Stand attributes or soil micro-environment exert greater influence than management type on understory plant diversity in even-aged oak high forests

Forest Ecology and Management 460 (2020) 117897

Contents lists available at ScienceDirect

Forest Ecology and Management journal homepage: www.elsevier.com/locate/foreco

Stand attributes or soil micro-environment exert greater influence than management type on understory plant diversity in even-aged oak high forests

T

⁎

Liping Weia,b, , Frédéric Archauxb, Florian Hulinb, Isabelle Bilgerb, Frédéric Gosselinb a

Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China b INRAE, UR EFNO, Domaine des Barres, F-45290 Nogent-sur-Vernisson, France

A R T I C LE I N FO

A B S T R A C T

Keywords: Ecological group Magnitude analysis Forest management Stand structure Stand composition Soil compaction Soil moisture

Understanding the determinants of forest understory diversity provides a theoretical basis for sustainable management practices. Our study aimed to identify the dominant factors determining understory diversity in temperate forests among three potentially important factors: management type (managed vs unmanaged), stand attributes (basal area by species and by diameter class, shrub layer cover) and soil micro-environment (soil compaction and soil moisture). Understory diversity was represented by the richness and abundance of plant ecological groups based on life form crossed with successional status, light and moisture requirements. We selected 50 stands in five French national forests, half of which were managed and half unmanaged. After model comparison, our results showed that, depending on the ecological groups studied, basal area and micro-environment rather than management type were the best explanatory models for understory diversity. Our magnitude analyses showed that understory diversity did not differ much between managed and unmanaged forests. Basal area, which had ecologically important effects, negatively correlated with understory diversity. For two ecological groups (mature-forest and shade-tolerant woody species), diversity was better explained by basal area in interaction with management type than by the sole effect of basal area. Soil micro-environment, which had ecologically important effects, mostly showed positive relationships with understory diversity; e.g. hygrophilous and intermediate-light herbaceous species positively correlated to soil moisture, while heliophilous herbaceous species were positively correlated to soil compaction. Our study indicated that forest management abandonment after 20–40 years did not induce higher understory diversity than in managed forests; abandonment’s role in understory diversity was weak compared to basal area or soil environment. Whether such results persist after longer periods of abandonment remains unknown.

1. Introduction Maintaining or improving biodiversity is an important goal of sustainable forest management (Lindenmayer et al., 2000). Understory plants, which represent most of the floristic diversity in temperate forests (Zenner and Berger, 2008), play multiple important roles in ecosystem functioning (Gilliam, 2002). Furthermore, due to its sensitivity to a variety of factors such as overstory characteristics (Augusto et al., 2003; Nagaike et al., 2005; Barbier et al., 2008), soil properties (Bassett et al., 2005; Small and McCarthy, 2005) and forest disturbances or management practices (Baltzinger et al., 2011; Duguid and Ashton, 2013; Wei et al., 2015a,2016), understory plant diversity is also

an important indicator of forest site quality and of the environmental impact of management (Gilliam, 2002). Human disturbance, overstory tree species composition and abundance as well as site-specific environmental conditions have long been recognized as major influences on the coexistence of species and the maintenance of understory plant species diversity (Cadotte, 2007). In Europe, most temperate forests have been managed for several hundred years (Heywood and Watson, 1995; Parvianen et al., 2000). Managed forests experience more frequent and regular disturbances and display more homogeneous tree species composition and vertical stratification than undisturbed forests (McCarthy and Burgman, 1995; Kuuluvainen et al., 1996; Commarmot et al., 2005; Paillet et al., 2010). Most current

⁎ Corresponding author at: Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China. E-mail address: [email protected] (L. Wei).

https://doi.org/10.1016/j.foreco.2020.117897 Received 4 September 2019; Received in revised form 13 December 2019; Accepted 8 January 2020 0378-1127/ © 2020 Elsevier B.V. All rights reserved.

Forest Ecology and Management 460 (2020) 117897

L. Wei, et al.

European forest reserves were managed until quite recently (generally after 1950 s or even only decades ago). Forest reserves differ significantly from managed stands most notably in their higher density of living trees, the quantity of very large trees and deadwood volumes (Bouget et al., 2014; Gossner et al., 2014; Paillet et al., 2015). Meanwhile, in managed stands, disturbance and changed stand attributes can modify micro-enviromental conditions (e.g. canopy openings, shrub densities, soil moisture, soil compaction, etc.). For example, during the last decades, forest management has evolved from manual felling and logging towards mechanized harvesting. Severe soil compaction by heavy machinery in managed forests can cause increased water runoff, thereby inducing soil nutrient loss (Akbarimehr and Naghdi, 2012). A number of studies have compared plant diversity in managed and unmanaged forests and found inconsistent results (Gosselin, 2004; Sitzia et al., 2012). For example, unmanaged forests in general are thought to contain more species - in particular fungi, bryophytes, lichens (Fenton and Bergeron, 2008) – than do managed forests (Okland et al., 2003), Nevertheless, some studies found that species richness of vascular plants tended to be higher in managed forests (Schmidt, 2005; Paillet et al., 2010) and still others (Sitzia et al., 2016) found no difference in vascular plant diversity between managed and unmanaged forests. These contrasting results could be accounted for by comparing the forest attributes in unmanaged and managed stands among studies. Indeed, stand attributes or soil conditions typical of unmanaged natural forests could also be developed in managed forests (deadwood, very large trees, uncompacted soils) (Gosselin et al., 2017; Paillet et al., 2017). Although the effects of forest management, stand attributes and micro-environments on understory plants have been studied separately, no previous study has ever combined the three factors to detect their effect on local-scale understory diversity. Knowledge is lacking on their interactions and their respective effects on understory diversity. Furthermore, the inconsistent results for differences in total or taxon diversity between managed and unmanaged forests also support studying diversity based on plant ecological groups defined by species ecological requirements or functional traits. For example, mature forest species could prefer unmanaged forests but peri-forest species might be favoured by management practices. Similarly, the increase in available light due to canopy opening after harvesting is likely to be detrimental to shade-tolerant species but may be favourable to heliophilous species. Thus, classifying ground flora into ecological groups is an important, though basic, step to better document biodiversity responses that may in turn help us to understand the mechanisms behind the effects of management (e.g. Gosselin, 2012). In this study, we classified ground flora into different ecological groups based on life-form and three species traits: successional status, and light and moisture requirements. We aimed to find the dominant factors affecting ground flora diversity and abundance among stand attributes (basal area by species and by diameter class, shrub layer cover), soil micro-environment (soil compaction and soil moisture) and management type (managed vs unmanaged). We hypothesized that stand structure or composition variables would better explain variations in ground floral communities than would management, especially depending on the successional status or light requirements of the species. Soil conditions could be also be the dominant factor for relevant sensitive species, e.g. we expected soil moisture to be the most important factor for species groups defined by soil humidity preferences. Our second objective was to determine whether the effects of stand attributes and micro-environment depended on management type. We assumed that the relationships between stand attributes or soil microenvironment and ground floral diversity would differ in unmanaged and managed forests. In particular, we assumed that the stochastic nature of the disturbance regime would produce different spatio-temporal regimes between managed and unmanaged stands (MacCarthy and Burgman, 1995), so that similar basal area attributes could be related to different dynamics in unmanaged and managed forests. Our questions were: (1) Do stand attributes (stand composition and

Fig. 1. Location of the five forests studied.

structure) and micro-environment (soil compaction and moisture) differ between managed and unmanaged stands? What is the relative importance of management type (managed vs unmanaged), stand attributes and micro-environment in explaining understory plant diversity? (2) Does the effect of stand attributes and micro-environment on understory plant diversity differ between managed and unmanaged forests (i.e. interacting factors)? (3) Are the dominant factors affecting ground floral diversity the same for different plant ecological groups?

2. Materials and methods 2.1. Study area Our study was carried out in five forest reserves (Fig. 1) with adjacent managed forests under the same site conditions. The mean elevation of the sites ranged from 162 m to 267 m a.s.l. (Table SM.1). The climate varies from suboceanic (Verrières, Rambouillet, Haut-tuileau and Parroy) to continental (Citeaux) (Bai et al., 2012) with a mean annual rainfall of about 760 mm and a mean temperature of 10.8 °C (Table SM.1). The soil texture at the study plots was confined to siltsand soil (though a higher clay content was detected in Citeaux compared to the other sites) (Table SM.1). Stands on soils with a high sand content were excluded as we did not expect them to be sensitive to compaction caused by forest harvesting operations. All five study sites were in lowland beech–oak dominated forests with a main management goal of producing quality timber. Therefore, all the area was managed as an even-aged high oak forest, or was in the process of conversion from coppice-with-standards (the previous management type) to evenaged high forest. A high oak forest rotation is typically 180 to 200 years or until trees reach 80 cm in diameter (ONF, 1996; Jarret, 2004). Evenaged high forest stands have typically experienced seed-tree natural regeneration. In France, a network of strictly protected areas has existed since the 1950 s and has considerably expanded since the 1980 s. The strict reserve conservation strategy assumes that, once wood harvesting has been abandoned, old-growth forest attributes such as large veteran trees, microhabitats and deadwood will restore themselves naturally (Peterken, 1981; Wirth, 2009). This has indeed been observed in strict reserves in France (Paillet et al., 2015). In our five study areas, the mean time since last harvesting was 38 years (min: 13.6, max: 58 years) for the unmanaged reserves, and six years (min: 3.7, max: 9.8 years) for 2

Forest Ecology and Management 460 (2020) 117897

L. Wei, et al.

one with a depth of at least 20 cm). For each successful measurement point, we retained the mean penetration resistance values from 0 to 20 cm in depth (20 records at 1 cm depth intervals) because the strongest impact of soil compaction on ground vegetation appears in this upper layer (Greacen and Sands, 1980; Ampoorter et al., 2007). Furthermore, this layer is generally free of the natural compaction that occurs in deeper soil layers (Godefroid and Koedam, 2004). Vascular plants below 2 m in height (including tree seedlings/saplings) were recorded following the Braun-Blanquet cover-abundance scale classification where (i) corresponds to a single unique individual with a total cover of < 5%; (+) to very few individuals with a total cover of < 5%; (1) to few to many individuals with a total cover of < 5%; (2) to species with a total cover of between 5% and 25%; (3) to species with a total cover of between 25% and 50%; (4) to species with a total cover of between 50% and 75%; and (5) to species with a total cover of ≥75%. These cover class coefficients were transformed into percentages of species abundance as follows: i = 0.1%; + = 0.5%; 1 = 5%; 2 = 17.5%; 3 = 37.5%; 4 = 62.5%; and 5 = 87.5%. The percentages were then summed among species in each understory ecological group.

the managed stands. 2.2. Data collection Within each of the five study sites, eight to 12 plots were randomly selected (Table SM.1), half of which were managed and half unmanaged, leading to a total of 50 plots sampled. The selected managed stands were mature stands composed exclusively of native tree species and located within 5 km of the forest reserve boundaries, on similar soil types to those in the reserves (Paillet et al., 2015). Very few of the 50 plots contained any large coarse elements (e.g. stones) in the soil from 0 to 50 cm in depth. In each stand, we delineated a 1000-m2 circular plot (Fig. SM 1 (a)) containing three 0.5 m × 5 m strips (subplots Sub_1, Sub_2, Sub_3) radiating out from the plot centre at angles of 67, 200 and 333°. The middle of each subplot was 10 m from the centre of the circular plot. (Fig. SM 1 (a)). In each plot, between 2010 and 2012, we determined species and diameter at 1.3 m in height (“DBH”, in cm) for each standing tree according to the following sampling scheme: trees with a DBH > 20 cm were measured when they were within a fixed relascopic angle of 2%. For example, trees with a DBH of 60 cm within a maximum distance of 30 m from the centre of the plot were included in the sample. Trees with 7.5 cm < DBH < 20 cm were measured within a fixed radius of 10 m from the plot centre. These data were used to calculate the basal area of different types of trees (by species or by diameter class). Foresters use basal area as a simple measurement of stand tree abundance; understory light availability can also be predicted by stand basal area (Sonohat et al., 2004; Perot et al., 2017). We complemented these basal area measures by visually estimating the cover of the tall (2–8 m high) and low (< 2 m high) shrub layers since they too could have similar influences in terms of light or other resources on understory plants. Shrub cover was estimated in or above each floristic survey plot. The tall shrub layer, comprised of all woody plants (trees and shrubs) from 2 to 8 m in height, was estimated during the floristic inventory. The total cover of the woody species below 2 m in height (the low shrub layer) was calculated based on the estimated cover of individual woody species recorded during the floristic inventory (see below). We carried out a floristic inventory in each subplot between April 28th and July 30th in 2013, and measured soil compaction and soil moisture. For micro-environment, we used three indicators for the degree of soil compaction: mean soil penetration resistance (PR) of the nine sampling spots at 0–20 cm in depth, mean number of measurements at each PR point (Nsam, varying between one and four) and mean maximum depth of the nine sampling spots (MaxD, varying between 20 and 80 cm). These three indicators were the best indicators of plant diversity for different ecological groups in our skid trail study (Wei et al., 2015a), though no single factor was unambiguously more appropriate than the others. PR was measured in March 2013, when soil water content was near field capacity in the forests in our research areas; penetrometer readings were thus less likely to be influenced by differences in soil moisture (Miller et al., 2001; Godefroid and Koedam, 2004). We took nine (Fig. SM 1 (b)) measurements per subplot. We used a field Theta probe sensor to measure soil surface moisture at the nine points simultaneously. We recorded penetration resistance via a static penetration test at 1 cm depth intervals by continuously inserting (2 cm/s speed) a penetrologger bipartite probing rod (with a coneshaped tip of 60°and 1 cm2 surface area) (Eijkelkamp Agrisearch Equipment, the Netherlands) into the soil until it stopped due to high soil compaction or because it encountered a root or stone. The maximum measuring depth of a penetrologger is 80 cm. If the probing rod stopped less than 20 cm below ground, we added measurement points (up to four) in a pre-determined direction and distance (10 cm) from the original point until the probe reached at least 20 cm in depth (Fig. SM 1 (b)). For the samples requiring additional measurements, we recorded the number of times we renewed the measurement, and kept the penetration resistance value from the final measurement only (i.e. the

2.3. Data analysis We used Gaussian generalized linear models (GLMs) to assess the variations in tree basal area (basal area by species composition and by diameter class, denoted as Gcompo and Gstruct), shrub layer cover (ct and cl), soil compaction (PR, Nsam and MaxD) and soil moisture with management type. The species list and the ecological group (18 groups, Table 1) each species belongs to was shown in Table SM.2. Ground floral richness and abundance were calculated for nine species groups classified according to successional status, light requirements and water preference, each crossed with life form (woody and herbaceous species) (Table SM.3). Successional status, light requirements and water preference are important species characteristics, which determine their response to disturbance or environmental conditions (e.g. Wei et al., 2015a,2016). We separated woody species from herbaceous species because previous studies had evidenced different responses to environmental gradients between these two life forms (Barbier et al., 2009; Zilliox and Gosselin, 2014). Due to low occurrence (present in less than 60 subplots), we removed five woody ecological groups (peri-forest, non-forest, Table 1 Summary of ecological groups. Species trait

Ecological group

Life form

Woody Herbaceous Mature-forest

Successional status

Peri-forest

Non-forest Light

Soil moisture

Shade-tolerant Intermediate Heliophilous Xerophilous Mesophilous Hygrophilous

Description

Mature forest species whose preferred habitat is mature forests Peri-forest species whose habitats are found close to mature forests successionally: shrubs, heathlands, early successional forest stages; or spatially along edges, clearings, and in forest gaps Non-forest species whose preferred habitats are not linked to forests. Ellenberg L value: 1–3 Ellenberg L value: 4–6 Ellenberg L value: 7–9 Ellenberg F value: 1–3 Ellenberg F value: 4–6 Ellenberg F value: 7–9

Data source: Hodgson et al., 1995; Julve, 2007. Ellenberg L value: Ellenberg indicator value of light (Ellenberg et al., 1992); Ellenberg F value: Ellenberg indicator value of soil moisture. 1 is the lowest value on the scale (e.g. very shady for L or very dry for F), and 9 is the highest value (e.g. full light for L or very wet for F). 3

Forest Ecology and Management 460 (2020) 117897

L. Wei, et al.

Table 2 Ecological variables used in the models. Variable

Description

Mean/SD

MAN Site G Gcompo

Forest management type: managed (MAN) and unmanaged (UNM) Sites: Citeaux (CI), Haut-Tuileau (HT), Rambouillet (RM), Verrières (VE), Parroy (PAR) Total stand basal area at breast height of all tree stands (m2/ha) Go + Ghb + Gother (Go: oak basal area, Ghb: total basal area of hornbeam and beech, Gother, total basal area of the other species, all in m2/ha)

Gstruct

G by diameter class category (m2/ha): G.VLT: very large trees (DBH > 67.5 cm) and large trees (47.5 cm < DBH < 67.5 cm), G.MT: medium trees (22.5 cm < DBH < 47.5 cm), and G.ST: small trees (7.5 cm < DBH < 22.5 cm) Mean penetration resistance from 0 to 20 cm in depth (MPa) Soil moisture (%) Cover of tall shrub layer (trees and shrubs at 2 to 8 m high) Cover of low shrub layer – sum of woody species (below 2 m high) cover Number of PR measurements per subplot (proxy for stone and root density) Maximum depth of probe (cm)

– – 25.04/8.13 Go: 14.60/8.25 Ghb: 6.92/5.90 Gother: 3.52/4.05 G.VLT: 12.03/8.3 G.MT: 7.73/4.92 G.ST: 5.28/4.75 2.08/0.50 47.99/21.75 35.46/34.54 18.80/22.17 1.10/0.27 69.51/12.23

PR Moisture ct cl Nsam MaxD

data that can be both over- and under-dispersed relative to the Poisson distribution while maintaining a correct parametrization of the mean (Huang, 2017). For species abundance, we used the frequentist version of the cumulative logit mixed model used in Barbier et al. (2009), in the “clmm” function of the R “ordinal” package (Amatya and Demirtas, 2015). A plot random effect was included in all the models while a site effect was incorporated as a fixed effect – due to the low number of sites. For the cumulative logit model, we distinguished the following discrete ordered classes for ecological group abundance: {0}; ]0;1]; ] 1;5]; ]5;10]; ]10;25]; ]25;50]; ]50;75]; ]75;∞[. We analysed the magnitude of the effects of all 17 models on the richness and abundance of each ecological group. Indeed, analyses based only on statistical significance (P-values) are unable to distinguish important differences in trends (Wei et al., 2015a). The important question is whether the true trend is ecologically negligible (or unimportant) or not (Dixon and Pechmann, 2005). Like Barbier et al. (2009) and Wei et al. (2015a), we distinguished two levels of ecologically importance in the multiplier of the mean of species richness and abundance for a given increase in an ecological variable: a more stringent one (b1), corresponding to strict ecologically importance and a less stringent one (b2, with (0 < b1 < b2)). In our analyses, we chose b1 = 0.1, b2 = 0.2 for species richness, and b1 = 0.25, b2 = 0.5 for abundance, as in Barbier et al. (2009) and Wei et al. (2015a) (Table SM.4). In other words, we considered that a change of 10% in species richness or 25% in abundance was an ecologically important change, whereas a change of 25% or 50%, respectively, was a strongly important change. The increases in the continuous ecological variables we considered were of about one standard deviation for PR, Nsam, MaxD, moisture and basal area (the latter corresponding to an increment of 5 m2/ha) (Table 2). For management type, we calculated the associated multiplicative coefficient by supposing the stand changed from UNM to MAN. Finally, in order to test for interaction effects between stand attributes/micro-environment and management type on ecological group richness and abundance, we also carried out a magnitude analysis on the best model in interaction with management and on the models that had ecologically important effects in interaction with management. Four different cases occur when describing ecologically importance effects: (1) unimportant effects denoted by “0″ when the value of the multiplier (β) follows P(-b2 < log(β) < b2) ≥ 0.975, and very unimportant effects denoted by “00” for the more stringent P(-b1 < log (β) < b1) ≥ 0.975; (2) importantly negative and very negative effects: “-” for P(log(β) < - b1) ≥ 0.975 and “–” for the stronger P(log(β) < b2) ≥ 0.975; (3) importantly positive and very positive effects: “+” for P(log(β) > b1) ≥ 0.975 and “++” for the stronger P(log (β) > b2) ≥ 0.975; and (4) inconclusive cases where the estimator cannot be classified in any of the above categories. Hereafter, unimportant (or very unimportant) effects will simply be called weak (or

heliophilous, xerophilous and hygrophilous woody species) and two herbaceous groups (non-forest and xerophilous herbaceous species) and kept 11 ecological groups for our modelling. We modelled the responses of the remaining ecological groups (Table 1) to variables that related to management type, tree stand basal area, shrub layer cover, soil compaction degree and soil moisture (Table 2). We applied a total of 18 explanatory models (including the null model, model [1], Table 3) to species richness and abundance for each ecological group (11 groups) (Table 3). There were two sets of ecological models: (i) additive models combining each set of variables with site as a co-variable (models [2] to [12], Table 3); and (ii), since several studies have detected non-linear relationships between PR and plant species cover (e.g. Godefroid and Koedam, 2004; Wei et al., 2015a), quadratic models related to degree of soil compaction (PR, Nsam) and soil moisture with site as a co-variable (models [13] to [17], Table 3). For each ecological group, we first compared the 17 models based on the Akaike Information Criteria (AIC) (Burnham and Anderson, 2002) to find the best model. To detect if the effects in the best model varied between managed and unmanaged forests (i.e. depended on management type), we included the best model in interaction with management (MAN) (model [18], Table 3) and compared it with the other 17 models (Table 3). For species richness, we used generalized linear mixed models (GLMMs) with the Conway-Maxwell-Poisson distribution (compois) and the log link function, estimated with TMB (R package glmmTMB; Brooks et al., 2017). This distribution makes it possible to model count Table 3 Summary of ecological models (with site as covariable in all models). Category

Additive model

Quadratic models

Interaction models

Models [1] Null model [2] MAN [3] G [4] Gcompo [5] Gstruct [6] Gstruct.ct: Gstruct + ct [7] Gstruct.cl: Gstruct + cl [8] Gstruct.ct.cl: Gstruct + ct + cl [9] PR [10] Moisture [11] Nsam [12] MaxD [13] PR.qua: PR + PR2 [14] Moisture.qua: Moisture + Moisture2 [15] Nsam.qua: Nsam + Nsam2 [16] PR.qua: PR + PR2 [17] MaxD.qua: MaxD + MaxD2 [18] Best model (selected from [1] to [17]) * MAN

Abbreviations are defined in Table 2. 4

Forest Ecology and Management 460 (2020) 117897

L. Wei, et al.

Fig. 2. Distribution of Gompo, Gstruct, Moisture, PR, Nsam, and MaxD depending on forest management type. Gcompo, Go. Ghb, Gother, Gstruct, Moisture, PR, Nsam, BD and MaxD, ct, cl, MAN and UNM are defined in Table 2. ‘‘*” indicates the difference in Go, Ghb, Gother, Moisture, PR, Nsam, BD, MaxD, ct and cl between management type (MAN and UNM). *p < 0.05, **p < 0.01, ***p < 0.001.

two broad categories (Tables SM.5 and SM.6) depending on the ecological group: i) models related to tree stand basal area – total basal area (G), basal area by species composition (Gcompo), basal area by diameter class (Gstruct) or its combined effect with shrub layer cover (ct and/or cl); and (ii) models related to soil micro-site factors – soil moisture and soil compaction. Models related to tree stand basal area, divided into tree species or tree diameter categories, and shrub cover (Gcompo, Gstruct, Gstruct.ct, Gstruct.cl and Gstruct.ct.cl), were the best for most of the 11 ecological groups, i.e. for seven groups when considering plant species richness and eight groups for species abundance. Meanwhile, no ecological group had any best model related to management type (MAN) or maximum penetration depth (MaxD) indicating soil compaction. From the set of best models, we created “interaction models” by adding an interaction between the selected best variable and management type (MAN); we then compared them with the previous 17 additive or quadratic models (Tables 4 and 5). The interaction models were preferred over the original best models for two ecological groups (mature forest and shade-tolerant woody species). Specifically, the best model for the richness and abundance of mature forest woody species was Gstruct.ct*MAN; while for richness of shade-tolerant woody species, it was moisture*MAN and for their abundance, Gstruct.ct*MAN.

very weak) effects; importantly negative (or very negative) effects will be called negative (or very negative) effects; importantly positive (or very positive) effects will be called positive (or very positive) effects. “Inconclusive” refers to cases where the estimator could not be classified in any of the above categories (see Table SM.4). 3. Results 3.1. Variation in tree basal area and soil environmental factors Oak basal area in unmanaged stands was significantly greater (P < 0.01) than in managed stands (Fig. 2), while no significant difference in total basal area of hornbeam and beech was found between managed and unmanaged stands. Only basal area of large or very large trees was detected significantly greater (P < 0.001) in unmanaged stands than in managed stands (Fig. 2). Neither shrub layer cover nor soil indicators (soil compaction and moisture) significantly differed between managed and unmanaged stands (Fig. 2). 3.2. Best models The best models among the 17 additive or quadratic models fell into 5

6

0.00 −1.66 1.73 5.27 −0.62 0.83 – – 1.80 −6.30 1.62 −0.16 3.05 −4.52 3.05 −3.41 – – −7.94 – – –

w.shade-tolerant

w.mature-forest

0.00 −0.18 0.20 3.91 −8.94 −8.99 – – 1.56 1.09 2.00 1.08 1.31 3.09 3.63 2.68 – – – – – −10.78

Light

Successional status

0.00 1.69 1.51 5.07 −5.24 −3.25 – – 1.98 1.92 1.81 1.73 3.60 3.73 3.64 3.71 – −0.86 – – – –

w.intermediate-light 0.00 −0.43 1.97 5.61 0.67 2.37 – – 0.66 −2.63 1.53 −1.36 2.65 −1.23 3.52 0.64 – – −0.28 – – –

w.mesophilous

Humidity

0.00 1.20 −0.47 −3.30 −0.71 – −10.20 −10.27 0.50 0.16 −0.73 −0.68 0.41 −0.46 1.27 −1.21 – – – – −3.75 –

h.mature-forest

Successional status

0.00 1.20 −3.94 −3.05 −6.30 – −4.70 −4.69 −2.04 −1.73 1.96 1.10 −0.30 −0.23 3.18 2.79 – −1.45 – – – –

h.peri-forest 0.00 −1.69 −8.26 −26.67 −12.43 – −15.71 −13.72 1.54 1.41 −1.48 −0.18 1.83 3.15 0.33 −0.97 –23.63 – – – – –

h.shade-tolerant

Light

0.00 2.00 1.47 0.99 3.66 – 1.74 3.39 0.94 −4.01 2.00 1.98 2.92 −4.04 3.33 3.68 – – −1.36 – – –

h.intermediate-light

0.00 1.96 −5.96 −6.93 −5.09 – −4.85 −4.15 −4.29 −4.58 1.45 1.46 −4.58 −3.25 2.78 3.45 −3.48 – – – – –

h.heliophilous

0.00 −1.44 −5.12 −5.87 −6.59 – −14.56 −13.17 −0.73 1.71 0.97 −0.93 0.97 3.68 2.07 0.57 – – – −7.00 – –

h.mesophilous

Humidity

0.00 1.84 −2.57 −0.24 −0.82 – −1.01 −0.29 1.60 −6.19 −0.13 1.78 2.31 −4.31 1.79 3.42 – – −2.74 – – –

h.hygrophilous

The smaller the AIC, the better the model with respect to the others. Within each ecological group, the model with the smallest AIC is in bold, and the AIC values within two units of this model are underlined. w.: woody, h.: herbaceous.

Null MAN G Gcompo Gstruct Gstruct.ct Gstruct.cl Gstruct.ct.cl PR Moisture Nsam MaxD PR.qua Moisture.qua Nsam.qua MaxD.qua Gcompo*MAN Gstruct*MAN Moisture*MAN Gstruct.cl*MAN Gstruct.ct.cl*MAN Gstruct.ct*MAN

Model

Table 4 Differences in AIC values between the different ecological models/best model (selected from Table SM.5)*MAN and the null model (with site as covariable in all models) for ecological group richness.

L. Wei, et al.

Forest Ecology and Management 460 (2020) 117897

7

0.00 −1.95 −3.30 0.60 −5.43 −9.57 – – 0.85 0.00 1.94 −0.71 2.42 1.95 3.75 1.01 – – – – −14.81 –

w.shade-tolerant

w.mature-forest

0.00 0.80 −0.15 3.27 −6.95 −14.37 – – 1.71 1.77 0.94 1.99 3.25 1.97 1.38 3.66 – – – – −20.99 –

Light

Successional status

0.00 1.98 1.31 4.24 −4.20 −2.94 – – 0.96 1.23 1.92 0.39 2.57 1.81 3.34 2.32 – – −2.98 – – –

w.intermediate-light 0.00 0.92 2.00 5.35 −0.14 −3.79 – – 1.47 1.86 1.98 1.93 3.38 3.86 3.80 3.73 – – – – −1.34 –

w.mesophilous

Humidity

0.00 1.98 0.33 −0.09 2.70 – −0.49 −0.63 0.66 0.20 −1.52 1.17 2.20 1.39 0.46 3.09 2.30 – – – – –

h.mature-forest

Successional status

0.00 1.31 −0.05 −8.99 −7.73 – −5.90 −4.72 0.57 0.99 1.88 0.15 2.56 0.62 3.61 1.88 – −7.58 – – – –

h.peri-forest 0.00 −1.41 −13.56 −29.24 −14.44 – −17.36 −15.36 1.14 −0.36 −4.83 1.09 2.83 1.47 −3.95 1.44 – −26.73 – – – –

h.shade-tolerant

Light

0.00 1.96 1.89 2.88 5.25 – 4.30 6.16 −0.63 −7.01 1.99 1.99 1.35 −6.73 3.63 3.51 – – – −4.61 – –

h.intermediate-light

0.00 1.97 −2.10 −9.75 −0.97 – 1.03 −1.50 1.28 −4.47 1.91 2.05 1.27 −4.67 3.73 3.24 – −6.33 – – – –

h.heliophilous

0.00 1.14 −2.43 −2.37 −2.14 – −5.71 −3.71 −1.15 1.52 1.37 0.83 0.70 1.87 3.35 2.74 – – – – – −5.01

h.mesophilous

Humidity

– −2.46 – –

0.00 1.99 −0.04 2.28 2.76 – 4.75 0.92 1.09 −6.34 −1.22 2.00 3.08 −4.37 0.29 2.04 –

h.hygrophilous

The smaller the AIC, the better the model with respect to the others. Within each ecological group, the model with the smallest AIC is in bold, and the AIC values within two units of this model are underlined. w.: woody, h.: herbaceous.

Null MAN G Gcompo Gstruct Gstruct.ct Gstruct.cl Gstruct.ct.cl PR Moisture Nsam MaxD PR.qua Moisture.qua Nsam.qua MaxD.qua Nsam*MAN Gcompo*MAN Gstruct*MAN Moisture*MAN Gstruct.ct*MAN Gstruct.cl*MAN

Model

Table 5 Differences in AIC values between the different ecological models/best model (selected from Table SM.6)*MAN and the null model (with site as covariable in all models) for ecological group abundance.

L. Wei, et al.

Forest Ecology and Management 460 (2020) 117897

8

Moisture Moisture*MAN

Gstruct.ct*MAN

Gstruct*MAN

Gstruct.ct

Gstruct

0.85 [0.66;1.11] 0.61**,[0.44;0.84]

0.75 [0.40; 1.42]

0.74 [0.42; 1.31]

1.0000 [0.89; 1.12] 0.94 [0.74; 1.19] 0.61***,– [0.47; 0.8] 0.970 [0.84; 1.12]

G.VLT.UNM G.MT.UNM G.ST.UNM

ct.MAN Moisture Moisture.MAN Moisture.UNM

1.19 [0.68; 2.06] 0.85 [0.27; 2.62] 0.28* [0.09; 0.87]

1.35 [0.75; 2.45] 0.46 [0.21; 1.01] 0.34***,- [0.18; 0.62] 1.16 [0.72; 1.87] 1.34 [0.50; 3.57] 0.23**,– [0.09; 0.59]

1.11 [0.98; 1.27] 0.86 [0.71; 1.03] 0.88 [0.77; 1.01]

0.32**,- [0.15;0.68] 0.92 [0.47;1.79] 0.25**,- [0.10;0.62] 1.16 [0.68;2.00] 0.91 [0.31;2.70] 0.28* [0.09;0.86] 0.30**,- [0.14; 0.64] 0.80 [0.40; 1.6] 0.29*,- [0.12; 0.74] 0.44** [0.24; 0.81]

w.shade-tolerant

G.MT.MAN G.ST.MAN ct.MAN

w.mature-forest

0.40**,- [0.21;0.77] 1.50 [0.83;2.71] 0.36* [0.17;0.78] 1.11 [0.69;1.79] 1.37 [0.53;3.58] 0.24**,- [0.10;0.61] 0.40**,- [0.21; 0.76]

0.78* [0.62; 0.98]

w.mesophilous

Light

0.95 [0.82;1.11] 1.13 [0.99;1.29] 0.83* [0.69;0.99] 0.990 [0.88;1.12] 0.94 [0.75;1.19] 0.61***,– [0.47;0.79] 0.960 [0.83; 1.11]

0.99 [0.86; 1.14] 1.14 [0.94; 1.37] 0.69**,- [0.55; 0.88]

0

w.shade-tolerant

w.mature-forest

w.intermediate-light

Successional status

Light

Successional status

Humidity

Abundance

Richness

G.VLT G.MT G.ST G.VLT G.MT G.ST G.VLT.MAN G.MT.MAN G.ST.MAN G.VLT.UNM G.MT.UNM G.ST.UNM G.VLT.MAN

Variable

0.89 [0.62; 1.28] 1.48 [0.87; 2.52] 0.41**,- [0.22; 0.75]

0

w.intermediate-light

1.040 [0.7; 1.56] 1.51 [0.82; 2.78] 0.57 [0.29; 1.12]

w.mesophilous

Humidity

Abbreviations are defined in Tables 2 and 3. w.: woody, h.: herbaceous. Variations were: increments of 5 m2/ha for basal area, and of one standard deviation for moisture (Table 2). For management type, we calculated the associated multiplicative coefficient by supposing the stand changed from UNM to MAN. Within each ecological group, the model with the smallest AIC is in bold. ‘‘0′’ and ‘‘00′’ indicate that the effect has a p-value of at least 0.975 of being non-important at two different levels (see text). ‘‘-’’ and ‘‘–’’ indicate that the effect has a p-value of at least 0.975 of being negative and ecological important at two different levels. ***,P < 0.001; **, P < 0.01; *, P < 0.05. Values in brackets are the 97.5% confidence intervals of the coefficients.

Soil environment

Stand attributes

Model

Table 6 Multiplicative effect of a substantial variation in ecological variables (with site as covariable in all models) on the richness and abundance of woody ecological groups. Here we displayed only the magnitude analysis results for the best models (see Tables 4 and 5) and for models that had ecologically important results selected from Tables SM.7 to SM.10.

L. Wei, et al.

Forest Ecology and Management 460 (2020) 117897

9

PR.qua

Moisture Moisture.qua

Gstruct.ct.cl

Gstruct.cl

Gstruct

G Gcompo

G Go Ghb Gother G.VLT G.MT G.ST G.VLT G.MT G.ST cl G.VLT G.MT G.ST ct cl Moisture Moisture at 1st quartile Moisture at median Moisture at 3rd quartile PR at 1st quartile PR at median PR at 3rd quartile

Variable

0.85* [0.74; 0.98] 1.05 [0.87; 1.27] 0.96 [0.77; 1.20] 1.11 [0.96; 1.29] 1.27***,+ [1.12; 1.44]

0.87* [0.77; 0.97] 1.030 [0.89; 1.19] 0.76** [0.63; 0.92]

0.69** [0.54; 0.87] 0.50***,– [0.38; 0.66] 0.87 [0.68; 1.10] 1.06 [0.77; 1.47] 0.55***,– [0.40; 0.76] 0.96 [0.71; 1.32] 0.55**,- [0.36; 0.83]

,-

h.shade-tolerant

h.mature-forest

h.peri-forest

Light

Successional status

2.28* [1.10; 4.73] 1.88*,+ [1.15; 3.07] 1.57**,+ [1.14; 2.16]

h.intermediate-light

1.21* [1.04; 1.42] 1.36**,+ [1.11; 1.66] 1.33** [1.10; 1.59]

0.85* [0.74; 0.98] 0.69***,- [0.56; 0.85] 0.93 [0.68; 1.27] 0.84* [0.72; 0.98] 0.91 [0.74; 1.14] 0.68**,- [0.52; 0.87]

h.heliophilous

0.85** [0.77; 0.93] 1.010 [0.88; 1.15] 0.85* [0.72; 1.00] 1.20** [1.07; 1.33]

h.mesophilous

Humidity

1.50**,+ [1.14; 1.98] 1.68 [0.82; 3.45] 1.61 [0.99; 2.60] 1.54**,+ [1.12; 2.11]

h.hygrophilous

Abbreviations are defined in Tables 2 and 3. w.: woody, h.: herbaceous. Variations were: increments of 5 m2/ha for basal area, and of one standard deviation for PR and moisture (Table 2). For management type, we calculated the associated multiplicative coefficient by supposing the stand changed from UNM to MAN. Within each ecological group, the model with the smallest AIC is in bold, and the AIC values within two units of this model are underlined. ‘‘0′’ and ‘‘00′’ indicate that the effect has a p-value of at least 0.975 of being non-important at two different levels (see text). ‘‘-’’ and ‘‘–’’ indicate that the effect has a p-value of at least 0.975 of being negative and ecological important at two different levels. ‘‘+’’ and ‘‘++’’ indicate that the effect has a p-value of at least 0.975 of being positive and ecological important at two different levels. ***, P < 0.001; **, P < 0.01; *, P < 0.05. Values in brackets are the 97.5% confidence intervals of the coefficients.

Soil environment

Stand attributes

Model

Table 7 Multiplicative effect of a substantial variation in ecological variables (with site as covariable in all models) on the richness of herbaceous ecological groups. Here we displayed only the magnitude analysis results for the best models (see Tables 4 and 5) and for models that had ecologically important results selected from Tables SM.7 to SM.10.

L. Wei, et al.

Forest Ecology and Management 460 (2020) 117897

10

Nsam.qua

Moisture.qua

Moisture Nsam PR.qua

Gstruct.ct.cl

Gstruct.cl

Gstruct

G Gcompo

G Go Ghb Gother G.VLT G.MT G.ST G.VLT G.MT G.ST cl G.VLT G.MT G.ST ct cl Moisture Nsam PR at 1st quartile PR at median PR at 3rd quartile Moisture at 1st quartile Moisture at median Moisture at 3rd quartile Nsam at 1st quartile Nsam at median Nsam at 3rd quartile

Variable

1.99***,++ [1.98; 2] 1.76***,++ [1.75; 1.76] 1.57***,+0 [1.56; 1.57]

1.60***,+0 [1.6; 1.61]

0.59***,- [0.58; 0.61] 0.97*,00 [0.94; 1.00] 0.79***,0 [0.76; 0.81]

0.70***,-0 [0.68;0.72] 0.70* [0.51; 0.98] 0.64 [0.41; 1.01] 2.46* [1.24; 4.86]

0.31***,– [0.17;0.58] 0.15***,– [0.07; 0.32] 0.64 [0.34; 1.18] 0.66 [0.28; 1.55] 0.21***,– [0.09; 0.49] 0.61 [0.27; 1.37] 0.22**,- [0.08; 0.62]

h.shade-tolerant

h.mature-forest

h.peri-forest

Light

Successional status

2.67**,+ [1.41; 5.08] 2.54**,+ [1.36; 4.74] 2.42* [1.23; 4.76]

2.58**,+ [1.39; 4.79]

h.intermediate-light

1.28***,0 [1.28; 1.29] 1.88***,++ [1.87; 1.90] 1.73***,++ [1.72; 1.74] 2.21* [1.11; 4.39] 2.47** [1.26; 4.87] 2.74**,+ [1.32; 5.71] 1.28***,0 [1.28; 1.29] 1.88***,++ [1.87; 1.9] 1.73***,++ [1.72; 1.74]

0.70 [0.47; 1.03] 0.35***,- [0.19; 0.63] 1.74 [0.77; 3.93]

h.heliophilous

0.59* [0.36; 0.95] 1.09 [0.55; 2.15] 0.49 [0.23; 1.06] 1.72* [1.10; 2.69]

0.52***,– [0.51; 0.54] 0.62***,- [0.6; 0.64] 1.45***,+0 [1.41; 1.49]

h.mesophilous

Humidity

3.32**,+ [1.46; 7.55] 3.22**,+ [1.47; 7.06] 3.14**,+ [1.37; 7.19]

0.52***,– [0.51; 0.52] 0.67***,-0 [0.67; 0.68] 0.87***,00 [0.87; 0.88] 0.48***,– [0.47; 0.48] 0.82***,00 [0.82; 0.82] 3.23**,+ [1.48; 7.05]

0.53***,– [0.53; 0.54] 0.74***,-0 [0.73; 0.74] 0.85***,00 [0.85; 0.86]

0.66***,-0 [0.64;0.68]

h.hygrophilous

Abbreviations are defined in Tables 2 and 3. w.: woody, h.: herbaceous. Variations were: increments of 5 m2/ha for basal area, and of one standard deviation for PR, Nsam, MaxD and moisture (Table 2). For management type, we calculated the associated multiplicative coefficient by supposing the stand changed from UNM to MAN. Within each ecological group, the model with the smallest AIC is in bold, and the AIC values within two units of this model are underlined. ‘‘0′’ and ‘‘00′’ indicate that the effect has a p-value of at least 0.975 of being non-important at two different levels (see text). ‘‘-’’ and ‘‘–’’ indicate that the effect has a p-value of at least 0.975 of being negative and ecological important at two different levels. ‘‘+’’ and ‘‘++’’ indicate that the effect has a p-value of at least 0.975 of being positive and ecological important at two different levels. ***,P < 0.001; **, P < 0.01; *, P < 0.05. Values in brackets are the 97.5% confidence intervals of the coefficients.

Soil environment

0

Stand attributes

Model

Table 8 Multiplicative effect of a substantial variation in ecological variables (with site as covariable in all models) on the abundance of herbaceous ecological groups. Here we displayed only the magnitude analysis results for the best models (see Tables 4 and 5) and for models that had ecologically important results selected from Tables SM.7 to SM.10.

L. Wei, et al.

Forest Ecology and Management 460 (2020) 117897

Forest Ecology and Management 460 (2020) 117897

L. Wei, et al.

Fig. 3. Standard error as a function of log-multiplicative effect of a substantial variation in ecological variables on the richness and abundance of each groups. The ecological magnitude class, and statistical significance of the ecological variables of the best models were shown in the plot by the color and shape of points respectively, with removing the “inconclusive” cases. Variations in ecological variables were: increments of 5 m2/ha for basal area, and of one standard deviation for moisture and Nsam. The abbreviation of ecological groups are explained in Table 1. w.: woody, h.: herbaceous. (MAN) or (UNM) means the estimate only holds for managed or unmanaged forests respectively. (Med) and (3rd) were used for quadratic model to represent the effect at medium and third quantile. The abbreviation of ecological variables can be found in Table 2.

understory richness and abundance (except for shade-tolerant woody species richness, which was negatively correlated to soil moisture) (Tables 6–8). The relationship between management type and understory richness or abundance was inconclusive (Tables SM.7–SM.10). For herbaceous species groups, more models showed ecologically important effects for abundance data (e.g. G, Gstruct, PR.qua, Moisture.qua and Nsam.qua, Table 8) than for richness data (Tables 6 and 7). Finally, in the vast majority of cases for the effects of our ecological variables, we could not conclude whether the effect was weak or ecologically important, due to confidence intervals that were generally large (Tables SM.7–SM.10). Concerning the effects of basal area, an ecologically important effect of total basal area (G) was only found for the richness and abundance of shade-tolerant herbaceous species (Tables 7 and 8), and G showed weak relationships with all woody groups (Table SM.7). The effect of oak basal area (Go) in the Gcompo model was similar to that of total basal area, except that Go also had a very negative relationship with the richness and abundance of shade-tolerant herbaceous species and the abundance of mesophilous herbaceous species (Tables 7 and 8, Fig. 3). Hornbeam and beech basal area (Ghb) had a negative relationship with the richness and abundance of heliophilous herbaceous species and the abundance of mesophilous herbaceous species. In addition, the basal area of other tree species (Gother) had no ecologically important effect

3.3. Magnitude of the effects We analysed the statistical significance and magnitude of the effects estimated from all the models for the richness and abundance of each ecological group (shown in Tables SM.7–SM.10). For the ecological groups whose best models were interaction models rather than the original additive models (shown in Tables 4 and 5), we carried out magnitude analyses for those interaction models (Tables SM.11 and SM.12). We also performed magnitude analyses for models that had ecologically important effects (in Tables SM.7–SM.10) in interaction with management type. We finally displayed only the magnitude analysis results for the best models (see Tables 6–8 and Fig. 3) and for models that had ecologically important results selected from Tables SM.7 to SM.10. Overall, among the three categories of models – models related to stand attributes, micro-environment and management type, ecologically important basal area effects were negatively related to understory richness and abundance (Tables 6–8), no matter which variable was used (G, Gcompo or Gstruct). For the effects of shrub layer cover, low and tall shrub layers showed opposite ecologically important relationships - respectively positive and negative - with specific ecological groups. On the other hand, the effects of ecologically important soil micro-environmental variable were all positively related to both 11

Forest Ecology and Management 460 (2020) 117897

L. Wei, et al.

(2010) – who reported greater vascular plant species richness in managed than in unmanaged forests – could be explained by a different ecological and historical context: the forests in Paillet et al. (2010) covered not only temperate but also boreal forests, and included many studies from northern and central Europe but no study in French forests. In addition, our methods of analysis were more precise, both in statistical terms (e.g. taking into account spatial structure) and in ecological terms (analysis of ecological groups). We found that the richness and abundance of a higher number of ecological groups were primarily related to stand attributes (basal area or shrub layer cover) rather than to soil micro-environment. Our tree species basal area models (Gcompo, Gstruct, Gstruct.ct, Gstruct.cl, Gstruct.ct.cl, and their interactive effect with management) were overall the best models for either the richness or abundance of a high proportion of ecological groups. The dominant role of basal area by tree species in determining herbaceous species had been found in previous studies in managed temperate forests (Barbier et al., 2009; Zilliox and Gosselin, 2014; Wei et al., 2015b). Yet, the novelty of our results is to show that the cover of shrub layers improved the basal area model – Gstruct – for a relatively high number of ecological groups, particularly for woody groups. Furthermore, the models related to Gstruct or their combined effect with shrub cover were more frequently the best models for the richness and abundance of woody groups than were those related to Gcompo, while the abundance of herbaceous groups was more dominantly affected by Gcompo than Gstruct. Our results were inconsistent with those of Zilliox and Gosselin (2014), who found that Gstruct models were the best models only for herbaceous species groups (four groups), and Gcompo or Ccompo (cover by tree species) models were best for two woody species groups and one herbaceous species group (Table 6 in Zilliox & Gosselin, 2014). However, both soil moisture and compaction were found to be the best indicators for specific ecological groups, as in Wei et al. (2015a,2016). Soil moisture best indicated mesophilous woody species richness, and both the richness and abundance of intermediate-light and hygrophilous herbaceous species, while soil compaction was the best indicator of the abundance of mature forest herbaceous species.

except for mesophilous herbaceous abundance (Tables 7 and 8, Fig. 3). For models related to basal area by diameter class (Gstruct), many ecological groups were negatively correlated to the basal area of small trees (G.ST). In other words, the richness of shade-tolerant and heliophilous herbaceous species, and the abundance of shade-tolerant herbaceous species had negative relationships with G.ST (Tables 7–8). Moreover, G.ST in the Gstruct*MAN model had a very negative relationship with the richness and abundance of mature-forest woody species in unmanaged stands (Table 6, Fig. 3) as well as with the abundance of shade-tolerant woody species in managed stands. For the basal area of very large trees (G.VLT), there was a negative relationship with the richness and abundance of shade-tolerant herbaceous species (Table 7) and the abundance of hygrophilous herbaceous species (Table 8). Moreover, G.VLT in the Gstruct*MAN and Gstruct.ct*MAN models had an importantly negative relationship with the abundance of mature-forest and shade-tolerant woody species in managed stands (Table 6, Fig. 3). Only unimportant effects were found for medium sized tree species (G.MT) in our study. Furthermore, in the model combining Gstruct and shrub layer cover (the Gstruct.ct.cl model), the cover of low shrub layer was positively related to the richness of mature forest herbaceous species; meanwhile, the cover of high shrub layer was negatively related to the abundance of hygrophilous herbaceous species in the Gstruct.ct.cl model and the abundance of mature forest woody species in managed stands in the Gstruct.ct.cl*MAN model. Regarding micro-environment (soil compaction and soil moisture variables), soil moisture had a positive relationship with the richness of hygrophilous herbaceous species (Table 7, Fig. 3) and the abundance of intermediate-light and hygrophilous herbaceous species (Table 8, Fig. 3). We also found a positive relationship between moisture (moisture.qua model) and the richness of intermediate-light herbaceous species as well as the abundance of hygrophilous and heliophilous herbaceous species (Tables 7 and 8). The only negative effect of moisture was found for the richness of shade-tolerant woody species in unmanaged stands in the moisture*MAN model (Table 6). For the effect of soil compaction (PR, Nsam and MaxD) (Tables 7 and 8), both PR in the PR.qua model and Nsam in the Nsam.qua model showed very positive relationships with the richness and abundance of heliophilous herbaceous species. Nsam was also positively correlated with the abundance of mature-forest herbaceous species.

4.2. Effect of tree basal area Few previous studies have dealt with the effect of basal area by diameter class on understory plants. We found that stand structure represented by tree diameter class significantly shaped understory richness and abundance, especially in terms of decreased richness or abundance of shade-tolerant herbaceous species under very large (DBH > 67.5 cm) /large (47.5 cm < DBH < 67.5 cm) or small trees (7.5 cm < DBH < 22.5 cm), though some other ecological groups were also found to have important decreases with one of these basal areas (the richness of heliophilous herbaceous species, the abundance of mature forest and hygrophilous herbaceous). We also studied another important stand attribute - stand composition - which mainly influenced herbaceous species rather than woody species. Among the three dominant tree species in our study (oak, hornbeam and beech), hornbeam and beech have consistently been found to have negative relationships with total understory or herbaceous understory diversity in previous studies (Kwiatkowska et al., 1997; Nagaike et al., 2005; Barbier et al., 2009; Zilliox & Gosselin 2014; Wei et al., 2015b). In our study, not all herbaceous groups were affected by hornbeam and beech, only certain groups that had high light requirements or low water preferences were negatively related to hornbeam and beech. The relationship between oak basal area and understory woody-species richness was weak in our study, as was the case in previous studies (Barbier et al., 2009; Wei et al., 2015b). In Wei et al. (2016), rather similar herbaceous ecological groups (shade-tolerant, heliophilous and mesophilous herbaceous species) were also negatively related with oak basal area in managed stands. Though the relationship between oak and herbaceous species was weak in Barbier et al. (2009), their studied sites

4. Discussion 4.1. Dominant factors explaining understory diversity The influence of management type (managed vs unmanaged), tree stand attributes and soil micro-environment on plant diversity have often been studied, yet their relative importance in explaining understory diversity at the fine scale has seldom been compared. Our results show that either basal area or micro-environment provided the best explanatory models for understory richness and abundance, depending on the plant ecological group studied. The role of management type was of secondary importance, and management type was important in explaining understory richness and abundance of some ecological groups only when considered in interaction with tree basal area or soil moisture. It should be remembered, however, that most of the European forests that are unmanaged today have undergone intensive management at some point in the last few centuries. The lack of a general management effect in our results could be accounted for by the fact that French forest reserves are quite recent, especially when compared to North America, and, as inferred by Paillet et al. (2015), biodiversity may still be recovering from past forest management. Indeed, Paillet et al.’s (2010) meta-analysis of European forest studies showed that the difference in species richness over all taxonomic groups between managed and unmanaged forests depended on time since management abandonment. The inconsistency between our results and Paillet et al. 12

Forest Ecology and Management 460 (2020) 117897

L. Wei, et al.

indicating that canopy cover was generally higher in unmanaged forests than in managed forests. Thus, our results might be consistent with the above-ground facilitation hypothesis (Holmgren, 2000), which demonstrated that shaded microsites reduce leaf and air temperatures, and also vapour-pressure deficit (Valladares and Pearcy 1997). This might allow species to use less water, thus the negative effects of soil moisture (e.g. lack of air for roots) could become predominant. Penetration resistance in our study was slightly higher in managed than in unmanaged stands and maximum depth of probe was slightly less. This indicates that soil compaction was higher in the managed than in the unmanaged stands, even though the differences were not statistically significant. Higher soil moisture is likely to occur in more compacted soils (Greacen and Sands, 1980; Williamson and Neilsen, 2000; McNabb et al., 2001; Tan et al., 2005). However, in our study, there was no strong relationship between penetration resistance and soil moisture. Penetration resistance values at which root growth is restricted are thought to be between 2500 and 3000 kPa for many plant species (Taylor et al., 1966; Greacen and Sands, 1980). In our study, the mean penetration resistance value was generally below the critical value of 2500 kPa; only a few subplots showed higher values. Zenner et al. (2007) similarly found that penetration resistance values after harvest were below the levels that restrict suckering and growth in aspen. Soil compaction levels in our study were therefore probably not high enough to negatively affect ground flora. On the contrary, we found that both the richness and abundance of heliophilous herbaceous species had strongly positive relationships with soil compaction (penetration resistance or number of repeated samples necessary to reach the minimum depth), though these models were not the best. Wei et al. (2015a,2016) also found that heliophilous species richness was positively affected by soil compaction (with bulk density as the best indicator). Higher soil compaction was often found on skid trails, where heliophilous herbaceous species were favoured due to larger canopy openings (Wei et al., 2015a,2015b). Thus, the positive association between soil compaction and heliophilous herbaceous species might be an artefact of relatively open skid trails.

were different from ours in terms of site conditions (e.g. a top silt soil layer with water logging), forest age (more mature stands) and forest types (include uneven-aged stands). It is noteworthy in our study that the relationship between basal area and understory plant diversity differed between managed and unmanaged stands for some woody ecological groups and certain tree types (very large trees and small trees). More precisely, our results indicated that very large and large trees resulted in a decrease in the abundance of mature-forest and shade-tolerant woody species in managed forests. Small trees, on the contrary, negatively affected the richness and abundance of mature-forest woody species only in unmanaged forests. The negative effect of basal area in either managed or unmanaged stands for different types of trees indicates that a variety of mechanisms related to biodiversity and forest structure or composition may be at play. For example, the basal area of very large trees might reflect management intensity in managed forests, since selective harvesting often favours oak trees of large/very large dimensions. Thus, in managed forests in France, the basal area of very large trees could reflect canopy openness maintained through forest management and the control of competing tree species such as beech. Further studies are needed to uncover the potential mechanisms behind these relationships. Shrub layer cover is an important factor that could modify the quantity of light let through canopy trees reaching the ground. We only found one study (Pérez-Ramos et al., 2008) that assessed the combined effect of stand structure and shrub cover on herbaceous species communities. Pérez-Ramos et al. (2008) found that the role of shrub clearing in herbaceous species richness and diversity was significant in open woodland sites but insignificant in closed forest sites. In our study, although the effect of shrub cover alone did not differ between managed and managed forests, an ecologically important effect of shrub layer cover was detected in combination with tree stand structure. According to our findings, maintaining or promoting a low shrub layer could help improve the richness of mature forest herbaceous species. However, increasing tall shrub cover in managed stands would decrease rather than increase understory diversity – with a negative relationship with the abundance of hygrophilous herbaceous species and of mature forest woody species. The picture therefore seems dynamic in terms of coupling between the woody and herbaceous layers: the development of low shrub cover is correlated with a higher diversity for some herbaceous groups, while the development of shrub cover in the higher layer is mostly negatively correlated, as is the basal area of small trees.

5. Conclusion Our study covered five lowland temperate forests in northern and eastern France, with a network design of strictly protected areas adjacent to managed stands. We found that understory richness and abundance did not differ between managed and unmanaged forests. However, we did provide further information on the diversity pattern mechanism: we incorporated and compared the effects on ecological group diversity of stand basal area by tree species or diameter class, shrub cover and soil micro-environment along with management type. Depending on the ecological groups studied, either basal area by tree species or by diameter class or micro-environment, rather than management type, explained understory diversity. Yet, our results also highlight that relationships between understory plant diversity and basal area and/or micro-environment sometimes vary between managed and unmanaged forests. Finally, in our study, when important effects were found, basal area and tall shrub cover negatively correlated with the richness or abundance of some understory plants, while soil micro-environmental conditions (soil moisture or soil compaction) and low shrub cover were positively related to the richness or abundance of understory plants (except for mature-forest woody species richness, which was negatively related to soil moisture). Meanwhile, species with similar traits, but belonging to different life forms, generally had different relationships with the basal area of large and small trees, with some exceptions. For example, when shrub cover was incorporated into the Gstruct model, it improved the explanation of the richness of both woody and herbaceous mature-forest species. Better understanding the mechanisms that cause these relationships, and investigating whether these relationships continue to hold true in a changing context (e.g. increased mechanization or levels of tree harvesting) are two important

4.3. Influence of soil micro-environment Resource quantity and heterogeneity are important factors for understory plant diversity, and their influence varies with stand development stage and disturbances in forest ecosystems (Bartels and Chen, 2010). Qian et al. (1997) suggested that levels of moisture and nutrients in the soil exert a strong influence on diversity patterns regardless of the successional or stand developmental stage. In our study, both the richness and abundance of intermediate-light and hygrophilous herbaceous species were found to be positively correlated to soil moisture, both for linear and quadratic moisture models. Wei et al. (2015a) also found a positive relationship between soil moisture and the richness of various ecological groups, though different from ours (not crossed with life form): shade-tolerant, heliophilous and low-humidity species, as well as transient and short-term seed bank species. This might be due to differences in the soils between the two studies, i.e. soils were generally wetter in our study sites than in the ones studied by Wei et al. (2015a). Wei et al. (2016) also found that the relationship between moisture and understory richness and abundance on tractor trails was systematically weak. We found that the richness of shade-tolerant woody species in unmanaged stands was the only one that was negatively correlated to moisture. In our study, total basal area was significantly higher in unmanaged forests than in managed forests – 13

Forest Ecology and Management 460 (2020) 117897

L. Wei, et al.

issues that our results raise.

Cadotte, M.W., 2007. Competition–colonization trade-offs and disturbance effects at multiple scales. Ecology 88, 823–829. https://doi.org/10.1890/06-1117. Commarmot, B., Bachofen, H., Bundziak, Y., Bürgi, A., Ramp, B., Sparky, Y., Sukhariuk, D., Voter, R., Zingg, A., 2005. Structure of virgin and managed beech forests in Uholka (Ukraine) and Sihlwald (Switzerland): a comparative study. For. Snow. Landsc. Res. 79, 45–56. https://www.dora.lib4ri.ch/wsl/islandora/object/ wsl:15328. Dixon, P.M., Pechmann, J.H.K., 2005. A statistical test to show negligible trend. Ecology. 86, 1751–1756. https://doi.org/10.1890/04-1343. Duguid, M.C., Ashton, M.S., 2013. A meta–analysis of the effect of forest management for timber on understory plant species diversity in temperate forests. For. Ecol. Manag. 303, 81–90. https://doi.org/10.1016/j.foreco.2013.04.009. Ellenberg, H., Weber, H.E., Dull, R., Wirth, V., Werner, W., Paulissen, D., 1992. Zeigerwerte von Pflanzen in Mitteleuropa. Scr. Geobo. 18, 1–248. Fenton, N.J., Bergeron, Y., 2008. Does time or habitat make old-growth forests species rich? Bryophyte richness in boreal Picea mariana forests. Biol. Conserv. 141, 1389–1399. https://doi.org/10.1016/j.biocon.2008.03.01. Gilliam, F.S., 2002. Effects of harvesting on herbaceous layer diversity of a central Appalachian hardwood forest in West Virginia. USA. For. Ecol. Manag. 155, 33–43. https://doi.org/10.1016/S0378-1127(01)00545-X. Godefroid, S., Koedam, N., 2004. Interspecific variation in soil compaction sensitivity among forest floor species. Biol. Conserv. 119, 207–217. https://doi.org/10.1016/j. biocon.2003.11.009. Gosselin, F., 2004. Imiter la nature, hâter son œuvre? Quelques réflexions sur les éléments et stades tronqués par la sylviculture. In: M. Gosselin and O. Laroussinie (Eds.), Gestion Forestière et Biodiversité: connaître pour préserver – synthèse bibliographique. Collection Etudes du Cemagref – Série gestion des territoires, n°20. Coédition GIP Ecofor – Cemagref Editions, Antony, pp. 217–256. Gosselin, F., 2012. Improving approaches to the analysis of functional and taxonomic biotic homogenization: beyond mean specialization. J. Ecol. 100, 1289–1295. https://doi.org/10.1111/1365-2745.12002. Gosselin, F., Paillet, Y., Gosselin, M., Durrieu, S., Larrieu, L., Marell, A., Boulanger, V., et al., 2017. Gestion, Naturalité et Biodiversité: présentation générale du projet de recherche et de son approche. Rendez Vous Techniques (ONF) 56, 20–32. Gossner, M.M., Schall, P., Ammer, C., Ammer, U., Engel, K., Schubert, H., Simon, U., Utschick, H., Weisser, W.W., 2014. Forest management intensity measures as alternative to stand properties for quantifying effects on biodiversity. Ecosphere 5, 113. https://doi.org/10.1890/ES14-00177.1. Greacen, E.L., Sands, R.R., 1980. Compaction of forest soils. A review. Soil. Res. 18, 163–189. https://doi.org/10.1071/SR9800163. Heywood, V.H., Watson, R.T., 1995. United Nations Environment Programme. Global biodiversity assessment/V.H. Heywood, executive editor; R.T. Watson, chair. Cambridge University Press, Cambridge; New York, NY, USA. Hodgson, J.G., Grime, J.P., Hunt, R., Thompson, K., 1995. The Electronic Comparative Plant Ecology. Chapman & Hall, London. Holmgren, M., 2000. Combined effects of shade and drought tulip poplar seedlings: Trade-off in tolerance or facilitation? Oikos. https://doi.org/10.1034/j.1600-0706. 2000.900107.x. Huang, A., 2017. Mean-parametrized Conway-Maxwell-Poisson regression models for dispersed counts. Stat. Model. 17, 359–380. https://doi.org/10.1177/ 1471082X17697749. Jarret, P., 2004. Guide des sylvicultures. Chênaie atlantique. Lavoisier – Office National des Forêts. Julve, P., 2007. Baseflor. http://perso.orange.fr/philippe.julve/catminat.htm. Kuuluvainen, T., Penttinen, A., Leinonen, K., Nygren, M., 1996. Statistical opportunities for comparing stand structural heterogeneity in managed and primeval forests: An example from boreal spruce forest in southern Finland. Silva. Fenn. 30, 315–328. https://doi.org/10.14214/sf.a9243. Kwiatkowska, A.J., Spalik, K., Michalak, E., Palinska, A., Panufnik, D., 1997. Influence of the size and density of Carpinus betulus on the spatial distribution and rate of deletion of forest-floor species in thermophilous oak forest. Plant. Ecol. 129, 1–10. https://doi.org/10.1023/A:1009708317294. Lindenmayer, D.B., Margules, C.R., Botkin, D.B., 2000. Indicators of biodiversity for ecologically sustainable forest management. Conserv. Biol. 14, 941–950. https://doi. org/10.1046/j.1523-1739.2000.98533.x. McCarthy, M.A., Burgman, M.A., 1995. Coping with uncertainty in forest wildlife planning. For. Ecol. Manage. 74, 23–36. https://doi.org/10.1016/0378-1127(94) 03523-Y. McNabb, D.H., Startsev, A.D., Nguyen, H., 2001. Soil wetness and traffic level effects on bulk density and air-filled porosity ofcompacted boreal forest soils. Soil Sci. Soc. Am. J. 65, 1238–1247. https://doi.org/10.2136/sssaj2001.6541238x. Miller, R.E., Hazard, J., Howes, S., 2001. Precision, accuracy, and efficiency of four tools for measuring soil bulk density or strength. USDA Forest Service, PNWRS, pp. 23. Nagaike, T., Kamitani, T., Nakashizuka, T., 2005. Effects of different forest management systems on plant species diversity in a Fagus crenata forested landscape of central Japan. Can. J. Forest. Res. 35, 2832–2840. https://doi.org/10.1139/x05-200. Okland, T., Rydgren, K., Okland, R.H., Storaunet, K.O., Rolstad, J., 2003. Variation in environmental conditions, understorey species number, abundance and composition among natural and managed Picea–abies forest stands. For. Ecol. Manage. 177, 17–37. https://doi.org/10.1016/S0378-1127(02)00331-6. ONF, 1996. Révision d'aménagement. Forêt domaniale de Montargis (4090, 42 ha). Département du Loiret. 1996–2015. ONF, pp. 120. Paillet, Y., Bergès, L., Hjältén, J., Ódor, P., Avon, C., Bernhardt-Römermann, M., Bijlsma, R., De Bruyn, L., Fuhr, M., Grandin, U., Kanka, R., Lundin, L., Luque, S., Magura, T., Matesanz, S., Mészáros, I., Sebastià, M., Schmidt, W., Standovár, T., Tóthmérész, B., Uotila, A., Valladares, F., Vellak, K., Virtanen, R., 2010. Biodiversity Differences

CRediT authorship contribution statement Liping Wei: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Writing original draft. Frédéric Archaux: Conceptualization, Methodology, Resources, Supervision, Writing - review & editing. Florian Hulin: Investigation. Isabelle Bilger: Investigation, Writing - review & editing. Frédéric Gosselin: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Writing - review & editing. Acknowledgements This research was supported by the French Ministry in Charge of Ecology through the DEB-Irstea convention (Action B – GNB-Sol) and the China Scholarship Council (CSC). We thank Richard Chevalier for helping with vegetation identification. We are grateful to Vicki Moore for verifying the English language in the manuscript. We warmly thank Ugoline Godeau for sharing her method and code for plotting the magnitude and significance of relationships (Fig. 3). We are also very grateful to two anonymous reviewers who helped us significantly improve the manuscript. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foreco.2020.117897. References Akbarimehr, M., Naghdi, R., 2012. Determination of most appropriate distance between water diversion on skid trails in the mountainous forest, north of Iran. Catena 88, 68–72. https://doi.org/10.1016/j.catena.2011.08.005. Amatya, A., Demirtas, H., 2015. Simultaneous generation of multivariate mixed data with Poisson and Normal marginal. J. Stat. Comput. Simul. 85, 3129–3139. https://doi. org/10.1080/00949655.2014.953534. Ampoorter, E., Goris, R., Cornelis, W.M., Verheyen, K., 2007. Impact of mechanized logging on compaction status of sandy forest soils. For. Ecol. Manage. 241, 162–174. https://doi.org/10.1007/s13595-012-0199-y. Augusto, L., Dupouey, J.L., Ranger, J., 2003. Effects of tree species on understory vegetation and environmental conditions in temperate forests. Ann. For. Sci. 60, 823–831. https://doi.org/10.1051/forest:2003077. Bai, Y., Chow, T.T., Ménézo, C., Dupeyrat, P., 2012. Analysis of a hybrid PV/thermal solar-assisted heat pump system for sports center water heating application. Int. J. Photoenergy. https://doi.org/10.1155/2012/265838. Baltzinger, M., Archaux, F., Gosselin, M., Chevalier, R., 2011. Contribution of forest management artefacts to plant diversity at a forest scale. Ann. For. Sci. 68, 395–406. https://doi.org/10.1007/s13595-011-0026-x. Barbier, S., Chevalier, R., Loussot, P., Berges, L., Gosselin, F., 2009. Improving biodiversity indicators of sustainable forest management: Tree genus abundance rather than tree genus richness and dominance for understory vegetation in French lowland oak hornbeam forests. For. Ecol. Manag. 258, S176–S186. https://doi.org/10.1016/j. foreco.2009.09.004. Barbier, S., Gosselin, F., Balandier, P., 2008. Influence of tree species on understory vegetation diversity and mechanisms involved – a critical review for temperate and boreal forests. For. Ecol. Manag. 254, 1–15. https://doi.org/10.1016/j.foreco.2007. 09.038. Bartels, S.F., Chen, H.Y., 2010. Is understory plant species diversity driven by resource quantity or resource heterogeneity? Ecology. 91, 1931–1938. https://doi.org/10. 1890/09-1376.1. Bassett, I.E., Simcock, R.C., Mitchell, N.D., 2005. Consequences of soil compaction for seedling establishment: Implications for natural regeneration and restoration. Austral. Ecol. 30, 827–833. https://doi.org/10.1111/j.1442-9993.2005.01525.x. Bouget, C., Parmain, G., Gilg, O., Noblecourt, T., Nusillard, B., Paillet, Y., Pernot, C., Larrieu, L., Gosselin, F., 2014. Does a set-aside conservation strategy help the restoration of old-growth forest attributes and recolonization by saproxylic beetles? Anim. Conserv. 17, 342–353. https://doi.org/10.1111/acv.12101. Brooks, T., Wagnerman, K., Artiga, S., et al., 2017. Medicaid and CHIP eligibility, enrollment, renewal, and cost sharing policies as of January 2017: Findings from a 50–state survey. Kaiser Commission on Medicaid and the Uninsured, Washington, DC http://files.kff.org/attachment/ Repor–Medicaid–and–CHIP–Eligibility–as–of–Jan–2017. Burnham, K.P., Anderson, D.R., 2002. Model selection and multimodel inference, 2nd ed. Springer, New York.

14

Forest Ecology and Management 460 (2020) 117897

L. Wei, et al.

Tan, X., Chang, S.X., Kabzems, R., 2005. Effects of soil compaction and forest floor removal on soil microbial properties and N transformations in a boreal forest long-term soil productivity study. For. Ecol. Manage. 217, 158–170. https://doi.org/10.1016/j. foreco.2005.05.061. Taylor, H.M., Roberson, G.M., Parker, Jr., 1966. Soil strength-root penetration relations to medium to coarse–textured soil materials. Soil. Sci. 102, 18–22. https://doi.org/ 10.1097/00010694-196607000-00002. Valladares, F., Pearcy, R.W., 1997. Interactions between water stress, sun-shade acclimation, heat tolerance and photoinhibition in the sclerophyll Heteromeles arbutifolia. Plant. Cell. Environ. 20, 25–36. Wei, L., Villemey, A., Hulin, F., Bilger, I., Dumas, Y., Chevalier, R., Archaux, F., Gosselin, F., 2015a. Plant diversity on skid trails in oak high forests: a matter of disturbance, micro-environmental conditions or forest age? For. Ecol. Manage. 338, 20–31. https://doi.org/10.1016/j.foreco.2014.11.018. Wei, L., Chevalier, R., Archaux, F., Gosselin, F., 2015b. Influence of stand attributes and skid trail area on stand-scale ground flora diversity. Can. J. For. Res. 45, 1816–1826. https://doi.org/10.1139/cjfr-2015-0189. Wei, L., Hulin, F., Chevalier, R., Archaux, F., Gosselin, F., 2016. Is plant diversity on tractor trails more influenced by disturbance than by soil characteristics? For. Ecol. Manage. 379, 173–184. https://doi.org/10.1016/j.foreco.2016.08.020. Williamson, J.R., Neilsen, W.A., 2000. The influence of forest site on rate and extent of soil compaction and profile disturbance of skid trails during ground-based harvesting. Can. J. For. Res. 30, 1196–1205. https://doi.org/10.1139/x00-041. Wirth, C., 2009. Old–growth forests: function, fate and value. A synthesis. In: Wirth, C., Gleixner, G., Heimann, M. (Eds.), Old-Growth Forests. Springer, Berlin and Heidelberg, Germany. Zenner, E.K., Berger, A.L., 2008. Influence of skidder traffic and canopy removal intensities on the ground flora in a clearcut-with-reserves northern hardwood stand in Minnesota, USA. For. Ecol. Manag. 256, 1785–1794. https://doi.org/10.1016/j. foreco.2008.05.030. Zenner, E.K., Fauskee, J.T., Berger, A.L., Puettmann, K.I., 2007. Impacts of skidding traffic intensity on soil disturbance, soil recovery, and aspen regeneration in north central Minnesota. North. J. Appl. For. 24, 177–183. https://doi.org/10.1093/njaf/24.3.177. Zilliox, C., Gosselin, F., 2014. Tree species diversity and abundance as indicators of understory diversity in French mountain forests: Variations of the relationship in geographical and ecological space. For. Ecol. Manag. 321, 105–116. https://doi.org/10. 1016/j.foreco.2013.07.049.

between Managed and Unmanaged Forests: Meta-Analysis of Species Richness in Europe. Conserv. Biol. 24, 101–112. https://doi.org/10.1111/j.1523-1739.2009. 01399.x. Paillet, Y., Gosselin, M., Gosselin, F., 2017. Quelles variables pour analyser la réponse de la biodiversité à la mise en réserve intégrale ? Une approche par naturalité(s). Rendez Vous Techniques (ONF) 56, 33–39. Paillet, Y., Pernot, C., Boulanger, V., Debaive, N., Fuhr, M., Gilg, O., Gosselin, F., 2015. Quantifying the recovery of old–growth attributes in forest reserves: A first reference for France. For. Ecol. Manage. 346, 51–64. https://doi.org/10.1016/j.foreco.2015. 02.037. Parvianen, J., Kassioumis, K., Bücking, W., Hochbichler, E., Päivinen, R., Little, D., 2000. COST action E4: Forest Reserves Research Network. Missions, Goals, Linkages, Recommendations and Partners. Final Report. Joensuu, Finland. 28. Pérez-Ramos, I.M., Zavala, M.A., Marañón, T., Díaz-Villa, M.D., Valladares, F., 2008. Dynamics of understorey herbaceous plant diversity following shrub clearing of cork oak forests: A five-year study. For. Ecol. Manage. https://doi.org/10.1016/j.foreco. 2008.01.069. Perot, T., Mårell, A., Korboulewsky, N., Seigner, V., Balandier, P., 2017. Modeling and predicting solar radiation transmittance in mixed forests at a within-stand scale from tree species basal area. For. Ecol. Manage. https://doi.org/10.1016/j.foreco.2017.01. 023. Peterken, G.F., 1981. Woodland conservation and management. Chapman and Hall, London. Qian, H., Klinka, K., Sivak, B., 1997. Diversity of the understory vascular vegetation in 40 year-old and old-growth forest stands on Vancouver Island, British Columbia. J. Veg. Sci. 8, 773–780. https://doi.org/10.2307/3237021. Schmidt, W., 2005. Herb layer species as indicators of biodiversity of managed and unmanaged beech forests. For. Snow Landsc. Res. 79, 111–125. Sitzia, T., Trentanovi, G., Dainese, M., Gobbo, G., Lingua, E., Sommacal, M., 2012. Stand structure and plant species diversity in managed and abandoned silver fir mature woodlands. For. Ecol. Manag. 270, 232–238. https://doi.org/10.1016/j.foreco.2012. 01.032. Small, C.J., McCarthy, B.C., 2005. Relationship of understory diversity to soil nitrogen topographic variation and stand age in an eastern oak forest. USA. For. Ecol. Manag. 217, 229–243. https://doi.org/10.1016/j.foreco.2005.06.004. Sonohat, G., Balandier, P., Ruchaud, F., 2004. Predicting solar radiation transmittance in the understorey of even-aged coniferous stands in temperate forests. Ann. For. Sci. 61, 1–13. https://doi.org/10.1051/forest:2004061.

15