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A Tree-related Microhabitat (TreM) is a distinct, well-delineated morphological singularity occurring on living or standing dead trees, which constitutes a crucial substrate or life site for various species. TreMs are widely recognized as key features for biodiversity. Current TreM typology identifies 47 TreM types according to their morphology and their associated taxa. In order to provide a range of resolutions and make the typology more user-friendly, these 47 TreM types have been pooled into 15 groups and seven forms. Depending on the accuracy required and the time available, a user can now choose to describe TreMs at resolution levels corresponding to type, group or form. Another way to more easily record TreMs during routine management work would be to use co-occurrence patterns to reduce the number of observed TreMs required. Based on a large international TreM database (2052 plots; 70,958 individual trees; 78 tree species), we evaluated both the significance and the magnitude of TreM co-occurrence on living trees for 11 TreM groups. We highlighted 33 significant co-occurrences for broadleaves and nine for conifers. Bark loss, rot hole, crack and polypore had the highest number of positive co-occurrences (N = 8) with other TreMs on broadleaves; bark loss (N = 4) had the highest number for conifers. We found mutually exclusive occurrences only for conifers: Exposed Heartwood excluded both dendrotelm and sap run. Among the four variables we tested for their positive contribution to significant co-occurrences, tree diameter at breast height was the most consistent. Based on our results and practical considerations , we selected three TreM groups for broadleaves, and nine for conifers, and formed useful short lists to reduce the number of TreM groups to assess during routine forest management work in the field. In addition, detecting potential similarities or associations between TreMs has potential theoretical value, e.g. it may help researchers identify common factors favouring TreM formation or help managers select trees with multiple TreMs as candidates for retention.
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Co-occurrence patterns of tree-related microhabitats: A
method to simplify routine monitoring
Laurent Larrieu, Alain Cabanettes, Benoit Courbaud, Michel Goulard,
Wilfried Heintz, Daniel Kozák, Daniel Kraus, Thibault Lachat, Sylvie Ladet,
Jörg Müller, et al.
To cite this version:
Laurent Larrieu, Alain Cabanettes, Benoit Courbaud, Michel Goulard, Wilfried Heintz, et al.. Co-
occurrence patterns of tree-related microhabitats: A method to simplify routine monitoring. Ecological
Indicators, Elsevier, 2021, 127, �10.1016/j.ecolind.2021.107757�. �hal-03237154�
Ecological Indicators 127 (2021) 107757
1470-160X/© 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Co-occurrence patterns of tree-related microhabitats: A method to simplify
routine monitoring
Laurent Larrieu
a
,
b
,
*
, Alain Cabanettes
a
, Benoit Courbaud
c
, Michel Goulard
a
, Wilfried Heintz
a
,
Daniel Koz´
ak
d
, Daniel Kraus
e
, Thibault Lachat
f
, Sylvie Ladet
a
, J¨
org Müller
g
, Yoan Paillet
h
,
Andreas Schuck
i
, Jonas Stillhard
j
, Miroslav Svoboda
d
a
Universit´
e de Toulouse, INRAE, UMR DYNAFOR, Castanet-Tolosan, France
b
CNPF-CRPF Occitanie, Tarbes, France
c
University Grenoble Alpes, INRAE, UR LESSEM, France
d
Czech University of Life Sciences, Czechia
e
Bayerische Staatsforsten A¨
oR (BaySF), FB Rothenburg, Germany
f
Bern University of Applied Sciences, School of Agricultural, Forest and Food Sciences HAFL, & Swiss Federal Institute for Forest, Snow and Landscape Research WSL,
Birmensdorf, Switzerland
g
Bavarian Forest National Park & Department of Animal Ecology and Tropical Biology, University of Würzburg, Germany
h
INRAE, UR EFNO, Nogent sur Vernisson, France & Univ. Grenoble Alpes, INRAE, UR Lessem, France
i
European Forest Institute, Bonn Ofce, Germany
j
Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
ARTICLE INFO
Keywords:
TreM monitoring
Biodiversity-friendly forest management
ABSTRACT
A Tree-related Microhabitat (TreM) is a distinct, well-delineated morphological singularity occurring on living or
standing dead trees, which constitutes a crucial substrate or life site for various species. TreMs are widely
recognized as key features for biodiversity. Current TreM typology identies 47 TreM types according to their
morphology and their associated taxa. In order to provide a range of resolutions and make the typology more
user-friendly, these 47 TreM types have been pooled into 15 groups and seven forms. Depending on the accuracy
required and the time available, a user can now choose to describe TreMs at resolution levels corresponding to
type, group or form. Another way to more easily record TreMs during routine management work would be to use
co-occurrence patterns to reduce the number of observed TreMs required. Based on a large international TreM
database (2052 plots; 70,958 individual trees; 78 tree species), we evaluated both the signicance and the
magnitude of TreM co-occurrence on living trees for 11 TreM groups. We highlighted 33 signicant co-
occurrences for broadleaves and nine for conifers. Bark loss, rot hole, crack and polypore had the highest num-
ber of positive co-occurrences (N =8) with other TreMs on broadleaves; bark loss (N =4) had the highest number
for conifers. We found mutually exclusive occurrences only for conifers: Exposed Heartwood excluded both
dendrotelm and sap run. Among the four variables we tested for their positive contribution to signicant co-
occurrences, tree diameter at breast height was the most consistent. Based on our results and practical consid-
erations, we selected three TreM groups for broadleaves, and nine for conifers, and formed useful short lists to
reduce the number of TreM groups to assess during routine forest management work in the eld. In addition,
detecting potential similarities or associations between TreMs has potential theoretical value, e.g. it may help
researchers identify common factors favouring TreM formation or help managers select trees with multiple
TreMs as candidates for retention.
* Corresponding author.
E-mail addresses: laurent.larrieu@inrae.fr (L. Larrieu), alain@o-art.fralai, n@o-art.fr (A. Cabanettes), benoit.courbaud@inrae.fr (B. Courbaud), michel.
goulard@inrae.fr (M. Goulard), wilfried.heinz@inrae.fr (W. Heintz), daniel.kraus@baysf.de (D. Kraus), thibault.lachat@bfh.ch (T. Lachat), sylvie.ladet@inrae.fr
(S. Ladet), joerg.mueller@npv-bw.bayern.de (J. Müller), yoan.paillet@inrae.fr (Y. Paillet), andreas.schuck@e.int (A. Schuck), jonas.stillhard@wsl.ch
(J. Stillhard), svobodam@d.czu.cz (M. Svoboda).
Contents lists available at ScienceDirect
Ecological Indicators
journal homepage: www.elsevier.com/locate/ecolind
https://doi.org/10.1016/j.ecolind.2021.107757
Received 29 May 2020; Received in revised form 23 April 2021; Accepted 25 April 2021
Ecological Indicators 127 (2021) 107757
2
1. Introduction
A Tree-related Microhabitat (TreM) is a distinct, well-delineated
morphological singularity occurring on living or standing dead trees,
which constitutes a crucial substrate for species (Larrieu et al., 2018).
Cavities, conks of lignivorous fungi and dead branches are examples of
TreMs. TreMs are widely recognized key features of biodiversity (Bütler
et al., 2013) and are useful indirect indicators for biodiversity (e.g.
Winter and M¨
oller, 2008; Paillet et al., 2018; Basile et al., 2020).
Therefore, Asbeck et al. (2021) have suggested using them as a moni-
toring tool to address biodiversity conservation issues in forest
ecosystems.
Larrieu et al. (2018) identied TreMs according to their morphology
and their associated taxa and allocated them into 47 types, the most
precise category, 15 groups, and seven forms, the more generic category,
by following a hierarchical way. Depending on the accuracy required
and the time available, a user can choose the suitable level to record
TreMs in the eld. For example, forest managers can record TreM forms
(e.g. cavities l.s.) during tree marking to estimate TreM diversity at the
stand scale whereas TreM groups (such as woodpecker breeding cav-
ities) could be applied in routine surveys and inventories like the na-
tional forest inventories, while elaborating management plans or for
Natura 2000 site evaluations. Researchers could use TreM types (e.g.
small, medium or large woodpecker breeding cavities) for more
exhaustive scientic surveys (Larrieu et al., 2018).
Another possible way to simplify and speed-up TreM recording
during routine management work would be to use non-random TreM co-
occurrence patterns (i.e. when TreM distribution on the tree is co-
dependent), to reduce the number of types to observe. In other words,
managers could use a shorter list of TreMs as a surrogate for the full list
that indicate the presence of further TreMs with a high probability.
However, TreM co-occurrence patterns are poorly known. Preliminary
studies revealed co-occurrence patterns at the tree scale for European
beech (Fagus sylvatica L.), pubescent oak (Quercus pubescens), holm oak
(Quercus ilex), silver r (Abies alba Mill.) and Douglas r (Pseudotsuga
menziesii Franco) (Larrieu and Cabanettes, 2012; Regnery et al., 2013a;
Winter et al., 2015; Puverel et al., 2019). However, these studies used
databases with a narrow geographical range and a limited number of
observed trees. Larrieu and Cabanettes (2012) highlighted, for example,
that bark loss and rot-holes co-occur in both beech and r while other
co-occurrences are tree-species specic: rot-holes and saproxylic fungi
co-occur only on beech, and dendrotelms and bark loss only on r.
Winter et al. (2015) showed TreM co-occurrence patterns for Douglas
r, e.g. for bark pockets and rot-holes. These results suggest that co-
occurrence patterns may be different between broadleaves and conifers.
Besides tree-species, other tree and stand features also inuence
these co-occurrence patterns. First, a greater diameter at breast height
(dbh) increases the probability of TreMs co-occurring on the same tree
(e.g. Winter and M¨
oller, 2008; Vuidot et al., 2011; Regnery et al., 2013a;
Larrieu et al., 2014; Courbaud et al., 2017; Asbeck et al., 2019).
Therefore, dbh is likely to be a crucial driver of co-occurrence patterns.
Second, pioneer species such as Salix spp., Populus spp. and Betula spp.
are relatively short-lived (Rameau et al., 1993) and individuals often
seem to simultaneously bear several TreM types early in their life cycles,
especially TreMs linked to reduced competitive ability (e.g. crown
deadwood) or early senescence (e.g. conks of polypores). In contrast,
small individuals of long-lived, shade-tolerant species such as Fagus
sylvatica and Quercus petraea for broadleaves, or Abies alba and Picea
abies for conifers (Rameau et al., 1993) rarely bear several TreM types
simultaneously (e.g. Larrieu et al., 2014). We therefore hypothesized
that tree species with distinct life cycles and succession dynamics would
show different co-occurrence patterns. Third, the CODIT system
(COmpartmentalization of Decay In Trees; Shigo, 1984) describes the
reaction of a tree following a trunk injury in order to limit the volume of
wood affected by pathogens. Tree species compartmentalize the decay in
unique ways and with a range of effectiveness, and exhibit a variety of
CODIT proles. While some species like the oaks can inhibit the spread
of pathogens within their organism by creating both chemical and
anatomical boundaries (Shigo, 1984), other tree species like the poplars
(Populus spp.) are less able to protect themselves and wood decay can
quickly affect a larger part of the trunk, thus creating, for example, wide
rot-holes. We hypothesized that the type of CODIT prole would affect
the development of saproxylic TreMs (i.e. those that involve decaying
wood) and would therefore inuence TreM co-occurrence. Fourth,
Winter et al. (2015) showed that management intensity has an impact on
TreM co-occurrence patterns for Douglas r. Although there are prop-
ositions for indices to assess management intensity (e.g., Kahl & Bauhus,
2014), the data required to calculate these indices are only seldomly
assessed during eld measurements. However, the time since the last
harvest is often available, at least broadly speaking, and can be used as a
proxy for management intensity to quantify its effect on TreM co-
occurrence.
Our study focused on living trees and co-occurrence patterns among
a set of TreMs at the tree scale. We expected that (i) co-occurrence
patterns of TreMs will differ between broadleaves and conifers, and
that (ii) tree dbh, time since last harvest (as a proxy for management
effect), succession dynamics of tree species and compartmentalization
capacity would drive co-occurrence patterns.
The practical outcome of this study was to develop short and
manageable lists to efciently record TreMs during routine eld visits.
We thus aim to provide forest managers with a practical tool to better
take into account the biodiversity associated with TreMs.
2. Materials and methods
2.1. Data
We collected data from a large range of temperate and boreal forests
from Northern Iran to Western Europe (Fig. 1; Table 1SM in Supple-
mentary Material). These forests cover a wide range of degrees of
naturalness, from regularly harvested stands to primeval forests (see e.g.
Commarmot et al., 2013; REMOTE project https://www.remoteforests.
org). The datasets from the managed stands cover various forest types
and silvicultural regimes and do not focus on TreM-rich stands only.
Each dataset provided was standardized according to the TreM typology
by Larrieu et al. (2018). However, since the typologies used by the eld
agents recording the TreMs differed slightly, we were not able to follow
exactly the same typology as Larrieu et al. (2018). In order to optimize
the available data, we designated eleven TreM subgroups (Table 1), very
close to the 15 TreM groups described by Larrieu et al.s (2018), and
discarded several TreM types that were rarely recorded or recorded with
protocols that differed too much to be merged (see Table 1 for the TreM
types analyzed). In addition, TreMs belonging to the form Epiphytic
and epixylic structures (Larrieu et al., 2018) - namely bryophytes, li-
chens, lianas, ferns and mistletoes - were not included since they have
been rarely recorded. Finally, the eleven TreM subgroups used, hereafter
referred to simply as TreMs, encompassed 24 TreM types.
Overall 70,958 living trees (including 54,740 broadleaves, 16,218
conifers and 78 tree species) from 2,052 plots were used for the calcu-
lations. According to Larrieu et al., 2018, TreMs occur on both living and
standing dead trees. However, we analyzed co-occurrence in living trees
only since snags are not routinely included in tree-marking for
harvesting.
2.2. Analyses
All calculations were performed with R v3.0.0 (R Development Core
Team, 2018).
2.2.1. Presence/absence of non-random TreM co-occurrences
The data used was an absence/presence matrix for the eleven TreM
subgroups, with one row for each tree observed. To quantify the nature
L. Larrieu et al.
Ecological Indicators 127 (2021) 107757
3
of a co-occurrence, we counted for the corresponding pair of TreMs (e.g.
crack and polypore) the number of (1,1) in the data matrix for the col-
umns associated to this pair (in this example, the rst column for crack
and the second column for polypore) meaning that both TreMs are
present on the same tree; there is no co-occurrence if both TreMs are
absent or if only one is present. If this count is low when a TreM is
present (e.g. crack) but the other (e.g. polypore) is often absent, the co-
occurrence can be qualied as negative; if the count is high since the two
TreMs are often both present on the same tree, the co-occurrence can be
qualied as positive. To decide if the co-occurrence is signicantly
positive/negative or random, we compared this count with a similar one
calculated on a sample where pairs of 0 and 1 are obtained by resam-
pling on the 0/1 vectors observed for each TreM of the considered pairs
independently; so we did a resampling test for each TreM pair. This
resampling procedure (the R-script is provided in the supplementary
material) gave min, mean and max counts and when an observed count
fell inside the minmax interval, the corresponding pair was considered
to be random. Otherwise, the co-occurrence was considered non-random
(negative or positive). As we observed considerable heterogeneity of
presences and co-occurrences at the plot level, we did the resampling at
each plot level to tackle specic plot characteristics (number of pres-
ences for each TreM and number of trees involved). We ran 10,000 it-
erations of this resampling. We then calculated, for each pair of TreMs,
the difference between the observed co-occurrence frequency (i.e. the
count of trees in the database that bore the TreM pair) and the mean
frequency obtained by the 10,000 iterations of the resampling; we called
this difference the magnitude of the co-occurrence. The results for
broadleaves and conifers were treated separately. Graphical represen-
tations (Figs. 2 and 3) were inspired by those provided in the co-occur
package (Grifth et al, 2016).
2.2.2. Modeling non-random co-occurrences to highlight key factors
To analyze the effect of four explanatory variables (detailled below)
on the probability of co-occurrence for each pair of TreMs at the tree
scale, we used for the 42 non-random co-occurrences found (33 for
broadleaves and 9 for conifers) a logistic model with a binomial error
distribution and a logit link-function (GLMM approach, glmer function,
R-package lme4; Bates et al., 2015). The dependent variable was a binary
variable (presence/absence of co-occurrence) since at least one of the
TreMs in the respective combination was present. For each combination
of TreMs, we considered only the trees bearing at least one of the TreMs
since we were looking for co-occurrence. Excluding trees without TreMs
did not affect the binomial distribution of the variable. As explanatory
variables, we used: (i) tree dbh, (ii) time since the last harvest on the plot
(ve classes: 1- <15 years, 2- from 15 to 30y, 3 - from 30 to 50y, 4 - from
50 to 100y and 5 - unharvested for at least 100y), (iii) tree-dynamic status
(two categories: long-lived and shade-tolerant species, and pioneer/post-
pioneer together in order to balance tree numbers between categories
since post-pioneers were underrepresented in the dataset) according to
Rameau et al. (1993), and (iv) compartmentalization capacity according
to Shigos, 1984 concept (two classes: weak and high; Gilman, 2011;
Oven and Torelli, 1999; Schneuwly-Bollschweiler and Schneuwly, 2012;
Dujesiefken and Liese, 2015; Table 5SM). We used the plot identity as a
random-effect variable (i.e. (1|SitePlot) since several plots were nested
in the same site). Using tree-species succession status instead of simply
tree species allowed us to include rarely observed tree species and to
follow a functional approach to stand dynamics. It should be noted that
compartmentalization capacity was not pertinent for conifers in our study
since all the conifers we assessed have a high compartmentalization
capacity according to the literature. The number of trees distributed
among the ve value classes of time since the last harvest was sometimes
very irregular. When the number of trees in a class was too small or
equalled zero, the model could not be calculated correctly or did not
converge; the variable time since the last harvest was therefore removed
from the model. We systematically used VIF >3 (Zuur et al., 2010) as
the cut-off point to remove collinear variables (vif.mer function).
Thirty-six models were tested for each signicant co-occurrence and
that separately for broadleaves and conifers (31 for broadleaves and 5
for conifers). We then used the MuMIn package (Barton, 2019) to
calculate the Second-order Akaike Information Criterion and R
2
values
(r.squaredGLMM and r.squaredLR) for each of the 36 models. The sig-
nicance of each explanatory variable was tested with the Anova func-
tion (R-package car; Fox and Weisberg, 2011). The signicance of the
different levels of the factorized variables was calculated with the model.
Fig. 1. Map of the TreM datasets; symbols identify the datasets; numbers indicate the dataset IDs shown in Table 1SM.
L. Larrieu et al.
Ecological Indicators 127 (2021) 107757
4
Table 1
TreM forms, groups, subgroups (level created for this study to optimize available data) and types (from Larrieu et al., 2018, and Kraus et al., 2016 for the illustrations);
TreMs belonging to the form Epiphytic and epixylic structures (Larrieu et al., 2018) were not included.
L. Larrieu et al.
Ecological Indicators 127 (2021) 107757
5
avg function (R-package MuMIn) based on the calculation of the condi-
tional average model.
2.2.3. Selecting tree-related microhabitat combinations for monitoring
We used the results obtained on TreM co-occurrence to identify the
combinations of TreMs that gave the most complete representation of
TreM diversity while reducing the monitoring effort as much as possible.
For this purpose, we assigned a score to each combination of TreMs
(from one to ten TreMs) based on their co-occurrences with other TreMs
and the reliability of their observation. Our reasoning was that a TreM
strongly co-occurring with others could be a proxy indicator for a larger
group of TreMs and should therefore have a higher score. In addition, we
considered that TreMs with high observational reliability (i.e. no man-
agement, season or observer bias highlighted in literature) and a high
occurrence rate (i.e. above median) should have higher scores. We
calculated two scores for each combination: one for broadleaved trees
and one for coniferous trees. We assigned a score of 0 to any combina-
tion of TreMs that did not have any signicant co-occurrences with any
other TreMs. For the other combinations, the total score was the
weighted sum of ve criteria (see below). Each criterion had a value
Fig. 2. TreM co-occurrences for broadleaves (top panels) and conifers (bottom panels). The left panels show positive co-occurrences while the right panels show
exclusive ones. Although only the results with p <0.0001 were considered signicant, here we show the whole range of signicance levels for a broader overview of
TreM relationships. X-axis labels are abbreviations of the full names of the TreM-subgroups indicated along the Y-axis, i.e. RH: rot hole, De: dendrotelm, RC: root
concavity, BL: bark loss, EH: exposed heartwood, Cr: crack, CD: crown deadwood, BC: burr canker, Po: polypore. Since plots with mixed stands were counted twice, i.e. for
both broadleaves and conifers, total plot number exceeds the total indicated in Table 1SM.
Fig. 3. Magnitude of TreM co-occurrences for broadleaves (left panel) and conifers (right panel). Expected co-occurrence (X-axis) corresponds to the average number
of co-occurrences between the 2 TreMs, resulting from a random reallocation of the TreMs observed on each plot over all the trees belonging to that plot. Each dot
corresponds to a co-occurrence between 2 TreMs (55 possible pairs). Values along the axes correspond to the number of trees bearing a TreM pair in the whole dataset
(for broadleaves and conifers, 1,859 and 902 plots respectively). The dashed black lines delimit the range of values (min and max) calculated for the random
assumption (p =0.0001; see Material and method section). Only the strongest 10% of the magnitudes are identied (see Tables 4SM and 5SM for magnitude values):
CD: crown deadwood, BL: bark loss, Cr: crack, RH: rot hole, BW: breeding woodpecker hole, Po: polypore, EH: exposed heartwood, SR: sap run, De: dendrotelm, RC:
root concavity.
L. Larrieu et al.
Ecological Indicators 127 (2021) 107757
6
between 0 and 1, which reected the mean of the values for each TreM
in the combination (see Table 4SM). We weighted the values to obtain a
clear hierarchy among the criteria. Our rst criterion was non-depen-
dence on management, with a weight of 5. We considered this criterion
the most important of all since harvesting can drastically modify both
TreM occurrence (Larrieu et al., 2012; Lassauce et al., 2013; Paillet et al.,
2017) and their co-occurrence (Winter et al., 2015). The second crite-
rion was co-occurrence with TreMs not included in the combination, with a
weight of 4. We considered this criterion highly important since our
main aim was to reduce the number of TreMs to observe. The third
criterion was the number of occurrences in the database, with a weight of
3. This criterion focused on the most frequent TreMs to increase the
probability of observing at least one TreM on the short list whatever the
stand; this criterion is also important in terms of data collection and
training practitioners in TreM observation. The fourth criterion was
TreM life-span (i.e. permanent versus temporary) with a weight of 2. This
criterion was deemed somewhat less important even though TreM
longevity makes year-round observation possible. The fth criterion was
observer effect (according to Paillet et al., 2015) with a weight of 1. We
included this criterion because the presence of an observer effect in
certain eld records could lead to stand mischaracterization. We
selected the best TreM combinations to create short-lists encompassing
from one to ten TreMs. We then analyzed how the total weighted scores
of these short lists varied as a function of the number of TreMs making
up the list, for conifers and broadleaves separately.
3. Results
3.1. Non-random TreM co-occurrences
We highlighted 33 non-random positive co-occurrences for broad-
leaves while we found seven positive and two mutually-exclusive co-
occurrences for conifers (p <0.0001; Fig. 2). All the TreMs on broad-
leaves showed at least one signicant co-occurrence with another TreM.
Burr canker never co-occurred with any other TreMs on conifers. Bark
loss, rot-hole, crack and polypore showed the highest number of positive
co-occurrences with other TreMs for broadleaves (N =8) as bark loss (N
=4) did for conifers. We found signicant mutually-exclusive co-oc-
currences only for conifers: exposed heartwood with dendrotelm and sap
run. Six co-occurrences were shared by broadleaves and conifers: Crown
deadwood with polypore, bark loss with sap run, bark loss with crack, root
concavity with crown deadwood, rot hole with bark loss, and nally
breeding woodpecker hole with bark loss. Dendrotelm with crack was the
only co-occurrence specic to conifers.
We found a wide range of magnitude values, mainly for broadleaves
(Fig. 3). The strongest magnitudes were observed for the co-occurrences
of bark loss with crack for broadleaves and breeding woodpecker hole with
bark loss for conifers.
3.2. Key factors for high-magnitude non-random co-occurrences
Among a set of four variables tested for their positive contribution to
signicant co-occurrences (i.e. dbh, time since the last harvest, tree-species
category in dynamic succession and compartmentalization capacity of the
tree species), dbh was the variable with the highest consistency. It
showed a signicant (p <0.05) effect on the likelihood of two TreMs co-
occurring for 88% and 71% of the high magnitude (i.e. the 10% stron-
gest magnitudes) non-random co-occurrences for broadleaves and co-
nifers respectively (Tables 2SM and 3SM). Longer time spans without
harvesting (time classes 4 and 5, both above 50 years) favored co-
occurrences between breeding woodpecker hole and both bark loss and
crown deadwood, rot hole and crown deadwood, bark loss and both exposed
hardwood and crack for broadleaves, and co-occurrences between bark
loss and crack for conifers. For broadleaves, a shorter time without
harvesting (time class 2, 1530 years) showed a positive effect on the co-
occurrence of rot hole with root concavity and bark loss with polypore,
while it had a signicant negative effect on co-occurrences between
crown deadwood and polypore, bark loss and crack, root concavity and
crown deadwood. Tree-species category and compartmentalization capacity
were sometimes collinear. Therefore, we were unable to evaluate their
contribution for all the co-occurrence combinations. However, for
broadleaves, tree-species category in dynamic succession did have a sig-
nicant, though sometimes opposite, effect for half of the co-occurring
pairs. For example, breeding woodpecker hole had a mainly positive ef-
fect often of high magnitude, as when it was combined with exposed
heartwood, but a negative effect among long-lived and shade-tolerant
species when it was combined with polypores. Compartmentalization ca-
pacity had a signicant effect for pairs including rot hole.
3.3. Selecting tree-related microhabitat assemblages for monitoring
The relationship between the scores of the best TreM combinations
and the number of monitored TreMs showed a bell-shaped curve both
for conifers and broadleaves (Fig. 4). The maximum score was reached
quickly for broadleaves, at three TreMs, whereas it was reached much
more slowly for conifers, requiring nine TreMs (Table 2).
3.3.1. Broadleaves
For broadleaves, several combinations of only three TreMs showed
signicant co-occurrences with all the unmonitored TreMs. The assem-
blage crack +burr-canker +crown deadwood had the highest score
(Table 2); it displayed strong co-occurrence with unmonitored TreMs
and it involved TreMs with relatively frequent occurrences, low sensi-
tivity to management, long life span and low observer effects. This
combination score was very similar to the scores obtained by combi-
nations of four TreMs.
3.3.2. Conifers
For conifers, the score increased slowly with the number of moni-
tored TreMs in the combination because co-occurrences were infre-
quent. Adding a new TreM to the combination did not result in a strong
increase in co-occurrence with the remaining TreMs. The maximum
score was reached for a combination of nine TreMs: breeding woodpecker
hole +exposed heartwood +polypore +root concavity +rot hole +sap run
in addition to the three TreMs selected above for broadleaves (Table 2).
Adding dendrotelm to this combination decreased the overall score
because of the sensitivity of dendrotelm to management.
4. Discussion
Based on a large-scale database combining 11 TreM groups, we
showed signicant high-magnitude co-occurrences between TreMs at
the tree scale. We also showed that these co-occurrences are more
frequent on broadleaves than on conifers, and that dbh had a consistent
effect on the co-occurrence, while life traits of trees (i.e. category in
dynamic succession and compartmentalization capacity) and forest
management had a lesser effect.
4.1. Co-occurrence between TreMs vary with tree species groups
Most of the co-occurrences between TreMs on broadleaves are likely
due to the propensity of some species to form certain types of micro-
habitats (e.g. crown deadwood in oaks, Paillet et al., 2019) that may, in
turn, lead to the occurrence of other TreMs linked to the same process (in
this case: crack and bark loss; Larrieu, 2014). More generally, the vital
status of a given tree is known to be a strong driver of microhabitat
dynamics (e.g. Vuidot et al., 2011; Larrieu and Cabanettes, 2012). We
can assume that when the vitality of a tree decreases, TreMs linked with
the decaying process appear (i.e. saproxylic TreMs). The patterns of co-
occurrence we observed in this study, where we worked only with living
trees, conrm this assumption. We found mutually-exclusive co-occur-
rences for conifers only. This is in accordance with the results of Winter
L. Larrieu et al.
Ecological Indicators 127 (2021) 107757
7
et al. (2015) who only found a slightly exclusive co-occurrence between
bark pockets and broken tree parts on Douglas r (Pseudotsuga menziesii
Mirb. Franco) while studying TreM co-occurrence patterns in European
Beech (Fagus sylvatica L.) and Douglas r forests. Although our TreM
group exposed heartwood is quite similar to the group broken tree parts
used by Winter et al. (2015), we could not consolidate the two results
since we were not able to analyze bark pockets through our database. At
the same level of signicance (p <0.0001), our results were in accor-
dance with co-occurrences highlighted by Larrieu and Cabanettes
(2012) for (i) European beech, between rot hole and root concavity, and
(ii) Silver r, between bark loss and crack, and sap run and rot hole.
4.2. Tree diameter mainly drives TreM co-occurrence patterns
The effect of tree dbh on TreM co-occurrence probability had not
been evaluated before the present study. For our dataset, dbh was the
most relevant variable explaining co-occurrence patterns, both for
broadleaves and conifers. Generally, the larger the tree, the greater
variety of TreMs it bears (e.g. Larrieu and Cabanettes, 2012; Paillet
et al., 2019). Thus, a larger dbh favors TreM co-occurrence both by
sampling(larger trees have more chances to have several types of mi-
crohabitats) and by ontogeny (the same processes apply for different
TreMs). Dbh is used as a proxy of tree-age since it is easier to record in
the eld than age. However, several TreMs might be linked with age
rather than with dbh since they are more likely to occur over a long
period, e.g. polypores (Boddy, 2008). Certain TreMs such as lightning
scars might benet from both age and dbh since lightning strikes on
trees are quite rare in temperate forests and a large dbh often accom-
panies tree dominance and canopy exposure. Finally, TreMs such as
woodpecker breeding holes require trees large enough to provide
adequate trunk volume (Rolstad et al., 2000). Moreover, the ontogenic
stage of the tree (i.e. juvenile, adult, mature and senescent, based on the
number of replications of the species-specic architectural unit, which is
only slightly correlated to age) can lead to TreM occurrence since e.g.
the senescent stage is characterized by the presence of sun-lit dead
branches. Therefore, the link between dbh and TreM co-occurrence
might actually hide the real links with age or ontogenic stage
(Rutishauer et al., 2011). For the few TreM co-occurrences that could be
assessed, we found mostly positive effects for a longer time without
harvesting, though there were three signicant negative effects for
TreMs that are rare in managed stands, such as polypore and crack.
Management might reduce co-occurrence for these TreMs in several
ways: (i) applying a low-rotation dbh is likely to reduce the number of
large trees in the stand (e.g. Asbeck et al., 2019); (ii) TreM-bearing trees
are often marked to be cut, thus reducing their proportion (Winter and
M¨
oller, 2008, Larrieu et al., 2012), particularly in broadleaf-dominated
Fig. 4. Scores of monitored TreM combinations. The score of the best combination of monitored TreMs is shown for different numbers of monitored TreMS, for
broadleaves (solid line) and conifers (dotted line).
Table 2
Best TreM assemblages revealing potential candidates for a short list of TreMs
for monitoring as a proxy for the set of the 11 TreMs studied; for the calculation
of the combined score, see Materials and Methods.
Broadleaves Number of
TreMs
observed
Best assemblages (i.e. highest total
scores)
Total
score
2 crack +polypore 4.911
3 crack +burr canker +crown deadwood 7.004
4 crack +burr canker +crown deadwood
+exposed heartwood
7.003
5 crack +burr canker +crown deadwood
+exposed heartwood +root concavity
6.898
6 crack +burr canker +crown deadwood
+exposed heartwood +root concavity
+sap run
6.672
7 crack +burr canker +crown deadwood
+exposed heartwood +root concavity
+sap run +polypore
6.662
8 crack +burr canker +crown deadwood
+exposed heartwood +root concavity
+sap run +polypore +breeding
woodpecker hole
6.733
9 crack +burr canker +crown deadwood
+exposed heartwood +root concavity
+sap run +polypore +breeding
woodpecker hole +rot hole
6.694
10 crack +burr canker +crown deadwood
+exposed heartwood +root concavity
+sap run +polypore +breeding
woodpecker hole +rot hole +
dendrotelm
5.212
Conifers 5 burr canker +crack +crown deadwood
+exposed heartwood +bark loss
4.425
6 burr canker +crack +exposed
heartwood +polypore +root concavity
+bark loss
4.532
7 burr canker +crack +breeding
woodpecker hole +crown deadwood +
exposed heartwood +rot hole +sap run
5.759
8 burr canker +crack +breeding
woodpecker hole +exposed heartwood
+polypore +root concavity +rot hole
+sap run
6.304
9 burr canker +crack +breeding
woodpecker hole +exposed heartwood
+polypore +root concavity +rot hole
+sap run +crown deadwood
6.353
10 burr canker +crack +breeding
woodpecker hole +exposed heartwood
+polypore +root concavity +rot hole
+sap run +crown deadwood +
dendrotelm
5.124
L. Larrieu et al.
Ecological Indicators 127 (2021) 107757
8
stands (Larrieu et al., 2014); and (iii) managers tend to eliminate trees
with trunk-borne TreMs, which strongly reduce the tree commercial
value, as is the case for polypores, since a conk indicates that the wood is
already decaying (Stokland et al., 2012) and is therefore unsuitable for
timber. All of these choices lead to a reduction in TreM diversity
(Larrieu et al., 2012) and thus, mechanistically, the the reduction of co-
occurrences. Winter et al. (2015) showed that management affects TreM
occurrence patterns in European Beech (Fagus sylvatica L.) and Douglas
r (Pseudotsuga menziesii Mirb. Franco) forests by strongly reducing the
number of signicant co-occurrences. Furthermore, they found that
management promotes co-occurrences not observed in more natural
unmanaged forests; these combinations include cavities and broken tree
parts or bark pockets and bark injuries for beech, and broken tree parts
and bark injuries for Douglas r (see Winter et al., 2015 for TreM
denitions). In our case, it seems that management through time since
the last harvest has relatively moderate effects.
To date, no studies have investigated the relationship between tree-
species life-traits and TreM co-occurrence. It is well known that all
woodpeckers excavate their breeding cavities in the part of the trunk
degraded by fungi (Schepps et al., 1999; Jackson and Jackson, 2004;
Matsuoka, 2008; Zahner et al., 2012). The Black woodpecker (Dryocopus
martius) may even trigger the colonization by the fungi, thus facilitating
cavity drilling (see e.g. Puverel et al., 2019). Therefore, woodpecker
breeding cavities and fungi are linked through functional processes.
However, conks of fungi may appear several years after the tree has
actually been colonized by the mycelium (Conner et al., 1976; Jackson
and Jackson, 2004) and thus shift the visible co-occurrence in time.
Pioneer broadleaves are often used by woodpeckers as breeding trees,
particularly birches (Betula spp.; e.g. Pakkala et al., 2019) and poplars
(Populus spp.; e.g. Hebda et al., 2017). This may be due to the fact that
they are susceptible to saproxylic fungi rather early in their life cycle.
They also have a weak compartmentalization capacity (see Table 5SM),
which allows the fungus, once introduced, to spread quickly inside the
wood (Kahl et al., 2017). These traits favor the creation of a large vol-
ume of favorable substrate for breeding holes.
For a tree, investing in defence against pathogens is a trade-off with
growth speed and life span (Loehle, 1988). Fast-growing broadleaved
pioneers, for example, are good at colonizing open areas and competing
with low ground/shrub vegetation, but they generally have a weak
compartmentalization capacity since their investment in defence bar-
riers is very low and they are short-lived (Morris et al., 2016). The strong
relationship between tree-species succession dynamics and compart-
mentalization capacity explains why we often found colinearity between
these variables in our models. We revealed a signicant positive effect of
a weak compartmentalization capacity for the TreM pair rot hole/bark
loss. This suggests that most bark loss leads to the development of a rot-
hole for broadleaved pioneers, since these trees are not able to isolate
the wound area effectively. However, another process might be involved
since we found no signicant difference between pioneers and long-
lived and shade-tolerant tree species for the co-occurrence of this
TreM pairs as a function of dbh (Fig. 1 SM).
5. Conclusion: Limitations and elds of application
5.1. TreM co-occurrences as clues to better understanding TreM
formation processes
Studies viewing TreMs as key features for biodiversity at the stand
level are quite recent (Winter and M¨
oller, 2008). Current knowledge of
TreM formation and dynamics is limited and is based only on expertise
or cross-sectional (synchronic) data (see e.g. Courbaud et al., 2017).
However, there is no doubt that certain TreMs are linked through dy-
namic processes; for example, we found a positive co-occurrence be-
tween bark loss and rot hole both for broadleaves and conifers. Indeed,
bark loss will irrevocably evolve towards a rot hole if the wound favors
infection by wood-decomposing fungi and if the bearing tree is not able
to overlay the wound. Although TreM life spans may be very different
(ranging from a few weeks for myxomycetes to several decades for large
rot-holes), TreMs evolve slowly on average. Therefore, obtaining
diachronic data would require both permanent plots dedicated to this
topic and long-term funding for periodic monitoring hard to imagine
given the area and time required to obtain enough trees in a dbh range
equivalent to the one in our synchronic data. In this context, TreM co-
occurrence patterns might help to identify certain TreM dynamic re-
lationships (e.g. shift of dominance between two TreMs when tree-dbh
increases), or at least to identify local conditions that lead to the for-
mation of different TreMs on a given tree. These patterns could guide
specic studies, as improved knowledge of TreM dynamics may lead to
better management of a continuous TreM supply, both at the stand and
forest levels.
Despite the large number of trees in our database, it was not possible
to perform analyses at the TreM-type level since some TreMs were rarely
recorded. Furthermore, due to the heterogeneity of the TreM denitions
in the available datasets, we were also unable to analyze all the TreM
groups sensu Larrieu et al. (2018). Further research should analyze co-
occurrence patterns on standing dead trees since they signicantly
bear TreMs (Larrieu and Cabanettes, 2012; Vuidot et al., 2011; Regnery
et al., 2013a; Paillet et al., 2017). However, thanks to the size of our
database and the conservative approach we used (signicance with a p-
value <0.0001), our results can benet forest managers during routine
practices (tree-marking, inspection visits or plot assessments) or can
provide input for management planning based on sound and robust
scientic data.
5.2. A short list of TreMs for monitoring based on co-occurrence patterns
Monitoring based on a limited number of TreMs inevitably di-
minishes the practitioners ability to precisely assess the full TreM di-
versity in a forest. However, the best-performing TreM lists we selected
(three TreMs for broadleaves and nine TreMs for conifers) are charac-
terized by a strong co-occurrence with unmonitored TreMs. The pres-
ence of these TreMs in a forest therefore indicates that TreM richness is
probably high in this forest.
Firstly, knowing co-occurrence frequencies can help managers
develop efcient strategies for the retention of TreM-bearing trees
(habitat-trees; Bütler et al., 2013). Indeed, if co-occurrence frequency is
high, managers may be able to conserve a wide range of TreM types
simply by protecting the habitat-trees which bear multiple TreMs. In
contrast, if co-occurrence frequency is low, managers must retain
different habitat-trees for each TreM type, or to target the habitat-trees
bearing the rarest TreMs.
Secondly, since practitioners often have limited time for tree
marking, reducing the number of TreMs to be monitored could help
forest managers incorporate TreM observation and recording, a time-
consuming process (Cosyns et al., 2019). Since every TreM has a mini-
mum required size for recording (Larrieu et al., 2018), shorter TreM
lists/TreM guides with only a few threshold size values to remember
may make TreM assessment more efcient and may also lead to higher
acceptance to do such assessments. However, if practitioners use a TreM
short-list rather than a more comprehensive one, they must be careful
not to reduce the time they dedicate to observing the trees. Since the
listed TreMs are not only important per se but are also surrogates for
other TreMs, missing them inadvertently could lead to signicant in-
formation loss and thus a higher likelihood that such a tree may be
marked for removal. Paillet et al. (2015) highlighted a signicant fa-
miliarity (i.e. the observer has already observed the TreM) observer ef-
fect for cracks, for instance, thus highlighting the need for careful
training. Using a short list of TreMs for monitoring does not justify
reducing the overall number of TreM-bearing trees to retain while
marking, since the density of habitat-trees is an important driver for
species richness and for species composition for taxa such as saproxylic
beetles, polypores, hoveries, bats and birds (Paillet et al., 2018;
L. Larrieu et al.
Ecological Indicators 127 (2021) 107757
9
Regnery et al., 2013b; Bouget et al., 2013; Winter and M¨
oller, 2008;
Larrieu et al., 2019). Furthermore, actively selecting trees bearing
different TreMs is the most efcient way to ensure TreM diversity at the
stand scale (Asbeck et al., 2020).
The higher number of TreMs selected for conifers as compared to
broadleaves was mainly due to the lower number of co-occurrence pairs
observed on conifers. Breeding woodpecker hole was selected in our best
TreM combination for conifers. This TreM is often targeted for biodi-
versity conservation or integrative forest management approaches.
Thus, many forest managers are used to assessing this TreM in their daily
work. Furthermore, breeding woodpecker hole is often deemed a keystone
feature for biodiversity since a wide range of taxa uses or depends on this
TreM (Bobiec et al., 2005; Roberge and Angelstam, 2004). Crown
deadwood was selected for both broadleaves and conifers. This form of
deadwood is crucial for numerous saproxylic taxa (e.g. Bouget et al.,
2011) and is very rarely assessed, even during deadwood monitoring
(see e.g. Larrieu et al., 2019). Although bark loss had a high number of
positive co-occurrences with other TreMs for both conifers and broad-
leaves, it was not selected in our procedure, partly because we assigned a
strong weight to the variable management effect, and this negatively
inuenced the score for bark loss. Indeed, bark loss can be a common
feature resulting from timber harversting (Larrieu et al., 2012) since
trees are often wounded along skidding trails . This may then lead to a
local overestimation of occurrence of other TreMs. Moreover, Paillet
et al. (2015) highlighted a double observer effect for bark loss (both
recording duration and familiarity effects).
For studies that aim at analyzing the relationship between TreMs and
biodiversity at the stand level, we recommend using the TreM-type level
to ensure a precise description of the stand; indeed, local conditions can
inuence co-occurrence patterns and there are many highly specialized
species whose habitat cannot be characterized by a group of TreMs.
CRediT authorship contribution statement
Laurent Larrieu: Conceptualization, Methodology, Resources,
Writing - original draft. Alain Cabanettes: Methodology, Formal anal-
ysis, Writing - original draft. Benoit Courbaud: Methodology, Software,
Formal analysis, Resources, Writing - original draft. Michel Goulard:
Methodology, Formal analysis, Writing - original draft. Wilfried Heintz:
Data curation. Daniel Koz´
ak: Resources, Writing - review & editing.
Daniel Kraus: Resources, Writing - review & editing. Thibault Lachat:
Resources, Writing - review & editing. Sylvie Ladet: Data curation,
Writing - review & editing. J¨
org Müller: Resources, Writing - review &
editing. Yoan Paillet: Resources, Writing - review & editing. Andreas
Schuck: Resources, Writing - review & editing. Jonas Stillhard: Re-
sources, Writing - review & editing, Resources, Writing - review &
editing. Miroslav Svoboda: Resources, Writing - review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Acknowledgments
Part of this research was funded by the French Ministry in Charge of
Ecology (Convention Cemagref-DEB (MEEDDAT), Action GNB; the
program Biodiversité, Gestion Foresti`
ere et Politiques Publiques
(BGF), convention GNB 10-MBGD-BGF-1-CVS-092, no CHORUS 2100
214651. The Uholka dataset was collected in a joint project of the Swiss
Federal Institute for Forest, Snow and Landscape Research WSL, the
Carpathian Biosphere Reserve and the Ukrainian National Forestry
University, with nancial support from the State Secretariat for Educa-
tion, Research and Innovation SERI, Switzerland. The REMOTE dataset
was collected within the MSMT projects (CZ.02.1.01/0.0/0.0/16_019/
0000803 and LTT20016). We thank Sergey Zudin (EFI) for data man-
agement and validation of the I +TreM Dataset as well as the German
Federal Ministry of Nutrition and Agriculture (BMEL) for funding the
projects Integrate +and Informar, which allowed EFI to build this
extensive I +TreM Dataset. We also express our sincere thanks to all the
data providers and numerous collaborators who recorded data in the
eld. We thank Vicki Moore for reviewing the English in the previous
manuscript and Fr´
ed´
eric Gosselin for his valuable comments on the rst
draft.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.ecolind.2021.107757.
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L. Larrieu et al.
... The presence and number of deadwood microhabitats, such as rot holes, large dead branches, exposed heartwood or sapwood, and fruiting bodies of polypores, is indeed positively related to tree trunk size (Asbeck et al., 2019;Larrieu et al., 2021;Percel et al., 2018). In contrast, smaller trees generally bear fewer or no microhabitats. ...
... For instance, α-and γ-diversity increased with increasing tree diameter ( Figure 4b). Diversity of saproxylic beetles tends to increase with diversity and abundance of available tree-related microhabitats and the microhabitats usually co-occur on trees of larger diameters (Larrieu et al., 2021). Large trees therefore provide a larger amount and variation in key niches. ...
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... However, compartmentalization capacity is currently available for only a few temperate tree species. To compensate for the lack of data at the tree species level, we assume that using data from a given species at the genus level could be an efficient and pertinent first step, while awaiting a more comprehensive assessment (see Larrieu et al., 2021). ...
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International conventions and resolutions on biological diversity, sustainable forest management and climate change have led in recent decades to an increasing interest in having reference values from forests undisturbed by man. An outstanding example of such an undisturbed forest is the primeval forest of Uholka-Shyrokyi Luh within the Carpathian Biosphere Reserve (Ukraine). It is approximately 9000 ha (90 km2) in area and is thought to be the largest primeval forest of almost pure European beech (Fagus sylvatica L.). In 2010, the Swiss Federal Institute for Forest, Snow and Landscape Research WSL, the Ukrainian National Forestry University UNFU and the Carpathian Biosphere Reserve CBR carried out a sampling inventory of the Uholka-Shyrokyi Luh forest (survey perimeter 10?282 ha) to obtain representative data for the main forest parameters. Given the remoteness of the area, long walking distances and difficult terrain, careful planning and organisation were required, as well as the logistic support of the local forest service. The field work was carried out by six mixed teams of Swiss and Ukrainian students and scientists, guided by three survey leaders from Switzerland and Ukraine. Two teams together shared a leader and a cook, and lived in decentra­lized camps, which were moved every week to minimize the walking needed to reach the sample plots. The collaboration between the Ukrainians and Swiss worked very well and was enriching for both sides. During the two-month sampling period, the teams assessed 314 sample plots laid out on a systematic grid. All living and standing dead trees = 6 cm DBH (diameter at 1.3 m above ground) within the 500 m2 circle plots were measured and assessed for features relevant for biodiversity. Lying deadwood was assessed with line-intersect sampling (3 lines each 15 m long per plot), and small trees (= 10 cm height and < 6 cm DBH) were surveyed on subplots consisting of three concentric circles 5, 10 and 20 m2 in area. The stand structure and any traces of anthropogenic use were assessed on a circular interpretation area of 2500 m2 around the sample plot centre. The primeval forest of Uholka-Shyrokyi Luh shows all the typical features of an old-growth forest shaped by small-scale disturbances. The structure was mainly three-layered, and most of the gaps encountered were not larger than the crown of a canopy tree. The growing stock per ha was 582 (± 14) m3 (mean ± standard error) and the deadwood volume 163 (± 8) m3. The ratio of standing to lying deadwood was 1:?5. The maximum DBH measured was 150 cm, and 10 trees per ha had a DBH of at least 80 cm. The density of habitat trees, i.e. living trees with features such as cracks, holes, bark damage or similar that provide microhabitats, was 150 (± 8) per ha (35?% of the living trees). Of all the trees recorded, 97?% were beech, although 14 other tree species were identified. All species found in the tree population = 6 cm DBH were also present in the regeneration. Traces of human presence were encountered on 19?% of the assessed plots (interpretation areas), mainly in the buffer and regulated protection zone of the protected massif. Most of these traces do not affect the integrity and pristine character of the forest. Nevertheless, they imply a certain pressure exerted from the nearby settlements and from the mountain pastures. The data obtained provide good reference values for old-growth beech forests and a valuable basis for more detailed analyses and comparisons with other old-growth and managed forests. The inventory was carried out and documented in a replicable way, and can thus be repeated if desired. The plots may also be used for other non-destructive studies, e.g. on fungi.
Thesis
La forêt couvre près de 30 % du territoire français. Pourvoyeuse de ressources pour l’Homme depuis des millénaires, sa gestion est organisée depuis le XVIIe pour fournir une large gamme de produits et de services correspondant aux demandes de la société. La gestion forestière repose sur des concepts techniques très orientés sur l’augmentation du volume de bois produit et le renouvellement des peuplements, et qui ont fait leurs preuves. Dans le contexte futur d’une perturbation du climat liée en grande partie au déstockage des énergies fossiles, le bois est considéré comme le matériau renouvelable par excellence. Il est amené à accroître encore sa place dans l’économie, mais dans un contexte socioculturel évolutif qui a intégré d’autres valeurs aux forêts, comme l’esthétique, un espace de loisirs et de détente, et un rôle essentiel dans la conservation de la biodiversité. Cette dernière est devenue un des axes fondamentaux de la gestion forestière dite « durable », définie lors du « Sommet de la Terre» de Rio de Janeiro (1992) et de la conférence ministérielle d’Helsinki (1993). Or, la tradition forestière freine parfois l’intégration de nouvelles orientations et l’émergence et la diffusion de concepts novateurs. Ce constat n’est pas surprenant si l’on considère la longueur des cycles de production pour la majorité des essences, et il faut reconnaître que, malgré les multiples atteintes qu’elles ont subies au cours de l’histoire, les forêts font partie des milieux terrestres qui comportent le plus de biodiversité. Pourtant, la résilience remarquable des écosystèmes forestiers ne doit pas nous dédouaner d’une réflexion sur la durabilité de nos systèmes de production. Pour que le forestier intègre plus aisément et efficacement la biodiversité dans ses actes de gestion, en compléments des aspects forestiers classiques (stationnels, sylvicoles), économiques et sociaux, il est nécessaire d’accroitre nos connaissances et de développer des outils performants et pratiques. En effet, s’il a bien en sa possession de multiples références vis‐à‐vis de la production de bois, le gestionnaire forestier en manque sur la composition biologique des types de forêts qu’il gère, le rôle que les espèces jouent et les dysfonctionnements liés à l’absence de certaines d’entre‐elles, présentes seulement dans les forêts non anthropisées. Comme il est impossible de réaliser des inventaires exhaustifs de la richesse en espèces d’un écosystème aussi diversifié qu’une forêt (plus de 10 000 espèces dans une grande forêt naturelle), une approche prometteuse est développée en analysant seulement certains taxons appelés bio‐indicateurs et considérés comme intégrateurs de la diversité de l’ensemble des espèces et des processus fonctionnels la soutenant. Mais pour un gestionnaire non naturaliste, une approche indirecte est souvent plus intuitive et pratique à utiliser dans le travail quotidien, notamment en utilisant pour le diagnostic des caractéristiques clés pour les espèces, comme la diversité des essences, la présence de bois mort ou de certaines singularités des arbres (cavités, fentes, etc.). Néanmoins, les recherches sur certaines de ces caractéristiques sont encore balbutiantes et les connaissances sur leurs liens fonctionnels avec les espèces sont encore fragmentaires. Ce travail de thèse résulte de l’opportunité d’un poste d’interface INRA/CNPF que j’ai eu la chance d’obtenir fin 2009 pour une durée de trois ans afin de concrétiser le développement d’un indicateur indirect de la diversité des espèces forestières au sein du laboratoire Dynafor de l’Inra Toulouse. Le contexte scientifique du laboratoire et l’appui sans réserve de la tribu des Dynaforiens m’a donné l’idée en 2011 de compléter ma formation d’Ingénieur Recherche et Développement par un travail plus académique, dans le but de valoriser une démarche exploratoire pluridisciplinaire et pluritaxonomique de la biodiversité des forêts amorcée dès 2003 sur un massif d’étude montagnard (Larrieu, 2007) puis, en 2008, sur un massif de plaine. Ce travail se réalisait sur la base d’un réseau de collaborations ponctuelles, tout en assurant mon rôle de conseiller forestier au sein du CRPF de Midi‐Pyrénées. Ces conditions ne permettaient pas une rigoureuse exploitation des nombreuses données de terrain recueillies. Les « choses sérieuses » d’un point de vue scientifique ont donc réellement commencé en 2010, mais en bénéficiant largement de données et de l’expérience de terrain acquises les sept années précédentes. J’ai également bénéficié de l’appui d’un grand nombre de scientifiques, non seulement au sein de l’INRA, mais aussi de l’IRSTEA, ainsi que de naturalistes. Tous ont répondu très volontiers à toutes mes sollicitations, que ce soit pour des appuis scientifiques ou pour partager leurs bases de données taxonomiques. Cette histoire explique pourquoi la recherche que nous avons menée sur les microhabitats est typiquement finalisée, dans un esprit de Recherche et Développement. Elle a ainsi pour objectif principal de fournir des éléments de réponse pratiques aux gestionnaires d’espaces forestiers, la plupart des questions posées émanant d’ailleurs de personnels de terrain. Les méthodes employées, comme par exemple la recherche quasisystématique de seuils numériques significatifs pour la biodiversité, ou bien les échelles de travail ‐ l’arbre, le peuplement ‐ sont aussi en partie sélectionnées pour tenter de fournir des résultats facilement utilisables en routine par les gestionnaires forestiers soucieux de pratiquer une gestion intégrant la biodiversité. Cependant, la réflexion préalable aux mesures s’est toujours efforcée de placer cette recherche dans des cadres écologiques théoriques afin de participer modestement à l’amélioration des connaissances sur le fonctionnement et les dynamiques des écosystèmes forestiers.
Article
Retaining trees during harvesting to conserve biodiversity is becoming increasingly common in forestry. To assess, select and monitor these habitat trees, ecologists and practitioners often use Tree-related Microhabitats (TreMs), which are assumed to represent the abundance and diversity of environmental resources for a wide range of forest-dwelling taxa. However, the relationship between TreMs and forest organisms is not fully understood. In this context, we attempted to identify and quantify the links between TreMs and three groups of forest organisms: insects, bats, and birds. Specifically, we tested whether species abundance is influenced by TreM abundance, either as direct predictor or as mediator of environmental predictors. We collected data in 86 temperate, 1-ha mixed forest plots and employed a hierarchical generalized mixed model to assess the influence of seven environmental predictors (aspect, number and height of standing dead trees, cover of herb and shrub layer, volume of lying deadwood, and terrain ruggedness index (TRI)) on the abundance of TreMs (15 groups) on potential habitat trees, insects (10 orders), bats (5 acoustic groups) and birds (29 species) as a function of seven environmental predictors: aspect, number and height of standing dead trees, cover of herb and shrub layer, volume of lying deadwood, and terrain ruggedness index (TRI). This allowed us to generate a correlation matrix with potential links between abundances of TreMs and co-occurring forest organisms. These correlations and the environmental predictors were tested in a structural equation model (SEM) to disentangle and quantify the effects of the environment from direct effects of TreMs on forest organisms. Four TreM groups showed correlations > |0.30| with forest organisms, in particular with insects and bats. Rot holes and concavities were directly linked with three insect groups and two bat groups. Their effect was smaller than effects of environmental predictors, except for the pairs “rot holes – Sternorrhyncha” and “rot holes – bats” of the Pipistrellus group. In addition, TreMs had indirect effects on forest organisms through mediating the effects of environmental predictors. We found significant associations between two out of fifteen TreM groups and five out of 44 forest organism groups. These results indicate that TreM abundance on potential habitat trees is not suited as a general indicator of the species abundance across broad taxonomic groups but possibly for specific target groups with proven links.
Article
The Black Woodpecker (Dryocopus martius L.) is both an ecosystem engineer and an umbrella species: it has the capacity to modify its environment through cavity excavation, which in turn favors a large range of species that depend on cavities but are unable to dig them themselves (secondary cavity nesters). However, the factors driving cavity excavation by the Black woodpecker at the tree scale remain poorly known. We analyzed the characteristics of trees bearing Black Woodpecker cavities to assess the bird's local habitat requirements and their conservation potential as habitat trees. We compared the traits and characteristics of trees bearing Black Woodpecker cavities (n=60) and control trees (n=56) in two managed lowland broadleave-dominated forests in France. We hypothesized that: (i) Cavity-trees would have lower wood density and display more conks of fungi than control-trees; (ii) The local environment of cavity-trees would be less crowded than those of the control trees. In particular, the first branch would be higher up, and their first neighboring tree would be further away from cavity-trees compared to control-trees; (iii) Cavity-trees would display a higher number of other woodpecker cavities and more saproxylic microhabitats than the control-trees. We validated most of our hypotheses and showed that cavity trees differed significantly from their control counterparts. Black Woodpeckers excavate trees with softer wood and higher first branches in a less crowded environment, thus minimizing both the energy dedicated to cavity excavation and predation risk. Second, cavitytrees bear more microhabitats and play a complementary umbrella role than what was documented before. They also appear a good candidate for habitat-tree conservation. In terms of biodiversity-friendly management measures, it would be beneficial to favor large isolated standing trees devoid of low branches (notably beech), especially in stands dominated by other tree species.
Article
The presence of rotted wood is often noted in descriptions of woodpecker nest and roost sites, and ornithologists have found that certain fungi and species of woodpeckers, such as the red heart fungus (Phellinus pini) and Red-cockaded Woodpeckers (Picoides borealis) are intimately linked. The relationship assumed is usually one of woodpecker dependence or preference for partially decayed wood in which to excavate cavities, but the woodpecker is also sometimes suggested as a vector for the fungus. In this paper we review such associations and describe patterns evident among woodpecker nest sites that suggest microclimatic and microhabitat characteristics favoring fungal colonization of trees, woodpecker-favorable responses of trees to fungi, and ultimate use of the trees for woodpecker cavity excavation. Factors that favor fungal invasion and tree use by woodpeckers include tree species, growth history, site characteristics such as proximity to water and exposure to sun or shade, nature and position of tree injury, local climate, forest age and species composition, fire frequency, and human management activities. Woodpecker cavity height and entrance orientation may be related to the dispersal dynamics of fungi, which in turn may be related to forest vegetation, thermal, and hydric characteristics. Relaciones Ecológicas entre Hongos y Cavidades de Pájaros Carpinteros Resumen. En las descripciones de los nidos y dormideros de los carpinteros, muchas veces se menciona la presencia de madera podrida. Los ornitólogos han encontrado que ciertos hongos y especies de carpinteros, como el hongo Phellinus pini y el carpintero Picoides borealis, están íntimamente ligados. Usualmente se supone que el carpintero depende de o prefiere la madera en cierto estado de descomposición para excavar las cavidades, pero a veces también se sugiere que el carpintero es el vector del hongo. En este trabajo revisamos estas asociaciones y describimos patrones evidentes entre los sitios de nidificación de los carpinteros que sugieren (1) que existen características micro-climáticas y micro-ambientales que favorecen la colonización de los árboles por parte de los hongos, (2) que los árboles responden a los hongos de modo favorable para los carpinteros y (3) que los carpinteros usan luego los árboles para excavar las cavidades. Los factores que favorecen la invasión de los hongos y el uso de los árboles por parte de los carpinteros incluyen la especie de árbol, la historia de crecimiento, las características del sitio tales como la proximidad al agua y la exposición al sol o a la sombra, el tipo y posición del daño que presenta el árbol, el clima local, la edad y composición de especies del bosque, la frecuencia de fuego y las actividades antrópicas de manejo. La altura y orientación de la entrada de las cavidades de los carpinteros pueden estar relacionadas con la dinámica de dispersión del hongo, la cual a su vez puede estar relacionada con las características térmicas, hídricas y de la vegetación del bosque.
Article
Managing forests to preserve biodiversity requires a good knowledge not only of the factors driving its dynamics but also of the structural elements that actually support biodiversity. Tree-related microhabitats (e.g. cavities, cracks, conks of fungi) are tree-borne features that are reputed to support specific biodiversity for at least a part of species' life cycles. While several studies have analysed the drivers of microhabitats number and occurrence at the tree scale, they remain limited to a few tree species located in relatively narrow bio-geographical ranges. We used a nationwide database of forest reserves where microhabi-tats were inventoried on more than 22,000 trees. We analysed the effect of tree diameter and living status (alive or dead) on microhabitat number and occurrence per tree, taking into account biogeoclimatic variables and tree genus. We confirmed that larger trees and dead trees bore more microhabitats than their smaller or living counterparts did; we extended these results to a wider range of tree genera and ecological conditions than those studied before. Contrary to our expectations, the total number of microhabitat types per tree barely varied with tree genus-though we did find slightly higher accumulation levels for broad-leaves than for conifers-nor did it vary with elevation or soil pH, whatever the living status. We observed the same results for the occurrence of individual microhabitat types. However, accumulation levels with diameter and occurrence on dead trees were higher for microhabi-tats linked with wood decay processes (e.g. dead branches or woodpecker feeding holes) than for other, epixylic, microhabitats such as epiphytes (ivy, mosses and lichens)