ONLINE RESOURCE – European Journal of Wildlife Research

Surviving on the edge: a conservation-oriented habitat analysis and forest-edge

manipulation for the hazel dormouse in the Netherlands

Contents:

- Detailed description of the methods

- Table S1: recorded plant species in the 2011 habitat study

- Table S2: results (AIC model ranking) of the 2011 habitat study

- Figure S1: map of the study area

- References

Jip J. C. Ramakers1,2*, Martijn Dorenbosch2 and Ruud P. B. Foppen3

1Department of International Wildlife Management, Van Hall Larenstein University of Applied

Sciences (part of Wageningen University and Research Centre), Agora 1, 8934 CJ

Leeuwarden, The Netherlands2Bureau Natuurbalans – Limes Divergens BV, Radboud University (Mercator III),

Toernooiveld 1, 6525 ED Nijmegen, The Netherlands3Dutch Mammal Society, Radboud University, Natuurplaza (Mercator III), Toernooiveld 1,

6525 ED Nijmegen, The Netherlands4Department of Animal Ecology and Ecophysiology, Radboud University, Huygens building,

Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands5Bureau Waardenburg, P.O. Box 365, 4100 AJ Culemborg, The Netherlands

*Correspondence author:

E-mail: jip.ramakers@gmail.com

Phone: +31(0) 6 33 10 20 52

Address: Hagedis 20, 1275 BR, Huizen, The Netherlands

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Methods

Study area

The study area is located in the most south-eastern part of the Netherlands (Fig.S1). The area

typically encompasses hills and valleys of the South-Limburgian Loess Area, in which the

typical forests are of the Luzulo-Fagetum, Fago-Quercetum and Carpinion betuli type, as well

as Rhamno-Prunetea and Lonicero-Rubetea thorny shrubs (Schaminée and Janssen 1998).

Mean temperatures in this area range from 15.5-16 °C in June and 3-4 °C in December, whilst

mean precipitation can be up to 80 mm in June (measured from 1971-2000; Berendsen 2008).

Within the study area, dormouse populations are found in three forest types: nutrient-poor

beech-oak Fago-Quercetum forests, nutrient-poor wood-rush-beech Luzulo-Fagetum forests,

and nutrient-rich hornbeam-oak Querco-Carpinetum (Foppen et al. 1999).

The present study was conducted at four forest locations that encompass a large part of the

distribution area of the dormouse in the Netherlands: (1) Groote Bosch (further referred to as

GB), (2) Schweiberger & Dunnenbosch (SDB), (3) Onderste & Bovenste Bosch (OBB), and

(4) Vijlenerbossen (VB). GB, SDB and OBB are separated from VB by open agricultural

fields, but are located within 7 km from one another. GD, SDN and OBB are connected to

forest complexes stretching into Belgium, whereas VB is connected to forest complexes in

Germany. In each forest, one study site was selected. Since VB forest covers an extensive

area, this forest was subdivided into three sites (south, west, and north). Consequently, six

study sites were distinguished in total (Fig. S1).

Annual dormouse census

As part of an annual dormouse monitoring programme (Verheggen and Boonman 2006), two

survey rounds take place each year between the second half of September and the first half of

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November. Because the main reproductive period in the Netherlands is from July to

September this reflects the time of the year when the population is at its peak (Foppen et al.

2002). During a survey, transects in forest edges or hedgerows are thoroughly searched for

dormouse nests, following Foppen et al. (2002). The number of observed nests in this period

is expected to provide a relative estimate of dormouse densities. The width of surveyed forest

transects depends on the edge vegetation structure, usually dense bramble shrubs, bracken,

occasionally young trees or holly (Foppen et al. 2002). In general, the surveyed forest

transects have a width of only a few meters; this could be more (up to 50 m) in cases with

absent or sparsely scattered mature trees. Hedgerows are sampled across their whole width.

The exact locations of dormouse nests are recorded in the field with a hand-held GPS and

saved on a mobile GIS device. For each nest a descriptive protocol is followed, filling out a

standard form with e.g., the plant species that supports the nest and the height of the nest from

the forest floor.

Forest-edge manipulation

In the areas VB-W and VB-S, 75-100% of the mature trees were cut in the winter of 2009-

2010 in parts of the forest edges. These forest-edge measures were directed at stimulating the

development of a well-developed forest edge, particularly bramble (Rubus fruticosus agg.),

with the expectance to boost the dormouse population. In total, ten forest-edge segments were

cleared in the VB-W and VB-S forests (mean ± SD length of each segment: 91.8 ± 43.8 m;

width: 17.1 ± 5.4 m). Dormouse nest counts were performed from 2009 through to 2013 in

these managed segments, as well as in ten unmanaged segments (length: 180.5 ± 21.8 m;

width: 8.9 ± 3.0 m) within the same study sites. The length and width of both managed and

unmanaged segments were a posteriori determined in an independent vegetation survey

conducted in 2011 (see next section).

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Forest-edge segments incorporated in the clearing management were arbitrarily chosen based

on their vegetation succession; segments used as ‘unmanaged controls’ were a random subset

from the remaining segments that were located next to managed segments. Hence, strictly

speaking, the study is a quasi-experiment (i.e. not fully random). To avoid confusion,

therefore, we shall refer to it as a forest-edge manipulation, and analysis and subsequent

interpretation of the results will take this limitation into account.

Habitat description study

In 2011 a study was conducted on habitat selection by the dormouse in forest edges and

hedgerows in all six study sites (totalling 19.7 km), using the census methods described

above. One census round was conducted from mid-September to mid-October, and a second

round from mid-October to mid-November. Independently of the dormouse census, a

vegetation survey of forest-edge and hedgerow transects was conducted from late September

to mid-October, by a qualified phytosociologist not a priori biased by knowledge of

dormouse habitat preferences. Transects were divided into spatial segments based on

homogeneity of both tree and shrub species (mean length ± SD: 118.9 ± 68.1; n = 166). The

relative abundance of each tree and shrub species (Table S1) in these segments was

determined using the Braun-Blanquet scale. The shape of each segment was recorded in the

field with a GPS and saved on a mobile GIS device. Plant species abundance in each segment

was automatically assigned a value of percentage cover using the programme TURBOVEG

(Hennekens and Schaminée 2001). Besides species abundances, several measures relating to

structural complexity were noted (Table 1 in main text).

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Data analysis

For each individual segment as obtained from the procedure described in the previous section,

the pooled number of dormouse nests from both survey rounds was determined by projecting

nest data onto the shape data containing all segments using MapInfo Professional 9.0.2 GIS

software (PB Mapinfo Corporation, Troy, New York).

We determined the effect of the 2009 clearing management on dormouse nest counts by

means of a Generalised Linear Mixed Model (GLMM) with a Poisson error structure, using

the LME4 package (Bates et al. 2014) in R 3.0.0 (R Development Core Team; http://www.r-

project.org/). Nest count per segment per year was the response variable, offset by the log of

segment length. ‘Treatment’ (managed vs. unmanaged), ‘year’ (2009–2013) and their

interaction were included as fixed factors; the approximate width of each segment was added

as a covariate. To alleviate the potential role of spillover effects of clearing management in

unmanaged segments as a result of the lack of a fully randomised design, an autocovariate

(Dormann et al. 2007) was added to the model. This autocovariate was constructed within the

SPDEP package (Bivand et al. 2014) using inverse-distance weighting of each segment’s nest

count; the neighbour radius to incorporate segments in the weighting was set to 500 m, which

should more or less cover dormouse dispersion distances (i.e. 250–500 m; Büchner 2008).

‘Segment ID’ was specified as random term, estimating an intercept for each year to account

for multi-year sampling. As direct comparison between managed and unmanaged segments

was not meaningful (because of the lack of complete randomisation), we instead tested

within-treatment changes in nest numbers, relative to their starting point. Significance levels

resulting from five pairwise comparisons (i.e. four comparisons between successive years +

the net change between 2009 and 2013) within each treatment were corrected using the

Bonferroni method.

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To analyse the effect habitat variables recorded in 2011 on dormouse nest counts in that year,

we determined for each segment: overall diversity of tree and shrub species (Simpson’s

reciprocal diversity index; Magurran 2004); diversity of hard-mast species (i.e. species

producing nuts or winged seeds; Table S1: species 17-21) and soft-mast species (i.e. species

producing fleshy fruits; Table S1: species 1-16); and the cumulative abundance of soft-mast

species including hazel (Corylus avellana) (= food-species abundance) (Table 1 in main text).

Hazel was included in the latter variable as this species is an important food source for

dormice (e.g., Bright and Morris 1993; Richards et al. 1984). We tested the effect of the

recorded soft- and hard-mast species only when they occurred in at least 40 segments (a

natural boundary in the data), as well as Bracken (Pteridium aquilinum) and Old man’s beard

(Climatis vitalba) (given their suitable nesting conditions). Honeysuckle (Lonicera spp.), a

potential food source for dormice (e.g., Bright and Morris 1993; Juškaitis and Šiožinytė 2008;

Richards et al. 1984), was not included as it was considered a rare species throughout the

study sites. To reduce the number of variables to be tested, each variable was first tested in

separate GLMMs; variables with an unadjusted P-value of ≥ 0.5 were disregarded in further

analyses (see below).

In each model (GLMM with Poisson errors) nest count was the dependent variable, offset by

the log of segment length. In addition to the independent variables listed above, an

autocovariate was added to account for potential spillover effects (inverse-distance weighting

with a 500-m neighbour radius; same procedures as outlined above). Each model contained

‘site’ and an observation-level factor (‘segment ID’) as random terms, to account for spatial

clustering and overdispersion, respectively. We adopted an information-theoretic approach

(Akaike’s Information Criterion or AICc; Burnham and Anderson 2002) to compare all

possible subsets with the restriction of using a minimum of four and a maximum of eight

independent variables in each model (based on prior, single-predictor analysis and to avoid

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overfitting) and excluding pairs of collinear (rSpearman > 0.5) variables. Model ranking based on

AICc is based on the “smaller is better” principle; final (standardised) variable coefficients

were generated using model averaging with the MUMIN package (Barton 2014) over all

models that were within six AICc units from the top-ranked one, ensuring that the best model

was chosen with high (> 95%) probability (Richards 2005).

Model fit in both sets of analyses was validated using residual plots, as well as observed-vs.-

fitted plots to assess the predictive accuracy of the final models (r ≥ 0.97; in the habitat

description study this was the model containing only significant model-averaged coefficients).

GLMM procedures closely followed Bolker et al. (2008).

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Table S1. Tree and shrub species identified in the field and used in analyses. Species under

‘Soft mast’ and ‘Hard mast’ include only those used as such in the data analysis; the rest is

filed under ‘Other’.

Species Common name Species Common nameSoft mast Other1 Cornus sanguinea L. Dogwood 22 Acer platanoides L. Norway maple2 Crataegus laevigata (Poir.) DC Woodland hawthorn 23 Acer pseudoplatanus L. Sycamore maple3 Crataegus monogyna Jacq. Common hawthorn 24 Alnus glutinosa L. Black alder4 Humulus lupulus L. Hop 25 Betula pendula Roth Silver birch5 Ilex aquifolium L. Holly 26 Betula pubescens Ehrh. Downy birch6 Mespilus germanica L. Medlar 27 Carpinus betulus L. Hornbeam7 Prunus serotina Ehrh. Black cherry 28 Clematis vitalba L. Old man's beard8 Prunus spinosa L. Blackthorn 29 Cytisus scoparius (L.) Link Broom9 Rhamnus frangula L. Alder buckthorn 30 Euonymus europaeus L. Spindle10 Rosa canina L. Dog rose 40 Fraxinus excelsior Ash11 Rubus fruticosus L. agg.* Common blackberry 41 Picea abies (L.) H. Karst Norway spruce12 Rubus idaeus L. European raspberry 42 Populus tremula L. Aspen13 Rubus spp.* Blackberry spp. 43 Populus x canadensis Hybrid black poplar14 Sambucus nigra L. Elder 44 Prunus avium L. Wild cherry15 Sorbus aucuparia L. Rowan 45 Pteridium aquilinum (L.) Kuhn Bracken16 Viburnum opulus L. Guelder rose 46 Quercus rubra L. Red oakHard mast 47 Ribes rubrum L. Red currant17 Acer campestre L. Field maple 48 Ribes uva-crispa L. Gooseberry18 Castanea sativa Mill. Sweet chestnust 49 Robinia pseudoacacia L. Black locust19 Corylus avellana L. Hazel 50 Salix alba L. White willow20 Fagus sylvatica L. Beech 51 Salix caprea L. Goat willow21 Quercus robur L. Pendunculate oak 52 Taxus baccata L. European yew

53 Tilia cordata Mill. Small-leaved lime54 Ulmus minor Mill. Field elm

*Grouping of blackberry (or bramble) was done based on morphological features, where Rubus spp. represents

low-growing bushes, with very little or no fruit production, often found in the forest interiors, and R. fruticosus

agg. represents higher (often > 0.5 m) bushes, generally richer in fruit production.

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Table S2. Rank list of all the GLMMs (< 6 ΔAICc ) for the 2011 habitat description study

(explanations given below the table).

Rank K ΔAICc Model formula w1 10 0.00 + AUTO + Cave + Cmon + Fsyl + SHHT + Rfru + Rspp + Sauc 0.042 10 0.12 + HAB + AUTO + WID + Cmon + SHHT + Rfru + Rspp + Sauc 0.033 10 0.15 + AUTO + WID + Cmon + Fsyl + SHHT + Rfru + Rspp + Sauc 0.034 9 0.53 + AUTO + Cmon + Fsyl + SHHT + Rfru + Rspp + Sauc 0.035 10 0.54 + HAB + AUTO + Cmon + Fsyl + SHHT + Rfru + Rspp + Sauc 0.036 9 0.64 + HAB + AUTO + Cmon + SHHT + Rfru + Rspp + Sauc 0.037 9 0.87 + AUTO + WID + Cmon + SHHT + Rfru + Rspp + Sauc 0.028 10 1.05 + AUTO + WID + Cave + Cmon + SHHT + Rfru + Rspp + Sauc 0.029 10 1.19 + AUTO + WID + Cmon + SHHT + Rfru + Rspp + DSOF + Sauc 0.0210 10 1.62 + HAB + AUTO + Cave + Cmon + SHHT + Rfru + Rspp + Sauc 0.0211 10 1.81 + AUTO + WID + Cave + Cmon + Fsyl + Rfru + Rspp + Sauc 0.0112 9 1.84 + AUTO + Fsyl + SHHT + Rfru + Rspp + Sauc + FDSP 0.0113 10 1.88 + AUTO + Fsyl + SHHT – Pspi + Rfru + Rspp + Sauc + FDSP 0.0114 8 2.01 + AUTO + WID + SHHT + Rfru + Rspp + Sauc 0.0115 10 2.02 + AUTO + WID + SHHT – Pspi + Rfru + Rspp + Sauc + FDSP 0.0116 10 2.03 + HAB + AUTO + Cmon + SHHT – Pspi + Rfru + Rspp + Sauc 0.0117 9 2.07 + AUTO + WID + SHHT – Pspi + Rfru + Sauc + FDSP 0.0118 10 2.13 + AUTO + WID + Cmon + SHHT – Pspi + Rfru + Rspp + Sauc 0.0119 9 2.13 + AUTO + WID + SHHT + Rfru + Rspp + DSOF + Sauc 0.0120 10 2.13 + AUTO + WID + Fsyl + SHHT + Rfru + Rspp + Sauc + FDSP 0.0121 10 2.16 + AUTO + Cmon + Fsyl + SHHT – Pspi + Rfru + Rspp + Sauc 0.0122 10 2.32 + HAB + AUTO + Cmon + SHHT + Rfru + Rspp + DSOF + Sauc 0.0123 9 2.33 + AUTO + WID + SHHT + Rfru + Rspp + Sauc + FDSP 0.0124 8 2.35 + AUTO + Fsyl + SHHT + Rfru + Rspp + Sauc 0.0125 9 2.39 + AUTO + WID + Fsyl + SHHT + Rfru + Rspp + Sauc 0.0126 10 2.42 + AUTO + WID + Cmon + SHHT + Rfru + Rspp + DTOT + Sauc 0.0127 10 2.43 + AUTO + Cmon + Fsyl + SHHT + Rfru + Rspp + DTOT + Sauc 0.0128 10 2.43 + AUTO + Cmon + Fsyl + SHHT + Rfru + Rspp + DSOF + Sauc 0.0129 9 2.57 + AUTO + Cave + Cmon + Fsyl + Rfru + Rspp + Sauc 0.0130 10 2.74 + HAB + AUTO + Cmon + SHHT + Rfru + Rspp + DTOT + Sauc 0.0131 10 2.76 + AUTO + WID + Fsyl + SHHT – Pspi + Rfru + Sauc + FDSP 0.0132 9 2.86 + HAB + AUTO + WID + SHHT + Rfru + Rspp + Sauc 0.0133 10 2.94 + AUTO + WID + Cave + Cmon + Rfru + Rspp + DSOF + Sauc 0.0134 10 3.04 + AUTO + WID + SHHT – Pspi + Rfru + Rspp + DSOF + Sauc 0.0135 9 3.06 + AUTO + WID + Cmon + Rfru + Rspp + DSOF + Sauc 0.0136 8 3.07 + HAB + AUTO + SHHT + Rfru + Rspp + Sauc 0.0137 9 3.09 + AUTO + Cave + Fsyl + SHHT + Rfru + Rspp + Sauc 0.0138 9 3.11 + AUTO + WID + Cave + SHHT + Rfru + Rspp + Sauc 0.0139 9 3.12 + AUTO + Fsyl + SHHT – Pspi + Rfru + Sauc + FDSP 0.0140 9 3.31 + AUTO + WID + SHHT – Pspi + Rfru + Rspp + Sauc 0.0141 10 3.34 + AUTO + WID + Fsyl + SHHT + Rfru + Rspp + DSOF + Sauc 0.0142 10 3.35 + AUTO + WID + Cmon + Fsyl + Rfru + Rspp + DSOF + Sauc 0.01

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43 10 3.38 + HAB + AUTO + WID + SHHT – Pspi + Rfru + Sauc + FDSP 0.0144 10 3.41 + AUTO + WID + SHHT + Rfru + Rspp + DSOF + Sauc + FDSP 0.0145 9 3.42 + AUTO + WID + Cave + Cmon + Rfru + Rspp + Sauc 0.0146 10 3.45 + AUTO + WID + Cave + Fsyl + SHHT + Rfru + Rspp + Sauc 0.0147 9 3.47 + AUTO + WID + Cmon + Fsyl + Rfru + Rspp + Sauc 0.0148 10 3.51 + HAB + AUTO + WID + SHHT + Rfru + Rspp + DSOF + Sauc 0.0149 10 3.53 + HAB + AUTO + WID + SHHT + Rfru + Rspp + Sauc + FDSP 0.0150 9 3.58 + HAB + AUTO + Fsyl + SHHT + Rfru + Rspp + Sauc 0.0151 9 3.59 + AUTO + Cave + Cmon + SHHT + Rfru + Rspp + Sauc 0.0152 10 3.59 + HAB + AUTO + Fsyl + SHHT + Rfru + Rspp + Sauc + FDSP 0.0153 10 3.62 + AUTO + WID + Cave + SHHT + Rfru + Rspp + DSOF + Sauc 0.0154 9 3.65 + AUTO + SHHT – Pspi + Rfru + Rspp + Sauc + FDSP 0.0155 10 3.77 + HAB + AUTO + SHHT – Pspi + Rfru + Rspp + Sauc + FDSP 0.0156 8 3.83 + AUTO + Cmon + SHHT + Rfru + Rspp + Sauc 0.0157 9 3.84 + HAB + AUTO + SHHT + Rfru + Rspp + Sauc + FDSP 0.0158 8 3.85 + AUTO + WID + SHHT + Rfru + Sauc + FDSP 0.0159 9 3.88 + AUTO + WID + SHHT + Rfru + Rspp + DTOT + Sauc 0.0160 10 3.93 + AUTO + WID + SHHT – Pspi + Rfru + DSOF + Sauc + FDSP 0.0161 9 3.95 + AUTO + Fsyl + SHHT – Pspi + Rfru + Rspp + Sauc 062 9 3.96 + AUTO + Fsyl + SHHT + Rfru + Rspp + DSOF + Sauc 063 7 4.00 + AUTO + SHHT + Rfru + Rspp + Sauc 064 9 4.03 + HAB + AUTO + SHHT – Pspi + Rfru + Sauc + FDSP 065 10 4.03 + AUTO + WID + Fsyl + SHHT – Pspi + Rfru + Rspp + Sauc 066 10 4.04 + AUTO + Fsyl + SHHT + Rfru + Rspp + DSOF + Sauc + FDSP 067 10 4.12 + AUTO + Fsyl + SHHT + Rfru + Rspp + DTOT + Sauc + FDSP 068 10 4.15 + HAB + AUTO + WID + Fsyl + SHHT + Rfru + Rspp + Sauc 069 9 4.18 + AUTO + WID + Fsyl + SHHT + Rfru + Sauc + FDSP 070 10 4.18 + AUTO + WID + Cave + SHHT – Pspi + Rfru + Rspp + Sauc 071 10 4.19 + AUTO + Cave + Cmon + SHHT – Pspi + Rfru + Rspp + Sauc 072 10 4.22 + AUTO + Cave + Cmon + Fsyl + Rfru + Rspp + DSOF + Sauc 073 9 4.23 + HAB + AUTO + WID + Cmon + SHHT + Rfru + Sauc 074 10 4.29 + AUTO + WID + SHHT – Pspi + Rfru + DTOT + Sauc + FDSP 075 8 4.30 + AUTO + SHHT + Rfru + Rspp + Sauc + FDSP 076 10 4.32 + HAB + AUTO + WID + SHHT – Pspi + Rfru + Rspp + Sauc 077 10 4.33 + SHCO + AUTO + Cave + Cmon + Fsyl + Rfru + Rspp + Sauc 078 9 4.39 + HAB + AUTO + SHHT + Rfru + Rspp + DSOF + Sauc 079 8 4.42 + AUTO + SHHT – Pspi + Rfru + Sauc + FDSP 080 9 4.44 + AUTO + Fsyl + SHHT + Rfru + Rspp + DTOT + Sauc 081 9 4.44 + HAB + AUTO + SHHT – Pspi + Rfru + Rspp + Sauc 082 10 4.44 + AUTO + Cave + Cmon + Fsyl + Rfru + Rspp + DTOT + Sauc 083 10 4.46 + HAB + AUTO + WID + Cave + SHHT + Rfru + Rspp + Sauc 084 10 4.47 + AUTO + WID + Fsyl + SHHT + Rfru + Rspp + DTOT + Sauc 085 10 4.50 + AUTO + Cave + Fsyl + SHHT – Pspi + Rfru + Rspp + Sauc 086 10 4.50 + AUTO + WID + Cmon + Fsyl + Rfru + Rspp + DTOT + Sauc 087 10 4.58 + AUTO + WID + SHHT + Rfru + Rspp + DTOT + Sauc + FDSP 088 9 4.63 + HAB + AUTO + Cave + SHHT + Rfru + Rspp + Sauc 0

89 10 4.64 + HAB + AUTO + Fsyl + SHHT – Pspi + Rfru + Sauc + FDSP 090 9 4.66 + HAB + AUTO + WID + SHHT + Rfru + Sauc + FDSP 091 10 4.68 + AUTO + Cave + Cmon + Fsyl – Pspi + Rfru + Rspp + Sauc 092 8 4.70 + AUTO + Cave + SHHT + Rfru + Rspp + Sauc 093 8 4.74 + AUTO + WID + Cmon + Rfru + Rspp + Sauc 094 9 4.74 + AUTO + WID + Fsyl + Rfru + Rspp + Sauc + FDSP 095 10 4.82 + AUTO + WID + Cave + Cmon + Rfru + Rspp + DTOT + Sauc 096 10 4.84 + AUTO + WID + Cmon – Pspi + Rfru + Rspp + DSOF + Sauc 097 9 4.85 + AUTO + Cmon + SHHT – Pspi + Rfru + Rspp + Sauc 098 10 4.87 + HAB + AUTO + Cave + Fsyl + SHHT + Rfru + Rspp + Sauc 099 10 4.87 + HAB + AUTO + Cave + Cmon + Fsyl + Rfru + Rspp + Sauc 0100 8 4.87 + AUTO + Fsyl + SHHT + Rfru + Sauc + FDSP 0101 8 4.88 + AUTO + SHHT + Rfru + Rspp + DSOF + Sauc 0102 9 4.89 + AUTO + Cmon + SHHT + Rfru + Rspp + DSOF + Sauc 0103 10 4.93 + AUTO + Cave + Fsyl + SHHT + Rfru + Rspp + DSOF + Sauc 0104 8 5.00 + AUTO + Cmon + Fsyl + Rfru + Rspp + Sauc 0105 10 5.01 + HAB + AUTO + WID + SHHT + Rfru + Rspp + DTOT + Sauc 0106 10 5.02 + AUTO + WID + SHHT – Pspi + Rfru + Rspp + DTOT + Sauc 0107 8 5.06 + AUTO + Fsyl + Rfru + Rspp + Sauc + FDSP 0108 10 5.08 + AUTO + Cave + Cmon + SHHT + Rfru + Rspp + DSOF + Sauc 0109 8 5.09 + AUTO + SHHT – Pspi + Rfru + Rspp + Sauc 0110 10 5.13 + HAB + AUTO + WID + Cmon + SHHT – Pspi + Rfru + Sauc 0111 10 5.16 + HAB + AUTO + WID + Cmon + Rfru + Rspp + DSOF + Sauc 0112 10 5.20 + SHCO + AUTO + WID + Cmon + Rfru + Rspp + DSOF + Sauc 0113 10 5.21 + AUTO + SHHT – Pspi + Rfru + Rspp + DSOF + Sauc + FDSP 0114 10 5.23 + AUTO + WID + Cave + SHHT + Rfru + Rspp + DTOT + Sauc 0115 9 5.25 + HAB + AUTO + SHHT + Rfru + Rspp + DTOT + Sauc 0116 10 5.25 + HAB + AUTO + Fsyl + SHHT – Pspi + Rfru + Rspp + Sauc 0117 10 5.27 + HAB + AUTO + Fsyl + SHHT + Rfru + Rspp + DSOF + Sauc 0118 10 5.28 + AUTO + WID + Cave + Cmon – Pspi + Rfru + Rspp + Sauc 0119 9 5.29 + AUTO + WID + Cmon + Rfru + Rspp + DTOT + Sauc 0120 10 5.33 + AUTO + Fsyl + SHHT – Pspi + Rfru + Rspp + DSOF + Sauc 0121 10 5.35 + AUTO + Cave + Fsyl + SHHT + Rfru + Rspp + DTOT + Sauc 0122 8 5.37 + HAB + AUTO + Cmon + SHHT + Rfru + Sauc 0123 9 5.38 + AUTO + Cmon + SHHT + Rfru + Rspp + DTOT + Sauc 0124 10 5.38 + AUTO + Fsyl + SHHT – Pspi + Rfru + DSOF + Sauc + FDSP 0125 10 5.41 + AUTO + Fsyl + SHHT – Pspi + Rfru + DTOT + Sauc + FDSP 0126 8 5.43 + AUTO + WID + Rfru + Rspp + Sauc + FDSP 0127 8 5.44 + AUTO + WID – Pspi + Rfru + Sauc + FDSP 0128 10 5.49 + SHCO + AUTO + WID + Cmon + Fsyl + Rfru + Rspp + Sauc 0129 10 5.49 + HAB + AUTO + SHHT – Pspi + Rfru + Rspp + DSOF + Sauc 0130 10 5.49 + SHCO + AUTO + WID + Cave + Cmon + Rfru + Rspp + Sauc 0131 9 5.52 + AUTO + Cave + SHHT – Pspi + Rfru + Rspp + Sauc 0132 8 5.52 + HAB + AUTO + SHHT + Rfru + Sauc + FDSP 0133 10 5.55 + AUTO + Cave + Cmon + SHHT + Rfru + Rspp + DTOT + Sauc 0134 10 5.55 + AUTO + Cmon + SHHT – Pspi + Rfru + Rspp + DSOF + Sauc 0

135 10 5.57 + HAB + AUTO + WID + Cave + Cmon + Rfru + Rspp + Sauc 0136 9 5.58 + AUTO + WID + Fsyl – Pspi + Rfru + Sauc + FDSP 0137 9 5.60 + AUTO + WID + Rfru + Rspp + DSOF + Sauc + FDSP 0138 9 5.60 + AUTO + SHHT – Pspi + Rfru + Rspp + DSOF + Sauc 0139 7 5.62 + AUTO + WID + SHHT + Rfru + Sauc 0140 10 5.65 + AUTO + WID + Fsyl – Pspi + Rfru + Rspp + Sauc + FDSP 0141 10 5.70 + HAB + AUTO + SHHT + Rfru + Rspp + DSOF + Sauc + FDSP 0142 8 5.71 + HAB + AUTO + WID + SHHT + Rfru + Sauc 0143 10 5.73 + HAB + AUTO + WID + Cave + Cmon + SHHT + Rfru + Sauc 0144 10 5.74 + AUTO + WID + Cmon + Fsyl – Pspi + Rfru + Rspp + Sauc 0145 10 5.76 + HAB + AUTO + WID + Cmon + Fsyl + Rfru + Rspp + Sauc 0146 8 5.78 + AUTO + WID + Cmon + SHHT + Rfru + Sauc 0147 10 5.79 + HAB + AUTO + Cave + SHHT – Pspi + Rfru + Rspp + Sauc 0148 10 5.81 + HAB + AUTO + Fsyl + SHHT + Rfru + Rspp + DTOT + Sauc 0149 8 5.82 + AUTO + SHHT + Rfru + Rspp + DTOT + Sauc 0150 10 5.87 + AUTO + SHHT – Pspi + Rfru + Rspp + DTOT + Sauc + FDSP 0151 9 5.88 + AUTO + Cmon + Fsyl + Rfru + Rspp + DSOF + Sauc 0152 9 5.88 + AUTO + WID + SHHT + Rfru + DSOF + Sauc + FDSP 0153 9 5.90 + AUTO + Cave + SHHT + Rfru + Rspp + DSOF + Sauc 0154 8 5.90 + AUTO + WID + Fsyl + Rfru + Sauc + FDSP 0155 9 5.91 + AUTO + Fsyl – Pspi + Rfru + Rspp + Sauc + FDSP 0156 9 5.92 + AUTO + SHHT + Rfru + Rspp + DSOF + Sauc + FDSP 0157 10 5.92 + HAB + AUTO + WID + Fsyl + SHHT + Rfru + Sauc + FDSP 0158 10 5.95 + AUTO + Fsyl + SHHT – Pspi + Rfru + Rspp + DTOT + Sauc 0159 9 5.95 + HAB + AUTO + Fsyl + SHHT + Rfru + Sauc + FDSP 0160 9 5.97 + AUTO + WID – Pspi + Rfru + Rspp + Sauc + FDSP 0

K = number of parameters; ΔAICc = difference in Akaike’s Information Criterion between current and top-

ranked model; w = Akaike weight of the model. Model terms: AUTO = autocovariate (weighting variable); Rfru

= Rubus frutiscosus agg.; Rspp = Rubus spp.; Sauc = Sorbus aucuparia; Pspi = Prunus spinosa; Fsyl = Fagus

sylvatica; Cmon = Crataegus monogyna; Cave = Corylus avellana; HAB = habitat type; WID = edge width;

SHHT = shrub height; DSOFT = Simpson’s D soft-mast species; DTOT = Simpson’s D total trees and shrubs;

FDSP = food-species abundance.

168169170171172173

Fig. S1. Geographic overview of the six study sites (see Online Resource text for details). The

forest edges and hedgerows surveyed are indicated by thick black lines; forest edges used in

the forest management experiment are indicated by white arrows.

174

175

176

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