Effects of management on aquatic tree

SUPPORTING INFORMATION
Effects of management on aquatic tree-hole communities in temperate
forests are mediated by detritus amount and water chemistry
Martin M. Gossner, Peggy Lade, Anja Rohland, Nora Sichardt, Tiemo Kahl, Jürgen Bauhus,
Wolfgang W. Weisser, Jana S. Petermann
Table of Contents
Appendix S1: Details on study plots and occurring tree-holes…………………………….
2
Appendix S2: Details on tree-hole mapping, sampling, and species identification……….
7
Appendix S3: Details on data analysis…………………………………………………….
13
Appendix S4: Details on water properties of sampled tree-holes……………………….
17
Appendix S5: Details on sampled dendrolimnotoxenes…………………………………...
18
Appendix S6: Community composition in tree-holes……………………………………
20
Appendix S7: Detailed results of Structural Equation Models…………………………….
21
Appendix S8: Tree-hole richness…………………………………………………………..
26
Appendix S9: Diversity partitioning……………………………………………………….
27
Appendix S10: Detailed Acknowledgements……………………………………………...
28
References………………………………………………………………………………….
29
1 SUPPORTING INFORMATION
Appendix S1: Details on study plots and occurring tree-holes
In the Schwäbische Alb (SA) 29 and in the Hainich-Dün (HD) 24 experimental plots (100 m x
100 m) were studied and represent a range of forest management intensities, from unmanaged
beech forests to differently managed beech- and conifer forests (Fig. S1-1, Table S1-1).
Unmanaged forests were set-aside 20-70 years ago, but all were influenced by humans at
some point, since no virgin forests exist in Central Europe. In the Schwäbische Alb, the
“unmanaged” forests (five plots) were formerly used as pasture woodland (approx. until the
beginning of the 20th century) resulting in very old beech trees (200 years) with large crowns
and low branching. The structural importance of these trees for biodiversity led to the
protection of the stands, but small interventions such as removal of spruce trees to protect the
old beech trees are still practiced in these forests. In the HD, unmanaged forests (six stands)
are located within the National Park Hainich which covers an area of 7,600 ha and was
founded in 1997. The core area of the national park was a protection zone of a military
training range from 1945 until 1990 and is characterized by uneven-aged broad-leaved forests
with tree ages of up to 250 years.
The managed forests in our study represent different silvicultural systems: 1) in
selection cutting forests only individual trees or small groups of trees are harvested according
to their target diameter and stem quality, resulting in forests with an uneven age-structure
(only in HD; 6 plots); 2) even-aged forests, where trees are from one cohort, are typically
regenerated either using small clear cuts or strip cuts (coniferous forests) or through extended
regeneration phases using shelterwood systems. Within age-class forests different
developmental stages were considered (see Table S1-1): immature beech stands (thickets and
stands of pole-sized trees; SA: 9 plots, HD: 5 plots), early-mature and mature beech stands
(SA: 9, HD: 7), early-mature and mature spruce stands (Schwäbische Alb: 6). Previous
2 SUPPORTING INFORMATION
studies have shown that plant diversity increases with forest management intensity in these
forests due to increased disturbance (Boch et al. 2013).
Fig. S1-1: Map showing the location of studied plots in the two regions Schwäbische Alb and
Hainich-Dün. Different forest management types are indicated by different symbols. Grey
shaded areas in the two regions represent forests, white areas predominantly represent arable
land, but also urban areas and water bodies. The mean distance between the two regions is
321km (MIN: 297km, MAX: 344km) and among plots within region it is 7.3km (MIN:
0.3km; MAX: 19km) in the SA and 16.4km (MIN: 0.3km; MAX: 36km) in Hainich Dün.
3 SUPPORTING INFORMATION
Table S1-1: The number of tree-holes occurring in the study plots (1ha) of the Schwäbische Alb and Hainch-Dün region, separated by stratum
(canopy >2m height, ground <2m height), type (pan/rot hole) and formation type. AC=Age-class forest
Canopy
Pan
Study plot
Schwäbische
Alb
AEW01
AEW02
AEW03
AEW04
AEW05
AEW06
AEW07
AEW08
AEW09
AEW13
AEW14
AEW15
AEW16
AEW17
AEW22
AEW23
AEW26
AEW27
Ground
Pan
Rot hole
Total
Rot hole
Main
tree
species
Management type
Forked Forked Branch Stem Forked Forked Root
stem
branch break hole stem
branch hole
Branch Stem Stump
break hole
Spruce
Spruce
Spruce
Beech
Beech
Beech
Beech
Beech
Beech
Spruce
Spruce
Beech
Beech
Beech
Beech
Beech
Beech
Beech
Age class, immature timber
Age class, immature timber
Age class, immature timber
Age class, pole wood
Age class, mature timber
Age class, immature timber
Unmanaged, mature timber
Unmanaged, mature timber
Unmanaged, mature timber
Age class, mature timber
Age class, mature timber
Age class, thicket
Age class, thicket
Age class, pole wood
Age class, immature timber
Age class, mature timber
Age class, pole wood
Age class, pole wood
0
0
0
0
11
20
5
6
9
0
0
0
0
0
10
24
0
0
0
0
0
0
0
0
1
2
7
0
0
0
0
0
0
0
0
0
5
0
4
0
3
0
2
5
1
0
0
0
0
0
2
7
0
0
0
0
0
0
0
0
15
11
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
4
1
0
0
0
0
0
0
0
0
0
0
0
0
0
16
4
14
16
3
0
0
2
2
0
4
12
0
0
0
0
0
0
0
0
4
6
2
0
0
0
0
0
0
0
0
0
0
13
3
0
8
0
0
0
2
12
8
0
2
0
0
0
0
0
5
13
7
0
38
24
43
50
31
12
8
2
4
0
16
43
0
0
4 SUPPORTING INFORMATION
AEW30
AEW32
AEW37
AEW38
AEW42
AEW43
AEW45
AEW47
AEW48
AEW49
AEW50
Beech
Spruce
Beech
Beech
Beech
Beech
Beech
Beech
Beech
Beech
Beech
Age class, mixed immature timber
Age class, immature timber
Age class, thicket
Age class, pole wood
Age class, immature timber
Age class, mature timber
Age class, thicket
Age class, mixed immature timber
Age class, mixed immature timber
Unmanaged, mature timber
Unmanaged, mature timber
12
0
0
0
11
8
0
10
9
9
13
0
0
0
0
8
1
0
0
0
4
6
0
0
0
0
0
0
0
0
0
7
11
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
11
2
0
0
0
0
0
7
8
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
0
0
0
16
0
13
8
19
9
0
10
9
28
38
HainichDün
HEW04
HEW05
HEW06
HEW08
HEW10
HEW15
HEW16
HEW17
HEW18
HEW19
HEW21
HEW22
HEW26
HEW28
HEW32
HEW34
HEW37
Beech
Beech
Beech
Beech
Beech
Beech
Beech
Beech
Beech
Beech
Beech
Beech
Beech
Beech
Beech
Beech
Beech
Age class, thicket
Age class, immature timber
Age class, mature timber
Selection cutting
Unmanaged, mature timber
Age class, thicket
Age class, pole wood
Age class, pole wood
Age class, pole wood
Age class, immature timber
Age class, mature timber
Age class, mature timber
Selection cutting
Selection cutting
Selection cutting
Unmanaged, mature timber
Unmanaged, mature timber
3
25
13
1
9
4
2
3
12
12
11
8
1
0
3
10
15
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
2
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
9
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
2
0
0
0
0
0
4
1
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
1
6
6
2
5
6
0
1
0
2
8
29
5
5
1
14
11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
1
2
1
0
0
1
0
0
0
3
1
1
0
1
0
0
1
0
0
0
1
0
1
0
0
0
0
0
0
2
1
0
0
0
5
34
20
4
16
16
3
11
13
17
21
39
8
7
4
27
40
5 SUPPORTING INFORMATION
HEW38
HEW39
HEW46
HEW47
HEW48
HEW49
HEW50
Total
Beech
Beech
Beech
Beech
Beech
Beech
Beech
Unmanaged, mature timber
Unmanaged, mature timber
Age class, immature timber
Age class, mature timber
Selection cutting
Selection cutting
Unmanaged, mature timber
14
14
10
7
6
2
6
4
1
5
2
0
0
1
8
14
4
0
0
0
4
1
9
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
18
12
5
5
7
6
2
0
0
0
0
0
0
0
0
6
1
0
0
0
4
0
0
0
0
0
0
0
45
56
25
14
13
8
18
348
65
91
16
10
10
262
12
37
59
910
6 SUPPORTING INFORMATION
Appendix S2: Details on tree-hole mapping, sampling, and species identification
Mapping of water-filled tree-holes
For assessing the availability of canopy tree-holes we first used binoculars to assess the
occurrence of potential water bodies in forked stems and branches, branch breaks and stem
holes (see Fig. S2-1). In total, we mapped 910 tree-holes (Table S1-1). We then used a 15
meter long telescopic pole with a camera attached at the top (tree-top camera) for detailed
assessment, in particular to check if the potential tree-hole was indeed water-filled and hence
a potential habitat for a local community (Fig. S2-2). This allowed the assessment of treeholes at heights of up to 17m when adding the height of the observer. Our approach thus did
not allow us to examine tree-holes higher than 17m in detail. Based on assessment with
binoculars we estimated that only 3% of the tree-holes were at heights above 17m. Since the
probability of desiccation increases with height because tree-holes located higher in the
canopy are usually smaller and more exposed to sunlight and wind (Yanoviak 1999) the large
majority of organisms can be reasonably expected to dwell in lower tree-holes. We thus
consider our sampling to be representative of the tree-hole communities in these plots.
Using the tree-top camera, each tree-hole was evaluated on a display attached to the
telescopic pole and a stick attached to the top was used to measure the depth of tree-holes.
The shape and surface area of each tree-hole was estimated and the maximum water volume
was assessed using equations for volumes of cones, hemispheres or prisms depending on the
shape of the tree-hole. For those tree-holes that were mapped but whose communities were
not sampled, the experience from sampled tree-holes (see below) was used to arrive at a
reliable estimation of water volume. Only tree-holes with a maximum water volume of >50ml
were used to calculate the density of tree-holes (per 1-ha plot). Additionally, the tree-hole
type (pan, maintaining an unbroken bark lining vs. rot hole, penetrating through to the wood
of the tree; Kitching 1971), tree-hole origin (forked stem, forked branch, root hole, branch
7 SUPPORTING INFORMATION
break, stem hole, stump), and the height above ground was noted. Water-filled holes did in
rare cases occur in deadwood logs and these were assigned to the category “stem hole”. Treeholes were classified as “canopy tree-holes” when found higher than 2m above ground and
“ground tree-holes” when found below 2m above the ground, following classifications by
Yanoviak and Fincke (2005) and Kitching (1971).
Fig. S2-1: Selection of tree-holes sampled in the present study. Stem forks are a common type
found in managed beech forests (Fagus sylvatica) (a-c). Sometimes birds (i.e. here the
common blackbird Turdus merula) build their nests above this type of tree-hole (b). Other
common types of tree-holes in the studied forests were branch forks (pans) in beech (d), root
holes (pans) in beech (e, k) and hornbean (Carpinus betulus) (j), and stumps (rot holes) in
spruce (Picea abies; h). Rot holes, such as branch breaks (e.g. in beech; f), stem holes (e.g. in
lime trees, Tilia spp.; g) and tree-hole in lying dead wood (i.e. in lying hornbean trunk; i),
were much rarer, particularly in managed forests.
8 SUPPORTING INFORMATION
Fig. S2-2: Mapping and sampling of tree-holes. a-c) A tree-top camera was used for mapping
tree-holes in the canopy. d) A photo of each tree-hole seen with the tree-top camera was
taken. e/i) Sampling of tree-holes in the canopy of (e) a managed age-class forest and (i) an
unmanaged former woodland pasture in the SA (i) was done using single rope climbing
technique. f) Sampling of a tree-hole at the base of a beech tree trunk (root hole). g/h) Treeholes in a branch fork with numerous larvae of the syrphid Myatropa florea with their long
posterior breathing tubes erected to the water surface.
9 SUPPORTING INFORMATION
Sampling of tree-hole communities
The sampling of selected tree-hole communities was conducted from June 23 - July 20 in the
SA and from June 10 -20, 2011 in the HD. Tree-hole communities at ground level were
sampled in a total of 14 plots in the SA and 24 plots in the HD across the whole range of
forest management intensities (Table S1-1). Due to time restrictions, canopy tree-holes were
sampled only in 9 and 18 plots, respectively. In each plot we randomly selected tree-holes
with a volume of 50ml (if not available smaller tree-holes were sampled), pans at ground
level (seven in SA and two at HD) and pans in the canopy (five in SA and one in HD).
Additionally, rot holes were sampled where available. In one plot of the HD and twelve plots
of the SA, fewer than the targeted number of tree-holes occurred and thus only these were
sampled. In two plots of the SA and one plot of the HD, tree-holes located higher than the
mapping limit of 17m were included because of an insufficient number of tree-holes at lower
heights. All sampled canopy tree-holes were reached by using the single-rope climbing
technique (Perry 1978). The ropes were installed using a crossbow.
The sampling of these tree-holes followed the protocol suggested by Yanoviak and
Fincke (2005). The tree-hole was photographed and the surrounding bark was checked for
insect larvae and pupae. Subsequently, pH, temperature, and oxygen saturation were
measured by a mobile sensor (see paragraph “Assessment of water properties”) as these
measures need to be taken prior to disturbing the system. Additionally, a 15-ml water sample
was taken from the free water body with the help of a 10-ml syringe. The water was filtered
using a 0.45-μm filter extension (PVDF diaphragm) and the filtrate was collected in a 15-ml
falcon tube, immediately transferred to a cooler and frozen at -20 °C in the evening of each
day. Subsequently, all water and detritus of the tree-hole were quantitatively removed and
transferred to sampling jars. The tree-holes were repeatedly flushed with a laboratory wash
bottle filled with rain water and subsequently checked for insect using a flashlight to ensure
10 SUPPORTING INFORMATION
complete sampling. Then, tree parameters (diameter at breast height, DBH) and parameters of
the tree-hole, such as exposition and height on the tree, type (pan/rot hole), origin (forked
stem, forked branch, root hole, branch break, stem hole, stump), shape, depth, length and
width were noted. The latter measurements were used to calculate the opening area the
maximum volume of non-sampled tree-holes. The maximum volume was measured by filling
the tree-hole with rain water. Finally, some leaves from around the tree were added to
facilitate the recovery of the system.
In the laboratory, the detritus of each water-filled tree-hole was separated into coarse
detritus (leaves, flower parts etc.) and fine detritus using a sieve of 0.5mm mesh size and
dried at room temperature.
Assessment of water properties
Oxygen saturation, pH, and water temperature were measured in the field with mobile
electrodes (Oxi 330, pH 330, WTW GmbH). Nitrate- (NO3-), ammonium- (NH4+), and
phosphate (PO34-) contents (mg/l) were measured by photometric determination of
concentrations. For the determination of nitrate content, a subsample of 1 ml of each sample
was diluted, if necessary, and mixed with 50 μl of a 5-% Resorcin solution and 1.3 ml of
concentrated acid sulfur. After mixing (vortex) and heat dissipation, an absorbance
measurement was taken in a 1.5 ml cuvette at 360 nm. The colorimetrical determination of
ammonium 1ml of each water sample was charged with 0.5 ml natrium-phenolate-solution,
0.25 ml natrium-nitroprussid- and natrium-hypochlorite-NaOH-solution. The test tubes were
subsequently stored in a water quench in the dark at 30°C for 30 minutes. Then, the
absorbance measurement was taken in a 1.5-ml cuvette at 630 nm. For the measurement of
phosphate concentration 2.5 ml of each water sample were charged with 100 μl Molybdat-
11 SUPPORTING INFORMATION
reagent solution and 25 μl ascorbic acid –solution and mixed subsequently. After a settlement
period of 20 minutes an absorbance measurement was taken in a 1.5-ml cuvette at 880 nm.
Due to technical constraints, temperature, oxygen content, and phosphate could only be
assessed in the HD.
Keys used for insect identification
Insect larvae and pupae were identified to family level following common insect larval
identification keys (Stehr 1987; Nilsson 1996; Stresemann et al. 2005). For genus and species
identification in Diptera we used for Ceratopogonidae Cranston (1982), Lindegaard (1997),
and Pankratova (1970) for Chironomidae, Nilsson (1997), for Culicidae Cranston et al.
(1987), Mohrig (1969), and Utrio (1976), for Muscidae Rozkošný and Gregor (2004), for
Syrphidae Dixon (1960), Hartley (1961), and Rotheray (1993), for Psychodidae Nilsson
(1997), and for Tipulidae Nilsson (1997). For species identification in the Coleoptera family
Scirtidae the key of Klausnitzer (1996) was used.
12 SUPPORTING INFORMATION
Appendix S3: Details on data analysis
Calculation of tree-hole diversity
Tree-hole density and the number of tree-hole types per plot were assessed using all 910
mapped tree-holes. We calculated the number of tree-hole types as follows. This method has
previously been used to calculate the richness of dead wood structures (Siitonen et al. 2000).
We included all tree-hole characteristics that were assessed for the mapped tree-holes (see
above); tree species, exposition, direction of opening, height, type, type of origin, shape,
opening area, depth, maximum water volume. The number of tree-hole parameters was
reduced based on ecological meaningfulness and then by removing highly correlated
parameter (Pearson’s correlation coefficient >0.4). Our final set of parameters for the variable
tree-hole diversity comprised tree-hole origin, tree species, direction of opening (top, top-side,
side), height (≤ 0.1 m, < 10 m, ≥ 10), and area of opening (≤ 20 cm², < 50 cm², ≥ 50 cm²).
Analyses of causal relationships with Structural Equation Models
To analyze causal relationships between forest management intensity, regional and local
factors and tree-hole communities we used structural equation modelling (SEM). Variables
and causal chains in our initial model were based on evidence from previous tree-hole studies
(e.g. Kitching 1971; Schmidl, Sulzer & Kitching 2008). Here, we used a model-generating
approach, where one tentative initial model is simplified until the model fit cannot be
improved (Grace 2006). Analyses were conducted with the package lavaan in R (Rosseel
2014) which fits models using Maximum Likelihood based on covariance matrices. The
hypothesized full models are shown in Figure S3-1. In the structural equation models, we
used the region (SA, HD) as a grouping factor and merged chemical and physical water
properties into a single composite variable, determined by pH, ammonium and nitrate
concentration (mg/l). Because other chemical properties were only available from the HD
13 SUPPORTING INFORMATION
region we excluded these from the models. We additionally calculated a separate model for
the HD region including all available water properties. The results were qualitatively similar
and our overall conclusions remained unchanged. Thus, we present only the model including
both regions.
All variables were tested for normality and homoscedasticity prior to analyses and
transformed where necessary. For ammonium, detritus amount, and insect abundance and
species richness an ln-transformation and for nitrate a square-root transformation was applied
and the density of tree-holes per plot, the height of the tree-hole above ground, and maximum
water volume were ln-transformed. We used two axes of the NMDS (see above) as response
variable in our SEM (Tylianakis et al. 2008; Blakely & Didham 2010).
For a measure of model fit in SEMs, the overall model p-value was used following
Grace (2006, p.128f). For models without an appropriate fit (model p-value <0.05), we first
used the modification indices to evaluate if important paths were missing from the model (MI
values larger than 3.84), included those and subsequently deleted non-significant variables in
a stepwise procedure, starting with those showing the highest p-values in both regions. We
followed this procedure until an adequate model fit was achieved. To calculate the total effect
(including all indirect effects) we multiplied path coefficients of each individual path and
summed up all paths between forest management intensity and species abundance and
richness, respectively.
14 SUPPORTING INFORMATION
Figure S3-1: Hypothesized full model for the indirect effects of forest management on
abundance and species richness (top) and community composition, measured as scores along
the first and second axis of a Nonmetric Multi Dimensional Scaling (NMDS) based on BrayCurtis distances (bottom). The composite variable “water properties” was not measured
directly.
15 SUPPORTING INFORMATION
Appendix S4: Details on water properties of sampled tree-holes
Table S4-1: Overview over the measured properties of the sampled tree-holes. Certain
properties (O2 concentration, temperature, phosphate) were only measured in the HainichDün.
Schwäbische Alb
Hainich-Dün
Property
N
Mean
SE
Min
Max
N
Mean
SE
Min
Max
O2 [%]
0
NA
NA
NA
NA
56
10.9
0.915
2.00
36.0
Temperature [°C]
0
NA
NA
NA
NA
64
15.7
0.502
7.00
25.0
pH*
68
7.08 6.69/7.49 4.93
8.85
66
6.70
6.30/7.08 4.10
8.50
Ammonium [mg/l]
96
1.27
0.190
0.00
15.0
50
4.68
1.24
0.00
57.0
Nitrate [mg/l]
96
0.29
0.073
0.00
5.05
50
22.6
1.95
0.68
72.0
Phosphate [mg/l]
0
NA
NA
NA
NA
47
1.79
0.22
0.00
6.34
Max. water volume
[ml]
112
982
188
6.00 16620
87
1094
184
5.00
10192
Coarse detritus [ml]
112
155
41.4
0.00 4030
87
427
73.7
0.00
3530
Fine detritus [ml]
112
125
28.1
0.00 1570
87
142
35.4
0.00
1892
Total detritus [ml]
112
280
63.2
0.00 5600
87
566
98.0
0.00
4982
Proportion detritus**
112 0.343
0.025
0.00
1.67
87
0.580
0.078
0.00
5.00
Height [m]***
112
0.554
0.03
34.0
87
3.89
0.593
0.10
18.3
4.37
* because pH is a logarithmic value median and quartiles are given
** proportion of detritus relative to maximum water volume
*** height above ground
16 SUPPORTING INFORMATION
Appendix S5: Details on sampled dendrolimnotoxenes
A total of 41,146 dendrolimnetoxenes were found in the studied tree-holes. These species
might not establish persistent populations there. They consisted mainly of oribatid mites
(please note that abundance of mites was not measured in the Schwäbische Alb).
Dendrolimnetoxenes comprised species from at least eight different orders (Tab. S5-1).
Araneae were observed occasionally in water-filled tree-holes near ground level, but might
have escaped during sampling. Similarly, Dermaptera were observed occasionally, but only
up to a height of five meters. Acari, Collembola, Diplopoda, Gastropoda, Isopoda, and
Oligochaeta (Lumbricidae, Enchytraeidae) frequently occurred in tree-holes and with the
exception of Enchytraeidae (only at ground level) up to 15 meters above ground.
Dendrolimnotoxenes were excluded from all analyses.
Table S5-1: Taxonomic groups of dendrolimnetoxenes found in the tree-holes of the present
study. Dendrolimnetoxenes are species that are present in tree-holes, but might not establish
persistent populations there according to the definition given by Kitching (1971; 2000). All
adult Lumbricidae were identified to species level. In the SA 112 and in the Hainich-Dün 87
tree-hole communities were sampled. The frequency gives the proportion of tree-holes in
which a particular species occurred. NA=not assessed.
Order
Family
Species
Individuals (total)
Schw. Alb Hainich
Frequency [%]
Schw. Alb Hainich
Araneae
0
2
0
2.30
Collembola
5
15
4.46
9.20
Dermaptera
2
0
0.89
0
Diplopoda
16
4
11.61
1.15
Gastropoda
7
137
2.68
40.23
31
34
13.39
10.34
47
24
22.32
8.05
Isopoda
Oligochaeta
Lumbricidae
total
17 SUPPORTING INFORMATION
.
Lumbricidae
Allolobophoridella eiseni
11
3
9.82
2.30
Lumbricidae
Not identified
36
21
16.96
8.05
8
9
2.68
5.75
NA
40,734
NA
75.86
Enchytraeidae
‚Acari‘
mainly
Oribatidae
18 SUPPORTING INFORMATION
Appendix S6: Community composition in tree-holes
The change in community composition as a function of forest management is illustrated in the
NMDS plots (Fig. S6-1). They show a higher relative abundance of filter feeders (green) in
tree-holes of plots under higher forest management intensity.
Fig. S6-1: Nonmetric multidimensional scaling (NMDS) of tree-hole communities (circles)
and individual species (numbered crosses referring to species IDs Fig. 1 of the main
manuscript) in the SA (stress value 0.185) and Hainich Dün (0.185). Forest management
intensity is fitted post hoc as a vector (black arrow) to illustrate the location of plots with high
or low forest management intensity in the ordination diagram. Green=filter feeders (FF),
orange=fine detritus feeders (FD), blue=macrosaprophages (MS), red=predators (P). All
sampled tree-holes with a full set of environmental variables were included (Schwäbische
Alb: 112, Hainich-Dün: 87). 19 SUPPORTING INFORMATION
Appendix S7: Detailed results from Structural Equation Models
Table S7-1: Results from structural equation models on the indirect effects of forest management on insect communities in water-filled tree-holes.
Covariances, R-squares, and total effects are given. For standardized path coefficients of regressions and overall model fit see Figure 5 and 6 of the
main manuscript and Figure S7-1. Region was included as grouping factor in the Abundance-Richness and Community composition model. In the Hainich model additionally measured water properties and distance to the next tree-hole were included.
Abundance-Richness Model
Schw. Alb
Covariances:
Plant species richness
~~Insect richness
~~NMDS1
~~NMDS2
Tree-hole density
~~Plant species richness
~~NMDS1
~~NMDS2
NMDS1~~NMDS2
pH
~~Ammonium
~~Nitrate
~~Phosphate
~~O2
~~ForMI
0.044
Community composition
model
Hainich-Dün
n.s.
Schw. Alb
Hainich-Dün
Hainich-Dün
0.349
-0.048
-0.051
-0.138
0.251
*
0.688
0.341
**
2.414
-0.011
Hainich model
-1.140
*
-0.130
0.136
-0.280
0.173
0.105
0.226
*
-0.236
-0.108
-0.167
0.130
0.257
0.343
*
**
0.136
0.337
-0.012
-0.182
*
0.111
0.143
0.447
-0.030
-0.268
*
20 SUPPORTING INFORMATION
~~Height
Ammonium
~~Nitrate
~~Phosphate
~~O2
~~ForMI
~~Height
Nitrate
~~Phosphate
~~O2
~~ForMI
~~Height
ForMI~~Height
0.273
*
1.873
0.277
*
0.306
0.287
*
0.431
0.325
*
0.108
0.004
0.200
-0.284
0.148
0.144
-1.132
2.363
*
0.537
1.312
-0.321
*
0.007
0.192
-0.299
0.175
0.147
-0.159
0.355
*
0.066
0.224
-0.042
*
0.221
0.167
-0.071
-0.296
*
-0.066
-0.107
0.041
Phosphate
~~O2
-0.003
~~ForMI
-0.362
O2~~ForMI
-0.114
R-square:
Water properties
Tree-hole density
Distance to next tree-hole
Plant species richness
Volume
Detritus
Insect abundance
Insect richness
NMDS1
NMDS2
1
0.558
1
0.330
1
0.575
1
0.331
0.099
0.042
0.529
0.578
0.532
0.010
0.005
0.589
0.393
0.616
0.102
0.048
0.517
0.009
0.009
0.572
0.374
0.254
0.384
0.262
*
0.325
0.214
0.174
0.072
0.559
0.532
0.571
21 SUPPORTING INFORMATION
Total effects:
Overall forest management
effect on
Insect abundance
Insect species richness
NMDS1
NMDS2
-3.021**
-2.984**
-0.151*
-0.164*
0.042 n.s.
0.292*
22 SUPPORTING INFORMATION
Figure S7-1: Indirect effects of forest management intensity on the abundance and species
richness of insect communities in water-filled tree-holes of the Hainich-Dün region. In
addition to the variables used in the model across regions distance to the next tree-hole
(Distance) and additional water properties (Phosphate, O2 content) were included in the model
23 SUPPORTING INFORMATION
which were not available for Schwäbische Alb. Top: Full hypothesized model; Bottom:
Results from a structural equation model carried out with the lavaan package in R. Black solid
arrows indicate paths of the final model. Values show standardized path coefficients for the
two regions (Chi²= 66.179). Model structure did differ significantly from observed data
(p=0.027) indicating low model fit.
24 SUPPORTING INFORMATION
Appendix S8: Number of tree-hole types
Fig. S8-1: Relationship of tree-hole density and number of tree-hole types per 1-ha plot
(F1,36=28.64, p<0.001; adjusted R²=0.428). Insets show the number of tree-holes (bottom
right; F1,51=36.88, p<0.001; adjusted R²=0.408) and number of tree-hole types (top left;
F1,36=32.28, p<0.001; adjusted R²=0.458) as function of forest management intensity. Please
note that the number of tree-hole types was only assessed in a subset of plots.
25 SUPPORTING INFORMATION
Appendix S9: Diversity partitioning
Table S9-1: Results from stand-level analyses on the differences of mean α-diversity, diversity, γ-diversity, and the proportion of -diversity (based on a multiplicative diversity
partitioning of species richness) between low (ForMI <0.85; Schwäbische Alb 2 plots,
Hainich-Dün 11 plots), medium (0.85x1.20; 5, 8) and high (>1.20; 7, 5) forest management
intensity. Results are based on linear models.
Df
F
p
Direction of effect
α-diversity
Region
ForMI
Residuals
1
2
33
5.081
5.239
0.031
0.028
-diversity
Region
ForMI
Residuals
1
2
34
0.263
0.342
0.612
0.563
γ-diversity
Region
ForMI
Residuals
1
2
34
2.110
17.980
0.155
<0.001 ↓
Proportion of -diversity
Region
ForMI
Residuals
1
1
49
0.337
5.082
0.565
0.031
Hainich-Dün>Schwäbische Alb
↓
↑
26 SUPPORTING INFORMATION
Appendix S10: Detailed Acknowledgements
We are grateful to Steffen Boch, Jörg Müller, Stephanie A. Socher, and Markus Fischer for
providing the data on plant species richness, Brenda Guidetti for assistance in combining the
data of the two regions, Norbert Höser for the identification of Lumbricidae, Reiner Bark and
colleagues for their engagement in building and optimizing the tree-top camera, Kirsten Küsel
for providing lab facilities for the analyses of water properties, Ulrich Kern for the graphical
illustrations, and two anonymous reviewers for their valuable comments which greatly
improved the manuscript. We thank the managers of the three Exploratories, Kirsten ReichelJung, Swen Renner, Katrin Hartwich, Sonja Gockel, Kerstin Wiesner, and Martin Gorke for
their work in maintaining the plot and project infrastructure; Christiane Fischer and Simone
Pfeiffer for giving support through the central office, Michael Owonibi for managing the
central data base, and Markus Fischer, Eduard Linsenmair, Dominik Hessenmöller, Jens
Nieschulze, Daniel Prati, Ingo Schöning, François Buscot, Ernst-Detlef Schulze and the late
Elisabeth Kalko for their role in setting up the Biodiversity Exploratories project. The work
was funded by the DFG Priority Program 1374 "Infrastructure-Biodiversity-Exploratories"
(DFG-WE 3081/21-1). Field work permits were issued by the responsible state environmental
offices of Baden-Württemberg and Thüringen.
27 SUPPORTING INFORMATION
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