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Old trees are important for biodiversity, and the process of their identification is a critical process in their conservation. However, determining the tree age by core extraction, ring counts, and eventually, cross-dating by means of dendrochronology is labor-intensive and expensive. Here we examine the alternative method of estimating determining tree age by visual characteristics for old Norway spruce and Scots pine trees. We used forest stands previously identified as “Old tree habitats” by visual criteria in Norwegian boreal forests. The efficiency of this method was tested using pairwise comparison of the age of core samples from trees within these sites, and within neighboring sites. Age regression models were constructed from morphological traits and site variables to investigate how accurately old trees can be detected. Cored trees in the Old-tree habitats were on average 41.9 years older than compared to a similar selection of trees from nearby mature forests. Several characteristics such as bark structure, stem taper and visible growth eccentricities can be used to identify old Norway spruce and Scots pine trees. Old trees were often found on less productive sites. Due to the wide range of environments included in the study, we suggest that these findings can be generalized to other parts of the boreal zone.
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Identifying old Norway spruce and Scots pine trees
by morphological traits and site characteristics
Eivind Handegard, Ivar Gjerde, Ole Martin Bollandsås & Ken Olaf Storaunet
To cite this article: Eivind Handegard, Ivar Gjerde, Ole Martin Bollandsås & Ken Olaf Storaunet
(2021): Identifying old Norway spruce and Scots pine trees by morphological traits and site
characteristics, Scandinavian Journal of Forest Research, DOI: 10.1080/02827581.2021.1996628
To link to this article: https://doi.org/10.1080/02827581.2021.1996628
© 2021 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group
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Identifying old Norway spruce and Scots pine trees by morphological traits and
site characteristics*
Eivind Handegard
a
, Ivar Gjerde
a
, Ole Martin Bollandsås
b
and Ken Olaf Storaunet
a
a
Department of Forest Genetics and Biodiversity, Norwegian Institute of Bioeconomy Research, Ås, Norway;
b
Faculty of Environmental Science
and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway
ABSTRACT
Old trees are important for biodiversity, and the process of their identication is a critical process in
their conservation. However, determining the tree age by core extraction, ring counts, and eventually,
cross-dating by means of dendrochronology is labor-intensive and expensive. Here we examine the
alternative method of estimating determining tree age by visual characteristics for old Norway spruce
and Scots pine trees. We used forest stands previously identied as Old tree habitatsby visual
criteria in Norwegian boreal forests. The eciency of this method was tested using pairwise
comparison of the age of core samples from trees within these sites, and within neighboring sites.
Age regression models were constructed from morphological traits and site variables to
investigate how accurately old trees can be detected. Cored trees in the Old-tree habitats were on
average 41.9 years older than compared to a similar selection of trees from nearby mature forests.
Several characteristics such as bark structure, stem taper and visible growth eccentricities can be
used to identify old Norway spruce and Scots pine trees. Old trees were often found on less
productive sites. Due to the wide range of environments included in the study, we suggest that
these ndings can be generalized to other parts of the boreal zone.
ARTICLE HISTORY
Received 17 June 2021
Accepted 16 October 2021
KEYWORDS
Biodiversity mapping; cost-
ecient conservation; forest
management; old trees;
woodland key habitats
Introduction
Old trees and old forests serve as important habitats for many
species (Wetherbee et al. 2020). The old trees feature many
microhabitats, such as hollows and cavities, dead wood,
bark with higher pH and rougher and more stable structures
than younger trees. These traits enable them to host a
complex diversity of vertebrates, insects, arachnids, fungi
and epiphytic lichens (Thunes et al. 2003; Lie et al. 2009; Nas-
cimbene et al. 2009; Kirby and Watkins 2015; Andersson et al.
2018). However, in addition to the general loss of forests due
to conversion to other land use categories, old and large trees
are often in decline because of commercial forest harvesting,
forest res or removed because they may pose a safety risk in
urban areas (Lindenmayer et al. 2014). Retaining old trees at
dierent spatial scales, from retention of individual trees at
clear-cuts to protection of old forest landscapes in national
parks, is, therefore, an important conservation measure
worldwide (Lindenmayer and Franklin 2002).
Successful conservation planning is a matter of acquiring
and assessing relevant biological and economic information
(Naidoo et al. 2006; Braun and Reynolds 2012). A well-
planned survey design and inventory methodology are there-
fore important. The answer is not necessarily to maximize the
survey intensity but to provide enough information to discern
land area that meets the conservation goal from those that
do not (Grantham et al. 2008). Due to budget and time con-
straints, ecological surveys must balance the information
gained for a given cost (Braun and Reynolds 2012). Arguably,
this is true for conservation targets such as the scattered old
trees of former selectively cut boreal forests.
Targeted conservation measures with regards to old trees
may require inventory methods adapted to cover extensive
areas. Ideally, for the optimal selection of old trees to con-
serve, one would prefer a situation where all the tree ages
were known. However, this is rarely achievable for extensive
surveys. Limited funds available for surveys means that the
method must provide enough information to locate the old
trees and to prioritize amongst those individuals in the
forest. The question that follows is, what kind of information
and how much?
Core extraction, laboratory preparation, tree-ring
measurements and dendrochronological cross-dating
provide the most accurate tree age estimates. However,
it translates poorly to spatially extensive surveys as it
leads to high eldwork labor costs. Determining the age
of old trees by other means can therefore be a viable
option. Visual discrimination of old and young trees
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
CONTACT Eivind Handegard eivind.handegard@nibio.no Department of Forest Genetics and Biodiversity, Norwegian Institute of Bioeconomy Research,
P.O. Box 115, NO-1431 Ås, Norway
*This article is based on the Master thesis Identifying old Norway spruce and Scots pine trees by visual inspection: An analysis of the relationship between age,
spatial distribution and morphological traits in trees(Handegard 2020).
Supplemental data for this article can be accessed at https://doi.org/10.1080/02827581.2021.1996628
SCANDINAVIAN JOURNAL OF FOREST RESEARCH
https://doi.org/10.1080/02827581.2021.1996628
reduces the time spent coring and may provide sucient
information.
Spatial distribution patterns and site characteristics
provide insight as to where old trees situated in forests are
more likely to be found. Old trees have often been associated
with low productivity sites, far away from roads and at higher
elevations (Rötheli et al. 2012; Sætersdal et al. 2016; Liu et al.
2019). This is partly because the longevity of trees is nega-
tively correlated with the growth rate (Black et al. 2008; Cas-
tagneri et al. 2013; Bigler and Zang 2016), but also due to the
general accessibility of the timber resources (Sætersdal et al.
2016). Nevertheless, tree age models that only use site
descriptors are generally substantially outmatched by
models that include morphological traits of the trees
(Matthes et al. 2008; Weisberg and Ko 2012; Brown et al.
2019).
Old trees can be separated visually from younger trees
using several morphological traits (Van Pelt 2007; Weisberg
and Ko 2012; Brown et al. 2019). Old trees are not always
large trees, and therefore, models used for age prediction
often include other morphological variables in addition to
tree height and diameter (Andersson and Östlund 2004;
Alberdi et al. 2013; Brown et al. 2019; Henttonen et al.
2019). Such indicators may include bark texture, attened
or wide crowns, large branches and high stem sinuosity
(Van Pelt 2007; Pederson 2010; Weisberg and Ko 2012;
Brown et al. 2019). Although visual age determination
appears promising, literature assessing the practical appli-
cation remains limited.
The boreal forests of Fennoscandia are generally regarded
as one of the most intensively managed areas (Gauthier et al.
2015; Kuuluvainen and Gauthier 2018; Linder and Östlund
1998, s. 19). Forest management is primarily directed at
Norway spruce (Picea abies) and Scots pine (Pinus sylvestris).
Centuries of forest utilization have transformed the forest
landscape and depleted the number of old trees (Linder
and Östlund 1998; Andersson and Östlund 2004). Due to
extensive former selection harvests, followed by a 70-year
period of clearcutting practice, large patches of old forest
stands are now scarce in Fennoscandia. Nonetheless, individ-
uals and groups of old trees can still be found within the
managed forest (Andersson and Östlund 2004; Sætersdal
et al. 2016).
Tree age regression models based on morphological
traits and site descriptions have been successfully con-
structed earlier for other tree species (Matthes et al. 2008;
Weisberg and Ko 2012; Brown et al. 2019), but to our
knowledge, this is the rst attempt to quantitively assess
a visual age determination methodology for tree species
in the boreal zone. Despite the extensive geographic distri-
bution and economic importance of Norway spruce and
Scots pine, literature on locating old individuals is scarce
and has only been studied in detail for Norway spruce
(Rötheli et al. 2012).
Norwegian boreal forests are a suitable testing ground for
old-tree identication since old trees are mapped nationwide
using morphological traits as a part of the Complementary
Hotspot Inventory (CHI) approach (Gjerde et al. 2007). This
Old treeshabitat type (hereby denoted as Old-tree
habitat) is one of 12 main habitat types considered of particu-
lar importance to overall forest biodiversity. The CHI is inte-
grated into forest planning on a property level basis and
also by a National Forest Inventory monitoring program
(Gjerde et al. 2007).
We used Old-tree habitats recorded by CHI to investigate
the visual age determination of old trees. Specically, the
aims were to assess the success of current practices and
potentially improve eld protocols with new knowledge
about the criteria used to visually evaluate age. Two research
questions were addressed: (1) How ecient is the present CHI
method in identifying the Old-tree habitats? (2) Which combi-
nation of morphological traits and site variables explains the
age of old Norway spruce and Scots pine trees the best?
Finally, we discuss the transferability of these ndings to
other forest types.
Material and methods
Study area
Four municipalities representative of the managed forest
landscapes in South-eastern Norway were chosen for this
study (Figure 1). Each of these selected areas is dominated
by Norway spruce and Scot pine forests managed for
timber production. By international standards, these are all
sparsely populated landscapes. The municipalities were
Aurskog-Høland and Midt-Telemark, in the southern boreal
zone and Nore og Uvdal and Sør-Aurdal in the northern
and middle boreal zones. They also diered in their distance
to the coastal areas (Figure 1), and they captured a relatively
wide range of boreal environmental conditions.
The average annual temperatures of the study areas range
from 0 to 6°C. The topography of Norway features jagged
mountains and deep valleys, and this was reected in most
of the study area. The sampled forests ranged from areas in
close proximity to the tree line to lowland forests. Aurskog-
Høland can, however, be regarded as more homogenous
overall compared to the other municipalities, as the terrain
is substantially atter. In addition, it is the municipality with
the strongest impact from forest harvest. Bilberry wood-
lands(Vaccinium myrtillus) and Bilberry-lingonberry wood-
lands(Vaccinium myrtillus-Vaccinium vitis-idaea) were the
most frequent category of vegetation types (Larsson 2000).
Study design and sample tree selection
Several terms, such as veteran, ancient, heritage trees and
champion trees, can overlap with the old tree, but these
also emphasize other features than age. A veteran tree
refers to a large tree with hollow trunks and receding
crowns. An ancient tree is an old tree with veteran traits. Heri-
tage and champions trees are mainly classied based on their
cultural signicance or size, respectively. Except for the term
ancient, none of these related terms directly state that the
trees must be old. For a more thorough discussion on these
terms, see (Nolan et al. 2020). In our study, Old treerefers
to a species-specic age threshold, i.e. Norway spruce trees
2E. HANDEGARD ET AL.
older than 150 years and Scots pine older than 200 years
(Baumann et al. 2001).
The eldwork was carried out over two eld seasons:
Nore og Uvdal and Aurskog-Høland in AugustOctober
2018 and Sør-Aurdal and Sauherad in AugustSeptember
2019. In each of the four municipalities, 10 old-tree habi-
tats were selected randomly from the CHI registry (Anon-
ymous 2021). Circular study-plots of 0.162 ha were laid
outatthecenteroftheold-treehabitats.Theve in
total assumed oldest living Norway spruce or Scots pine
trees were carefully chosen within the study-plot following
the CHI eld guides descriptions. GPS coordinates were
collectedforeachsampletree.Thestudy-plotsizeand
the selected number of sample trees corresponded to
the minimum density threshold for the delimitation of
old-tree habitats, i.e. at least three old trees per 0.1 ha
(Baumann et al. 2001).
For each study-plot, a reference plot was established
outside the respective Old-tree habitat. The purpose of the
reference plots was to obtain similar tree age data from the
oldest trees within a forest stand with comparable forest con-
ditions in the vicinity of each of the Old-tree habitats. Two
primary criteria were used to classify the reference plots.
Firstly, they belonged to a mature forest cutting class V, i.e.
forest old enough for harvest by foresters and located
within 200500 m from the Old-tree habitats. Secondly, the
reference plots had the same elevation and composition of
the main tree species. In one instance, an Old-tree habitat
plot had no mature forest in the immediate vicinity. In this
case, a reference plot was selected from the highest cutting
class present (cutting class IV)
The paired-plot combination of study- and reference plots
is referred to collectively as a locality. In total, 40 localities
were selected, and consequently, 80 plots were included.
Site characteristics
The basal area (m
2
ha
1
) around each sample tree was
measured with a relascope. The vertical forest structure and
vegetation type (Larsson 2000) were classied within a 7-m
radius of each sample tree. Vertical forest structure was
divided into three categories: one-storied, two-storied or mul-
tistoried, based on treesrepresentation in dierent canopy
layers. An assessment of the topography within each study-
plot was recorded in the following set of categories; (1)
hilltop, (2) south-facing hill, (3) north-facing hill and (4) at
terrain (Table 1).
Other site characteristics were based on geographic fea-
tures and forest productivity measures analyzed in QGIS
(QGIS Geographic Information System 2020). The K-nearest
neighbors algorithm provided data on the proximity of
forest access roads suited for timber harvests. Digital terrain
models enabled the collection of elevation data in meters
and the percentage slope of the 10-m radius buer area sur-
rounding each sample tree. Forest stand productivity was
supplied by site indexes from forest inventory maps. In this
case, the H40 index was adopted, which is based on the dom-
inating tree species height at 40 years (Tveite and Braastad
1984). The forest inventory data was also collected from kil-
den.nibio.no (Anonymous 2021) and access road datasets
from vbase. Data for DTM 10 were compiled from the geo-
norge.no database which is hosted at the Norwegian Map
Authority (The Norwegian Mapping Authority 2021).
Morphological tree variables
Morphological traits reecting size and shape were measured
for every sample tree. Tree height in decimeters and the
length of the living crown from the lowest living branch to
Figure 1. Location of the four dierent municipalities included in the study area.
SCANDINAVIAN JOURNAL OF FOREST RESEARCH 3
the tree apex were measured with a vertex hypsometer. The
relative crown length was calculated as the length of the
living crown in percent of the total tree height. Cross cali-
pered diameter at 1.3-m height (DBH) was measured to the
nearest half-cm. Height and diameter registrations were com-
bined into a diameter height ratio. Stem taper was estimated
as the relative (%) change in diameter from breast height to
the height of 5 m. The diameter at 5 m was estimated from
photographs of trees, utilizing the known DBH (details in
Appendix).
Dierent types of eccentricities along the tree stems were
measured for each sample tree. The degree of stem crooked-
ness was visually assessed as the deviation of the stem from a
straight line, a leveled factor from 1 to 5 and devised from the
Table 1. A full list of all the collected variables, along with descriptive statistics for the Norway spruce sample trees.
Variable
Spruce old-tree habitats Spruce references
Mean Range Mean Range
Tree variables
Age (years) 192.3 70313 167.4 52317
Height (m) 19.2 8.531.6 18.8 8.230.4
Diameter at 1.3 m (cm) 32.6 13.065.0 29.7 16.057
Diameter Height: ratio 1.7 1.13.6 1.6 0.94.5
Live crown (%) 80.6 53.799.3 77.5 29.2100
Crown shape 2.6 14 2.6 14
Rot 0101
Stem taper (%) 4.7 0.74 to 12.3 6.1 016
Bark structure 3.0 14 2.6 14
Bark color 2.8 24 2.7 14
Spiral grain 0101
Branch thickness (cm) 2.8 09 3.2 011
Drooping branches 0101
Crookedness 2.0 14 1.8 14
Broken top/spike knot 0101
Forked stems 1212
Visible wounds 0101
Site variables
Basal area (m
2
ha
1
) 19.4 545 21.9 450
Vertical forest structure One multistoried One multistoried
Vegetation type Lichen Tall herb Lichen Tall herb
Site index (m) 10.3 617 9.8 617
Topographic position 2.5 14 2.6 14
Distance to road (m) 650.2 46.31657.8 505.6 51.11726.7
Elevation (m) 650.0 207.91034.1 679.4 188.71018
Slope (%) 27.1 3.282.9 19.4 2.960.4
Note: The summary statistics is presented separately for each plot type.
Table 2. A full list of all the collected variables, along with descriptive statistics for the Scots pine sample trees.
Variable Pine old-tree habitats Pine references
Mean Range Mean Range
Tree variables
Age (years) 241.6 108414 185.8 54442
Height (m) 15.1 6.137.4 17.0 5.130.6
Diameter at 1.3 m (cm) 37.3 15.084 37.6 19.069
Diameter Height: ratio 2.8 1.19.6 2.4 0.85.4
Live crown (%) 43.0 6.284.4 77.5 29.2100
Crown shape 2.3 14 1.7 14
Rot 0101
Stem taper (%) 5.6 1.8 to 21.4 5 17.9 to 15.48
Bark structure 2.8 14 2.1 14
Bark color 3.1 15 3.0 15
Spiral grain 0101
Branch thickness (cm) 0 to >20 0 to >20
Drooping branches 0101
Crookedness 2.8 14 2.6 14
Broken top/spike knot 0101
Forked stems 1212
Visible wounds 0101
Site variables
Basal area (m
2
ha
1
) 19.1 536 21.9 450
Vertical forest structure One multistoried One multistoried
Vegetation type Lichen Tall herb Bilberry-lingonberry Tall herb
Site index (m) 9.5 617 10.3 617
Topographic position 2.1 14 2.1 14
Distance to road (m) 574.3 56.61396 433.0 51.11726.7
Elevation (m) 464.0 148.6926.7 414.0 161890
Slope (%) 23.8 3.282.9 23.8 1.384
The summary statistics are presented separately for each plot type.
4E. HANDEGARD ET AL.
lower ¾ of the tree stem. The scale measured the deviance
from a straight line, and the classes were (1) < 2 cm, (2) 2
5 cm, (3) 615 cm, (4) 1630 cm and (5) >30 cm. However,
the crookedness also measured stem sinuosity. Trees with a
strongly sinuous pattern were always classied as (>3) as
they all were highly curved. Other registered eccentricities
were the number of forked stems, the presence or absence
of visible spike knots after a top breakage and the presence
of wounding on the stem.
Additional species-specic morphological variables were
dened to account for species-specic traits in pine and
spruce trees (Table 2). Discriminant values of the dierent
classes of bark texture, bark color, branch thickness and crown
shapeweredened and recorded. Initially, bark texture and
bark color were encoded as ve leveled factors, but this was sim-
plied after the eldwork to a four leveled factor because only
two observations were found for the highest levels (Table 2).
Spiral grain and drooping branches were recorded for both
species, but since no spruce trees had visible spiral grain, and
only one tree was without drooping branches, these parameters
were only applicable for pine (Table 3).
Core extraction
The height of 0.5 m was chosen as the primary extraction
point for core samples, whereas cores located at 1.3 m in
height were also taken to achieve a better age estimate if
butt rot was detected. The dierences in coring height
come as a compromise between ascertaining the total tree
age while still being able to successfully extract a sample
core. Often this was related to turning the handle of the
corer or detecting butt rot within the sample core. Trees
with rot were cored again at 1.3 m, and if rot was present
on that core, this was also noted (Table 1).
Age determination of trees
Tree rings were measured using lintab 6 and the dendrochro-
nology software TSAP-Win (Rinntech 2003). Incomplete core
samples were corrected for the remaining distance to the
pith. This was done by comparing the curvature of the last
intact tree rings with a plastic template that had concentric
circles (Applequist 1958). The distance to the pith was trans-
lated into an estimate of the remaining tree rings by dividing
it with the mean width of the ten closest growth rings. Four
of the trees had age corrections > 100 years, and these were
capped at 100 years. Of the trees included in the sample,
eight trees had more than 4 cm missing to the center of the
pith. However, these were either characterized by large ring
widths throughout their life or they were conrmed old trees
by the total number of rings present already on the core.
The number of growth rings in the samples was adjusted
according to the core extraction height. One growth ring
was added for every 5 cm in core extraction height above
ground to provide an approximate for the true tree age (Kuu-
luvainen et al. 2002). Some core samples from Nore og Uvdal
were unfortunately corrupted and were excluded from the
material. This resulted in a total of 373 trees with estimates
of total age that were included in the analysis.
Statistical analysis
To test the eciency of the CHI method to visually identify
patches of old trees, we compared age measurements of
trees from Old-tree habitats to similar measurements from
similar forest stands in the vicinity that were not recorded
as Old-tree habitats. For this analysis, we used a pairwise t-
test to test the dierence in the mean age of the oldest
trees on each plot pair (locality).
For the investigation of which tree variables and site
characteristics that best function as indicators of old trees,
we used regression models with tree age as a response and
tree variables and site characteristics as dependent variables.
All the analyses were performed in the statistical software R (R
Core team 2018), and the presented gures were made using
the package ggplot2.
Comparing the age of the oldest trees in the old-tree
habitats with references
TheOld-treehabitatsandthereferenceplotsweretreated
as pairs under the assumption that each locality, at some
Table 3. Species-specic tree variables.
Variable Datatype Norway spruce Scots pine
Crown
measurements
Ordered factor 1
4
Crown width: (1) > 0.7 m, (2) 0.71.5 m, (3) 1.54 m and (4)
>4 m
Crown atness was a measure of declining height growth
with increasing age. (1) was cone-shaped, (2) strongly
ellipsoid, (3) some curvature and (4) completely at
Bark structure Ordered factor 1
4
The bark thickness and roughness were expected to
increase with age. The dierent bark morphologies were:
(1) Smooth, (2) furrowed, (3) rough and (4) very rough.
This variable reects the change from a cracked juvenile
bark to a bark consisting of large, tightly interlocking
plates: (1) Wide ssures, (2) ssured, (3) Bark plates and
(4) dense plates.
Bark color Ordered factor 1
4
A color variation on the rst 5 meters of the stem from
brown to increasingly light gray. These were brown, dark
gray, gray and light gray.
Bark color variation on the rst 5 m of the stem from
orange to gray. (1) Orange, (2) dark brown, (3) brown
gray and (4) gray
Branch thickness Continuous/
Ordered factor
14
The branch thickness was gathered by measuring the
thickest branch in cm from within the stems lower 2.3 m. The crowns thickest branch was categorized into four
levels: 05 cm, 610 cm, 1120 cm and >20 cm.
Visible spiral
grain
Binomial Absent Trees with visibly twisted right-oriented patterns on the
stem were noted as spiral grained trees.
Drooping
branches
Binomial Only one found To qualify as drooping branches, more than half of the
branches on the tree inclined downwards.
SCANDINAVIAN JOURNAL OF FOREST RESEARCH 5
degree, shared a common forest history and site conditions.
The mean age of the trees sampled in each study-plot was
calculated. The mean plot age fullled the assumption of
normality, and a pairwise t-test was deemed an appropriate
test. We tested the null hypothesis that the mean of the age
dierence is equal. A complete list of the study-plot pairs
containing the mean age of the oldest trees, locality
number and the municipality is available in supplementary
material 1.
Age regression models
The age models were constructed using a generalized linear
mixed modeling approach to account for spatial autocorrelation
between the sample trees within each of the localities (Crawley
2013). Localities and study-plots were represented in the models
as random intercepts. The random eects structure was
modeled by nesting each of the study-plot pairs by their locality.
Arandomeect structure that also included municipality
showed that the random eect variance from the municipality
variable was exceedingly small (4.52 * 10
10
), and thus this
was left out from further analyses. The variation explained by
the random clustering eect was measured through the intra-
class correlation (ICC) (Crawley 2013). Continuous explanatory
variables were centered and scaled to account for the large
dierence in scale between some variables.
Both tree species featured skewed age distributions
(Figure 2). To accommodate for positive and skewed values,
we used a generalized linear mixed model with a gamma dis-
tribution (Crawley 2013). Generalized linear models provide
exibility in that they can account for error structures that
are not normally distributed yet have a non-constant variance
(Nelder and Wedderburn 1972). Age models were tted with
a log-link function by maximum likelihood in the package
glmmTMB(Brooks et al. 2017). In this case, the age
models were shown to all converge.
The models and their assumptions were described in the
following way. (Notation from Nakagawa et al. 2017). The
two sides of the equation
yijk =gamma(
l
ijk,v)
refers to; yijk, the ith observation/tree of the jth study-plot
in the kth locality is dened by the two gamma-distributed
parameters
l
and v.vis the shape parameter of the distri-
bution, while
l
is the mean of the gamma distribution, and
the variance is
l
2
v. The expression
ln (
l
ijk)=
b
0+
k
h=1
b
hxhijk+Yk+
a
jk
refers to the log-link function. ln (
l
ijk) is the latent value for
the ith tree of the jth study-plot in the kth locality.
YkGaussian(0,
s
2
a)
signies each locality, which is assumed to be normal dis-
tributed around 0 with variance
s
2
Y.
a
jk Gaussian(0,
s
2
a)
refers to each study-plot pair which is nested inside each
locality. This is assumed to be normally distributed around
0, with a variance of
s
2
a. The residual variance was approxi-
mated using the trigamma function:
eijk =log (
c
)
Model assumptions were tested with the package
DHARMawhich provides simulated residuals for general-
ized linear mixed models (Hartig 2020). The simulated
residuals indicated that the gamma distribution was an
appropriate t for Norway spruce. The Scots pine models
had a good t, but these models were shown to underesti-
mate the medium-aged treesage slightly, since bimodality
was evident in the age distribution (Figure 2). Bimodality
was, however, not caused by the dierences between the
study-plots in Old-tree habitats and/or reference plots
(results not shown).
Figure 2. The age distributions in the sample represented as a combination between density plots and histogram with a bin width of 30 years
6E. HANDEGARD ET AL.
Eect size
Marginal R
2
and conditional R
2
determinations were
extended into generalized linear mixed models with
gamma as a measure of the goodness of t (Nakagawa
et al. 2013; Nakagawa et al. 2017). The marginal R
2
determi-
nation measures the variance explained by the xed eects
of the models, while the conditional R
2
included the
random eect variance (Nakagawa et al. 2013). Supplying
both determinations was seen as advantageous since they
provided an intuitive comparison of both the t from the
explanatory models and then the relative contribution from
random eects (Nakagawa et al. 2013; Nakagawa et al.
2017). Both R
2
s were calculated using the trigamma
method in r2.squared.glmmfrom the package MuMIn
(Nakagawa et al. 2017; Barton 2020).
Variable selection
Second-order terms were allowed in the models, and all vari-
ables were inspected for potential interactions. This inspec-
tion was related to the objective that interpretated the
causal relationships between the variables. Kendalls tau, a
rank-based correlation coecient, was used to look at
relationships between categorical and continuous variables
(supplementary material for full correlation matrix). In two
cases, the interpretation of the relationships was problematic.
Stem taper could not be calculated for all the trees, and thus
analyses were made with and without this variable to
compare the eect. The rot variable was also not included
in the nal models as it can only be found by coring, and
thus, this did not align with the purpose of determining
age visually.
Model selection
Model selection was made by comparing the AICc weight and
residual analysis from dierent realistic models. The main aim
was to nd the most parsimonious models. Unless stated
otherwise, the models shown in the results had an AICc
weight >0.7. All the models were inspected for collinearity
using generalized variance ination and visual inspection
by scatter plots.
Cross-validation
The robustness of the tree age models in terms of prediction
was tested by cross-validation. A resampling procedure was
repeated with 500 iterations for both tree species to estimate
the bias of the estimator and the corresponding standard
error. The prediction models were retted on a xed
number of resampled trees randomly selected for each iter-
ation. For each iteration, the retted models were used to
predict the age of the sample trees left for validation, and
the mean dierence between the measured tree ages and
the predicted ages was calculated. The standard error of
the overall mean dierence was estimated as the standard
deviation of the 500 individual mean dierences. The Scots
pine model was t with 160 sample trees and validated on
the remaining 31 sample trees. Similarly, the Norway spruce
model was t with 120 sample trees and validated on 20
sample trees.
Results
Comparing the age of the oldest trees in the old-tree
habitats with references
The mean age of the trees in the Old-tree habitats was signi-
cantly older than those in the reference plots. The age dier-
ences between the plots were, on average, 41.9 years (p-
value > 0.001, 38 pairs, CI (24.9, 61.6), Figure 3). A general
trend was that localities with a high mean age in the Old-
tree habitats had reference plots with correspondingly old
trees (Simple linear R
2
= 0.21, p-value > 0.005). Of the 38
pairs, seven had reference plots with higher mean age com-
pared to the Old-tree habitats. These seven discrepancies
still fullled the age requirements (200 for Scots pine and
150 for Norway spruce) dened for the given tree species.
However, there were some indications that pine-dominated
Old-tree habitats were more clearly dierentiated than the
spruce-dominated ones (Table 2).
Age regression models
The measured morphological variables explained more age
variance than the site variables, and the Scots pine models
showed stronger relationships than the Norway spruce
models. The age models for the two species were markedly
dierent (Tables 4 and 5and Figures 4 and 5). Several
species-specic morphological traits were prominent in the
models. Importantly no positive relation between age and
large size was found. Instead, a negative correlation with tree
height was identied. The majority of the oldest trees were
not tall, and this was especially evident for Scots pine, where
none of the trees older than 300 years stood taller than 20 m.
Table 4. The selected Scots pine model.
Age model Scots pine
Fixed eects Estimate SE zValue Pr(>|z|)
(Intercept) 4.586 0.085 53.87 ***
Bark structure [2] 0.197 0.056 3.48 ***
Bark structure [3] 0.277 0.065 4.23 ***
Bark structure [4] 0.518 0.090 5.76 ***
Visible spiral grain 0.128 0.042 3.06 **
Branch thickness [2] 0.186 0.060 3.08 **
Branch thickness [3] 0.263 0.070 3.76 ***
Branch thickness [4] 0.263 0.083 3.19 **
Crookedness 0.071 0.021 3.41 ***
Drooping branches 0.119 0.039 3.02 **
Scaled (Site index) 0.094 0.031 3.09 **
Random eects
ICC 0.53 ––
N
Localities
27 ––
N
plots
53 ––
Observations 191 ––
Marginal R2
gamma 0.51 ––
Conditional R2
gamma 0.77 ––
Notes: Bark structure, visible spiral grain and branch thickness are represented
in the model as dummy variables, while site index and crookedness are con-
tinuous. ns corresponds to non-signicant, *, ** and *** corresponds to p-
values of >0.05 (non-signicant), <0.05 and <0.
SCANDINAVIAN JOURNAL OF FOREST RESEARCH 7
Including site variables in addition to morphological vari-
ables did not provide additional explanatory power, except
when the productivity variable site index (H40) was included.
Most productivity measures were, however, weakly linked
with age (Kendall correlation coecient τ< 0.22). The only
multicollinearity was observed between the H40 site index
and with the elevation data for Norway spruce. This had a
Kendall correlation coecient of τ= 0.63.
Scots pine
Age models for Scots pine performed better than for Norway
spruce. However, Scots pine models also had the largest
random eect term (n= 192, Marginal R2
gamma = 0.51,
Conditional R2
gamma = 0.77). The oldest Scots pine trees were
characterized by densely spaced bark plates, drooping
branches, visible spiral grain and the selected model also
included the H40 site index. The same model (Table 4),
when height included, was similar (AICc weight = 0.655).
Diameter at breast height and age were positively correlated,
but the diameter was excluded from the nal selected model.
Norway spruce
The selected Norway spruce model moderately explained the
age variance (n= 139, Marginal R2
gamma = 0.40, Conditional
R2
gamma = 0.47). The oldest spruce trees were characterized
by light gray rough bark and columnar stems. The best
model, when considered without the stem taper variable,
and which included all the spruce tree samples, was,
however, substantially weaker (n= 183, Marginal R2
gamma =
0.26, Conditional R2
gamma = 0.34). Site index was also shown
to correlate with tree height. A comparison with a model
where the same variables were considered but where the
site index was included instead of tree height showed a
highly similar t (AICc weight = 0.63).
Cross-validation
The models only featured a minor bias. The result of the cross-
validation with 500 resampling runs led to a bias of 5.4 with
a standard deviation of 10.9 for Scots pine. Norway spruce
had a bias of 1.6 with a standard deviation of 11.9.
Discussion
The oldest trees in the Old-tree habitats, identied visually
using morphological traits, were on average 41.9 years
older than comparable mature forests. Age regression
Table 5. The selected Norway spruce model.
Age model Scots pine
Fixed eects Estimate SE zvalue Pr(>|
z|)
(Intercept) 4.369 0.161 27.18 ***
Bark structure [2] 0.635 0.217 2.93 **
Bark structure [3] 0.646 0.212 3.04 **
Bark structure [4] 0.782 0.225 3.48 ***
Branch color [2] 0.077 0.146 0.53 ns
Branch color [3] 0.166 0.144 1.15 ns
Branch color [4] 0.329 0.150 2.19 *
Scaled (Stem taper) 0.076 0.021 3.71 ***
Scaled (Height) 0.067 0.025 2.69 **
Scaled(Stem taper) * Scaled (Height) 0.062 0.023 2.75 **
Random eects
ICC 0.14 –– –
N
Localities
22 –– –
N
plots
40 –– –
Observations 191 –– –
Marginal R2
gamma 0.40 –– –
Conditional R2
gamma 0.47 –– –
Notes: Bark roughness and Bark color are represented in the model as dummy
variables, while Stem taper and Height are continuous. ns, *, ** and *** cor-
responds to p-values that are >0.05 (non-signicant), <0.05, <0.01 and 0.001.
Figure 3. Boxplot comparison of the mean age of the oldest trees on each plot where the line shown in each box is the median age. The observations have been
jittered using a width of 0.37 in the geom_jitter() function in ggplot2 to avoid overplotting.
8E. HANDEGARD ET AL.
models based on morphological and site characteristics
explained around half of the age variation, and they per-
formed better for Scots pine than for Norway spruce. Further-
more, the results show that visual age determination of
Norway spruce and Scots pine benetted from the inclusion
of several discrete morphological characteristics used in a
combination.
Most Old-tree habitats in this study included trees with
total age estimates older than the dened age limits of 150
and 200 years (Baumann et al. 2001) for Norway spruce and
Scots pine, respectively. Additionally, several of the reference
plots had estimated ages well above these limits (Figure 3).
The registration of Old-tree habitats requires a density corre-
sponding to >30 old trees ha
1
on a minimum of 0.2 ha to be
mapped (Baumann et al. 2001). Given the size of the study-
plots (0.162 ha), these reference plots would be eligible as
Old-tree habitats. Some of these reference plots may
indeed represent potential Old-tree habitats that border the
requirements in registration or are overlooked by the forest
planners during the survey. Nevertheless, applying the
visual method did succeed in nding areas with old trees
present.
Visual identication of old trees is not a measure without
error (Pederson 2010; Brown et al. 2019). To determine the
age of old trees using visual traits can be dicult and requires
experience. Extra information gathered from the eld may aid
the process of selecting which areas to set aside. In this sense,
photographs and rough classication of the tree ages can
serve as additional documentation to later help the selection
process. Likewise, the knowledge gained from this study may
also likely reduce the future error rate. However, it is impor-
tant to acknowledge that some old trees will remain undis-
covered and similarly, some younger trees will also be
falsely registered as old trees.
Forest history and current developments also aect the
area, which can be eligible as Old-tree habitats. It is expected
that there would be old trees outside the areas delimited by
the forest planners as nearby localities tend to share general
forest conditions. Because the amount of old forest in Norway
is increasing, some areas adjacent to the Old-tree habitats
now may meet the age requirements. Nevertheless, untan-
gling which reference plots that could qualify as Old-tree
habitats or were marginally classied as those habitats, was
deemed outside the scope of this study since comparisons
Figure 4. All the variables are included in the best model. Scots pine model excluding drooping branches. Categorical variables are shown as boxplots with jitter-
ing of the observations to avoid overplotting. Continuous variables are shown as scatterplots.
SCANDINAVIAN JOURNAL OF FOREST RESEARCH 9
were only made between the ages of the paired study and
reference plots.
Areas with old Scots pine trees were identied with more
certainty compared to their spruce counterparts. Firstly, this
may be because they have wider age demography than
Norway spruce. Secondly, spruce forests are often darker
and dense, making it harder to assess the older trees.
Lastly, but perhaps the most important reason according to
the age regression models: Scots pine trees have several
better performing characteristics that correlate with age.
When these factors are taken together, old Scots pine trees
were easier to identify when compared to the variation
observed in Norway spruce trees.
Of the site characteristics, the H40 site index contributed
the most to tree age prediction. Although the maximum
age of pines to some degree seems to be explained by
aspect and slope (Bigler and Zang 2016), for spruce and
pine trees as a whole, site characteristics are shown to be
weak predictors of age (Bigler and Veblen 2009; Rötheli
et al. 2012; Castagneri et al. 2013). Nevertheless, the site
index parameter appears to be easiest to utilize as it both
directly summarizes the various site conditions which regu-
late tree growth, and in many cases, can readily be accessed
from forest resource maps (Sharma et al. 2012). This index can
also be used to distinguish between medium-aged trees and
old trees of the same size (Alberdi et al. 2013; Brown et al.
2019).
Site index and tree height were confounded (Supplemen-
tary) but seemed to diverge in older trees. The site index
values are based on the height of the dominating trees
(Tveite and Braastad 1984). Thus, in low productivity localities,
one would expect shorter trees at a given age and lower
potential maximum heights. Moreover, the oldest trees in
the stand and the dominating trees are also not necessarily
the same. The oldest trees measured in the current study
were not the tallest. This may be because the utilized site
indexes were initially developed for even-aged production
forests, and old trees exceed the forest management cycles.
Additionally, the age of the trees used to measure site
index is also underestimated if they have been suppressed
by neighboring trees in the past. In other words, site
indexes aim to indicate site productivity and not age, but
they can likely provide indications on the whereabouts of
old trees.
The potential dierence between the dominating trees
and the oldest trees can be illustrated with two alternative
life-history strategies; grow fast or grow slow (Bigler and
Zang 2016). The rst of these results in a competitive advan-
tage early and while the latter increases the time available to
reproduce. Slow growth seems to be a prerequisite for long-
evity (Black et al. 2008; Castagneri et al. 2013). Moreover,
longer exposure times can increase the likelihood of
treetop breakage (Kuuluvainen et al. 2002). Together these
two factors can make tree height an unreliable indicator of
age.
Old Scots pine trees often featured distinctive crowns,
crooked stems and closely spaced bark plates. Younger indi-
viduals tended to have thin branches facing upwards and
ssured bark. Such a morphology indicates that the trees in
question are still vigorously growing and have not yet devel-
oped the individual traits associated with old pines (Weisberg
and Ko 2012; Brown et al. 2019). Furthermore, the crooked
stems may indicate selection from former harvesting
periods that avoided trees with poor timber quality. Never-
theless, most of the characteristic morphological traits likely
result from the legacy of a long life.
Figure 5. All the variables included in the best model exclude the interaction between stem taper and height. Categorical variables are shown as boxplots with
jittering of the observations to avoid overplotting. Continuous variables are shown as scatterplots.
10 E. HANDEGARD ET AL.
The spiral grain pattern may reect a life-history strategy
that contributes to longevity in conifers. Spiral grain is a bio-
logical phenomenon in which the orientation of the outer
wood forms a helical pattern (Kubler 1991). This pattern
becomes stronger with age and is hypothesized to either
stabilize the trees against the wind (Skatter and Kučera
1997) or increase drought resistance by spreading the
water into a larger part of the crown (Kubler 1991). Thus,
spiral grain may prove an advantageous trait for pine trees
and improve their ability to grow old.
Old Norway spruce trees were identied by their rough
and light-colored gray bark. The properties of the bark are
known to change with increasing age. However, age-related
bark characteristics appear less prominent in spruces than
pines (Van Pelt 2007; Pederson 2010; Weisberg and Ko
2012). The age varied considerably in Norway spruce trees,
even when these trees had similar bark features. Bark color
was the most statistically signicant of the two bark variables,
although bark color was prone to change with moisture
content (own observation). When bark color was left out of
the models (results not shown), the bark structure
variable increased in statistical signicance. Therefore, bark
structure may serve an important role in identifying old
individuals.
The stems of Norway spruce trees became less tapered
with age. Juvenile trees generally tend to increase the stem
diameter on the lower parts of the stem, but this dierence
evened out with increasing age. Thus, older trees usually
have less tapered stems (Pederson 2010). However, some
spruce trees are known to develop large root buttresses
(Van Pelt 2007). Root buttresses were accounted for by calcu-
lating the stem taper between 1.3 and 5 m in tree height. Still,
stem taper was not found independent of tree size, but this
was adjusted for with an interaction term. Tapering was
shown to be less in tall trees, but there are uncertainties as
to how vital this interaction is for practical registration.
It seems that old age morphology is more distinct in old
pine trees than old spruces (Rötheli et al. 2012; Weisberg
and Ko 2012). Individual traits such as bark structure appear
to be more distinctively associated with age for pine trees
compared to that of spruce trees. With age, pines accumulate
signicant crown dieback leading to shorter, more open
crowns (Weisberg and Ko 2012). This openness also makes
it easier to assess other old age indicators as one gets a
better view of the stem and the individual branches. In com-
parison, spruces show less variation with age in the bark and
may retain their symmetric crowns for 200300 years (Van
Pelt 2007). Consequently, investigating other age indicators
may improve the discovery of older spruces. One such
alternative may be the presence or absence of visible scars
from branches on the bole directly underneath the crown.
Remarkably, the age variation explained by the random
eects for Scots pine was considerable, meaning that tree
ages from each plot were more homogenous than that of
Norway spruce. The substantial random eect for Scots
pine trees may reect their regeneration strategy. Scots
pine predominantly regenerates in patches, whereas
Norway spruce often regenerate by small-scale gap dynamics
(Kuuluvainen and Aakala 2011). On a scale of 0.2 hectares,
more Scots pine trees will belong to the same cohort. As a
practical result, for this species, a single core sample using tra-
ditional age determination may also give relevant infor-
mation on the age of surrounding trees.
The selected age regression models did not depend
strongly on tree size. While the relationship between age
and size in old trees is generally acknowledged to be weak
(Kuuluvainen et al. 2002; Castagneri et al. 2013;Brown et al.
2019), it was still surprising how weak the relationship in
our study was. This can be lingering eects from former
periodical harvests selecting and removing the best and
largest trees from the stand. The historic forest use in
Norway is spatially extensive and dicult to disentangle
from this dataset. While we cannot state for certain, we
believe that the weak age-size relationship is inherently
weaker in old trees. Sampling more young trees would
likely reveal a monotonic positive relationship between age
and size (Kuuluvainen et al. 2002; Castagneri et al. 2013).
However, in old trees, the relation between size and age
becomes weak due to a plateau eect. Once past 150250
years old and depending on the site conditions, size seems
to oer little information on the age for Norway spruce and
Scots pine (Kuuluvainen et al. 2002; Brown et al. 2019).
The age regression models are explanatory but have gen-
eralizable results. The selected models maximize the
explained age variation within the study area and are not
directly aimed at prediction. Nevertheless, the sampling cap-
tures a wide environmental gradient, which might explain the
low bias for the models. In addition, the mixed-eects models
reduce overtting by shrinking the random eect of locality
(Crawley 2013). We deem that the currently devised models
would translate to various forest types, including those with
old characteristics.
Similar to our methods, comparable approaches have
been successfully applied in a range of forest types, including
those from ancient trees on near untouched clis (Matthes
et al. 2008), montane areas (Brown et al. 2019) and arid
environments (Weisberg and Ko 2012). It is uncertain how
well our models would work in dierent types of natural
forests, but the tree ages in the sample are arguably compar-
able to old trees present in old boreal forests (Kuuluvainen
et al. 2002). Recognizing old trees visually may serve as a
useful component in locating or circumscribing remnants of
old forests.
Conclusion
This is the rst comprehensive study of visual morphological
traits that indicate old age in Norway spruce and Scots pine
trees. We found several characteristics such as bark texture,
stem taper and visible growth eccentricities that can be
used to obtain age estimates for old trees. For Norway
spruce, the models indicate that the tapering at the lower
stem, and the bark structure and color, are the most impor-
tant visual morphological traits for nding the oldest individ-
uals. Determining the age of Scots pine visually benets from
assessing the whole tree, where characteristics like bark
plates, branch morphology and stem eccentricities seem to
SCANDINAVIAN JOURNAL OF FOREST RESEARCH 11
be the most inuential. For both species, a low site index may
increase the probability of nding the old trees.
If the goal is to preserve or manage old trees within the
forest matrix, one needs information about their where-
abouts. Old trees represent habitats that require centuries
to accumulate. Unlike dead wood such as logs and snags,
one cannot simply make old trees, as they are unavoidably
bound with time. Finding the old trees within the forest land-
scape is therefore vital to aid management decisions.
Acknowledgments
The authors thank Kajsa Sivertsen and Alexander Saša Bjelanovićfor their
assistance with the eldwork, and Adam Vivian-Smith and Wibecke
Nordstrøm for proofreading the manuscript. We are also grateful to the
reviewers for their valuable input, which considerably improved the
manuscript.
Disclosure statement
No potential competing interest was reported by the authors.
Funding
This work was supported by Norwegian Ministry of Agriculture and Food.
Data availability
The data that support the ndings of this study are available
from the corresponding author, E.H., upon reasonable
request.
ORCID
Eivind Handegard http://orcid.org/0000-0003-1166-2539
Ivar Gjerde http://orcid.org/0000-0001-6592-961X
Ole Martin Bollandsås http://orcid.org/0000-0002-1231-7692
Ken Olaf Storaunet http://orcid.org/0000-0002-7258-5592
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Appendix
Stem taper (%) was calculated from photographs as the dierence
between the diameter at 1.3 m and the diameter at 5-m height. These
heights were chosen to enable a standardized comparison of the stem
taper trees of varying tree heights and avoid the extreme stem taper in
the lower part of the stem. Colored bands were tied around 1.3 m to
locate the sample trees and connect the photographs with the eld
measurements. We translated the known eld measurements into
pixels for each sample tree. Therefore, we could use the ratio between
the eld measurements and the pixels to locate the height and diameter
of 5 m on the stem. The dierence in size at 5 m caused by the increased
distance was adjusted with trigonometry. A triangle was calculated with
the distance to the sample tree as length and 5 m as height. The diameter
at 5 m height on the tree was corrected with the dierence between the
hypotenuse and length of the triangle.
SCANDINAVIAN JOURNAL OF FOREST RESEARCH 13
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