly used
attribute of tree species diversity was not used since the underlying
research was done in the single- tree species forest. We used digital
hemispherical photography and WinSCANOPY (Regent Instruments,
Inc.) software to obtain data on understory light availability. The mean
and standard deviation of ‘openness’ were calculated and used as the
response variable to detect mean and variability of light dis tribution in
the understory within the plot.

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2.3.3 | Statistical models
Spatial regression models were fitted in order to assess the response
of habitat quality indices to disturbance severity and the number of
years since the disturbance. We used a generalised additive mixed
model (GAMM, R package mgcv; Wood, 2015, 2017) to fit a linear
combination of smooth functions of the predictor variables (plus one
smooth interaction of time and severity), while additionally consid-
ering random spatial effects, which allowed us to account for sys-
tematic variability in space.
For i- th stand and j- th plot was used a model as follows:
Yij ~ N(μij, σ2), for habitat quality response,
log(
𝜇ij
)
=𝛽0+bi+sloc
(
xij,yij
)
+stime
(
timeij
)
+sseverity
(
severityij
)
+
s
time
∗
severity(
time
ij
, severity
ij)
b
i∼N
(
0, 𝜎
2),
where bi are (independent) random effects of individual stands;
sloc,stime,sse ve rit y, stime*severity are smooth functions to be estimated (pe-
nalised splines are used here); sloc, location with the x and y coodi-
nates: reflects the smooth spatial trend; stime, reflects the (smooth)
marginal effect of time since disturbance; sseverity, reflects the
(smooth) marginal effect of disturbance severity; stime*severity, reflects
the (smooth) interaction of time and severity (obtained as a tensor
product spline); and β0, σ2 parameters are estimated from data (to-
gether wit h the random effects and smooth functions), based on op-
timization of penalised likelihood function.
The habitat heterogeneity of each forest stand was evaluated
on the stand scale level. Habitat heterogeneity of the stand was
calculated as habitat index (Figure 1) using ranges among compo-
nents within a stand in the first step of the calculation. We tested the
dependence of the habitat heterogeneity on variability of the distur-
bance regime within stand using linear regression models with log-
transfomed response variables. The following variables were tested:
the variability of the time since the main disturbance (Gini coeffi-
cient); the variability of disturbance severities (Gini coefficient); the
interaction between time and severity; age of the latest disturbance
occurring within the stand (5% quantile of years since the main dis-
turbance); time of the earliest disturbance occurring within the stand
(95% quantile of years since the main disturbance); severity of the
least severe disturbance (5% quantile of severity); and severity of the
most severe disturbances (95% quantile of severity). Statistical analy-
ses and visualisations were performed in R 4.1.3 (R Core Team, 2022).
3 | RESULTS
3.1 | Plot scale
The decline of habitat quality in mid- seral stages following a severe
disturbance was detected (Figure 2, Table 1). Both additive and mul-
tiplicative habitat quality indices showed a significant difference be-
tween the low and high severity disturbance around 90 years after
a disturbance. Additive habitat quality index (Figure 2a; assuming
each habitat attribute can compensate for each other) was signifi-
cantly influenced by the time and severity interaction, which means
that the response with time is different for different disturbance
FIGURE 1 The graphical scheme showing how habitat quality indicators were constructed and calculated. Primary values of components
and attributes were scaled to the maximum of one to achieve that all the components and attributes have the same weight in the attributes
and final values. The range of components within the stand were used to express the variability and stress minimum and maximum values
for stand habitat heterogeneity indicators.

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severities. Habitat quality decline deepened with the disturbance
severity for this index. The response after a severe disturbance de-
creased at its minimum to approximately 70% of the initial maximum
value. The additive habitat quality index after a severe disturbance
was lower for most of the time than for plots permanently under
low severity disturbance regimes with the exception of the start (the
first 20– 30 years) and the end (more than 200 years after the main
disturbance). Multiplicative habitat quality index (Figure 2b) (which
penalises plots with a very low value of even a single attribute) was
higher at all the times for plots permanently under low severity dis-
turbance regime and showed a minimum of biodiversity at about
80 years after the main disturbance for all severity classes. The U-
shape response of both forms of habitat quality index after a high
severity disturbance was caused mainly by the following significant
aspects: dimension diversity of live trees, maximum dimension of
live trees, amount of standing deadwood, amount of lying dead-
wood, diversity of understory vegetation types, and canopy open-
ness (Table 2).
3.2 | Stand scale
The stand habitat heterogeneity significantly (linear regression:
p < 0.001, R2 = 0.38; Figure 3a) increased with the increasing vari-
ability of times since the main disturbance and with increasing
variability of disturbance severities (linear regression: p = 0.006,
R2 = 0.21; Figure 3b). The interaction between both disturbance
diversity measures was not significant (p = 0.563). These two
basic indicators of the disturbance regime variability proved to be
significant for the spatial diversity of the individual attributes of
habitat quality index. However, their mutually synergistic interac-
tion was not proved.
Some plots within the stands were recently disturbed which is
important for high stand habitat heterogeneity. The stand habitat
heterogeneity decreased with increasing age of the latest distur-
bances in the stand (Figure 4a). The stand habitat heterogeneity did
not change depending on the age of the oldest main disturbances
(Figure 4b). It should be noted, however, that all stands (except one)
had the oldest disturbances older than 150 years. The smallest se-
verity and the largest severity disturbance are both needed for the
high stand habitat heterogeneity (Figure 4c,d).
4 | DISCUSSION
Our study is the first one to test the effect of mixed severity distur-
bances on habitat quality. This study confirms the premise that forest
habitat quality changes over a long time and exhibits the U- shaped
response following the disturbance (Donato et al., 2012; Thom &
Seidl, 2016). Additive as well as multiplicative habitat quality indices
revealed a decline of biodiversity in mid- seral stages with a minimum
appearing around 90 years after severe disturbances (Figure 2). The
U- shaped response of habitat quality following a severe disturbance
was revealed in the case of understory diversity, large tree occur-
rence, and diameter diversity of both standing and lying deadwood.
Our study presents empirical evidence of the premise that spa-
tial and time variability of bark beetle and wind disturbances creates
high habitat heterogeneity (He et al., 2019; Willig & Presley, 2017).
The stand- level within- stand habitat heterogeneity increased with
increasing variability of both times and severities after disturbance
as expected (Figure 3). The habitat heterogeneity (a proxy of spatial
heterogeneity in species composition) decreased with increasing age
of the latest disturbances that had occurred in the stand (Figure 4).
This suggests that the absence of young successional stages results
in the absence of elements that are important for stand scale bio-
diversity; for example, high number of sun- exposed standing dead
trees (Table 2; positive effects of severity and U- shaped response of
the amount of standing deadwood).
4.1 | Habitat quality response to disturbance
history on a plot scale
The habitat quality response to the time since disturbance exhib-
ited the U- shaped pattern following a severe disturbance in the
European mountain spruce forests that we studied. This general
U- shaped response of biodiversity response to the sequence of
successional stages was also found in temperate forests of cen-
tral Europe (Hilmers et al., 2018). The relationship between bio-
diversity and the time since disturbance has been under debate
since the 1960's (e.g. Habeck, 1968; Monk, 1967). It is important
to note that a different trend to the one found by our study was
generally accepted in the past; that is, the peak in the diversity
of forest communities was expected 100– 200 years after the
FIGURE 2 The influence of time since
the main disturbance and the severity
of the main disturbance on additive
biodiversity indicator (a) and multiplicative
biodiversity indicator (b).

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disturbance had occurred (when elements of both the pioneer and
the late- successional communities are present) and that a down-
turn in both diversities takes place when the entire community
comprises of shade- tolerant climax species (Loucks, 1970). Some
studies focusing on temporal changes in diversity reported differ-
ent results; for example, Monk (1967) found diversity to increase
with the time since disturbance; Habeck's (Habeck, 1968) results
revealed an opposite (decreasing) trend; and Schoonmaker and
McKee (1988) reported a hump- shaped response of diversity dur-
ing the first 40 years following the disturbance. Such variation in
results is mainly due to the fact that diversity is not only a func-
tion of time since the disturbance but also, in particular, a func-
tion of type, intensity/severity, size and timing of the disturbance
(Miller et al., 2011), and species pool (Kuneš et al., 2019). The cur-
rent model (Miller et al., 2 011) supposes a wide diversity of pos-
sible relationships, including a hump- or U- shaped responses of
TAB LE 1 The influence of time since the main disturbance and the severity of this main disturbance on habitat quality indices. The
results displayed are explanatory variables included in the model along with their significance (based on F- tests; values with p < 0.05 are in
bold). The overall proportion of deviance explained (% dev) for the model is also presented. The estimated degrees of freedom (edf ) indicate
linearity: edf = 1 represents a linear relationship, edf ≥ 2 represents a significantly non- linear relationship.
Response variable Explanatory variable edf F value p value % dev
Habitat quality (additive index) Random spatial ef fect 2.0 1.6 0.208 44.6%
n = 407 Random effect of stand 24.3 3 .1 <0.0001
Smooth main effect of years since main disturbance 2.8 6.4 <0.0001
Smooth main effect of disturbance severity 2.3 10.9 <0.0001
Interaction between the main effects 2.8 3.2 0.029
Habitat quality (multiplicative
index)
Random spatial ef fect 2.0 0.2 0.823 38.5%
n = 407 Random effect of stand 26.6 4.8 <0.0001
Smooth main effect of years since main disturbance 3.7 7.0 <0.0001
Smooth main effect of disturbance severity 1.0 7.0 0.008
Interaction between main effects 1.0 0.8 0.899
TAB LE 2 The influence of time since the main disturbance and the severity of the main disturbance on individual aspects, which
contribute to the final biodiversity index.
Individuals aspects and indicators
Time since
disturbance
Shape after
high severity
disturbance
Shape after
low severit y
disturbance
Disturbance
severity
Effect of severity
on the indicator
value
Interactions
(disturbance
severity*time since
disturbance)
Dimension diversity (live trees) * UՈ*** −**
Maximum dimension (live trees) n.s. UՈ*** − *
Maximum age (live trees) n.s. *** −n.s.
Amount of standing deadwood n.s. UՈ*** +***
Decay diversity of standing
deadwood
n.s. n.s. n.s.
Dimension diversity of standing
deadwood
n.s. ** −n.s.
Amount of lying deadwood *** U U n.s. n.s.
Decay diversity of lying deadwood n.s. / \ ** +*
Dimension diversity of lying
deadwood
n.s. ** −n.s.
Diversity of understory vegetation ** UՈ*** −n.s.
Canopy openness ** U U n.s. n.s.
Canopy patchiness n.s. n.s. n.s.
Habitat quality (additive index) *** U _ *** − *
Habitat quality (multiplicative idex) *** U U ** −n.s.
Note: n.s.: p > 0.05; ⁎: 0.01 < p ⩽ 0.05; ⁎⁎: 0.001 < p ⩽ 0.01; ⁎⁎⁎: p ⩽ 0.001. U— U- shaped response; ꓵ— hump- shape response; /— increasing response;
\— decreasing response; _— constant response. The shape of response is shown if the time since disturbance or its interaction was significant on 0.05
level or lower. Effect of disturbance severity (+ positive, − negative) is shown if it was significant on 0.05 level or lower.

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BACE et al .
biodiversity to the time since disturbance. The U- shaped patterns
obser ved in our st udy likel y occu r in tem pera te forests wi th sim ila r
disturbance agents and successful natural regeneration processes.
The depth of the U- shaped response of habitat quality to the time
since disturbance was deepening with increasing disturbance severity.
Nonetheless, this was not the case for all plots (see small explained
variability; Table 1). The gradual decline of habitat quality is caused
by the degradation of post- disturbance biological legacies (e.g. dead-
wood, old large trees, understory diversity, and little light in the un-
dergrowth; Table 2), which happens over time and by the presence of
natural regeneration that is dense and uniform and whose develop-
ment is rapid (Donato et al., 2012). The variations of all of these factors
may contribute to the variability and relatively high habitat quality of
some of the heavily disturbed plots in the critical period of 90 years
after the disturbance. The initial setting of the biological legacies after
disturbance in the form of density and spatial structure of the regener-
ation process determine whether the self- thinning phase, manifested
by low light levels in the undergrowth, small deadwood fragments and
the abse nce of old and large trees occur s after an inte nsive disturba nce
(Turner et al., 1998). Although the low post- disturbance regeneration
densities have been recently reported due to the climate change in the
forests limited by drought in many regions (Rammer et al., 2021). This
is not the case for central European mountain spruce forests, where a
rapid and predominantly spatially uniform natural regeneration occurs
even after large and intensive disturbances accompanied by unprec-
edentedly dry seasons (Gelnarová et al., 2022; Moravec et al., 2021;
Zeppenfeld et al., 2015). Therefore, we can expect European mountain
spru ce for est s to ret ain rich open habitats afte r la rge disturbances onl y
temporarily. On the other hand, as the future climate is increasingly
conducive to massive insect outbreaks, young dense stands can be
disturbed soon and our projections based on the past will no longer be
valid (Sommerfeld et al., 2021).
4.2 | Stand- scale disturbance diversity and its
effect on habitat heterogeneity
The within- stand disturbance regime was variable thanks to a wide
range of disturbance severities or sizes and the timing of distur-
bances. This resulted in an observation of a greater habitat heter-
ogeneity and consequently also to a higher contribution of spatial
turnover in species composition. This is likely to be the first study
where the hypothesis testing the disturbance variability positively
influencing the habitat heterogeneity was executed on a stand scale
in primary forests (Willig & Presley, 2017). Our data showed that
the patch affected by a severe disturbance may have a low level of
habitat quality at a certain point of succession. However, severe
disturbances are necessary in order to contribute to overall habitat
heterogeneity on a large scale. This implies that a landscape perma-
nently under low severity disturbance regime may have a higher but
uniform plot level habitat quality and therefore a lower landscape
level heterogeneity. Our results also showed that forest stands that
had not been affected by a recent disturbance (i.e. several decades)
had a significantly lower habitat heterogeneity on a stand level com-
pared to stands that had experienced a full range of disturbance
times and severities. Those forest stands where no plots or just a
small number of plots have been recently disturbed can miss some
important biodiversity attributes such as the sun- exposed dead-
wood (Winter et al., 2015).
4.3 | Implications for the management of high
habitat quality in forest landscape
The lessons learnt from natural disturbance regimes are essential
for guiding forest management aimed at sustainability of ecosystem
FIGURE 3 Scatterplots of forest habitat heterogeneity of stand as dependent on (a) the variability of time since the main disturbance and
(b) the variability of disturbance severities. The log- transformed linear- regression R2- values and p- values are shown. Habitat heterogeneity
of stand was calculated as habitat quality index using ranges among components within a stand (Figure 1).
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BACE et al .
functions and biodiversity (Čada et al., 2020). Although we found a
decline in habitat quality following strong disturbances in primary
forests, it is unlikely to fall below the level of habitat quality as in
the case of planted forests. The major difference between dis-
turbances in managed and natural forests is in biological legacies
(Swanson et al., 2011); the presence of deadwood and the surviving
old and big trees is essential for biodiversity (Vitková et al., 2018).
The habitat quality index utilised in our study can provide informa-
tion on how to increase the biodiversity levels of planted forests.
Our paper demonstrates a substantial variability in stand- level
habitat heterogeneity in natural forest landscape due to various
levels of mixed- severity disturbance regime. The current paper
further demonstrates the importance of early- seral stages that are
necessary for specific habitat qu alit y af ter severe disturbances on a
st and scale. Earl y ser al st ages ar e charac te rised by high bi odiversity
levels since crucial biological legacies such as light and heat avail-
ability in the understory and resource richness are present (Müller
et al., 2008). The early seral stages are typically missing in planted
forests managed by fine- grained uneven- aged management (e.g. se-
lective cutting). Such a setting represents an example where forest
management does not mimic the full range of disturbance regimes
on a broader scale and consequently causes the loss of biodiver sity.
This was shown on the example of managed European beech (Fagus
sylvatica L.) forests where a coarser mosaic of different age classes
FIGURE 4 The dependence of habitat heterogeneity of stand on (a) age of the latest disturbances occurring within the stand (5%
quantile of years since the main disturbance), (b) age of the oldest disturbances occurring within the stand (95% quantile of years since the
main disturbance), (c) severity of the least severe disturbances (5% quantile of disturbance severity) and (d) severity of the most severe
disturbances (95% quantile of disturbance severity). The log- transformed linear- regression R2- values and p- values are shown. Habitat
heterogeneity of a stand was calculated as the biodiversity index uses the ranges among components within a stand (see Figure 1).
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Journal of Applied Ecology
BACE et al .
was more important for regional biodiversity than high within-
stand small scale heterogeneity (Schall et al., 2017).
Stands permanently under low severity disturbance regime
and stands affected by one severe disturbance are not able to pro-
vide a complete habitat for biodiversity (Roberts & Gilliam, 1995).
However, emulating them by selective cutting and large clear- cuts,
respectively, has been a widespread practice in forest management.
Retention forestry instruments with various sizes limited by a local
natural disturbance regime shall be adopted in order to mimic the
natural state and the natura l dynamic s by the pr act ice of for est man-
agement (Augustynczik et al., 2 019). Diversified forest management
at multiple spatial scales is a suitable way to face the increasing in-
tensity, size, and frequency of natural disturbances in the future.
AUTHOR CONTRIBUTIONS
Radek Bace, Jenyk Hofmeister and Miroslav Svoboda conceived the
ideas and designed the methodology; Radek Bace, Kresimir Begovic,
Vojtech Cada, Pavel Janda, Daniel Kozak, Martin Mikolas, Jakob
Pavlin, Ruffy Rodrigo, Thomas A. Nagel and Miroslav Svoboda col-
lected the data; Radek Bace, Marek Brabec, Pavel Janda, Vojtech
Cada and Ondrej Vostarek analysed the data; Radek Bace, Lucie
Vitkova and Jenyk Hofmeister led the writing of the manuscript. All
authors contributed critically to the drafts and gave their final ap-
proval for publication.
ACKNO WLE DGE MENTS
The study and its authors were supported by the Czech Science
Foundation (project No. 21- 27454S) and the project TR ANSFER
MSMT LTT20016. We thank all staff involved in the data collection
and their processing.
CONFLICT OF INTEREST STATEMENT
None declared.
DATA AVAIL AB ILI T Y STAT EME N T
Data available via the Dryad Digital Repository ht t p s : //doi.
org/10.5061/dryad.h70rx wdpb (Bace et al., 2023).
ORCID
Radek Bace https://orcid.org/0000-0001-6872-1355
Jenyk Hofmeister https://orcid.org/0000-0002-3915-5056
Lucie Vitkova https://orcid.org/0000-0002-6994-5067
Marek Brabec https://orcid.org/0000-0001-6367-5791
Kresimir Begovic https://orcid.org/0000-0001-7166-6190
Vojtech Cada https://orcid.org/0000-0002-3922-2108
Pavel Janda https://orcid.org/0000-0003-4732-6908
Daniel Kozak https://orcid.org/0000-0002-2622-370X
Martin Mikolas https://orcid.org/0000-0002-3637-3074
Thomas A. Nagel https://orcid.org/0000-0002-4207-9218
Jakob Pavlin https://orcid.org/0000-0001-8514-3446
Ruffy Rodrigo https://orcid.org/0000-0002-6539-1511
Ondrej Vostarek https://orcid.org/0000-0002-0657-0114
Miroslav Svoboda https://orcid.org/0000-0003-4050-3422
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SUPPORTING INFORMATION
Additional supporting information can be found online in the
Supporting Information section at the end of this article.
Appendix 1. Map of the study area.
Figure S1. Hierarchical distribution of the study plots (black dots) at
landscape, mountains and stand scale.
Appendix 2. List of stands and their environmental characteristics.
Table S1. List of stands and their environmental conditions.
Temperatures and precipitations were calculated as averages for
stands using the IIASA Climate Database. This grid- based database
was created from existing historical weather records. The weather
records from up to eight different sources were standardized,
ranked in quality, selected, interpolated and smoothed to fit a one-
half degree latitude/longitude terrestrial grid surface (Janda et al.,
2019).
Appendix 3. Region- specific parameters used for disturbance
history reconstruction.
Table S2. Region- specific parameters and threshold values to
calculate disturbance proxy evidence in individual tree growth
series.
Appendix 4. Schematic illustration of the forest structure on a plot
with high and low habitat quality index.
Figure S2. Sch emat ic il l ustratio n of th e for est str ucture on a pl o t with
high and low habitat quality index. Plots with high quality habitat
are characterised by high tree and undergrowth plant diversity,
the presence of old and large trees, live tree dimensions diversity,
volume and decay and dimension diversity of lying and standing
deadwood, more light levels and more variable light conditions in the
understory. On the other hand, plots with low habitat quality values
are represented by a simple forest stand structure.
Appendix 5. The graphical results of the GAMM models presented
Table 2.
Figure S3. The influence of time since the main disturbance, the
severity of the main disturbance and its interaction on individual
aspects which contribute to the final biodiversity index (see Figure
1 and Table 2 for aspect explanation). Only results where at least
one variable or their interaction were significant are shown. The
confidence intervals of the models for the two levels of disturbance
severity are depicted in gray.
How to cite this article: Bace, R., Hofmeister, J., Vitkova, L.,
Brabec, M., Begovic, K., Cada, V., Janda, P., Kozak, D.,
Mikolas, M., Nagel, T. A., Pavlin, J., Rodrigo, R., Vostarek, O.,
& Svoboda, M. (2023). Response of habitat quality to mixed
severity disturbance regime in Norway spruce forests.
Journal of Applied Ecology, 60, 1352–1363. htt p s : //d oi .
org /10.1111/1365-2664.14 409