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Kraus et al. Marteloscopes (2018) 26:3
Seeing is building better understanding - the Integrate+
Marteloscopes
Daniel Kraus*1,8, Andreas Schuck2, Frank Krumm3, Rita Bütler4, Hannes Cosyns3, Benoit Courbaud5,
Laurent Larrieu6,7, Ulrich Mergner8, Patrick Pyttel1, Simo Varis2, Georg Wilhelm9, Manfred Witz9, Eric
Zenner10 and Sergey Zudin2
Summary
Marteloscopes are multifunctional training tools that can create a better understanding of forest
management and have been developed as didactic tools for virtual tree selections. With this
paper the authors provide explanatory information on the more than 40 Marteloscopes that were
established in the course of the project Integrate+. It presents the Marteloscope plot design, gives
insight on their set up and the type of data that is recorded for each site. Methods are described
on how to calculate e.g. habitat and economic values. The paper elaborates on the use of
Marteloscopes as silvicultural training tools and their value in forest education. With the help of
the tablet software “I+” virtual management interventions can be performed and the results
immediately retrieved. We exemplary present a few options on how the Marteloscope dataset of
more than 15,000 recorded trees may serve as stimulus for scientific investigations. Examples are
stand development projections, future evolution of tree microhabitats and the calculating of
structural complexity and competition indices. An annex separate to this paper contains the bulk
of the Integrate + Marteloscopes in the form of information fact sheets.
Keywords:
Silviculture, Marteloscopes, Tree related Microhabitats (TreMs), habitat value, I+ software,
training, structural complexity, competition index
* Correspondence: daniel.kraus@waldbau.uni-freiburg.de
1 Chair of Silviculture, University of Freiburg, Tennenbacherstr. 4,
79085 Freiburg im Breisgau, Germany
Full list of author information is available at the end of the report
Background
Silvicultural concepts and forest management
practices have evolved towards ensuring not
only wood production, but also at making
forest stands more resilient against natural
disturbances and climate change effects,
conserving biological diversity and providing a
multitude of ecosystem services.
These concepts are frequently referred to as
integrative management systems when they
strive to optimize conservation efforts,
economic return and ecosystem services
provision (Kraus and Krumm 2013). Increasing
forest structural complexity is often advocated
as a means to increase resilience and
biodiversity (e.g. Puettman et al. 2009). Thus
silvicultural practices take advantage of
competition effects among trees to alter stand
structure and composition by removing or
retaining trees since competition is one of the
main drivers determining the structure and
Integrate+ Technical Paper
Integrate+ is a demonstration project funded by the German
BMEL to establish a European network of demonstration sites for
the integration of biodiversity conservation into forest
management.
Kraus et al. Marteloscopes (2018) 26:3
composition of tree communities (Oliver and
Larson 1996). However, practical knowledge
of the interacting effects of competition in tree
communities from both an ecological and
economic point of view is often still limited
despite its importance. Growth of adult trees is
mainly affected by competition for crown space
whereas competition for light is particularly
important for smaller trees. The most important
silvicultural method to promote the growth and
quality of residual trees is thinning by reducing
competitors although growth response largely
depends on site fertility and stand age
(Assmann 1970). Often thinning intensity,
however, is thought to be negatively related to
structural complexity and species diversity.
Especially the reduction of microhabitat
structures on trees through silvicultural
interventions may contribute considerably to
the loss of biodiversity in managed forests.
Furthermore the careful retention of such tree
related microhabitats has the potential to
contribute to increasing both the productivity,
resistance and long-term resilience of forest
ecosystems. In this context, a better
understanding of tree and stand responsiveness
to removal or retention becomes crucial to
support silvicultural decisions. Best available
knowledge from science and practice are thus
the foundation for educated decision making.
For ensuring continuity in silviculture, training
is pivotal as scientific findings, policy
orientation, societal demands and management
requirements evolve over time. By adapting
teaching and providing innovative, multi-
disciplinary training opportunities forest
managers will acquire up-to-date knowledge
and expertise.
In forestry the main challenge is seen in
conveying practice oriented forest management
content. A novel approach in silviculture
training to further develop forest management
skills are so called Marteloscopes
(Bruciamacchie et al. 2006, Schuck et al.
2015). These innovative training tools are
applicable for a variety of educational aims and
participants having different experience levels
around topics including forest ecology and
silviculture or forest management in general.
Main focus for participants of training courses
is to receive insight to stand structures and their
dynamics while at the same time evaluating
individual trees in terms of wood quality,
economic and nature conservation value. To
visualize and demonstrate effects of
silvicultural decisions on tree growth and stand
development, we used inventory data from
Marteloscope plots of a wide range of different
forest types across Europe.
To visualize and demonstrate effects of
silvicultural decisions on tree growth and stand
development, we used inventory data from
Marteloscope plots of a wide range of different
forest types across Europe. Within the
Integrate+ project we focused on the following:
(i) presenting practice examples in which
integrative forest management concepts are
being applied, and (ii) performing virtual tree
selection exercises based on different
silvicultural aims and forest management
strategies. Furthermore, we evaluated (iii)
silvicultural decisions in terms of ecological
impacts and economic consequences.
Marteloscopes
The concept of Marteloscopes was originally developed in France. The term is derived from the
French word for tree selection (‘martelage’) and the Greek term "skopein" (look), meaning
literally “having a closer look” at a tree selection. The concept was at first mainly applied in
private forests but its potential for field-based training and education for both forestry
professionals and students was already recognised in the 1990s (Bruciamacchie et al. 2006). The
use of the usually 1 hectare sized Marteloscope plots found application not only in France but
soon after also in its neighbouring countries, becoming more and more known also far beyond.
The demonstration project Integrate+ considerably contributed to this development in Europe
(Kraus et al. 2016a, Schuck et al. 2016).
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Kraus et al. Marteloscopes (2018) 26:3
The aim of this paper is to compile in one
document all explanatory information related to
the Marteloscopes established in the course of
the Integrate+ project. It describes the
Marteloscope plot design, introduces recorded
data and corresponding calculation methods
(e.g. tree related microhabitat and economic
values). It highlights the use as a training tool
and introduces potential applications using the
Marteloscope data. A main component of the
paper is Annex I in which the bulk of the
Integrate + Marteloscopes are presented. This is
done in the form of individual Marteloscope
Information Sheets. With this paper we intend
to raise interest in the Marteloscope tool and
the corresponding existing dataset which
includes more than 15,000 recorded trees (see
Kraus et al. 2017). Especially we hope to
convey to the reader their use for education and
training, as destinations for field visits and
stimulus for scientific investigations. As new
Marteloscopes are continuously being
established the dataset will also steadily grow.
Marteloscopes plots
Methods and plot design
For this project, a total of 42 Marteloscope
plots were established in a range of
representative forest types across Europe
(Figure 1). They cover a broad range of forest
types including e.g. beech-oak, beech-fir (-
spruce), oak-hornbeam, pine-spruce, altitudinal
gradient (from 25 m – 1850 m) and site
conditions (e.g. oligotrophic Luzulo-Fagetum
or Vaccinio-Pinetum to mesotrophic Galio-
Fagetum or Milio-Fagetum). Due to their
demonstration character the selection of plots
was based on either availability or particular
demonstration criteria, e.g. representative
silvicultural systems for a region, high
abundance of habitat structures etc.. Further
plot selection was limited by the frequency of
future silvicultural interventions (i.e. no
measures during the next 5 to 10 years) and
their accessibility. Most plots are in public
ownership (state and community forests) with a
few also situated in private forest estates.
Fig. 1 Distribution of the 42 Integrate+ Marteloscopes across Europe
Page 3 of 22
Kraus et al. Marteloscopes (2018) 26:3
The standard size for the Marteloscope plots
was 1 ha (100 m x 100 m) with a rectangular
shape. Some of the plots, however, deviated
from size and outline due to local conditions.
The plots were divided into 4 quadrants to
facilitate orientation and the use of data subsets.
All corners and centre points (incl. the centre
points of each quadrant) were permanently
marked. All trees within the plot with dbh > 7.5
cm were numbered and marked.
We recorded the following data in each plot for
trees above 7.5 cm breast height (dbh) (Table
1): (1) tree species, (2), tree location (stem base
map) (3) tree status as dead/alive, (4) forest
mensuration data (dbh, tree height and crown
base height), (5) timber quality (estimated) and
(6) tree related microhabitats (TreMs). Height
measurements were conducted with a digital
hypsometer (VERTEX IV, Haglöf, Sweden),
dbh with a measuring tape. Tree locations were
determined by using a compass (Suunto,
Finland) and the distance function of the Vertex
digital hypsometer as a standard.
Measurements took place from the tree to fixed
centre points within the Marteloscope, in our
case the centre points of the four Marteloscope
quadrants. In some plots the measurements
were carried out using specialized inventory
software (Fieldmap, Czech Republic; GPS
Trimble for some plots in France). TreMs
recording was based on a specially developed
catalogue for field data collection (Kraus et al.,
2016b).
In addition to the spatial dendrometric data we
collected information on management history
(year of last intervention), forest type, plot
location (state, region, country), elevation,
means for annual precipitation and temperature,
and the natural forest community. All trees
were permanently marked with consecutive
numbers. From the measured data each tree
was assigned an economic and a habitat value.
Derived parameters such as basal area and tree
volumes were calculated based on standard
calculation methods differentiated by tree
species.
Table 1 Parameters recorded in the Marteloscope plots
Type
Unit
Tree
species
Fagus
sylvatica (Fasy), Abies alba (Abal) etc.
Tree
location*
polar
coordinates
Tree
status
dead
(0), alive (1)
Diameter
at breast height
dbh
[cm] (>7.5 cm)
Tree
height
h
[m]
Crown
base height
h
cb [m]
Timber
quality
Class
(A, B, C, D/IT, F for fuelwood ) and section length [m]
TreMs
abundance
Habitat value
Particular attention in the plots was given to
TreMs (Kraus et al. 2016b) as these structures
provide a multitude of ecological habitat
functions for a large number of species that are
closely associated to them (Larrieu et al. 2018).
Retaining and restoring such structures in
managed forests by selecting habitat trees can
be well integrated into the work portfolio of
forest managers. Their selection in turn can
contribute to biodiversity conservation.
In order to describe the effect of forest
management interventions on the quantity and
quality of TreMs we calculated a habitat value
for each tree.
The habitat value is intended to support
visualizing the impact of harvesting on such
tree related structures. A standardized
assessment of the habitat value is based on a
catalogue of tree microhabitats and serves as
reference document for identifying and
classifying TreMs (Kraus et al. 2016b).
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Kraus et al. Marteloscopes (2018) 26:3
Microhabitats
Tree related microhabitats can be considered as keystone structures for forest ecosystems (Tews
et al. 2004, Möller 2005). They provide a wide range of specific conditions to specialized taxa,
notably microclimatic conditions and substrates for sheltering, foraging or breeding. They are
used by a large variety of animals including insects, arachnids, gastropods, birds, mammals,
amphibians and reptiles, by vascular plants, bryophytes, fungi and lichens. Species assemblages
can be very diverse, based on the composition of conditions. Some species can be exclusively
linked to particular tree microhabitats. For example more than half of all European dendrotelm-
dwelling insects are strictly dependent on this microhabitat (Dajoz 2007, Gossner et al. 2015).
Base mould cavities supply habitat for the full life cycle of the click-beetle Limoniscus violaceus
(Gouix 2011) and additionally serve as a simple and temporary shelter e.g. for rodents (Le Louarn
and Quéré 2003). Even though certain tree related microhabitat types are relatively persistent
(e.g. large mould cavities), they are still considered as ephemeral structures. They can change
from one type to another over time supplying different conditions (missing bark evolving to a
mould cavity), be periodically unavailable (dentrotelms without water in dry periods) or disappear
when a tree either dies or a microhabitat bearing-tree is removed.
Fig. 2 Examples of different tree microhabitats
Page 5 of 22
Kraus et al. Marteloscopes (2018) 26:3
Table 2 The microhabitat types from Kraus et al. (2016b) used to derive the habitat value
Code
Type
Sub
-type
CV11
Woodpecker
cavities
ø = 4 cm
CV12
ø = 5
-6 cm
CV13
ø > 10 cm
CV14
ø ≥ 10 cm (feeding hole)
CV15
Woodpecker "flute" / cavity string
CV21
Trunk and mould cavities
ø ≥ 10 cm (ground contact)
CV22
ø ≥ 30 cm (ground contact)
CV23
ø ≥ 10 cm
CV24
ø ≥ 30 cm
CV25
ø ≥ 30 cm / semi
-open
CV26
ø ≥ 30 cm / open top
CV31
Branch holes
ø ≥ 5 cm
CV32
ø ≥ 10 cm
CV33
Hollow branch, ø ≥ 10 cm
CV41
Dendrotelmata
ø ≥ 3 cm / trunk base
CV42
ø ≥ 15 cm / trunk base
CV43
ø ≥ 5 cm /
crown
CV44
ø ≥ 15 cm / crown
CV51
Insect galleries and bore holes
Gallery with single small bore holes
CV52
Large bore hole ø ≥ 2 cm
IN11
Bark
loss / Exposed sapwood
Bark loss 25
- 600 cm2, Decay stage < 3
IN12
Bark loss > 600 cm
2, Decay stage < 3
IN13
Bark loss 25
- 600 cm2, Decay stage = 3
IN14
Bark loss > 600 cm
2, Decay stage = 3
IN21
Exposed heartwood / Stem and crown breakage
Broken trunk, ø ≥ 20 cm at the broken end
IN22
Broken tree crown / fork, Exposed wood ≥ 300 cm²
IN23
Broken limb, ø ≥ 20 cm at the broken end
IN24
Splintered
stem, ø ≥ 20 cm at the broken end
IN31
Cracks and scars
Length ≥ 30 cm ; width > 1 cm ; depth > 10 cm
IN32
Length ≥ 100 cm ; width > 1 cm ; depth > 10 cm
IN33
Lightning scar
IN34
Fire
scar, ≥ 600 cm²
BA11
Bark pockets
Bark shelter, width > 1 cm ; depth > 10 cm ; height > 10 cm
BA12
Bark pocket, , width > 1 cm ; depth > 10 cm ; height > 10 cm
BA21
Bark
structure
Coarse
bark
DE11
Dead branches and limbs / crown deadwood
ø 10
- 20 cm, ≥ 50 cm, Sun exposed
DE12
ø > 20 cm, ≥ 50 cm, Sun exposed
DE13
ø 10
- 20 cm, ≥ 50 cm, Not sun exposed
DE14
ø > 20 cm, ≥ 50 cm, Not sun exposed
DE15
Dead top ø ≥ 10 cm
GR11
Root buttress cavities
ø ≥ 5 cm
GR12
ø ≥ 10 cm
GR13
Trunk cleavage, length ≥ 30 cm
GR21
Witch broom
Witches broom, ø > 50 cm
GR22
Epicormic shoots
Epicormic shoots
GR31
Cankers and burrs
Cancerous growth, ø > 20 cm
GR32
Decayed canker, ø > 20 cm
EP11
Fruiting
bodies of fungi
Annual polypores, ø ≥ 5 cm
EP12
Perennial polypores, ø ≥ 10 cm
EP13
Pulpy agaric, ø ≥ 5 cm
EP14
Large
ascomycetes, ø ≥ 5 cm
EP21
Myxomycetes
Myxomycetes
, ø ≥ 5 cm
EP31
Bryophytes
Epiphytic bryophytes, coverage > 25%
EP32
Foliose lichens
Epiphytic foliose and fruticose lichens, coverage > 25%
EP33
Lianas
Lianas, coverage > 25%
EP34
Ferns
Epiphytic ferns, > 5 fronds
EP35
Mistletoe
Mistletoe
NE11
Nests
Large vertebrate nest, ø > 80 cm
NE12
Small vertebrate nest, ø > 10 cm
NE21
Invertebrate
nest
OT11
Sap and resin flow
Sap flow, > 50 cm
OT12
Resin flow and pockets, > 50 cm
OT21
Microsoil
Crown Microsoil
OT22
Bark
Microsoil
Page 6 of 22
Kraus et al. Marteloscopes (2018) 26:3
The catalogue comprises 64 saproxylic
(encompassing decaying wood) and epixylic
(without decaying wood) microhabitat types
such as cavities, large dead branches, cracks
and loose bark, epiphytes, sap runs, or trunk rot
characteristics (Table 2).
The habitat value is calculated for each tree
based on the number of recorded TreMs. The
calculation takes into account the relative rarity
of a habitat in near-natural forests and the time
span needed for it to develop.
The result is then expressed in so called
‘habitat points’.
(eq.1)
where Hi is the habitat value of tree i, Nj the
number of microhabitat type j, R is a value for
the rarity of a TreM, D is a value for the time a
microhabitat takes to develop or is available,
and s is a size score (physical size of a TreM)
within a TreMs group (see Table 3 and 4).
Table 3 R and D values for TreMs in near natural-forests
Economic value
A visual assessment of timber quality classes
was performed in order to provide an estimate
of the economic value (market price) for each
tree. We used local criteria and knowledge of
timber markets to decide which timber qualities
a tree provides. We allowed up to five
categories on each tree corresponding to a
section of a distinct quality class.
Only general timber quality classes were used
such as ‘veneer' (A - quality), B and C- quality
‘sawnwood’ , ‘industrial timber’ (IT or D –
quality timber) and ‘fuelwood’ (F or energy
wood). The volume of each quality section was
calculated based on a locally adapted and
species-specific tapering factor (see Fig. 3).
Rarity
gradient in near-natural
forests
(R
-value)
Development
time
(D
-value)
very
common
1
fast
or linked to very common event
1
common
2
fairly
fast or linked to fairly common event
2
fairly
rare
3
from
fairly slow to slow or linked to uncommon event
3
rare
4
slow
or linked to rare event
4
very
rare
5
very
slow or linked to very rare event
5
R-value D-value
Size
s
core
Code
Type
Sub
-type b c
CV11
Woodpecker
cavities
ø = 4 cm
3 5 2 1
CV12
ø = 5
-6 cm 3 5 2 2
CV13
ø > 10 cm
4 5 2 3
CV14
ø ≥ 10 cm (feeding hole)
2 2 1 3
CV15
Woodpecker "flute" / cavity
string
5 5 4 3
Table 4 R and D-values for the TreM group CV1 (example). R-values are different for broadleaves (b)
and conifers (c)
Page 7 of 22
Kraus et al. Marteloscopes (2018) 26:3
Fig. 3 Volume calculation for a tree with 3 sections. More volume sections can be added following
the same method. dm1, dm2, dm3 are mid diameters for each section in [cm], L1, L2, L3 are lengths
in [m] for each section, fT is a species specific tapering factor, d1.3 is the diameter at breast height
(dbh) in [cm], h is total height in [m], hcb is the crown base height in [m]. A stump height of 0.5 m
is subtracted from all harvested volumes.
dm
3 = dm2 – L2/fT – L3/fT
if crown base height (
hcb
) is lower than the section height
(L
1 + L2 + L3) a different tapering is assumed:
dm
3 = dm2 – L2/fT – (L3/fT) x (d1.3/h)
dm
2 = dm1 – L1/fT – L2/fT
if crown base height (
hcb
) is lower than the section height
(L
1 + L2) a different tapering is assumed:
dm
2 = dm1 – L1/fT – (L2/fT) x d1.3/(h – 1.3)
dm
1 = d1.3 – L1/fT + 0.8
Volumes are then calculated based on the mid
diameters for each quality section accordingly:
(eq.2)
where V1 is the volume of Section 1 in [m3].
Timber market prices for each quality class
were provided by local forest managers at the
time of data collection (see Tab. 5). It is noted
that timber market prices fluctuate so the
monetary values attached to individual trees (in
Euro or national currencies) are only rough
indicators. They are however sufficient for
Marteloscope training exercises.
V1 = (dm1/100)2 x
x L1
Page 8 of 22
Kraus et al. Marteloscopes (2018) 26:3
Table 5 Example of a local timber price list used as a basis to determine economic values of each tree
Timber
Class A B C D/IT Fuel
€/m3 €/m3 €/m3 €/m3 €/m3 €/m3
Oak
0 0 0 0 0 0
1a 0 0 0 0 25
1b 0 0 0 0 25
2a 0 45 45 0 25
2b 0 45 45 0 25
3a 0 135 90 0 25
3b 0 170 110 45 25
4 500 250 130 45 25
5 600 390 170 45 25
6 800 390 170 45 25
Beech
0 0 0 0 0 25
1a 0 0 0 0 25
1b 0 0 0 0 25
2a 0 0 0 0 25
2b 0 0 0 0 25
3a 0 62 62 0 25
3b 0 71 63 45 25
4 130 100 68 45 25
5 200 120 73 45 25
6 250 126 74 45 25
Hornbeam
0 0 0 0 0 25
1a 0 0 0 0 25
1b 0 0 0 0 25
2a 0 70 64 0 25
2b 0 70 64 0 25
3a 0 92 68 0 25
3b 0 92 68 45 25
4 140 115 74 45 25
5 180 125 82 45 25
6 200 125 82 45 25
Maple
0 0 0 0 0 25
1a 0 0 0 0 25
1b 0 0 0 0 25
2a 0 0 0 0 25
2b 0 0 0 0 25
3a 0 110 70 0 25
3b 0 150 90 45 25
4 600 200 110 45 25
5 800 300 130 45 25
6 1000 400 150 45 25
Page 9 of 22
Kraus et al. Marteloscopes (2018) 26:3
Fig. 4 Habitat and economic values for selected plots: a) Steinkreuz, Germany,
b) Groenendaal, Belgium, c) Křivoklát, Czech Republic
0
50
100
050 100
Habitat value [points]
0
50
100
050 100
Economic value [EUR]
0
50
100
050 100
0
50
100
050 100
0
50
100
050 100
0
50
100
050 100
a)
b)
c)
Page 10 of 22
Kraus et al. Marteloscopes (2018) 26:3
Deadwood and natural regeneration
Additionally, we recorded spatial information
on lying and standing deadwood as an
important structural element in some of our
plots (see Table 6). Since the accumulation of
large dimensioned deadwood and the creation
of gaps through dying and decaying trees
results in a three-dimensional restructuring of a
forest stand (Juutilainen et al. 2011), new
niches are created enriching species
assemblages. It also serves as an important
substrate for many specialized species, acts as
water storage and supplies nutrients through its
slow decay through the soil to plants and trees
(Jonsson et al. 2005).
Deadwood is delivered continuously under
natural forest development or may occur in
large quantities following disturbances such as
windthrow, wildfires or bark beetle
infestations. It can also be accumulated during
silvicultural interventions. Intensive forest
management over the past centuries, however,
resulted in low levels of both standing and
lying deadwood and thus a loss of numerous
deadwood dependent species (Müller et al.
2005).
Many managed forests have less than 10 m³ ha-
1 of deadwood on average whereas natural
forests can have up to 200 m³ ha-1, in some
cases even 400 m³ ha-1 (Lassauce et al. 2011,
Müller and Bütler 2010). Scientific evidence on
the role of deadwood in forest ecosystems has
led to a rethinking also for managed forests.
Thus larger amounts are increasingly being
accepted and may even become part of a
strategy to accumulate deadwood as a long-
term nutrient reservoir or as structural element
(see Table 6). Therefore mapping deadwood in
Marteloscopes can add an additional
information layer to the dataset since the effect
of harvesting on deadwood dynamics can be
demonstrated.
We recorded the following data in the plots for
deadwood (Table 7): (1) tree species (if not
identified we noted down
broadleaved/coniferous), (2) deadwood type
(standing: snag, stump; lying: log, tree crown),
(3) object location as polar coordinates and
orientation of lying deadwood (deadwood
map), (4) decomposition stage (5 decay classes
according to Hunter 1990), (5) deadwood
mensuration data (diameter, height or length of
object). Diameters were measured with a
caliper (Haglöfs, Sweden). For logs and tree
crowns we took the diameter at the larger end
(d1) and at the smaller end (d2), for stumps
(created through tree fellings) we took the
diameter at the top (d0). Snags, being a
standing, dead tree and high stumps (resulting
from management measures) were recorded
with the tree measurements when larger than
1.3 m. Deadwood locations were determined,
for the few plots where deadwood was
recorded, by using a compass (Suunto, Finland)
and the distance function of the Vertex digital
hypsometer as a standard.
Table 6 Deadwood volumes per hectare for selected plots
Decay class
Plot Snags Stumps Logs Total 1 2 3 4 5
[ ] [m³ ha-1 ]
[m³ ha-1]
[m³ ha-1]
[m³ ha-1]
[m³ ha-1]
[m³ ha-1]
[m³ ha-1]
[m³ ha-1]
[m³ ha-1]
Steinkreuz
0,0
6,6
18,2
24,8
0,1
1,3
16,8
6,1
0,6
Löran 3,3
0,6
8,1
11,9
2,5
3,2
1,9
0,7
0,3
Rosskopf 1,5
9,1
19,7
30,3
2,8
8,3
11,6
5,4
0,4
Mooswald
0,0
3,8
8,4
12,3
0,0
6,6
2,7
1,9
1,1
Sihlwald 13,3
1,4
80,7
95,5
2,4
27,3
7,4
43,6
1,4
Heches 6,7
2,5
65,8
74,9
0,0
16,2
35,8
13,1
2,4
Waldhaus
0,5
29,8
127,7
158,1
35,8
25,3
54,4
41,6
0,0
Page 11 of 22
Kraus et al. Marteloscopes (2018) 26:3
Table 7 Deadwood parameters recorded in the Marteloscope plots
Type
Unit
Tree
species
Fagus
sylvatica (Fasy), Abies alba (Abal) etc.
Deadwood
type
Snag,
log, stumps, crown
Location
polar
coordinates and orientation of logs in [°]
Decay
stage
5
classes (according to Hunter 1990)
Diameter
d
1.3 [cm] for snags, d0 [cm] for stumps, d1 and d2 for logs
Height
h
[m] for stumps and snags
Length
L
[m] for logs
Fig. 5 Spatially explicit map showing the distribution of lying deadwood and tree stumps in
the plots Steinkreuz (a) and Waldhaus (b)
a) b)
Potential stand development trajectories
including regeneration and ingrowth dynamics
are difficult to predict. The information from
the regeneration layer is important to evaluate
the effect of tree removals on future stand
development, especially when using a growth
simulator. Thus, for some plots we also
estimated coverage of natural regeneration of
the stand, and mapped seedlings (height ≥ 20
cm and < 200 cm) and saplings (height ≥ 200
cm and DBH < 5 cm) differentiated by their
height (Fig. 6). Also, browsing damage during
the previous year, and annual terminal shoot
length of the previous three years of the largest
individual per tree species were measured.
Fig. 6 Natural regeneration (in green) at
the plot Steinkreuz, Germany
Page 12 of 22
Kraus et al. Marteloscopes (2018) 26:3
Martelscopes and training
Each Marteloscope possesses unique stand and
individual tree characteristics and together with
the data collected on this stand and its
individual trees, it determines which subjects
can be discussed and trained at a particular site.
Typical teaching examples in a Marteloscope
are e.g. to comprehend potential management
conflicts induced by the need to address
multiple ecosystem services such as protection,
timber harvesting, recreation and biodiversity
conservation or which stand regeneration
method to best apply for reaching set
silvicultural targets. The distinguishing feature
of Integrate+ Marteloscopes is that for each
individual tree detailed data on their economic
value and habitat value were determined.
Whereas the recording of economic tree values
is common in Marteloscopes, the assessment of
trees’ microhabitats and habitat values is rather
unique. This makes the Marteloscopes
particularly suited to discuss and learn about
biodiversity-related topics as well as about
trade-offs between economic and ecological
(habitat) objectives in forests. Hence, the
majority of training sessions within the
Integrate+ Marteloscopes focus on these topics.
In Marteloscopes, different teaching methods
can be applied in accordance to predefined
learning objectives. In general, self-directed
learning formats are favoured that encourage,
problem-oriented learning. Conventional
lecture formats can be embedded into
Marteloscope exercises and can show useful to
provide additional explanation to a limited
extent. However, participants are encouraged to
seek their own solutions for a given task. They
move independently in a Marteloscope, which
fosters self-learning processes and stimulates
the application of already acquired knowledge
and motivates to educate oneself further. A
discussion session at the end of the exercise
frames the individual’s made observations and
collected experiences in a broader context and
fuels self-reflection.
Marteloscope exercises are either carried out
individually or in small groups. Both
approaches have their benefits.
Main advantage of working in small groups is
that they provoke already discussions during
the tree selection process. Exercises last
between one to two hours and are accompanied
by a trainer. The “I+” tablet software is used to
record the exercise decisions by training
participants. Own selection of single trees in a
stand makes parameters such as basal area, tree
volume or height more tangible and provides a
better understanding of forest practitioners’
skills acquired through years of field practice.
By adapting the degree of independence and
difficulty levels of given tasks, Marteloscopes
offer a high variation of training levels. All
exercises are supported by an innovative tablet
based software, “I+” which allows virtual
silvicultural interventions. The training
participant can virtually implement also
management scenarios which are rather
unrealistic or excessive to demonstrate their
consequences. This raises lively discussions
directly in the Marteloscope.
Fig. 7 Example of a tree selection exercise
at the Steinkreuz Marteloscope, Germany.
Removed trees are shown in dark grey,
selected habitat trees in green
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Kraus et al. Marteloscopes (2018) 26:3
Further actual management guidelines
including nature conservation objectives
(deadwood accumulation, habitat tree selection)
can be easily practiced and tested.
Important will be to analyse exercise results in
social science research to investigate tree
selection behaviours of individuals and
different stakeholder groups. This will help to
better understand what drives decision-making
in forests. However, scientific research has to
meet several requirements for hypothesis
testing considering i.a. sampling design,
objectivity and comparability, which are often
not compatible with the educational objectives
of the Marteloscope trainings. Therefore, in the
scope of the Integrate+ project exercises
protocols were developed to find synergies and
combine particular educational objectives with
specific research objectives.
Pommerening et al. (2015) have investigated
human tree selection behaviour using
Marteloscopes. First results indicate that there
is rarely consensus between different test
persons given the same task. Indications of this
high interpersonal variation have been
confirmed by Spinelli et al. (2016) and Vitkova
et al. (2016). We applied the methods
suggested by Pommerening et al. (2015) to
some of our Marteloscope exercises to assess
participants tree selection: we used thinning
type as a suitable indicator and thinning
intensity as a characteristic of impact since it
affects the development and structure of a
forest stand. Thinning type was measured as
the NG ratio, defined as the relative number of
trees removed divided by the relative basal area
removed. Thinning intensity was defined by the
proportion of basal area removed (measured on
the abscissa in Fig. 8a in relative basal area,
rG).
Also it can be useful information to see how
sustainable an intervention suggested by a
participant is. When using growth rates, tree
volume or basal area removed in an exercise
can be compared to the increment over the next
10 years (Fig. 8b).
All participants above the horizontal line
representing the initial quadratic mean diameter
performed a tree selection corresponding to a
crown thinning, those below this line made
decisions leading to a thinning from below. The
vertical solid line marks the basal area
increment over a 10 year period (dashed
vertical lines give a region of allowance of
±10%). Accordingly, basal-area values of
removed trees smaller than 6.4 m2 lead to an
increase of stand basal area, values larger than
6.4 m2 result in a decrease.
Fig. 8 The NG ratio of seven exercise
participants in the Steinkreuz Marteloscope,
Germany (a), and quadratic mean diameter
over basal area of trees selected for harvest
by four test persons (b)
Page 14 of 22
a)
b)
Kraus et al. Marteloscopes (2018) 26:3
Synthesis and applications
Our Marteloscope plots have proven further
valuable as exploratory forests for other
research applications since they provide
datasets with spatially explicit information on
trees, structure and TreMs (Kraus et al. 2017).
Such types of datasets are currently rather rare.
They can serve research targeted at better
understanding for example tree related
microhabitat formation, their dynamics and the
effects of their spatial distribution on associated
taxa (Larrieu 2014; Courbaud et al. 2017). In
the following a set of exemplary applications
are presented to illustrate how Marteloscope
plot data and any derived or processed
information (e.g. results from virtual
interventions) can be further used and applied.
Stand development projections
Currently we can only provide snapshots of the
immediate effects of harvesting in our
Marteloscope plots. The use of growth
simulators can process the information
generated by Marteloscope interventions and
project these into the future. We used the
Samsara2 model (Courbaud et al. 2015) to run
simulations after different harvesting scenarios
in Marteloscope plots. Samsara2 is
implemented in the Capsis simulation platform
(de Coligny et al. 2003; Dufour-Kowalski et al.
2012) which enables both interactive or
automatic simulations and the visualization of
simulation results. Harvests can be simulated
using specific algorithms (Lafond et al. 2012,
2014). In our case all simulated stands are
Marteloscopes of 1 ha size with ground cell
area of 25 m2 (5 m × 5 m). Radiation
interception, which is the process requiring
most of the computing time, is usually updated
only every 5 years, whereas demographic
processes are calculated on an annual basis.
A stand simulated in Samsara2 is based on a
list of trees and a list of saplings that have
explicit 3D coordinates on a plot (Fig. 9). This
plot is attributed a slope and an exposure value,
and is divided into ground cells. Trees are
characterized by species, trunk diameter at
breast height (dbh), crown dimensions, and
location.
Seedlings are simply characterized by their
species, height, and location. Individual tree
crown dimensions are calculated using
allometric relationships relating total height,
crown base height, crown base radius, and dbh
(Vieilledent et al. 2010). The irradiance of each
cell under canopy and the amount of radiation
intercepted by each adult tree during a growing
season are calculated together, in an integrated
approach based on light ray interception by
crowns in 3D (Courbaud et al. 2003). The
annual basal area increment of a tree depends
on the amount of radiation intercepted during a
growing season. This relationship integrates
both an ontogenetic effect (interception
depends on tree size) and a competition effect
(incident radiations depend on neighbors) on
growth. In Samsara2, the mortality submodel
simulates only background mortality. The death
of a tree is the result of a Bernoulli trial, the
probability of mortality depending on dbh and
local competition. When saplings reach an
arbitrary height defined by the user, they are
recruited as adult trees in the model.
The spatially explicit, individual-based
Samsara2 model was designed to determine the
relationships between stand structure and
dynamics in uneven-aged mixed temperate
forests and to predict the impact of
management strategies (i.e. variations in the
Fig. 9 3D projection of the Steinkreuz
Marteloscope plot before harvesting using
the Samsara2 model. Grey shading
represents the effect of radiation
interception (dark grey high, light grey
low)
Page 15 of 22
Kraus et al. Marteloscopes (2018) 26:3
distribution of cuttings over time and space and
among trees) at the population scale (i.e. a
forest stand). This makes it possible to analyze
the development of individual trees within a
stand and the resulting collective dynamics,
summarized by synthetic variables such as
density, basal area, distribution of trees among
size classes, indices summarizing the spatial
distribution of trees, cumulated harvests and the
like. Giving specific focus on the dynamics and
management of uneven-aged stands which are
composed of trees at different development
stages requires the simultaneous simulation of
demographic processes (growth, mortality, and
recruitment), and interactions among trees of
different sizes (e.g. competition). Light
interception by tree crowns is the key driver in
uneven-aged stand dynamics as they present a
strong vertical heterogeneity favoring
asymmetric competition both between trees
canopy and seedlings (Schütz 1997).
In the model, light distribution among trees,
irradiance on the ground, and seed dispersion
are spatially explicit and their spatial
heterogeneity drives the changes in forest
structure. In Fig. 10 we show the results of a
simulation of two relatively contrasting
interventions during a Marteloscope exercise at
Steinkreuz. The projection of stand
development and the light model was run for
20 years.
Microhabitat development and future habitat
potential
A pressing question stated by forest managers
is often not directed at how to retain sufficient
habitat structures but how to ensure a
continuous supply of TreMs formation on trees
also in future. Consequently implementing
negative selection often depletes those trees
which display promising future habitat
potentials. A simple method to estimate the
TreM formation rate would be to use the
variation of their numbers on trees which are
observed repeatedly. Unfortunately, such
repeated measurements are still largely missing
due to the relatively recent interest researchers
have taken in this subject (Lindenmayer et al.
2011). Moreover, trees have rarely been
permanently labeled in the field during
previous studies, which does not allow re-
measurements. Certain TreM types such as
cracks are rare even in near-natural forests.
Their detection and formation thus requires
large tree samples. Extensive measurement
efforts will become necessary to build large
databases with repeated observations of TreMs.
We identified a list of structures (Table 8) on
trees we believe have a relatively high
probability to develop into TreMs during the
lifetime of a tree. Based on these criteria the so
called Future Habitat Potential can then be
calculated for each individual tree. These
structures can be revisited periodically e.g. in
Marteloscopes where all TreMs have been
recorded in the initial set up for measuring the
rate and quality of TreM formation on a single
tree. Repeating TreM inventories on the same
trees will then allow to improve the accuracy
of the Future Habitat Potential predictions.
Fig. 10 Simulation of different interventions
with Samsara2 performed for the
Marteloscope Steinkreuz (simulation period
20 years): a) harvest of approximately 85
m3/ha with a strong focus on the removal of
defective trees (negative selection), b)
harvest of approximately 45 m3/ha with a
positive selection of elite and habitat trees.
a)
b)
Page 16 of 22
Kraus et al. Marteloscopes (2018) 26:3
We also tested a new method proposed by
Courbaud et al. (2017) where we estimated the
probability of TreM formation during tree
growth based on cross-sectional data from our
Marteloscope plots (i.e. the presence of TreMs
on trees of different diameters). The challenge
is that usually there is no information on tree
ages making it difficult to relate TreM
formation to a time scale.
Therefore, age is replaced by dbh. Further
survival analysis techniques are applied which
can estimate the expected duration of time until
an event such as death in biological organisms
or failure in mechanical systems occurs
(Hosmer et al. 2008; Meeker and Escobar
1998). With this input the probability of TreM
formation can be estimated as a function of tree
species, tree dbh and tree dbh increment. In Fig
12 we show the results of modelled TreM
formation after two different interventions at
Steinkreuz using the TreM submodel of
Samsara2.
Structural complexity
An increasing complexity of stand structure
often leads to a higher number of animal and
plant species and to greater ecological stability
(Larrieu et al. 2015). However, the
multidimensional character of forest stands
makes it hard to characterize structural
complexity.
Fig. 11 Future habitat potential in the
Marteloscope Steinkreuz: size of the
circles is proportional to the habitat value
of a tree. Over the next 30 years the
habitat value will increase in general but
also trees with actually no microhabitats
are likely to develop new TreMs
Type
Score
Forks
0,6
Branch scars
0,3
Dead branches
0,4
Frost scar
0,3
Bulges
0,5
Spiral grain
0,2
Exposed sapwood
0,8
Necroses
0,6
Fissures
0,4
Table 8 Structures with a high probability to
develop into TreMs
Fig. 12 TreM formation modelled after
harvesting for the Marteloscope Steinkreuz:
a) harvest of approximately 85 m3/ha with a
strong focus on the removal of defective
trees (negative selection), b) harvest of
approximately 45 m3/ha with a positive
selection of elite and habitat trees
a)
b)
Page 17 of 22
Kraus et al. Marteloscopes (2018) 26:3
However, the multidimensional character of
forest stands makes it hard to characterize
structural complexity. The horizontal
distribution pattern of trees, stand density, the
differentiation of dimensions, and species
intermingling constitute the most important
aspects of stand structure that influence growth
processes, habitats, species richness, and
stability of forest ecosystems (Pretzsch 2009).
Additionally the spatial arrangement of plants,
both horizontally and vertically, the structure of
tree canopies and the presence of canopy gaps,
snags, and coarse woody debris are the
principal characteristics that influence the
diversity of animals (Kimmins 2005). The
density of TreM-bearing trees is positively
correlated with the saproxylic beetle species
richness in several forest contexts (Bouget et al.
2013, 2014). While some of these attributes are
hard to define and difficult to measure in the
field, tree stem diameter and position are
standard in measurement protocols of forest
inventories. For quantification, the Structural
Complexity Index (SCI) describes structural
complexity by means of an area ratio of the
surface that is generated by connecting the tree
tops of neighbouring trees to form triangles to
the surface that is covered by all triangles if
projected on a flat plane (Zenner and Hibbs
2000). Hence, in our plots we focused on these
variables and defined structural complexity as
the spatial arrangement of tree dimensions,
both horizontally and vertically according to
Zenner and Hibbs (2000). The SCI integrates
both vertical (size differentiation) and
horizontal (spatial position) components of
forest structure. It is based on the position of
trees whose xy-coordinates are complemented
with a tree attribute, such as dbh or height, as a
z-coordinate. By a spatial tessellation approach
(Delaunay 1934) each tree is connected to its
neighbours such that triangles are defined.
Those triangles then form a continuous faceted
surface, i.e. a triangulated irregular network
(TIN) (Figure 13a). If tree height is selected as
the z-coordinate, this TIN can be visualized as
connecting the tops of neighbouring trees (Fig.
13b). Instead of tree height, any measured
continuously or ordinally scaled tree attribute
can be chosen as the z-coordinate.
The SCI is defined as the surface area of the
TIN in three dimensional space divided by the
area covered by its projection on a plane
surface. If all trees have the same z-value (e.g.
all trees have the same height or basal area as in
an even aged plantation) the SCI equals 1, the
lower limit of the SCI. For structurally more
complex forest stands the SCI is >1.
Competition indices
Indices of spatial competition are commonly
based on the nearest-neighbour (NN) concept
where the immediate neighbours surrounding a
subject tree are likely to have a competitive
effect (Schneider et al. 2006). Using this
approach, a competition index is calculated for
each tree as a measure of the competition
intensity exerted by neighbouring trees.
Fig. 13 Structural Complexity Index (SCI) of
the plot Steinkreuz visualized as a
triangulated irregular network (TIN) with dbh
(a) and height (b) as selected tree attribute
Page 18 of 22
a)
b)
Kraus et al. Marteloscopes (2018) 26:3
Competition index values typically are
associated with the point locations of the
subject trees. By contrast a different approach
producing spatial competition fields has been
developed where potential competition pressure
is known for every point in a research plot.
Such competition kernels are functions that
describe how biological processes such as
growth, survival and reproduction of an
individual depend on its own size and the size
of and distance to other individuals (Snyder and
Chesson 2004; Vogt et al. 2010).
In our plots we quantified tree-tree competition
by using a combination of a traditional
competition index and a competition kernel as
suggested by Pommerening and Maleki (2014).
First we defined a zone of influence (ZOI) and
then derived the actual competition index (CI)
sensu stricto. We assumed that the ZOI is a
circular area around a tree in which it
predominantly draws on resources like light,
water and nutrients (Berger and Hildenbrandt
2000). Where the ZOIs overlap, trees interact
via competition for resources (Grimm and
Railsback 2005). In this context, we considered
symmetric competition as an equal sharing of
resources among individuals whereas
asymmetric competition is an unequal sharing
of resources resulting from larger individuals
having a competitive advantage over smaller
ones (Schwinning and Weiner 1998;
Freckleton and Watkinson 2001; Begon et al.
2006). Hence, we adapted an approach
described in Pommerening and Maleki (2014)
and calculated the radius of the Competition
Zone (rCZ) for each tree:
(eq.3)
where dbh is tree diameter at 1.3 m and α and β
are parameters defined by species.
The Competition Index (CI) for every tree was
calculated as follows:
(eq.4)
where dbh is diameter for a given tree, dt is
distance between the given tree and another
tree in the plot, C is a set of trees which
competition zones overlap with the competition
zone of a given tree:
(eq. 5)
T is a set of living trees in the plot.
Another way of displaying the space available
for each tree and hence indirectly where
competition is high, is the dual graph of the
Delaunay tessellation: we used Voronoi
diagrams to make changes after interventions
visible (Fig. 14).
Concluding remarks
For all of the above applications, we used tree
selection results from exercises performed in
different Marteloscopes. Those exercises
allowed us to test in how far they are suitable
for practical silvicultural training or how they
may be further applied in research activities.
We encourage the reader interested in our work
and the practical application of Marteloscopes
to contact the authors for more information or
support.
Fig. 14 Voronoi diagram of the Steinkreuz
plot.
Page 19 of 22
Kraus et al. Marteloscopes (2018) 26:3
Acknowledgements
The project “Establishing a European network of
demonstration sites for the integration of biodiversity
conservation into forest management (Integrate+)”
was financially supported by the Federal Ministry for
Food and Agriculture (BMEL) between December 2013
and December 2016.
Author details
1Chair of Silviculture, University of Freiburg,
Tennenbacherstr. 4, 79085 Freiburg im Breisgau,
Germany, 2European Forest Institute EFI, Yliopistokatu
6, 80100 Joensuu, Finland, 3Swiss Federal Research
Institute WSL, Zürcherstrasse 111, CH-8903
Birmensdorf, Switzerland, 4Swiss Federal Research
Institute WSL, EPFL, Bât. GR Station 2, ECOS, 1015
Lausanne–Ecublens, Switzerland, 5Irstea, UR EMGR,
Centre de Grenoble, F-38402 St-Martin-d’Hères,
France, 6INRA, UMR1201 DYNAFOR, Chemin de Borde
Rouge, Auzeville, CS 52627, 31326 Castanet Tolosan
Cedex, France, 7CRPF-Occitanie, 7 chemin de la Lacade,
31320 Auzeville Tolosane, France, 8BaySF, Forstbetrieb
Ebrach, Marktplatz 2, 96157 Ebrach, Germany,
9Landesforsten Rheinland-Pfalz, Le Quartier Hornbach
9, 67433 Neustadt an der Weinstraße, Germany, 10
Department of Forest Ecosystem Science and
Management, Penn State University, University Park
16801, PA, USA
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Additional file
Annex I: Integrate+ Marteloscope plot information
sheets (COM-I+ 26:3)
