# Are biodiversity patterns of saproxylic beetles shaped by habitat limitation or dispersal limitation? A case study in unfragmented montane forests

P. Janssen, Eugénie Cateau, M. Fuhr, B. Nusillard, Hervé Brustel, Christophe Bouget

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P. Janssen, Eugénie Cateau, M. Fuhr, B. Nusillard, Hervé Brustel, et al. Are biodiversity patterns of saproxylic beetles shaped by habitat limitation or dispersal limitation? A case study in unfragmented montane forests. Biodiversity and Conservation, 2016, 25 (6), pp.1167-1185. 10.1007/s10531-016-1116-8. hal-02603471

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1 Are biodiversity patterns of saproxylic beetles shaped by habitat limitation or dispersal limitation? A case

2 study in unfragmented montane forests

3

4 Philippe Janssen, Eugénie Cateau, Marc Fuhr, Benoit Nusillard, Hervé Brustel, Christophe Bouget

5

6 P. Janssen and M. Fuhr

7 Université Grenoble Alpes, National Research Institute of Science and Technology for Environment and

8 Agriculture (Irstea), UR EMGR, 2 rue de la Papeterie BP 76, F-38402 Saint-Martin-d'Hères, France

9

10 C. Bouget and B. Nusillard

11 National Research Institute of Science and Technology for Environment and Agriculture (Irstea), UR EFNO,

12 Domaine des Barres, F-45290 Nogent-sur-Vernisson, France

13

14 E. Cateau and H. Brustel

15 Université de Toulouse, Ecole d'Ingénieurs de Purpan, UMR 1201 Dynafor, F-31076 Toulouse Cedex 3, France

16

17 Corresponding author: P. Janssen (philippe.janssen@irstea.fr), phone +33 476762879, National Research

18 Institute of Science and Technology for Environment and Agriculture (Irstea), UR EMGR, 2 rue de la Papeterie

19 BP 76, F-38402 Saint-Martin-d'Hères, France
20 Abstract

21 Understanding the processes that shape biodiversity patterns is essential for ecosystem management and
22 conservation. Local environmental conditions are often good predictors of species distribution and variations in
23 habitat quality usually positively correlate to species richness. However, beside habitat limitation, species
24 presence-absence may be constrained by dispersal limitation. We tested the relative importance of both
25 limitations on saproxylic beetle diversity, using forest continuity as a surrogate for dispersal limitation and
26 stand maturity as a surrogate for habitat limitation. Forest continuity relies on the maintenance of a forest
27 cover over time, while stand maturity results in the presence of old-growth habitat features. Forty montane
28 beech-fir forests in the French pre-Alps were sampled, under a balanced sampling design in which forest
29 continuity and stand maturity were crossed. A total of 307 saproxylic beetle species were captured using flight-
30 interception traps and Winkler-Berlese extractors. We explored the response of low- versus high-dispersal
31 species groups to forest continuity and stand maturity. Saproxylic beetle diversity increased significantly with
32 stand maturity and was mostly influenced by variables related to deadwood diversity at the stand scale and
33 suitable habitat availability at the landscape scale. Surprisingly, no evidence of dispersal limitation was found,
34 as diversity patterns were not influenced by forest continuity and associated variables, even for low-dispersal
35 species. Our study demonstrates that in an unfragmented forest landscape, saproxylic beetles are able to
36 colonize recent forests, as long as local deadwood resources are sufficiently diversified (e.g. tree species,
37 position, diameter and/or decay stage).

38

39 Keywords: biodiversity patterns, forest continuity, stand maturity, saproxylic beetles.

40
41 **Introduction**

42 Understanding the processes that shape local species composition and diversity is a fundamental
43 question in ecology (Sutherland et al. 2013). Variability in local species diversity is usually assumed to reflect
44 differences in environmental conditions. This assumption relies both on the island biogeography theory
45 (MacArthur and Wilson 1967) and the ecological niche theory (Hutchinson 1957), where species-rich sites
46 result from species coexistence through habitat availability and heterogeneity, two complementary qualities
47 usually correlated. However, beside habitat limitation, variability in local species diversity may result from
48 dispersal limitations that constrain species movements toward suitable habitats (Pulliam 2000). Understanding
49 the relative influence of dispersal versus habitat limitations has implications for biodiversity conservation and
50 the management of ecosystems (Hodgson et al. 2011).

51 Species dispersal plays several fundamental roles in communities dynamics through the colonization of
52 newly available habitats and genetic flow (Clobert et al. 2012). Dispersal is closely related to spatio-temporal
53 variations in resource availability, given that species tend to move from poor-quality to high-quality habitats
54 (Lowe 2009). However, dispersal abilities vary within and among species groups, which can be defined
55 according to species traits such as flight ability for animals. Depending on dispersal abilities and distance to
56 habitat source, colonization of newly available habitats may take more or less time and explain the absence of
57 species from suitable habitats (Pulliam 2000).

58 Even for high-dispersal species, the ability to successfully establish a population in new habitat patches
59 remains dependent on species-specific requirements. Species niche breadth shows the ability of a given
60 individual species to perform as a function of resource availability (Devictor et al. 2010). Furthermore, habitat
61 quality may contribute to species presence-absence patterns (Pulliam 2000). Indeed, habitats that include both
62 a large amount and a diversity of resources may support a greater diversity of species, including specialists
63 which are able to perform thanks to very specific resources. Variations in habitat quality therefore provide a
64 practical framework in which to study patterns of species diversity.

65 In forest ecosystems, forest continuity and stand maturity have contrasting effects on biodiversity.
66 Forest continuity is related to the maintenance of forest cover over time, regardless of stand maturity and
67 management type. Ancient forests have been in continuous existence for at least 200 years while recent forests
68 are mainly agricultural areas afforested during the last 200 years (Hermy and Verheyen 2007). Experimental
studies testing the role of forest continuity in shaping community structure have largely focused on plants (Hermy and Verheyen 2007). Results have shown that plant species closely related to ancient forests were usually more adapted to forest interior conditions (e.g. shade tolerant) and have short-distance dispersal abilities (e.g. myrmecochory) (Verheyen et al. 2003; Sciama et al. 2009). However, the influence of forest continuity on other taxa remained unclear since very few studies have been conducted to date (e.g. Assmann 1999; Gossner et al. 2008; Buse 2012). Stand maturity is usually related to old-growth habitat features. Compared to mature stands, overmature stands are characterized by a greater proportion of very large trees (e.g. Whitman and Hagan 2007), and a larger abundance and diversity of deadwood (e.g. Meyer and Schmidt 2011) and tree-related microhabitats (e.g. Winter and Möller 2008). By enhancing habitat quality, overmature stands may improve species coexistence, especially for saproxylic species depending on specific deadwood resources (e.g. Stenbacka et al. 2010; Lassauce et al. 2012). Finally, overmature stands may highlight the effect of habitat limitation on biodiversity patterns.

Using forest continuity as a surrogate for dispersal limitation and stand maturity as a surrogate for habitat limitation, we evaluated the relative importance of both limitations on saproxylic beetle diversity patterns. Saproxylic beetles are a rich group of specialized species that depend on deadwood for at least a part of their lifecycle (Speight 1989). They are among the first organisms to colonize dying trees and, consequently, play a fundamental role in wood decomposition and nutrient recycling (Speight 1989; Siitonen 2001). Previous works have shown that saproxylic beetle diversity is closely linked to deadwood quantity (Lassauce et al. 2011; Lachat et al. 2012; Bouget et al. 2014) and that many species are more closely linked to specific deadwood qualities such as tree species, diameter, stage of decay and microhabitat presence (Jonsell et al. 2007; Brin et al. 2011; Bouget et al. 2013). Saproxylic beetle diversity may therefore respond to stand maturity. Moreover, dispersal abilities vary greatly among saproxylic species, i.e. between flightless and flying beetles. Since probability of colonization is a function of patch isolation in space and time (Clobert et al. 2012), response of saproxylic beetles to forest continuity may depend on their dispersal abilities. This suggests that low- and high-dispersal species may have different response patterns to both environmental conditions (Lassau et al. 2005; Janssen et al. 2009) and distance to sources (Irmler et al. 2010; Bouget et al. 2015).

Based upon a sampling design crossing forest continuity and stand maturity, we quantified the relative contribution of dispersal and habitat limitation on saproxylic beetle richness and abundance. We used
97 information on flight ability to build species groups, i.e. flightless beetles (brachypterous and apterous) were
98 considered as low-dispersal species and flying beetles (macropterous) were considered as high-dispersal
99 species. We then studied the response of (i) total species, (ii) low-dispersal species and (iii) high-dispersal
100 species to forest continuity and stand maturity. Specifically, we explored the following two hypotheses:

101 (1) Forest continuity induces dispersal limitation from habitat sources, i.e. ancient forests, to newly
102 available habitats, i.e. recent forests. This limitation should influence low-dispersal species more than high-
103 dispersal species, and may be a function of isolation from habitat sources. Regardless of stand maturity, we
104 hypothesized that the richness and abundance of low-dispersal saproxylic beetle species would increase from
105 recent to ancient forests, and that the proportion of ancient forests in the surrounding landscape and the
106 distance at which they are found would be key parameters explaining diversity patterns.

107 (2) Stand maturity induces habitat limitation between structurally homogeneous habitats, i.e. mature
108 stands, and structurally heterogeneous habitats, i.e. overmature stands. This limitation may influence
109 specialized species, such as saproxylic beetles, and may be driven above all by resource availability. Regardless
110 of forest continuity, we hypothesized that the richness and abundance of low- and high-dispersal saproxylic
111 beetle species would increase from mature to overmature stands. We further hypothesized that deadwood
112 quantity and quality would be key parameters explaining diversity patterns.

113

114 **Materials and methods**

115 **Study area and sampling design**

116 The study was carried out in the French pre-Alps, west of the Northern Alps Mountains (Fig. 1). The
117 area encompasses the Vercors, Chartreuse and Bauges ranges (three Regional Natural Parks) characterized by a
118 limestone substratum and a temperate climate. Landscapes are mainly covered by forests (63%) among which
119 beech-fir stands are the most widespread. Owing to topographic and social constraints, forest management has
120 been quite extensive and certain forest stands have not been managed for decades.

121 In 2014, we sampled 40 sites located in montane beech-fir forests at an altitude of 800 – 1500 m. The
122 dominant tree species are European beech *Fagus sylvatica*, silver fir *Abies alba* and Norway spruce *Picea abies*.
123 Our stratified and balanced sampling design crossed forest continuity and stand maturity. Forest continuity was
124 characterized using 1:40 000 État-Major maps of France (Cateau et al. 2015), charted in the middle of the 19th
century, and 1:10 000 up-to-date vegetation maps. Forest cover overlapping in both maps was considered to indicate ancient forests (AF, n=20), while current forest cover overlapping with crops or meadows in the État-Major maps was considered to indicate recent forests (RF, n=20). Stand maturity was *a priori* approached by forest prospections and confirmed after stand attribute measurements were taken. Among a larger sampling design of 70 stands, we selected 20 mature (AF=10, RF=10) and 20 overmature stands (AF=10, RF=10). To improve the independence of our observations and avoid edge influence, all sampling sites were established >1.2 km away from any other site, were located in ancient or recent forest stands >5 ha in area, and were >68 m from the nearest stand edge.

## 133 Insect sampling

Insects were collected using two sampling methods: flight-interception traps efficient at capturing flying beetles and Winkler-Berlese extractors of litter samples efficient at capturing flightless soil-dwelling beetles. From May to August 2014, three replicates of flight-interception traps were installed in each stand approximately 30 m apart and 1.5 m above the ground, for a total of 120 traps. Each trap consisted of two perpendicularly intercepting transparent plastic panes (40–60 cm), for a cumulative panel area of 1 m², with a funnel below leading to collecting vials filled with a mixture of 50% propylene glycol, 50% water and detergent to kill and preserve the insects. Flight-interception traps were emptied monthly. Flightless soil-dwelling beetles were sampled by sifting litter through 0.5 cm-mesh Winkler bags. In each stand, a total of six liters of litter samples was collected at the base of the six largest living trees: two beech, two fir and two spruce trees. Litter sifting was conducted in October 2014 and insects were extracted at the laboratory with Berlese funnels for one month (Cateau et al. 2016). Except for Staphylinidae beetles (excl. Pselaphinae and Dasycerinae), most saproxylic beetles were identified to the species or genus level, depending on the information available in the literature, by the authors and independent experts. Staphylinidae is one of the most diversified beetle families, and its identification is quite difficult and time-consuming. Moreover, saproxylic species richness and composition, with or without staphylinidea, respond similarly to stand and landscape gradients (Parmain et al. 2015).

## 150 Characterization of sampling sites

At the stand scale, each plot was characterized using a 10-m-radius and a 20-m-radius subplot. Within the 10-m-radius subplot (area: 314 m²), we recorded all standing trees with a diameter at breast height (DBH) ≥
7.5 cm and all lying trunks with a diameter ≥ 7.5 cm at the base and ≥ 1 m in length. Within the 20-m-radius
subplot (area: 1 256 m²), we recorded all standing trees with a DBH ≥ 30 cm and all lying trunks with a diameter
≥ 30 cm at the base and ≥ 1 m in length. For each standing tree and lying trunk, tree species, decay stage and
tree microhabitats (cavities, sporophores of saproxylic fungi, ivy, sap runs, missing bark, cracks and shelter
bark) were recorded whenever possible. For lying trunk, the diameter at both ends and in the middle section as
well as length was recorded (only the portion located inside the plots was considered). Five decay stages were
estimated based on resistance to tree caliper penetration: (I) hard wood, (II) caliper penetration < 1/4 of the
tree diameter, (III) caliper penetration < 1/2 but > 1/4 of the tree diameter, (IV) caliper penetration < 3/4 but >
1/2 of the tree diameter, (V) caliper penetration > 3/4 of the tree diameter.

At the landscape scale, a Geographic Information System managed with ArcGIS 10.1 (Environmental
Systems Research Inst., Redlands, CA, USA) was used to characterize the geographic context within a 500-m-
radius around each sampling site. We used local scale vegetation maps provided by Regional Natural Parks to
describe forest stand types and landscape composition around each plot.

Environmental variables

At the stand scale, latitude, longitude, altitude, exposure and slope were recorded at the center of the
plots. The number of diameter classes was calculated using 2-cm-diameter classes. Deadwood diversity was
estimated as the number of combinations formed by position (snags versus logs), species, decay class and 2-
cm-diameter classes (Table 1).

At the landscape scale, variables based on spatial composition and configuration were computed.

Landscape composition refers to the relative proportion of forests, beech-fir stands and ancient forests within a

500-m-radius around each sampling site, while landscape configuration refers to the spatial arrangement of

patches within the landscape, i.e. perimeter-area ratio, distance to the nearest forest edge and distance to the

nearest ancient/recent forest edge.

Statistical analysis

All statistical analyses were performed with R version 3.1.2 (R Core Team 2014).

Based upon data exploration (Zuur et al. 2010), independent variables with a skewness >1 were log or
log+1 transformed to approximate normal distribution (see also correlation matrix, Online Resource Fig. 1 & 2).
We then test the significance of each environmental variable to forest continuity, stand maturity and the interaction term using two-ways ANOVA with Tukey's post-hoc tests (Table 1).

At regional scale (γ diversity), we estimated the performance of our sampling design by comparing the total number of species collected with the extrapolated species richness using incidence-based Chao estimator (vegan package). We then used rarefaction curves to compare accumulated species richness between ancient and recent forests, mature and overmature stands, ancient mature and ancient overmature stands, recent mature and recent overmature stands (BiodiversityR package).

At local scale (α diversity), we used two-ways ANOVA with Tukey's post-hoc tests to determine whether diversity patterns were influenced by forest continuity, stand maturity and their interaction. We then investigated whether saproxylic beetle richness and abundance were predicted by habitat and landscape features, using 30 *a priori* biologically plausible candidate linear models (Online Resource Table 2). Because, richness and abundance may not be independent, we furthermore assessed models performance with standardized richness as dependent variable, by including abundance as a covariate. To avoid biasing estimates toward forest continuity or stand maturity, ten models included variables describing forest continuity only, ten models included variables describing stand maturity only, and ten models included both types of variables. We used either Poisson or negative binomial regressions depending on the overdispersion of the count data and controlled for multicolinearity among explanatory variables with variance inflation factors (car package), i.e. only models with VIF <3 were considered (Zuur et al. 2010). To assess spatial autocorrelation, we used Moran's correlogram in the model residuals among sampling sites (ncf package). The significance of Moran's *I* coefficient at each distance class was evaluated with 999 permutations while the overall significance of the correlogram was tested with Bonferroni's correction (Legendre and Legendre 1998). To identify the most parsimonious regression model, we used Akaike's information criterion corrected for small sample sizes (Burnham and Anderson 2002). Moreover, as recommended by Burnham and Anderson (2002), we used model averaging to estimate parameter and associated unconditional standard errors based on the subset of top ranking models for which the sum of AICc weights reached ≥0.95 (MuMln package). To determine the relative importance of forest continuity versus stand maturity for saproxylic beetles, we compared the sum of AICc weights for the three categories of models: forest continuity variables only, stand maturity variables only, and both types of variables (Burnham and Anderson 2002).
208

209 **Results**

210 **Forest continuity and stand maturity**

211 Compared to recent forest sites, ancient forest sites were included in a matrix that contained more
212 forests (p = 0.028), more beech-fir stands (p = 0.025) and more ancient forests (p < 0.001) (Table 1). Ancient
213 forest sites were also located at a greater distance from the forest edge (p = 0.021) and in patches with less
214 complex shapes (p = 0.005) than recent forest sites.

215 Compared to mature stands, overmature stands were poorer in beech (p = 0.033) but richer in fir (p < 0.001) (Table 1). Overmature stands were also more heterogeneous (p = 0.002), with a greater number of very large trees (p < 0.001), a smaller number of stems (p < 0.001) and a higher diversity of tree microhabitats (p < 0.001). The deadwood compartment was more diversified (p < 0.001) and more abundant (volume of large logs, p < 0.001; number of large snags, p < 0.001) in overmature than in mature stands.

220 Finally, except for the perimeter-area ratio of forest cover (p = 0.043) and the volume of large logs (p = 0.049), all of the environmental variables were not influenced by the interaction term. Tukey HSD tests showed that the perimeter-area ratio of forest cover was more important in recent-mature stands than in ancient-mature stands (p = 0.005) and ancient-overmature stands (p = 0.011), and that the volume of large logs was more important in ancient-overmature stands than in ancient-mature stands (p < 0.001) and recent-mature stands (p = 0.001).

226 **Diversity patterns of saproxylic beetles**

227 A total of 307 saproxylic beetle species (18 729 individuals) belonging to 53 families were captured at the 40 sites (flight-intercept traps: 284 species, 16 884 individuals; Winkler-Berlese extractors: 52 species, 1 845 individuals). High-dispersal beetles encompassed 275 species (17 032) from 48 families, while low-dispersal beetles encompassed 27 species (1 666 individuals) from 5 families (Online Resource Table 1). Extrapolated species richness (Chao) indicated that species pool may encompass 385 (SD ±149) species, meaning that 80% of the saproxylic beetle diversity in the area was sampled.

233 The species pool of saproxylic beetles was much more influenced by stand maturity than by forest continuity (Online Resource Fig. 3). From sample-based rarefaction curves, overmature stands encompassed a pool of 281 (SD±5.56) species versus 239 (SD±5.04) species in mature stands, while ancient forests

234

235
encompassed a pool of 267 (SD±5.34) versus 249 (SD±5.06) species in recent forests. Interaction between forest continuity and stand maturity confirmed previous patterns: ancient overmature stands were more diversified than ancient mature stands, with 235 (SD±5.37) and 196 (SD±5.03) species respectively. Recent overmature stands were also more diversified than recent mature stands, with 216 (SD±5.02) and 192 (SD±4.96) species respectively.

At local scale, diversity patterns were influenced by stand maturity but not by forest continuity or interaction term (Table 2). Total richness (p = 0.012) and abundance (p = 0.003), and high-dispersal richness (p = 0.014) and abundance (p = 0.004) all increased from mature to overmature stands.

## Key parameters driving saproxylic beetle richness and abundance

Observed Moran's *I* in model residuals were non-significant for all of the dependent variables, indicating that spatial patterns were accounted for by forest continuity and stand maturity variables (Online Resource Fig. 4).

## Model ranking

Overall, stand maturity models were far more powerful than forest continuity models in explaining diversity patterns (Table 3). Total and high-dispersal richness were best predicted by stand maturity model #1 (pseudo-R² range from 37% to 38%), while total and high-dispersal abundance were best predicted by forest continuity and stand maturity model #21 (pseudo-R² = 29%). Low-dispersal richness and abundance were best explained by the null model. When accounting for abundance in models, standardized richness was best predicted by the same best model as total richness (i.e. model # 1) but model performance increased substantially (pseudo-R² = 68%). Although some of these models show quite a strong support to explain diversity patterns, model selection uncertainty remains since associated AICc weight and evidence ratio were relatively low. We therefore used model averaging, ranging from the 5 to the 20 best models, to draw inferences about the variables influencing saproxylic beetles.

## Model parameters

Forest continuity-associated variables mostly influenced abundance patterns (Table 4 & 5). Total and high-dispersal abundance increased with the proportion of beech-fir stands in the surrounding landscape but decreased with increasing distance to forest edge. Stand maturity-associated variables positively influenced both saproxylic beetle abundance and richness (Table 4 & 5). Standardized richness, total and high-dispersal
richness and abundance increased with the diversity of deadwood types. Moreover, high-dispersal richness increased with the number of large snags, while standardized richness increased with the total abundance of saproxylic beetles but decreased with the number of very large trees. The other forest continuity- and stand maturity-associated variables we tested had little influence and none of them significantly influenced low-dispersal saproxylic beetles (Table 4 & 5).

Relative influence of forest continuity and stand maturity on saproxylic beetles

Judging from the sum of AIC<sub>c</sub> weights for the models, richness patterns were mostly influenced by stand maturity (Fig. 2). For Total, standardized richness and high-dispersal richness, the cumulative AIC<sub>c</sub> weight of stand maturity models was > 0.80. Indeed, six of the top ten models included only stand maturity variables. Remaining top ten models included both stand maturity and forest continuity variables. In contrast to richness patterns, abundance patterns were more heterogeneous. For total and high-dispersal abundance, the relative influence of stand maturity was lower (from 0.49 to 0.51) and models combining both stand maturity and forest continuity variables gained in importance (from 0.41 to 0.43). For these dependent variables, the cumulative AIC<sub>c</sub> weight of forest continuity models was < 0.07. Finally, because low-dispersal richness and abundance were best explained by the null model, none of the models and associated metrics (i.e. AIC<sub>c</sub> weight) is of any inferential value.

280

## Discussion

Our results clearly show that saproxylic beetle diversity patterns were shaped by habitat limitation. No evidence of dispersal limitation was found. As indicators of habitat limitation, stand maturity models and associated variables performed much better than did forest continuity models and associated variables in explaining richness and abundance patterns of saproxylic beetles. In unfragmented montane forests, key parameters related to deadwood diversity at the stand scale and suitable habitat availability at the landscape scale appeared to be major drivers of saproxylic beetle diversity.

Dispersal limitation

Contrary to our first hypothesis, no evidence of dispersal limitation was found, i.e. diversity patterns were not influenced by forest continuity. This indicates that saproxylic beetles, even low-dispersal species, were able to successfully disperse and colonize newly available habitats. Previous studies have reported a
292 significant effect of forest continuity on plants (e.g. Sciama et al. 2009), epiphytic bryophytes and lichens (e.g.
293 Fritz and Brunet 2010), and carabids (e.g. Assmann 1999). For saproxylic beetles, the few existing studies have
294 reported that recent forests were species poor sites (Gossner et al. 2008; Irmler et al. 2010; Buse 2012). In our
295 study, forest continuity had no significant effect on saproxylic beetles richness and abundance; species
296 accumulation curves simply indicated that ancient forests encompassed a larger species pool than recent
297 forests. Given that most previous studies were conducted in fragmented landscapes, these apparently
298 contradictory results may indicate confounding effects between forest continuity and fragmentation, as
299 previously showed for plants (Jamoneau et al. 2012). In the Northern Alps, landscapes are dominated by
300 unfragmented forests and recent forests have colonized agricultural areas next to ancient forest areas.
301 Afforestation adjacent to ancient forests reduces the distance to habitat source and limits dispersal barriers
302 within the habitat matrix (Honnay et al. 2002). This may have facilitated the colonization of recent forests by
303 saproxylic beetles. Moreover, the assumption that flightless saproxylic beetles are dispersal-limited species is
304 questionable. In our study, four of the six flightless saproxylic weevils (Curculionidae) presented as ancient
305 forest relict species (Buse 2012) were recorded – *Acalles lemur*, *Kyklioacalles navieresi*, *Echinodera hypocrita*,
306 *Trachodes hispidus* – and all of them were evenly present in both ancient and recent forests. In accordance
307 with Horák et al. (2013), we believe that the dispersal ability of these flightless saproxylic species has probably
308 been underestimated.

309 Beside dispersal limitation, it has been argued that forest continuity influences biodiversity patterns
310 through habitat limitation (Hermy and Verheyen 2007). Even for highly connected patches, differences in
311 habitat characteristics between ancient and recent forests may persist for centuries, especially for soil
312 properties (Dupouey et al. 2002). These differences in habitat quality can limit species establishment in newly
313 available forests by recruitment limitation (Honnay et al. 2002). Such long-lasting effects act directly on plant
314 communities (Baeten et al. 2009) and indirectly on plant consumers (Gossner et al. 2008). Saproxylic beetles,
315 however, should be more influenced by stand structural attributes than by understory vegetation and soil
316 properties. If stand maturity parameters are not controlled for between ancient and recent forests (e.g. Irmler
317 et al. 2010; Buse 2012), it may be hazardous to infer that the differences observed in biodiversity patterns are
318 related to dispersal limitation (Nordén et al. 2014). Our sampling design was designed to disentangle the
319 relative effect of dispersal limitation from habitat limitation and our results show that stand maturity was
320 indeed independent from forest continuity. Habitat quality can therefore be regarded as homogeneous
321 between ancient and recent forests. Although forest continuity may have acted on soil proprieties and
322 associated plant communities, no effects were observed for saproxylic beetles. This indicates that, in
323 unfragmented montane forests, dispersal limitation is not a major driver in shaping saproxylic beetle diversity
324 patterns.

325 Habitat limitation

326 Habitat limitation was powerful to explain variation in species diversity. In accordance with our second
327 hypothesis, the richness and abundance of saproxylic beetles increased with stand maturity, in both ancient
328 and recent forests, from the regional species pool scale to the local diversity scale. This indicates that species
329 requirements were best supported in overmature stands and that stand maturity allowed greater species
330 coexistence regardless of forest continuity. This pattern may be related to a diversification in resource
331 availability, given that habitat heterogeneity usually positively correlates to stand maturity (McElhinny et al.
332 2005). In our sampling design, structural attributes such as the number of diameter classes of standing trees,
333 the number of very large living trees or the diversity of deadwood types, strongly increased with stand
334 maturity. Stand maturity may therefore be viewed as a surrogate for habitat heterogeneity, which is known to
335 have significant effects on biodiversity patterns, e.g. for wood-inhabiting fungi (Junninen et al. 2006), epiphytic
336 bryophytes (Dittrich et al. 2013) and birds (Poulsen 2002). For saproxylic beetles, numerous studies have also
337 reported a positive relationship between stand maturity and diversity patterns (e.g. Martikainen et al. 2000;
338 Stenbacka et al. 2010; Lassauce et al. 2012). Our results are therefore consistent with knowledge related to the
339 ecological niche theory and highlight the significance of habitat limitation as a major driver in shaping
340 biodiversity patterns. However, low-dispersal saproxylic beetles were not influenced by stand maturity. This
341 finding may indicate that, in our study, the variation in habitat quality between mature and overmature stands
342 was not strong enough. Indeed, flightless saproxylic weevils depend on twigs for their development (Heijerman
343 2004), which are not a limiting resource in mountain forests (Barbati et al. 2014). Flightless saproxylic beetles,
344 on the other hand, may be more dependent on environmental factors related to litter depth, soil moisture
345 (Sroka and Finch 2006), altitude (Horák et al. 2013), climate and topography (Buse 2012).
346 Key parameters at stand scale
At the stand scale, parameters related to stand maturity, especially those linked to deadwood quality, provided relevant explanatory variables for species richness and abundance variation patterns. Although stand structural attributes have been pointed out as key parameters for saproxylic beetles (Ranius and Jansson 2000; Grove 2002), in our study, the diversity of deadwood types was by far the most powerful explanatory variable for diversity patterns. Deadwood diversity is related to habitat diversity and allows a larger range of species, including specialists, to coexist through an increase in the heterogeneity of the resource supply. The importance of deadwood diversity for saproxylic beetles has already been pointed out in boreal (Økland et al. 1996; Similä et al. 2003) and temperate forests (Brin et al. 2011; Bouget et al. 2013). However, contrary to our expectations, deadwood quantity was not as powerful as deadwood diversity in explaining diversity patterns. While richness of high-dispersal species was significantly influenced by large snag density, large log volume had no significant effect on saproxylic beetles. Large log volume has been pointed out as a key parameter for saproxylic species (e.g. Økland et al. 1996; Lachat et al. 2012; Bouget et al. 2014) and is a commonly used indicator for sustainable forest management (Forest Europe, UNECE and FAO 2011). Müller and Bütler (2010) stated that the amount and diversity of deadwood are usually correlated and, consequently, that it may be difficult to disentangle their relative influence. In our montane mixed forests, as in others forests (Grove 2002; Similä et al. 2003; Bouget et al. 2013), the total amount and diversity of deadwood were correlated (R = 0.75). Nevertheless, our deadwood diversity models consistently performed better than our deadwood amount models in explaining richness patterns of saproxylic beetles, even when controlling for species abundance. In line with Lassauce et al.'s meta-analysis (2011), our results confirm that deadwood diversity is of more importance than deadwood amount in shaping saproxylic diversity patterns in forest ecosystems.

**Key parameters at landscape scale**

At the landscape scale, parameters related to forest continuity had contrasting effects on saproxylic beetles but were found to be relevant in explaining abundance patterns. The proportion of beech-fir stands in the surrounding landscape increased the abundance of all dependent variables, except for low-dispersal species, while the proportion of forests and ancient forests had no effect. As previously demonstrated for plant species recovery in recent forests (De Frenne et al. 2011), our results underlines the importance for saproxylic beetles of suitable habitat availability at a larger scale. The abundance of many saproxylic species has been shown to increase with the proportion of suitable habitat at landscape scale in boreal forests (Gibb et al. 2006;
Laaksonen et al. 2008). These findings may indicate that larger source populations are available for colonization in stands surrounded by a larger proportion of suitable habitat (Gibb et al. 2006). This may be viewed as an additive effect of habitat limitation versus dispersal limitation at the landscape scale. However, total and high-dispersal species abundance decreased with distance to forest edge. This may be related to the attractiveness of the sunny conditions found along forest edges (Vodka and Cizek 2013). Moreover, several adult forms of saproxylic beetles are floricolous. The likelihood of them being captured increases with the availability of flowering plants, i.e. with the proximity to forest edge. This response pattern may be linked to edge effect, rather than to a negative effect of forest continuity. Our overall results highlight the relevance of considering multiple spatial scales to better characterize diversity patterns, in accordance with previous studies (e.g. Økland et al. 1996; Janssen et al. 2009).

## Conclusion

We found that habitat limitation was the main ecological mechanism explaining saproxylic beetle diversity patterns in unfragmented montane forests. Habitat limitation was primarily driven by variations in deadwood diversity at the stand scale and by availability of suitable habitat at the landscape scale. In the ecological context of the Northern Alps, i.e. unfragmented forests, our results clearly show that dispersal limitation was not a causative factor for saproxylic beetle diversity. This suggests that biodiversity responses to forest continuity may depend on species groups and/or on the regional setting. Indeed, caution must be used in studies based on a limited group of taxa when generalizing the ecological mechanisms resulting from complex ecosystem modifications such as land use change. Saproxylic beetle conservation would benefit from forestry practices that maintain a diversity of deadwood types within stands, e.g. snags and logs of various diameters and in different stages of decay, and a high availability of overmature stands in the surrounding landscape.

## Acknowledgements

We thank Gilles Favier, Sophie Labonne, Fanny Lebagousse, Carl Moliard and Pascal Tardif from IRSTEA for help on the field. We are grateful to Thierry Noblecourt (Scolytinae) and Olivier Rose (Ciidae) from ONF, Olivier Courtin, Julien Haran (Curculionidae) and Julien Delnatte (Elateridae) for taxonomic assistance. We also
403 thank George Kunstler for his comments on the paper and Vicki Moore for correcting the English manuscript.

404 The Vercors, Chartreuse and Bauges Natural Regional Parks as well as public (ONF) and private forest managers facilitated our work in the study area. Financial support was provided by IRSTEA and local contributors (Conseil Général de l'Isère and Bauges Natural Regional Park).

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532
533 **Figure captions**

534

535 **Fig. 1** Study area and distribution of sampling sites among ancient and recent forests and mature and overmature stands in the Vercors, Chartreuse and Bauges ranges, Northern Alps, France

537

538 **Fig. 2** Influence of parameters related to forest continuity and/or stand maturity on saproxylic beetle richness and abundance in the Vercors, Chartreuse and Bauges ranges, Northern Alps, France. Sums of AIC<sub>c</sub> weights are provided for models based exclusively on forest continuity variables (10 models), on stand maturity variables (10 models) or on both types of variables (10 models) plus the null model; Low-disp. = Low-dispersal, High-disp. = High-dispersal

542
<figure><img src="image_2.png" /><figcaption>543 Fig. 1</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Fig. 2</figcaption></figure>
545 Table 1 Variation in forest continuity and stand maturity variables used to model saproxylic beetle richness and abundance in the Vercors, Chartreuse and Bauges ranges,

546 Northern Alps, France (BD = basal diameter, DBH = diameter at breast height, p-value based on two-ways ANOVA)

<table><thead><tr><th>Variables</th><th>Description</th><th>Ancient<br>Mean (±SD)</th><th>Recent<br>Mean (±SD)</th><th>p-value</th><th>Mature<br>Mean (±SD)</th><th>Overmature<br>Mean (±SD)</th><th>p-value</th></tr></thead><tbody><tr><td>lati</td><td>Latitude in decimal degrees</td><td>45.44 (±0.25)</td><td>45.42 (±0.21)</td><td>0.7777</td><td>45.38 (±0.23)</td><td>45.48 (±0.22)</td><td>0.1551</td></tr><tr><td>long</td><td>Longitude in decimal degrees</td><td>5.86 (±0.27)</td><td>5.88 (±0.26)</td><td>0.8203</td><td>5.84 (±0.27)</td><td>5.91 (±0.25)</td><td>0.3856</td></tr><tr><td>alti</td><td>Altitude in meters</td><td>1172 (±156.1)</td><td>1137 (±159.5)</td><td>0.5008</td><td>1171 (±160.0)</td><td>1137 (±155.7)</td><td>0.5070</td></tr><tr><td>slope</td><td>Slope in percentage</td><td>22.20 (±7.22)</td><td>25.63 (±7.21)</td><td>0.1409</td><td>24.02 (±7.76)</td><td>23.82 (±7.08)</td><td>0.9323</td></tr><tr><td>expo</td><td>Exposure in degrees</td><td>216.3 (±103.7)</td><td>226.0 (±113.7)</td><td>0.7841</td><td>206.1 (±108.1)</td><td>236.2 (±107.5)</td><td>0.3941</td></tr><tr><td colspan="8">Stand maturity variables measured at stand scale</td></tr><tr><td>G_beech</td><td>Beech basal area in m²</td><td>1.42 (±0.85)</td><td>1.53 (±1.07)</td><td>0.7123</td><td>1.80 (±0.80)</td><td>1.15 (±1.01)</td><td>0.0334</td></tr><tr><td>G_fir</td><td>Fir basal area in m²</td><td>3.30 (±2.28)</td><td>2.97 (±2.21)</td><td>0.5594</td><td>1.79 (±1.15)</td><td>4.48 (±2.24)</td><td>0.0000</td></tr><tr><td>G_spruce</td><td>Spruce basal area in m²</td><td>1.07 (±1.04)</td><td>1.49 (±1.27)</td><td>0.3093</td><td>1.50 (±1.28)</td><td>1.05 (±1.01)</td><td>0.3345</td></tr><tr><td>NTrees_classes</td><td>Number of diameter classes of standing trees</td><td>21.55 (±3.05)</td><td>21.60 (±3.10)</td><td>0.9542</td><td>20.15 (±2.35)</td><td>23.00 (±3.03)</td><td>0.0022</td></tr><tr><td>NTrees</td><td>Number of living trees per hectare</td><td>93.8 (±40.66)</td><td>108.1 (±48.32)</td><td>0.2476</td><td>124.5 (±46.94)</td><td>77.4 (±26.92)</td><td>0.0004</td></tr><tr><td>NLargeTrees</td><td>Number of very large living trees (DBH > 62.5 cm)</td><td>3.35 (±4.15)</td><td>3.45 (±3.36)</td><td>0.9001</td><td>0.60 (±0.88)</td><td>6.20 (±3.35)</td><td>0.0000</td></tr><tr><td>VolLarge_cwd</td><td>Volume of large logs (BD > 30 cm) in m³</td><td>2.47 (±3.65)</td><td>1.20 (±1.82)</td><td>0.0986</td><td>0.31 (±0.61)</td><td>3.36 (±3.49)</td><td>0.0002</td></tr></tbody></table>
<table><tr><td>N_large snags</td><td>Number of large snags (DBH > 30 cm)</td><td>1.95 (±2.39)</td><td>1.25 (±1.89)</td><td>0.2090</td><td>0.35 (±0.59)</td><td>2.85 (±2.43)</td><td>0.0000</td></tr><tr><td>Alpha_cwd</td><td>Diversity of deadwood types</td><td>13.30 (±9.70)</td><td>10.05 (±5.33)</td><td>0.1174</td><td>7.30 (±4.46)</td><td>16.05 (±8.23)</td><td>0.0001</td></tr><tr><td>Alpha_mh</td><td>Diversity of tree microhabitats</td><td>2.20 (±2.07)</td><td>2.60 (±1.76)</td><td>0.4211</td><td>1.25 (±1.02)</td><td>3.55 (±1.90)</td><td>0.0000</td></tr><tr><td colspan="8">Forest continuity variables measured at landscape scale</td></tr><tr><td>Prop_Forest</td><td>Forest proportion within a 500-m-radius (%)</td><td>93.72 (±7.85)</td><td>86.73 (±11.62)</td><td>0.0289</td><td>88.06 (±12.90)</td><td>92.39 (±6.78)</td><td>0.1666</td></tr><tr><td>Prop_BF</td><td>Beech-fir proportion within a 500-m-radius (%)</td><td>84.99 (±16.18)</td><td>72.61 (±17.04)</td><td>0.0252</td><td>77.13 (±17.32)</td><td>80.48 (±18.08)</td><td>0.5311</td></tr><tr><td>Prop_Ancient</td><td>Ancient forest proportion within a 500-m-radius (%)</td><td>86.06 (±10.71)</td><td>39.35 (±25.65)</td><td>1.1158</td><td>62.90 (±32.09)</td><td>62.51 (±29.86)</td><td>0.9507</td></tr><tr><td>Complex_Forest</td><td>Perimeter-area ratio of forest cover</td><td>0.005 (±0.001)</td><td>0.008 (±0.004)</td><td>0.0054</td><td>0.007 (±0.004)</td><td>0.006 (±0.002)</td><td>0.1011</td></tr><tr><td>Dist_Forest</td><td>Distance to the nearest forest edge in meters</td><td>367.3 (±243.4)</td><td>216.8 (±125.2)</td><td>0.0212</td><td>312.5 (±234.8)</td><td>271.6 (±175.5)</td><td>0.5165</td></tr><tr><td>Dist_Past_Edge</td><td>Distance to the nearest ancient/recent edge in meters</td><td>386.1 (±320.7)</td><td>249.7 (±320.3)</td><td>0.1940</td><td>335.2 (±329.8)</td><td>300.5 (±325.3)</td><td>0.7382</td></tr></table>
547 **Table 2** Mean (±SD) of dependent variables in relation to forest continuity and stand maturity in the Vercors,

548 Chartreuse and Bauges ranges, Northern Alps, France (p-value based on two-ways ANOVA)

<table><thead><tr><th rowspan="2">Dependent variable</th><th colspan="3">Forest continuity</th><th colspan="3">Stand maturity</th></tr><tr><th>Ancient</th><th>Recent</th><th>p-value</th><th>Mature</th><th>Overmature</th><th>p-value</th></tr></thead><tbody><tr><td>Total richness</td><td>75.1 (±19.95)</td><td>72.8 (±17.44)</td><td>0.6887</td><td>66.6 (±15.44)</td><td>81.3 (±18.79)</td><td>0.0125</td></tr><tr><td>Total abundance</td><td>457.8 (±207.05)</td><td>478.6 (±182.94)</td><td>0.7128</td><td>379.8 (±152.23)</td><td>556.6 (±192.18)</td><td>0.0033</td></tr><tr><td>Low-dispersal richness</td><td>8.3 (±1.90)</td><td>9.0 (±2.44)</td><td>0.3103</td><td>8.3 (±2.16)</td><td>9.0 (±2.21)</td><td>0.3103</td></tr><tr><td>Low-dispersal abundance</td><td>38.4 (±16.75)</td><td>44.9 (±16.41)</td><td>0.2282</td><td>39.6 (±16.22)</td><td>43.6 (±17.33)</td><td>0.4555</td></tr><tr><td>High-dispersal richness</td><td>66.4 (±19.81)</td><td>63.0 (±16.04)</td><td>0.5372</td><td>57.8 (±14.77)</td><td>71.6 (±18.38)</td><td>0.0149</td></tr><tr><td>High-dispersal abundance</td><td>419.1 (±209.29)</td><td>432.5 (±177.96)</td><td>0.8114</td><td>339.6 (±156.13)</td><td>512.0 (±188.55)</td><td>0.0040</td></tr></tbody></table>

549

550
<table><caption>**Table 3** Top-ranking models among 31 models predicting saproxylic beetle richness and abundance in the Vercors, Chartreuse and Bauges ranges, Northern Alps, France, as assessed with Akaike's information criterion corrected for small sample size ($AIC_c$). Dependent variable, model number, related group model (continuity, maturity or both), number of estimated parameters including the intercept (k), $AIC_c$, $AIC_c$ weight (W), adjusted $R^2$ and evidence ratio (ER), i.e. Akaike weight of best model/Akaike weight of second best model, are provided</caption><thead><tr><th>Dependent variable</th><th>Top-ranking</th><th>Type</th><th>k</th><th>AIC<sub>c</sub></th><th>W</th><th>R<sup>2</sup></th><th>ER</th></tr></thead><tbody><tr><td>Total richness</td><td>1</td><td>Maturity</td><td>2</td><td>332.7</td><td>0.546</td><td>0.38</td><td>3.4</td></tr><tr><td>Total abundance</td><td>21</td><td>Continuity + Maturity</td><td>4</td><td>534.2</td><td>0.211</td><td>0.29</td><td>1.4</td></tr><tr><td>Standardized richness</td><td>1</td><td>Maturity</td><td>3</td><td>311.5</td><td>0.380</td><td>0.65</td><td>1.3</td></tr><tr><td>Low-dispersal richness</td><td>31</td><td>Null</td><td>1</td><td>184.1</td><td>0.122</td><td>0.00</td><td>1.0</td></tr><tr><td>Low-dispersal abundance</td><td>31</td><td>Null</td><td>1</td><td>339.3</td><td>0.124</td><td>0.00</td><td>1.3</td></tr><tr><td>High-dispersal richness</td><td>1</td><td>Maturity</td><td>2</td><td>329.5</td><td>0.525</td><td>0.37</td><td>3.4</td></tr><tr><td>High-dispersal abundance</td><td>21</td><td>Continuity + Maturity</td><td>4</td><td>534.9</td><td>0.239</td><td>0.29</td><td>1.4</td></tr></tbody></table>

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<table><caption>**Table 4** Average coefficients (AC) and confidence intervals (CI) for each variable of the top ranking models (i.e. sum of AIC<sub>c</sub> weights ≥0.95) predicting total saproxylic beetles richness and abundance in the Vercors, Chartreuse and Bauges ranges, Northern Alps, France. The 95% confidence interval of coefficients in bold excluded 0, NA = not available</caption><thead><tr><th rowspan="2">Parameter</th><th colspan="2">Total abundance</th><th colspan="2">Total richness</th><th colspan="2">Standardized richness</th></tr><tr><th>AC</th><th>CI</th><th>AC</th><th>CI</th><th>AC</th><th>CI</th></tr></thead><tbody><tr><td>(Intercept)</td><td>6.000</td><td>(4.012; 7.988)</td><td>3.821</td><td>(3.408; 4.234)</td><td>3.620</td><td>(3.319; 3.923)</td></tr><tr><td>Abundance</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>0.001</td><td>(0.001; 0.001)</td></tr><tr><td>N_tree_classes</td><td>0.041</td><td>(-0.007; 0.089)</td><td>0.001</td><td>(-0.022; 0.024)</td><td>-0.009</td><td>(-0.026; 0.008)</td></tr><tr><td>N_largeTrees</td><td>0.160</td><td>(-0.015; 0.336)</td><td>-0.029</td><td>(-0.114; 0.056)</td><td>-0.069</td><td>(-0.133; -0.007)</td></tr><tr><td>Vol_large_cwd</td><td>0.110</td><td>(-0.103; 0.323)</td><td></td><td></td><td></td><td></td></tr><tr><td>N_largeSnags</td><td>0.193</td><td>(-0.010; 0.397)</td><td></td><td></td><td></td><td></td></tr><tr><td>Alpha_cwd</td><td>0.281</td><td>(0.082; 0.480)</td><td>0.225</td><td>(0.130; 0.319)</td><td>0.148</td><td>(0.066; 0.230)</td></tr><tr><td>Alpha_mh</td><td>-0.016</td><td>(-0.098; 0.066)</td><td>0.012</td><td>(-0.026; 0.050)</td><td>0.012</td><td>(-0.014; 0.039)</td></tr><tr><td>Prop_Forest</td><td>0.003</td><td>(-0.013; 0.020)</td><td>-0.003</td><td>(-0.011; 0.006)</td><td>-0.004</td><td>(-0.010; 0.002)</td></tr><tr><td>Prop_BF</td><td>0.010</td><td>(0.002; 0.019)</td><td></td><td></td><td></td><td></td></tr><tr><td>Prop_Ancient</td><td>-0.001</td><td>(-0.005; 0.003)</td><td>0.000</td><td>(-0.002; 0.002)</td><td>-0.0001</td><td>(-0.002; 0.002)</td></tr><tr><td>Complex_Forest</td><td>0.160</td><td>(-0.282; 0.602)</td><td></td><td></td><td></td><td></td></tr><tr><td>Dist_Forest</td><td>-0.282</td><td>(-0.556; -0.007)</td><td>-0.010</td><td>(-0.148; 0.128)</td><td>0.073</td><td>(-0.034; 0.180)</td></tr><tr><td>Dist_Past_Edge</td><td>-0.130</td><td>(-0.276; 0.015)</td><td>-0.051</td><td>(-0.121; 0.019)</td><td>-0.008</td><td>(-0.065; 0.049)</td></tr></tbody></table>
Table 5 Average coefficients (AC) and confidence intervals (CI) for each variable of the top ranking models (i.e. sum of AIC<sub>c</sub> weights ≥0.95) predicting low-dispersal (Low-disp.) and high-dispersal (High-disp) saproxylic beetles richness and abundance in the Vercors, Chartreuse and Bauges ranges, Northern Alps, France. The 95% confidence interval of coefficients in bold excluded 0, NA = not available

<table><thead><tr><th rowspan="2">Parameter</th><th colspan="2">Low-disp. abundance</th><th colspan="2">Low-disp. richness</th><th colspan="2">High-disp. abundance</th><th colspan="2">High-disp. richness</th></tr><tr><th>AC</th><th>CI</th><th>AC</th><th>CI</th><th>AC</th><th>CI</th><th>AC</th><th>CI</th></tr></thead><tbody><tr><td>(Intercept)</td><td>3.761</td><td>(2.644; 4.878)</td><td>2.175</td><td>(1.090; 3.259)</td><td>5.932</td><td>(3.800; 8.065)</td><td>3.669</td><td>(3.210; 4.128)</td></tr><tr><td>Abundance</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>N_tree_classes</td><td>0.030</td><td>(-0.014; 0.074)</td><td>0.027</td><td>(-0.011; 0.065)</td><td>0.043</td><td>(-0.012; 0.097)</td><td>-0.001</td><td>(-0.026; 0.024)</td></tr><tr><td>N_largeTrees</td><td>0.057</td><td>(-0.089; 0.202)</td><td>0.007</td><td>(-0.115; 0.130)</td><td>0.169</td><td>(-0.035; 0.372)</td><td>-0.027</td><td>(-0.121; 0.067)</td></tr><tr><td>Vol_large_cwd</td><td>-0.013</td><td>(-0.178; 0.152)</td><td>-0.092</td><td>(-0.259; 0.076)</td><td>0.126</td><td>(-0.113; 0.366)</td><td></td><td></td></tr><tr><td>N_largeSnags</td><td>-0.028</td><td>(-0.206; 0.151)</td><td>0.018</td><td>(-0.154; 0.190)</td><td>0.222</td><td>(-0.010; 0.453)</td><td>0.205</td><td>(0.105; 0.306)</td></tr><tr><td>Alpha_cwd</td><td>-0.044</td><td>(-0.240; 0.152)</td><td>0.039</td><td>(-0.130; 0.208)</td><td>0.325</td><td>(0.104; 0.547)</td><td>0.245</td><td>(0.141; 0.349)</td></tr><tr><td>Alpha_mh</td><td>0.033</td><td>(-0.036; 0.102)</td><td>0.023</td><td>(-0.034; 0.080)</td><td>-0.026</td><td>(-0.117; 0.065)</td><td>0.010</td><td>(-0.031; 0.052)</td></tr><tr><td>Prop_Forest</td><td>-0.011</td><td>(-0.028; 0.006)</td><td>-0.003</td><td>(-0.015; 0.009)</td><td>0.005</td><td>(-0.014; 0.024)</td><td>-0.003</td><td>(-0.012; 0.007)</td></tr><tr><td>Prop_BF</td><td>-0.001</td><td>(-0.008; 0.007)</td><td>0.003</td><td>(-0.005; 0.011)</td><td>0.011</td><td>(0.001; 0.021)</td><td></td><td></td></tr><tr><td>Prop_Ancient</td><td>-0.003</td><td>(-0.007; 0.001)</td><td>-0.001</td><td>(-0.004; 0.003)</td><td>-0.001</td><td>(-0.006; 0.004)</td><td>0.000</td><td>(-0.002; 0.002)</td></tr><tr><td>Complex_Forest</td><td>0.165</td><td>(-0.145; 0.475)</td><td>0.179</td><td>(-0.125; 0.482)</td><td>0.134</td><td>(-0.377; 0.645)</td><td></td><td></td></tr><tr><td>Dist_Forest</td><td>0.126</td><td>(-0.185; 0.436)</td><td>-0.048</td><td>(-0.261; 0.165)</td><td>-0.333</td><td>(-0.645; -0.021)</td><td>-0.004</td><td>(-0.157; 0.148)</td></tr><tr><td>Dist_Past_Edge</td><td>0.015</td><td>(-0.129; 0.158)</td><td>-0.011</td><td>(-0.134; 0.111)</td><td>-0.150</td><td>(-0.317; 0.016)</td><td>-0.056</td><td>(-0.133; 0.022)</td></tr></tbody></table>

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