# Macroclimate modulates the positive dead-wood influence on bryophyte diversity in managed and unmanaged temperate lowland forests

Marion Gosselin, Yann Dumas, Serge Cadet, E. Cateau, Yoan Paillet, Frédéric Gosselin

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## To cite this version:

Marion Gosselin, Yann Dumas, Serge Cadet, E. Cateau, Yoan Paillet, et al. Macroclimate modulates the positive dead-wood influence on bryophyte diversity in managed and unmanaged temperate lowland forests. Journal of Environmental Management, 2024, 367, pp.121840. 10.1016/j.jenvman.2024.121840. hal-04806013

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https://hal.inrae.fr/hal-04806013v1

Submitted on 26 Nov 2024

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Research article

# Macroclimate modulates the positive dead-wood influence on bryophyte diversity in managed and unmanaged temperate lowland forests

M. Gosselin<sup>a</sup>, Y. Dumas<sup>a</sup>, S. Cadet<sup>b</sup>, E. Cateau<sup>c</sup>, Y. Paillet<sup>a</sup>, F. Gosselin<sup>a,*</sup>

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## ARTICLE INFO

### Keywords:

- Forest biodiversity

- Bryophyte

- Forest specialists

- Deadwood

- Climatic variables

- Management abandonment

## ABSTRACT

Extending the network of strict forest reserves is one of the conservation measures promoted by the French National Strategy for Biodiversity. However, there is a lack of evidence concerning the efficiency of setting aside strict forest reserves to preserve biodiversity in the case of temperate forests. Moreover, there are potentially unexplored underlying ecological mechanisms that forest management could usefully mimic. In order to disentangle the respective roles of management abandonment, stand structural attributes and climatic and topographic variables in determining forest biodiversity, we conducted the first national-scale study in France comparing biodiversity in managed and in unmanaged forests. Here we focus on bryophytes (all species combined and forest specialists separately). We analyzed data from 127 plots in ten lowland forests in France. Our aim was to disentangle the relationships between bryophyte richness and (i) management abandonment per se, (ii) associated forest-structure variables like deadwood volume, and (iii) macroclimatic variables important for bryophytes (temperature, precipitation, relative humidity, solar radiation and vapor pressure deficit). For each studied combination of variables (univariate, additive or interactive models), we compared hierarchical models of several types: linear with a fixed slope, linear with a random slope, quadratic, sigmoid or threshold models. We found that deadwood variables were the main drivers of bryophyte richness in managed as well as in unmanaged stands. We observed a sigmoid relationship of total deadwood volume to overall richness, and a threshold effect of large and very large deadwood volume on forest specialist richness. The effect of management abandonment was globally non-significant, though impact varied strongly among the different forest sites. A combination of deadwood and macroclimatic variables best predicted bryophyte richness, through non-linear relationships: 1) higher solar radiation reinforced the positive effects of large deadwood on forest-specialist bryophyte richness; and 2) higher mean annual temperatures counteracted the positive effects of total deadwood amount on total bryophyte species richness. Maintaining high amounts of deadwood in both managed and unmanaged forests is likely to improve bryophyte richness and will be particularly important under ongoing climate change.

## 1. Introduction

In order to conserve forest biodiversity in European forests, extending the network of strict forest reserves is one of the conservation measures promoted by the European Biodiversity Strategy. Strict forest reserves are set-aside forests, which evolve naturally after the abandonment of silvicultural practices. This strategy is consistent with the worldwide literature on the effects of setting aside strict forest reserves on the biodiversity threatened by forestry practices (Lindenmayer et al., 2006) and with the knowledge on ecological succession and niches. However, the scientific rationale is not so obvious as far as European

temperate forests are concerned. At the European scale, Paillet et al. (2010a, b) carried out a meta-analysis to compare managed and unmanaged forests (defined as forests that had either never been harvested or were no longer being harvested). They found mostly positive trends of the effects of unmanaged forests on local species richness (SR), except for vascular plants, but strong variations occurred between taxa. Only four groups had significant or nearly significant higher diversity in unmanaged forests, namely fungi, bryophytes, lichens and birds. A more recent work centered on management abandonment (Langridge et al., 2023) updated and enhanced the meta-analysis by taking into account the comments put forward by Halme et al. (2010). It found an overall non-significant effect of harvesting abandonment but specified that

<footnote><sup>a</sup> INRAE, UR EFNO, F-45290 Nogent-sur-Vernisson, France</footnote>

<footnote><sup>b</sup> Office National des Forêts, 46 avenue Paul Cézanne, F-13098, Aix-en-Provence, France</footnote>

<footnote><sup>c</sup> Réserves Naturelles de France, La Bourdonnerie, 2 allée Pierre Lacroute - CS 67524 - F-21075, Dijon cedex, France</footnote>

<footnote>* Corresponding author. INRAE, UR EFNO Ecosystèmes Forestiers, Domaine des Barres, 45290 Nogent-sur-Vernisson, France.
E-mail address: marion.gosselin@inrae.fr (F. Gosselin).</footnote>
Abbreviations

SR species richness

DICm marginalized deviance information criterion

LVL large (67,5 cm ≥ diameter >47,5 cm) and very large (>67,5 cm), for trees or deadwood pieces

App Appendix

time-since-management-abandonment may interact with climatic variables. Hence, the effects of setting aside forest stands appear to vary among taxa and potentially interact with climatic or stand variables. In the context of climatic change, which is known to impact several sectors, among which biodiversity conservation, and forestry (Abbass et al., 2022), it is worth understanding such interactions between management practices, stand variables, and climatic variables.

Bryophytes appear to be among the taxa that most benefit from management abandonment (Gosselin, 2004; Paillet et al., 2010b; Hofmeister et al., 2015b; Horvat et al., 2017). Bryophytes are also influenced by forest stand or regional characteristics like macroclimate (Raabe et al., 2010), soil (Saetersdal, 1994) or forest history (Táborská et al., 2017).

In temperate and boreal forests, some stand attributes enhance bryophyte richness because they provide a diversity of suitable substrates (Lóhmus et al., 2007; Chen et al., 2017). These include in particular old-growth forest attributes like uprooted trees (Jonsson and Esseen, 1990; Von Oheimb et al., 2007), decaying wood (Vellak and Paal, 1999; Rambo, 2001) at various decay stages (Rambo and Muir, 1998a), large deadwood pieces (Dittrich et al., 2014; Hofmeister et al., 2015a; Czerepko et al., 2021), density of large trees (Odor et al., 2013; Hofmeister et al., 2015b; Tinya et al., 2021), among other attributes like tree species diversity (Odor et al., 2013; Czerepko et al., 2021). In most cases, the highly influential old-growth forest attributes are related to deadwood and to large trees (dead or alive).

Climatic variables, especially those related to water supply and desiccation, may influence bryophytes because they are poikilohydric, i. e. they are unable to control their water potential (Nakatsubo, 1997 quoted in Proctor et al. (2007)). Since they absorb water through their entire above-ground structure, relative humidity, precipitation, or vapor pressure deficit (VPD) are then more important than substrate humidity for the plant's hydration and for active photosynthesis. VPD is a proxy for the drying power of the air, depending on temperature and relative humidity, which may be a driver of bryophyte growth and community structure (Rambo, 2010). In addition, high temperatures increase the risk of desiccation and may consequently limit bryophyte growth or survival rate (Proctor et al. (2007) and bryophytes, as photosynthetic organisms, are also influenced by solar radiation (Rambo and Muir, 1998b; Spitale, 2016).

Several studies have tried to disentangle the relative effects of management abandonment, local stand variables and context – for example, regional climatic conditions – over multiple scales, but the observed patterns of additional or interactive effects are complex and vary considerably from one study to the next.

First, several results suggest potential additional effects between “uncontrollable” climatic variables and “controllable” ones like management abandonment or stand characteristics. In some cases, macroclimatic or regional variables appear to have stronger effects than local stand variables. Zellweger et al. (2015) showed that variations in bryophyte richness at the stand level were explained first by climatic variables, and only secondarily by topographic (for total richness) or dendrometric (for forest SR) variables. Odor et al. (2006) showed that, at the tree level, geographic region had a stronger effect than local deadwood characteristics in explaining bryophyte SR. In other cases, local stand variables appeared to have stronger effects than regional ones.

Müller et al. (2019) and Chen et al. (2017) concluded that bryophyte SR was mostly explained by the availability of diverse suitable habitats. Odor et al. (2014) showed that landscape and historical variables had less influence than stand scale factors on epiphytic bryophytes.

Secondly, several studies showed interactive effects on bryophyte diversity, involving climatic variables, management abandonment and dendrometric variables. Müller et al. (2019) found interactive effects between management type (managed versus unmanaged) and stand structural variables when studying corticolous bryophyte SR in temperate beech forests. Hofmeister et al. (2015b) showed that only unmanaged forest stands with suitable substrates such as large dead woody debris and large diameter beech trees were able to support rare bryophyte species, thus suggesting an interaction between management abandonment and deadwood variables. Kropik et al. (2021) evidenced interactions between climatic and deadwood variables. Below a threshold of 900 mm of annual precipitation, increasing deadwood amounts did not result in any gain of obligate epixylic or liverwort species.

Furthermore, results may differ among bryophyte ecological groups, being higher for forest specialists (e.g. Müller et al., 2019 for management or Zellweger et al., 2015 for precipitation effect).

Further research is needed to expand study contexts to diverse forest types and to demonstrate whether or not general or contextual trends that could be useful for forest managers actually exist. Is it worth setting aside forest stands or retaining deadwood when the associated climatic or stand conditions are not appropriate, as illustrated by Kropik et al. (2021) and Langridge et al. (2023)?

In the present study, we focus on bryophytes and used data from a national-scale project dedicated to analyse the relative effects of forest management abandonment and of forest structure variables on biodiversity. Hereafter, the term “managed” refers to forest stands that are currently being managed with some form of harvesting, whereas the term “unmanaged” refers to stands inside strict forest reserves where harvesting operations were stopped at least 20 years ago. We aimed to answer two questions: 1. What are the respective roles of management abandonment, deadwood attributes and climatic variables in determining bryophyte SR in French temperate lowland forests? 2. Can their potential effects be additive and can they interact with one another?

We considered bryophyte SR at the stand level for two ecological groups: the forest specialist group and the overall species group. In line with the literature cited above and the community assembly theory (Keddy, 1992) – a general framework for understanding the organization and diversity of natural communities, which assumes that species co-occurrence patterns result from interacting ecological processes that filter the species depending on their traits, we put forward the following hypotheses.

**H1.** Overall and forest-specialist bryophyte SR should be significantly favoured by management abandonment (H1a) and best explained by variables related to deadwood variables (H1b).

**H2.** Both overall and forest-specialist SR should be positively related to relative humidity, precipitation and visible incident solar radiation; and negatively related to mean annual temperature and vapor pressure deficit.

**H3.** There are possible additive or interactive effects between management abandonment and deadwood variables.

**H4.** There are possible additive or interactive effects between management abandonment and macro- or microclimatic variables, or between deadwood and macro- or microclimatic variables.

The community assembly theory integrates several ecological processes, like environmental filtering theory (for climatic variables here), ecological niche and biotic interactions (for living trees or deadwood as suitable habitats) and disturbance (for forest management factor).

Each of these four hypotheses were tested statistically through
Bayesian extensions of generalized mixed linear models. In addition, we carried out a model comparison approach to identify the best combination of variables explaining overall and forest-specialist SR. Both approaches are presented in the method flowchart, Fig. 1.

## 2. Materials and methods

### 2.1. Study area

The study was carried out in France at ten lowland broadleaved temperate-forest sites (See Figure SM1 in App.1) dominated by either *Fagus sylvatica*, *Quercus petraea* or *Quercus robur*. The mean elevation of the sites ranged from 134 m to 693 m a.s.l. (mean elevation 282 ± SD 162 m a.s.l.). The mean annual rainfall over the last 38 years ranged from 646 to 988 mm and the mean annual temperature from 9.2 to 12 °C (mean temperature and precipitation data from the SAFRAN database, based on re-processed data (Durand et al., 1993; Vidal et al., 2010) over the 1980–2018 period). The choice of the sites was guided by the presence of a strict forest reserve with at least twenty years of management abandonment. The longest management abandonments in France are of about 150 years (Reserves in the Fontainebleau Forest). All other reserves are younger: the reserve network expanded mostly in the 1980’s by setting-aside stands that had been regularly harvested before. The duration since last harvest ranged from eight (and not 20, due to punctual harvesting operations for safety and sanitary reasons, see Paillet et al. (2015)) to 148 years for the unmanaged plots in our dataset (mean ± SE: 48 ± 5.12 years). For the managed plots, the duration from the last harvest ranged from 0 to 25 years (mean ± SE: 6 ± 0.63 years) – See Table SM1 in App.2.

### 2.2. Study design

The dataset comprised 63 managed and 64 unmanaged plots (see Table SM2 in App.2). At each site, the plots were randomly selected on a squared grid of 100 m × 100 m. Managed and unmanaged plots were paired to guarantee similar site conditions (elevation, soil type, topography). Stand age was not a selection criterion, however, because we wanted the plots to be representative of all possible successional stages for both the managed and unmanaged stands. Due to the limited surface area of the forest reserves (a smaller extent than the managed areas), the plots inside reserves generally tended to be closer to each other than the plots in managed areas.

The forest structure at each plot was described between 2008 and 2013 following Paillet et al. (2015)'s protocol – See Section SM1 in App. 1 for an overview of this protocol.

We also inventoried the tree-related microhabitats (TreMs) on each sampled living tree or standing dead tree or snag. The microhabitats taken into account were cavities, cracks, bark features, percentage of deadcrown, conks of fungi, cankers, sap or resin, bryophyte or ivy cover. See Vuidot et al. (2011) for details. We used these data to assess the total number of microhabitats per ha on standing living and dead trees.

Shrub layer (trees and shrubs from 2 to 6 m high) and herbaceous

layer (<2m) covers were visually evaluated in percentage.

The duration since last harvest was obtained through a survey completed by forest managers.

### 2.3. Bryophyte sampling

We used the sampling protocol published in Gosselin et al. (2021) summarized below. Between 2010 and 2016, we recorded the bryophytes on circular plots with a radius of 20m corrected for slope, centered on the same plot center as the stand structure plots. The surveys were carried out once per plot and the same year for all the plots at a given site.

First, we recorded the bryophytes on a given number of predefined substrate pieces, which included, when available:

(i) Random selection of five large living trees (DBH ≥30 cm), three small living trees (DBH <20 cm), five stumps (diameter ≥30 cm), five large lying deadwood pieces (median diameter ≥30 cm), five snags (DBH ≥30 cm and height >1.30 m);

(ii) Selection of five small lying woody debris (5 cm ≤ diameter <30 cm) along three transects at angles of 0, 120 and 240° from magnetic North. The first and the last piece encountered on each transect was recorded until we obtained five pieces.

(iii) Three circular subplots with a radius of 2 m, 10 m from the center of the plot along axes at angles of 0, 120 and 240° from magnetic North. All the ground-dwelling bryophytes growing inside the subplots were recorded.

(iv) One windthrow mound, consisting of soil retained between the roots of an uprooted tree.

Next, we performed a complementary survey on the whole 20-m-radius plot for 30 min. This allowed us to inventory additional, less frequent supports potentially overlooked in the systematic inventory, for example, boulders, rare tree species or parts of lying tree crowns. The complementary survey also made it possible to compare results at the plot scale, since the final census (systematic + complementary inventories) applied to the whole plot surface, regardless of the number and diversity of the supports present in the plot.

For each bryophyte individual, we noted the species and the cover class (defined as the percentage of its cover in comparison to the total surface of the item considered). The bryophytes were identified to species level following the nomenclature in the list established by Gargominy et al. (2015). It was not possible to identify *Orthotric*, *Ulofa*, *Zygodon*, *Schistidium* and *Weissia* at the species level when capsules or propagules were absent and in this case, they were recorded only at the genus level, e.g. *Ulofa* sp. They were, however, included in the calculations for SR when the genus was not encountered elsewhere in the plot.

The species were assigned to two categories: “forest specialist” or “other”, according to the forest species list established for Germany by Schmidt et al. (2013). Bryophytes were considered “forest specialist species” when the value of the D attribute in this list was M1.1 or M1.2.

The final bryophyte data set used in this article are the total SR per

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Method flowchart. Solid arrows = statistical inductive approach; dotted arrows = model comparison approach. The variables involved in univariate models are in italics. Those involved in multivariate additive or interactive models are in bold. Hypotheses number are those presented at the end of the introduction.</figcaption></figure>
plot and the forest-specialist SR per plot.

## 2.4. Environmental data

Microclimatic bioindicated variables were obtained by calculating the mean Ellenberg indicator value for edaphic humidity, light and temperature (Ellenberg et al., 1992) from the vascular plant survey recorded on a circular sub-plot with a radius of 18 m at the center of each plot. We did not weight the data according to the abundance of each plant species. Vascular plant surveys were conducted by two experienced botanists during a controlled census time of 35 min (± 5 min). We recorded each vascular plant species (herbaceous or ligneous) less than 2 m in height.

Macroclimatic variables were obtained by calculating mean values over the last 38 years (1980–2018) for re-analyzed data from the SAFRAN and ISBA Météo-France analytical platforms (Durand et al., 2017), provided on a grid of 8 km × 8 km. By allocating a plot to the nearest grid square, we obtained the mean daily relative humidity (HUM.Saf, in %) and temperature (TEM.Saf, in °C), the cumulative annual solid and liquid rainfall (PREC.Saf, in mm), and the cumulative annual radiation (RAD.Saf, in J/cm²) for each plot. As vapor pressure deficit (VPD.Saf, in Pascals) is defined as the saturated vapor pressure minus the actual vapor pressure (SVP - VP<sub>actual</sub>), and as VP<sub>actual</sub> = (HUM. Saf*SVP)/100, we obtained vapor pressure deficit by applying the formula:

$$VPD = ((100 - HUM.Saf) / 100) * SVP,$$

where HUM.Saf is the relative humidity (in %), SVP (Pascals) = 610.7*10<sup>7.5</sup>TEM.Saf/(237.3+TEM.Saf), and TEM.Saf is temperature (in °C) (Murray, 1967).

Landscape data were measured on maps using GIS (ESRI© Arc-MapTM 10.0). We included the surface area of the strict forest reserves within 500 m of the plot, the distance to the nearest reserve limit (in

meters) for the managed (outside) and unmanaged (inside) plots.

## 2.5. Statistical methods

### 2.5.1. Models

Analyses were performed with Bayesian models that were fitted with the R (version Rx64 3.1.0) software through Markov Chains Monte Carlo (MCMC) performed with the R2WinBUGS package, which provided convenient functions to call the WinBugs software, version 1.4 (Lunn et al., 2000) from R. It also included the GeoBUGS extension for spatial autocorrelation modelling and the WBdev interface to code user specific probability distributions.

First, to address hypotheses H1 and H2, we explained either total or forest specialist SR by using hierarchical parametric univariate models, whose structure we explain below, that included the following main explanatory single variables: (i) nothing (Null model, intercept only); (ii) management factor (managed versus unmanaged); and (iii) each separate stand, landscape, microclimatic and macroclimatic variables – See Table 1.

Then, to address hypotheses H3 and H4, we tested additive or interactive models combining the management factor effect, two deadwood-related variables (total deadwood volume and LVL deadwood volume), the bioindicated climatic variables and the macroclimatic variables – See Table 1.

Our response variables were count data, namely species richness (SR). In order to allow for potential under- or over-dispersion of the data, we used a user-specific probability distribution (Barbier et al., 2009; Gosselin, 2011; Zilliox and Gosselin, 2014), which allows the dispersion parameter to be < 1 or > 1. We used the log function as a link function.

In each case, we tested four model types to evaluate the link between the response and the explanatory variables: linear, quadratic, sigmoid and threshold model types. The linear model type was divided into two types: a fixed-slope linear model, where the explanatory variable had a

<caption>**Table 1**</caption>

Studied models. For each response variable (Y = overall bryophyte species richness or Y = forest bryophyte species richness), we first modeled the effects of each single explanatory variable, and then the additive or interactive effects of the management factor, the total deadwood volume or the volume of LVL (large and very large) deadwood, on the one hand; and each of the climatic explanatory variables (W<sub>i</sub>) on the other hand. For interactive models involving the management factor, the sign ":" means that the management effect was included in the linear combination only through an interaction with the ecological variable – but not as a single management effect in addition to this interaction. The effect of the ecological variables differed only between managed and unmanaged stands.

<table><thead><tr><th>Models</th><th>Explanatory variables</th><th>where X<sub>i</sub> can be replaced by:</th><th>where W<sub>i</sub> can be replaced by:</th></tr></thead><tbody><tr><td>Single models</td><td>Management factor<br>(MANaged versus<br>UNManaged)<br>Stand and landscape<br>variables (X<sub>i</sub>)<br>Microclimatic and<br>macroclimatic variables<br>(W<sub>i</sub>)</td><td>19 stand variables:<br>Total Deadwood Volume<br>Large Deadwood Volume (Diameter ≥47.5 cm)<br>Standing Deadwood Volume (Snags)<br>Stump Volume<br>Lying Deadwood Volume<br>Number of Deadwood Types<br>Ratio between deadwood volume and total volume</td><td>5 macroclimatic<br>variables (SAFRAN<br>Data):<br>Mean daily relative<br>humidity<br>Mean daily temperature<br>Cumulative annual<br>rainfall</td></tr><tr><td>Additive models (+)</td><td>Management + Total<br>Deadwood Volume<br>Management + LVL<br>Deadwood Volume<br>Management + W<sub>i</sub><br>Total Deadwood Volume +<br>W<sub>i</sub><br>LVL Deadwood Volume +<br>W<sub>i</sub></td><td>Number of Microhabitats on Standing Deadwood<br>Number of Tree-related Microhabitats on Living Trees<br>Total Basal Area of living trees<br>Mean quadratic Diameter of living trees<br>Basal Area of Fagus trees + Basal Area of Quercus trees + Basal Area of Carpinus trees + Basal Area of other broadleaved trees<br>Tree Species Richness<br>Basal Area of Small trees (7.5 ≤ DBH &lt;22.5 cm) + Basal Area of Medium trees (22.5 ≤ DBH &lt;47.5 cm) + Basal Area of LVL trees (DBH ≥47.5 cm)</td><td>Cumulative annual visible<br>radiation<br>Vapor Pressure Deficit<br>3 microclimatic<br>bioindicated variables:<br>Ellenberg's edaphic<br>humidity<br>Ellenberg's temperature<br>Ellenberg's light</td></tr><tr><td>Interactive models<br>The sign * applies for interactions between numeric variables; the sign: applies for interactions involving qualitative factors.</td><td>Management: Total<br>Deadwood Volume<br>Management: LVL<br>Deadwood Volume<br>Management: W<sub>i</sub><br>Total Deadwood Volume *<br>W<sub>i</sub><br>LVL Deadwood Volume * W<sub>i</sub></td><td>Basal Area of Very Large trees (DBH ≥67.5 cm)<br>Basal Area of LVL trees (DBH ≥47.5 cm)<br>Duration since last harvest<br>Shrub layer Cover<br>Herbaceous layer Cover<br>3 landscape variables:<br>Distance to the nearest reserve (managed plots)<br>Distance to the reserve edge (unmanaged plots)<br>Surface of strict forest reserves within 500 m around the plot</td><td></td></tr></tbody></table>
single slope parameter; and a random-slope linear model, where the slope parameter varied randomly among forest sites – See Fig. 2.

Hereafter, the term “studied model” refers to the single, additive or interactive variable combinations presented in Table 1 and Fig. 1, whereas “model type” refers to the form of the relationship the model was built on, as presented in Fig. 2. For each studied model, we fitted two to six model types. Indeed, we did not modelize the quadratic model type for the studied models that included an interaction between a quantitative variable and the management factor variable, and we studied only the two linear models (fixed- and random-slope) for the “management” model, where the only explanatory variable was a factor.

Bayesian models allowed us to correctly (Saas and Gosselin, 2014) take into account the spatial autocorrelation among plots and the potential under- or over-dispersion of our count data (See Section SM2 in App.1 and Table SM3 in App.2). All the model types were hierarchical and thus included a “plot” spatially correlated random effect to take into account the spatial autocorrelation between plots. In addition, all the models included the forest site as a fixed effect.

### 2.5.2. Model selection

In order to identify the best model (coupling a studied model and a model type) for each response variable, we compared their DICm (marginalized deviance information criterion) values. The DICm does not indicate if the model fits the data well, but rather aims at identifying the model whose predictive capacity is the best. Following Millar (2009), we chose DICm rather than DIC because our models were hierarchical, with one random effect per plot. The smaller the DICm, the better the model.

## 2.6. Interpreting model results: analyzing the magnitude of the effect

For each response variable and each studied model, we analyzed not only the statistical significance but also the magnitude of the effect following Barbier et al. (2009). We simulated a change in the explanatory variable (either a change in level for the management factor or an increase of one standard deviation for numeric variables – See Table SM4 in App 2) and studied the magnitude of the consecutive change in the estimated mean of the response variable. Hereafter, what

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Different types of curves showing a relationship between the response variable and the explanatory variable. In all the studied models, our response variables were the logarithm of mean species richness (either for total or forest-specialist species richness). In the linear model type, the effect of the explanatory variable is the slope of the line, which remains constant over the whole gradient of the explanatory variable. a) The linear fixed-slope model gives one global effect of the variable, regardless of forest site. b) In the linear random-slope model, the slope may vary depending on the forest site. In the non-linear model types (c, d, e), the effect of a given variation of the explanatory variable depends on the place of the starting point over the gradient: in the quadratic type (c) for example, the effect may be positive and strong at the beginning of the gradient, then null in the middle of the curve and negative at the end. Note that our explanatory data may not cover the whole gradient, and in this case, the fitted model would cover only a part of the curves.</figcaption></figure>
we call the “magnitude multiplicative factor” is the ratio R between the final and the initial value of the response variable. See Section SM3 in App.1 for more details. See Table 2 for the notations used, or Table SM5, App. 2.

# 3. Results

## 3.1. Descriptive statistics

For the 127 study plots, we recorded 131 bryophyte species, comprising 109 bryophytes *sensu stricto* and 22 liverworts (these figures do not take into account some *Orthotric*, *Uloata*, *Zygodon*, *Schistidium* and *Weissia* species that could be determined only to the genus level). Fifty-one species were classified forest-specialist species (36 mosses and 15 liverworts) – see Table 3.

## 3.2. Statistical model results

### 3.2.1. Effect of management abandonment (H1a)

Contrary to our expectations, management abandonment, taken as a single categorial variable, yielded no significant effect on bryophyte richness.

For total SR, the relationship with the management factor varied among sites (linear random model type), the global mean effect of management abandonment was non-significant and the magnitude multiplicative factor was weak and inconsistent (See Table 4). The management abandonment effect was significantly positive for only one forest site (FBL) and was non-significant for the other sites (Table SM7 in App.2).

For forest-specialist SR, the effect of management abandonment was also best modeled by the linear random model type. The global mean effect was non-significant (Table 4) but there were significant positive effects for two forest sites (FBL, HT, Table SM7 in App.2). The median value of the magnitude multiplicative factor, when shifting from managed to unmanaged plots, was 1.20 [0.97; 1.46]. This meant that forest-specialist SR in unmanaged plots was between – 3% and +46% compared to managed ones: the effect was not systematically positive.

### 3.2.2. Best single-variable models (H1b)

Among all the stand and landscape variables taken as single explanatory variables, the ones with the best explanatory power were LVL deadwood volume for forest-specialist SR and total deadwood

<table><caption>**Table 2**
Notations for the magnitude of the effect.</caption><thead><tr><th>For a simulated standard variation ΔX of the explanatory variable, the variation ΔY in the mean of the response variable:</th><th>Notation</th><th>Meaning</th></tr></thead><tbody><tr><td colspan="3">Is strictly positive (resp. negative) in more than 95% of the cases and:</td></tr><tr><td>• increases (resp. decreases) by more than 20% in more than 95% of the cases.</td><td>++ (resp. - -)</td><td>very strong effects</td></tr><tr><td>• increases (resp. decreases) by more than 10% in more than 95% of the cases.</td><td>+ (resp. -)</td><td>strong effects</td></tr><tr><td>• increases (resp. decreases) by less than 10% in more than 95% of the cases.</td><td>W+ (resp. W -)</td><td>weak effects</td></tr><tr><td colspan="3">Is in none of the previous cases and is strictly positive (resp. negative) in less than 95% of the cases and:</td></tr><tr><td>• changes by less than 20% in absolute value in more than 95% of the cases.</td><td>0</td><td>weak and inconsistent effect</td></tr><tr><td>• changes by less than 10% in absolute value in more than 95% of the cases.</td><td>00</td><td>very weak and inconsistent effect</td></tr><tr><td>• changes by more than 20% in absolute value in more than 5% of the cases (The effects are neither systematically positive nor systematically negative, but may be strong in more than 5% of the cases).</td><td>nc</td><td>No possible conclusion concerning magnitude</td></tr></tbody></table>

<caption>**Table 3**</caption>

Total number of species (top) and forest-specialist species (bottom) identified at the species level inventoried on the managed and unmanaged plots. Liverwort and moss species are listed separately.

<table><thead><tr><th></th><th>Full data set</th><th>Managed plots</th><th>Unmanaged plots</th></tr></thead><tbody><tr><td>All species</td><td>131</td><td>106</td><td>113</td></tr><tr><td>liverworts</td><td>22</td><td>16</td><td>20</td></tr><tr><td>mosses</td><td>109</td><td>90</td><td>93</td></tr><tr><td>Forest specialist species</td><td>51</td><td>39</td><td>46</td></tr><tr><td>forest liverworts</td><td>15</td><td>11</td><td>13</td></tr><tr><td>forest mosses</td><td>36</td><td>28</td><td>33</td></tr></tbody></table>

At the plot scale, the total SR ranged from 8 to 38 species (mean = 21.3; sd = 6.2), and forest-specialist SR ranged from 1 to 16 species (mean = 7.1, sd = 3) – See Table SM6 in App.2 for the distinction between managed and unmanaged plots.

volume for total SR (Table 4). This confirmed hypothesis H1: overall and forest bryophyte species richness was best explained by deadwood related variables when considering only single-variable models.

Total deadwood amount best explained total bryophyte SR, with a weak positive sigmoid effect. Around an inflexion point value corresponding to an unscaled deadwood volume of 14.8 [-80.55; 188.37] m³ ha⁻¹, SR increased by 9–91% (and in half of the cases by more than 21%) when simulating a variation of one standard deviation of the deadwood volume (Table 4). The evaluation of the inflexion point value was very imprecise; the magnitude of the effect when the threshold was crossed was weak (almost strong).

LVL deadwood volume best explained forest-specialist SR, with a very strong positive threshold effect: when crossing an estimated threshold scaled value corresponding to a volume of LVL deadwood pieces of 22.3 [10.13; 25.6] m³ ha⁻¹, the forest-specialist SR increased by 49–97% (Table 4 and Fig. 3). The evaluation of the threshold value was rather imprecise, but the magnitude of the effect when the threshold was crossed was clearly very strong.

### 3.3. Effects of macroclimatic variables in single-variable models (H2 – see Table SM8 in App.2)

Surprisingly, and contrary to hypothesis H2, relative humidity showed a strongly negative quadratic effect on both forest-specialist species and overall SR.

Rainfall amount had a very strong positive quadratic effect on forest-specialist SR, as expected, but no significant or conclusive effect on total richness.

As expected, we observed negative effects of an increase in the mean daily temperature, both on overall and forest-specialist SR. The effect was very strong for forest-specialist richness (which fell by 33–65%, and by more than 51 % in half of the cases, when the threshold – a scaled value corresponding to a daily temperature of 20.01 °C [19.05; 20.97] – was crossed). The effect was weak for overall SR (average decrease of 43 %, but less than 10% in more than 5% of the cases).

We found no significant effect of the solar radiation and the Vapor Pressure Deficit, taken as single variables.

To sum up, hypothesis H2 was confirmed only for the negative effect of an increase in mean daily temperature and, for forest-specialist richness, for the positive effect of increasing rainfall. Contrary to our expectations, we found negative effects for relative humidity, and no effect of solar radiation or VPD.

All of these univariate models ranked far below the best models (presented in Table 5) in terms of predictive power, with deviations between 24 and 37 units from the best model’s DICm for overall SR, and between 32 and 52 for forest-specialist SR (See Table SM8 in App.2).

**Microclimatic bioindicated variables** in univariate models had generally non-significant or, if significant, weak or inconclusive effects. These models were far from best in terms of DICm (Table SM8, App.2).
<caption>**Table 4**</caption>

Results for the single management factor model (management abandonment effect) and the best single-variable models (grey cells). For each studied model, the model type presented here is the best one according to the marginalized DIC value. Explanatory variables: LVLDW.vol = large and very large deadwood volume; DW.vol = total deadwood volume; Management = management factor. Model types: lr = linear random effects; s = sigmoid; t = threshold. Significance: *** = significant effect (p < 0.005), ** = significant effect (p < 0.01), * = significant (p < 0.05), ns: non-significant effect. Magnitude: see Table 2. Magnitude multiplicative factor: median value of the multiplicative factor from the initial value to the final value of the mean of the response variable when the explanatory variable is increased by one standard deviation of the data (in case of quantitative variables) or switches from “managed” to “unmanaged” in the case of the Management variable. The values in square brackets are those of the 95 % credibility interval. See Table SM7 in App.2 for more details (estimates of the parameters).

<table><thead><tr><th>Response variable</th><th>Studied model</th><th>Model type</th><th>DICm</th><th>Explanatory variable</th><th>Significance</th><th>magnitude</th><th>Magnitude multiplicative factor</th></tr></thead><tbody><tr><td>SR.for</td><td>Management</td><td>lr</td><td>558.3</td><td>Management</td><td>ns</td><td>nc</td><td>1.20 [0.97; 1.46]</td></tr><tr><td>SR.for</td><td>LVLDW.VOL</td><td>t</td><td><strong>529.19</strong></td><td>LVLDW.VOL (scaled)</td><td>***</td><td>++</td><td>1.71 [1.49; 1.97]</td></tr><tr><td>SR.tot</td><td>Management</td><td>lr</td><td>752.25</td><td>Management</td><td>ns</td><td>nc</td><td>1.09 [0.95; 1.22]</td></tr><tr><td>SR.tot</td><td>DW.VOL</td><td>s</td><td><strong>734.89</strong></td><td>DW.VOL (scaled)</td><td>***</td><td>W+</td><td>1.21 [1.09; 1.91]</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Graphical relationship between the observed species richness corrected for the forest-site fixed effect as assessed in the models (circles) or the modeled mean species richness (Solid line = median value of the modelized response variable. Dashed lines = lower and upper values of the 95% credibility interval around the median estimated value) and the best variables explaining total bryophyte species richness (SR.tot, sigmoid model, left part), and forest-specialist bryophyte species richness (SR.for, threshold model, right part) in univariate models.</figcaption></figure>

### 3.3.1. Additive or interactive effects between management abandonment and deadwood variables (H3 – see Table SM9 in App.2)

Adding the management factor to deadwood variables did not improve the models, since this led to a higher DICm than for the single-variable models. Hypothesis H3 was therefore not confirmed. All the interactive models showed that deadwood variables had significant – and often strongly – positive effects on bryophyte richness, and that the effects were generally stronger in managed than in unmanaged stands. For example, LVL deadwood volume had a very strong positive effect on forest-specialist SR in managed plots, and a weaker effect, though still

strong, in unmanaged plots. This interactive model (DICm = 535) was among the best models for forest-specialist SR but it was not better than the single-variable “LVL deadwood” model (DICm = 529).

Similarly, although the interactive “Management: total deadwood volume” model was among the best models for total richness (DICm = 735.33), it was not better than the single-variable “total deadwood volume” model (DICm = 734.89).

These results meant that bryophyte species richness was explained first and foremost by deadwood amounts, rather than by management abandonment.

<caption>**Table 5**</caption>

The best models explaining forest-specialist bryophyte species richness (SR.for) and total bryophyte species richness (SR.tot), according to the marginalized DIC value. Explanatory variables: LVLDW.vol = large and very large deadwood volume, DW.vol = total deadwood volume, TEM.Saf: mean daily temperature (SAFRAN Data), RAD.Saf: cumulative annual visible radiation (SAFRAN Data). Model types: s = sigmoid; q = quadratic. Significance: *** = significant effect (p < 0.005), ** = significant effect (p < 0.01), * = significant (p < 0.05), ns: non-significant effect. Magnitude: see Table 2 for the notations. Magnitude multiplicative factor: median value of the multiplicative factor between the final and initial values of the response variable, when the explanatory variable is increased by one standard deviation for the data. The values in square brackets are those of the 95 % credibility interval.

<table><thead><tr><th>Response variable</th><th>Studied model</th><th>Model type</th><th>DICm</th><th>Explanatory variable</th><th>Significance</th><th>magnitude</th><th>Magnitude multiplicative factor</th></tr></thead><tbody><tr><td rowspan="4">SR.for</td><td rowspan="4">LVLDW.vol: RAD. Saf</td><td rowspan="2">s</td><td rowspan="2">524.48</td><td>LVLDW.vol</td><td>***</td><td>++</td><td>5.67 [2.88; 25.3]</td></tr><tr><td>RAD.Saf</td><td>ns</td><td>nc</td><td>0.994 [0.0159; 30.4]</td></tr><tr><td rowspan="2">q</td><td>LVLDW.vol*RAD.Saf</td><td>***</td><td>+</td><td>1.42 [1.18; 3.13]</td></tr><tr><td>DW.vol</td><td>***</td><td>W+/W+/W+</td><td>1.17 [1.06; 1.28]/1.15 [1.07; 1.23]/1.13 [1.07; 1.19]</td></tr><tr><td>SR.tot</td><td>DW.vol + TEM.Saf</td><td>q</td><td>733.72</td><td>TEM.Saf</td><td>ns</td><td>nc/nc/-</td><td>1.28 [0.574; 2.9]/0.826 [0.513; 1.35]/0.535 [0.306; 0.94]</td></tr></tbody></table>
### 3.3.2. Additive or interactive effects between management abandonment and climatic variables (H4 – first part; Table SM10 in App.2)

We observed significant effects of several climatic variables (temperature, reltive humidity, rainfall) in addition to the management variable. This could render management effects significant and sometimes strong for forest species.

Some climatic variables (radiation, VPD, rainfall, Ellenberg's light and temperature indices) significantly interacted with the management factor, but the estimations for these parameters were very imprecise and did not lead to any conclusion concerning the magnitude of the effect.

Furthermore, additive or interactive models were not among the best models, maybe because these models lacked deadwood variables, which appeared as key variables for bryophyte diversity (cf. 3.2.4. above).

### 3.3.3. Additive or interactive effects between deadwood and climatic variables (H4 – second part) (Table SM11 in App.2)

We observed some significant, strong additive effects between deadwood and mean daily temperature. Below are the results for the models involving total deadwood volume (which had similar trends but better predicting capacity in terms of DICm than those involving LVL deadwood volume).

- When added to the positive effect of total deadwood volume on bryophyte SR, increasing mean daily temperature had a negative quadratic effect on total richness and a very strongly negative linear effect on forest-specialist SR.

- In both cases, this additive model had slightly better predictive capacity than the single “Total deadwood volume” model and was far better than the univariate “Temperature” model (See the DICm in Table SM12, App. 2). This means that bryophyte SR was first and foremost explained by total deadwood volume, and that once this effect had been taken into account, daily temperature had a secondary additional negative quadratic effect.

- For total SR, this additive model was the best among all models tested (cf. Table 5; Table SM12 in App.2).

Our analyses also revealed that the positive relationship between LVL deadwood volume and bryophytic richness was reinforced (positive interactive effects) when mean daily relative humidity or cumulative annual rainfall increased (for both forest-specialist and total SR) or when cumulative annual visible radiation increased (only for forest-specialist richness – and this was the best model in this case, see below).

Among the bioindicated microclimatic variables, only the Ellenberg's humidity value had a weak positive effect on forest-specialist SR, when added to the effect of deadwood volume. This model was among the best for forest specialists.

### 3.3.4. Best models (Table 5)

For each response variable, Table 5 presents the best model among all the models tested, according to their DICm (Table SM12 in App.2).

Total bryophyte SR was best explained by adding the quadratic effects of deadwood volume and mean daily temperature. Mean daily temperature had a strong negative effect at the third quartile of its gradient (namely values over 10.8 °C), although the global effect was statistically non-significant (Table 5). In addition, deadwood volume had a very significant weak positive effect all along the gradient of its values (with an increase of 6–28 % in SR for a simulated increase in deadwood volume of one standard deviation: 58 m³ ha⁻¹). This means that temperatures over 10.8 °C would have a negative effect in addition to the weak positive effect of total deadwood volume on total bryophyte SR.

Forest-specialist SR was best explained by a sigmoid model including an interaction between LVL deadwood volume and solar radiation. The single variable “solar radiation” had no conclusive effect, LVL deadwood volume had a very strong positive effect, and their interaction also had a strong positive effect. This means that the positive effect of LVL

deadwood volume on forest-specialist richness was reinforced when solar radiation increased.

## 4. Discussion

The effects of forest management abandonment on bryophytes remain relatively poorly studied in temperate lowland forests. Here, we compared paired set-aside and managed stands at ten sites (127 plots) located in lowland forests in France. By comparing several linear and non-linear model types and taking into account spatial autocorrelation, we showed that.

- Management abandonment did not significantly improve bryophyte SR, whereas deadwood related variables did (they appeared in the best univariate models and were involved in the majority of the top-ranked models, ΔDICm ≤7).

- The best models revealed combined effects of both deadwood and climatic variables (including a negative effect of mean daily temperature added to the positive effect of total deadwood amount on total SR; a strong positive interaction between LVL deadwood volume and incident visible radiation for forest-specialist SR).

- The effects were stronger on forest-specialist species than when considering all bryophyte species.

- The top-ranked models often yielded non-linear relationships, in particular in the case of interaction or additive models. This opens perspectives that may help to disentangle the relative effects of climate, deadwood variables and management type, since most previous studies mainly tested linear relationships, which may not be appropriate.

### 4.1. Management abandonment and deadwood effects

Firstly, and surprisingly, we found no significant overall effect of management abandonment on bryophyte SR, whatever the group (total bryophytes or forest-specialist species). The single management abandonment models were far from best, although the linear fixed-slope model showed a clearly positive, albeit weak, relationship (increase of 4–21%) with overall SR.

These results do not confirm those of a first European meta-analysis (Paillet et al., 2010a, 2010b), but they do agree with recent studies by Müller et al. (2019) and Czerepko et al. (2021).

Secondly, we observed strong variations in the management abandonment effect among sites (the best univariate models were the linear random ones, Table 4 and Table SM7 – meaning that the effect of management abandonment varied significantly among sites). These variations may be explained by time since last harvest (values between 8 and 148 years, depending on the site – Table SM2 in App.2). Two results support this idea. Firstly, the only site where management abandonment yielded significant, strong positive effects was in the Fontainebleau Forest (FBL), which contained the oldest reserves (117–148 years since last harvest). Secondly, time since last harvest had a highly significant and rather strong linear effect when taken as a single explanatory variable on the whole dataset (increase of 5–15% in overall SR, increase of 10–26% in forest-specialist SR when duration was increased by one standard deviation). This may be linked to the fact that differences in deadwood features between managed and unmanaged plots increase with the duration of protection, as shown by Paillet et al. (2015). However, the Haut-Tuileau forest (HT) also showed significant positive results although the time since last harvest was only between 20 and 35 years.

Thirdly, we also observed significant positive effects of management abandonment once the effects of certain climatic variables (daily mean temperature and relative humidity, annual rainfall, Ellenberg's bioindicated edaphic humidity) had been taken into account (Tables SM10 and SM13 in App.2). However, these models were far from best (ΔDICm
>7 in all cases).

For the second part of H1, our results confirm our hypothesis. Forest-specialist SR was best explained by the volume of LVL pieces of dead-wood (threshold model, very strong positive effect). Overall SR best responded to total deadwood volume (sigmoid model, weak positive effect). The positive effect of deadwood features supports previous findings in temperate or boreal forests (Vellak and Paal, 1999; Rambo, 2001; Dittrich et al., 2014; Hofmeister et al., 2015a; Müller et al., 2019; Czerepko et al., 2021), which differ from results in other contexts, namely, no effect in Mediterranean forests (Brunialti et al., 2010).

For forest specialists, we observed a very strong threshold effect with LVL deadwood (Table 4, Fig. 3, threshold value (22.3 [10.13; 25.6] m³ ha⁻¹, multiplicative factor 1.71 [1.,49; 1.97]), meaning that if we pass the threshold value in a forest stand, the gain in forest-specialist species would be 71 % on average. In our dataset, the mean stock of LVL deadwood was 2.7 ± SD 6.7 m³ ha⁻¹ in managed plots (about 8 times less than the threshold value) and 35.2 ± SD 61 m³ ha⁻¹ in unmanaged plots. More generally, the stock of mid-large (diameter >32.5 cm) deadwood pieces in managed French forests is less than 5 m³ ha⁻¹ (Ministère de l'Agriculture et de la Pêche, 2016; Piton, 2021). Since the maximal value of our estimated threshold is 25.6 m³ ha⁻¹, ensuring levels above the threshold would require a more than five-fold increase in the average LVL deadwood stock, which seems unachievable in managed forests, but could occur in unmanaged forests (Paillet et al., 2015). Focusing on epixylic species only, Kropik et al. (2021) also found a threshold effect for total deadwood amounts (not only LVL deadwood), with the strong effects of increasing deadwood values persisting until a very high threshold value of 60 m³ ha⁻¹, then becoming weaker above that amount.

For overall SR, the scaled value of the median inflexion point was negative (-14.8 [-80.55; 188.37] m³ ha⁻¹), like Godeau et al. (2020) in their study on saproxyic beetles, which means that our dataset corresponded to the final part of the sigmoid curve and the estimator was very imprecise. As a result, we could not correctly assess the effort required to reach it.

Concerning our third hypothesis, our results showed that deadwood was, and irreducibly remained, crucial for forest bryophyte richness both in managed and in unmanaged stands (Table SM9 in App. 2). Once the deadwood variables had been taken into account, the management factor yielded no significant additional effect. Interactive models showed positive relationships between deadwood variables and bryophyte richness both in managed and in unmanaged stands. In line with Odor and Standovar (2001), our study shows that conservation requires sufficient amounts of deadwood (especially large pieces) in the matrix of managed stands as well as in strict forest reserves. These reserves, in fact, increase deadwood amounts at the landscape scale thanks to their set-aside status and are even more important since sufficient deadwood amounts seem difficult to reach in managed stands, especially for large and very large deadwood pieces.

Note that not only LVL deadwood, but also total deadwood volume, had strong positive linear effects on forest-specialist richness in managed and in unmanaged stands (magnitude multiplicative factors of 1.48 [1.2; 1.8] and 1.2 [1.13; 1.28], respectively). This suggests that not only large deadwood pieces favour forest bryophyte richness; so do all the other types of deadwood that constitute the essential part of the deadwood volume (Paillet et al., 2015). This supports the results of Müller et al. (2019), who concluded that the amount of available substrates appeared to be the key factor explaining the richness of four ecological groups (epixylic, corticolous, terricolous and saxicolous bryophytes), regardless of management type. This also supports the results of Dittrich et al. (2014), who observed that deadwood, and especially large-diameter deadwood, hosted a large proportion (84%) of the bryophyte assemblages in unmanaged forests. In our case, deadwood hosted 56 % of the bryophyte assemblages over the whole dataset, and 58% in the unmanaged plots.

Other deadwood variables involved in the top-ranked models

(Table SM14 in App.2) can help elucidate what is behind this deadwood volume effect. The number of deadwood types had a strong positive threshold effect on overall SR. For forest-specialist and for overall SR, the additive model of snags, stumps and lying deadwood volume revealed a strong quadratic effect for lying deadwood volume, whereas snags and stumps had negligible effects. This suggests that the diversity of deadwood pieces and the volume of lying deadwood play an important role in the total-deadwood-volume effect.

## 4.2. Other forest structure variables involved in the top-ranked models

For forest-specialist SR, the top-ranked models involved no other dendrometric variables than the two main deadwood variables (LVL deadwood volume, total deadwood volume).

For total bryophyte SR, basal area and mean quadratic diameter had weak positive quadratic effects and were included in the top-ranked models (see Table SM14 in App.2). This is in line with Gosselin et al. (2017) for the quadratic effect of basal area.

Contrary to Czerepko et al. (2021), Marialigeti et al. (2009) and Király et al. (2013), we did not find any significant effect of tree-layer diversity, shrub cover or herbaceous cover. Surprisingly, time since last harvest did not appear among the best models, although it had a significant positive effect on its own.

## 4.3. Effects of climatic variables and their interaction with deadwood or management variables

The effects of the climatic variables we studied were surprising. When taken alone, the macroclimatic variables yielded either non-significant effects (solar radiation, VPD) or, inversely, strong effects (rainfall, temperature, relative humidity), whose models remained, however, far from best in terms of DICm. Yet, temperature was involved in the best model explaining total SR, as was solar radiation for forest-specialist SR. It seems that the macroclimatic variables acted only as secondary or contextualising factors, by reinforcing the positive effects of deadwood amount in the case of solar radiation (positive interaction), or by counteracting them in the case of temperature (the positive effect of deadwood minus the negative effect of mean daily temperature).

### 4.3.1. Temperature

The negative – as expected – effect of mean daily temperature was strong for the forest specialist species and weak for overall SR. These results confirm those of Spitale (2016) and Zellweger et al. (2015) for mean summer temperature. This negative effect, when added to the positive effect of deadwood volume, best explained total bryophyte SR in our study. Interestingly, for saproxylic beetle SR and using linear (not sigmoidal) models, Müller et al. (2015) did not observe additive effects but a negative interaction between temperature and deadwood: taken alone, temperature and deadwood each had positive effects, but when the temperature decreased, the positive effects of deadwood increased, suggesting a compensation mechanism. Kropik et al. (2021), who focused on epixylic bryophytes, found that deadwood variables and precipitation, but not temperature, were the best predictors of the total richness (either mosses only or mosses and liverworts). For liverworts alone, the best model also included the temperature variable (negative effect) in addition to the precipitation and deadwood variables (positive effects). In our case, we studied the whole bryophyte community and not only the epixylic species and the deadwood effect remained the same, regardless of temperature (no interaction). Temperature only partially counteracted the positive deadwood effect, similarly to Kropik et al. (2021)'s additive model for liverworts. In a boreal context, Barbé et al. (2020) found no negative effect of temperature, contrary to our results, even though they also considered the whole bryophyte community (terricolous, epiphytic and epixylic species). All these results point toward a highly contextualised response of bryophytes to deadwood.
### 4.3.2. Solar radiation

Contrary to Zellweger et al. (2015) who found negative effects for mean summer solar radiation, we found no significant effect of solar radiation on bryophyte SR in our univariate model. Furthermore, the best model for forest-specialist richness richness evidenced a positive effect of solar radiation, interacting with LVL deadwood volume.

### 4.3.3. Rainfall

In their study of epixylic bryophytes, Kropik et al. (2021) found interacting threshold effects between deadwood and rainfall, suggesting that insufficient rainfall could counteract the positive deadwood effect, as in our best model for overall SR. Other studies observed significant effects of mean annual rainfall on species composition (e.g. Calleja et al. (2016)) but did not study the effect on the SR. The effects of rainfall reported in the literature are various: sometimes surprisingly negative (Spitale, 2016) or hump-shaped (Zellweger et al., 2015), sometimes positive (Medina et al., 2014), and sometimes absent (Medina et al., 2018)). These results may depend on the season considered, the average level of rainfall or be influenced by other climatic variables like winter temperatures (Medina et al., 2014). We found a strong quadratic effect of annual rainfall amount on forest-specialist SR (although not among the best models). It supports the findings of Kropik et al. (2021) (hump-shaped effect of rainfall amount), and Zellweger et al. (2015), who found a hump-shaped relation between rainfall (daily mean over the whole year in our case, summer mean in their case) and forest SR.

### 4.3.4. Relative humidity

Surprisingly, considering the poikilohydric character of bryophytes, the relationship between relative humidity and forest-specialist, as well as total SR, was strongly negative and opposite to rainfall. Rainfall and relative humidity were not correlated (Figure SM2 in App1). For forest-specialist SR, increasing the relative humidity counteracted the positive effect of setting aside the stand. However, the predictive capacity of these models was not good (34 DICm units from the best model), and the relative humidity effect was quadratic, suggesting that the hypothesized positive effect may occur only for low relative humidity values. In our dataset, the values were rather high (mean 81%, min 75%, max 83%), which would place our data in the last, decreasing part of the quadratic curve. Our results showing a strong negative quadratic effect of atmospheric humidity contradict Király et al. (2013)'s findings showing a correlation between atmospheric humidity and bryophyte richness, as well as recent experimental results showing a slight effect of relative humidity on terricolous bryophytes (Ingerpuu et al., 2019).

## 4.4. Focus on forest specialist species

For all the studied models, the relationships observed between bryophyte richness and ecological, management, climatic or landscape variables were stronger for the subgroup of forest specialist species. This suggests that specialist species are particularly sensitive to variations in the forest environment, which is consistent with other studies where total bryophyte SR did not respond the same way as forest-specialist SR (Müller et al., 2019) or SR for other guilds (Barbé et al., 2020).

Under the hypothesis that forest specialist species may contain epixylic species, and that deadwood may be an explanatory factor for bryophyte forest-specialist richness (Rambo, 2001; Dittrich et al., 2014; Hofmeister et al., 2015a; Czerepko et al., 2021), our results are consistent with those of Müller et al. (2019), who showed that epixylic bryophyte species in Central European forests were more numerous with high amounts of deadwood.

## 4.5. Limitations and perspectives

Our results apply only to oak and beech lowland forests of northern France Hexagone.

Our results apply only to SR. However, it would be worth using our

dataset to further analyse abundance or composition, first because climatic variables probably influence abundance more than SR, and because bryophyte ecological groups may have contrasted responses, as shown in Müller et al. (2019) or Barbé et al. (2020).

Another limitation of our study is that our threshold and sigmoid models did not vary by site, whereas this could be the case (see Godeau et al. (2020) for sigmoid models). Indeed, improving estimator precision, as we discussed in the present study for total deadwood volume, is key to helping forest managers to decide target conservation objectives. In terms of statistical methodology, it would be interesting to confirm the model comparison results we obtained through marginalized DIC with results obtained through marginalized WAIC, looIC or cross-validation (Godeau et al., 2020).

Lastly, our dataset suffers from the relative recentness of the strict forest reserves in France, as well as from our non-experimental design. For obvious practical reasons, we opted for a synchronic observational design guided by existing forest reserves. Even though we ensured that the site conditions were similar between the managed and unmanaged stands, we did not have full control over the previous management history of the stands.

# 5. Conclusion

In line with previous works studying the effects of one or of two interacting factors, our study disentangled for the first time for bryophyte diversity the relative roles of three main drivers together, namely management, stand structure and climatic variables.

From an academic point of view, comparing several models and analysing the magnitude of the effects enabled us to show that bryophyte diversity depended first and foremost on deadwood volume, and especially large deadwood pieces for forest specialist species, and that macroclimatic variables were prone to modulate the positive effects of deadwood amounts.

Our study provides new results by revealing links between deadwood amounts and bryophyte assemblages that are broader than epixylic species alone. Numerous previous studies showed that large-diameter deadwood pieces host richer epixylic assemblages than do small-diameter pieces (see Odor et al. (2006) and the references therein), but fewer studies established a link between large-deadwood volume and broad (i.e. not restricted to epixylic species) bryophyte assemblages.

Our results further enlarge the list of publications pointing to the context-dependency of relationships between stand compositional or structural variables and biodiversity (e.g. Zilliox and Gosselin, 2014; Müller et al., 2015; Godeau et al., 2020; Langridge et al., 2023). They call for paying attention to such patterns in future studies.

From a forest management and applicability point of view, we showed that the drivers of bryophyte richness can be influenced by forest management techniques. Although management abandonment yielded no significant overall effect in our dataset, the following points suggest that a network of set-aside forests is required complementary to deadwood retention. On the one hand, creating strict forest reserves is not the main driver for bryophyte diversity; its impact appears to vary highly among sites, with positive effects increasing over time. On the other hand, deadwood variables are the best drivers of bryophyte richness, both in managed and in unmanaged stands – with higher effects in managed stands. Even though climatic conditions like mean daily temperature or annual precipitation may counteract or, inversely, enhance the beneficial effects of deadwood variables, maintaining high amounts of deadwood is an efficient way to enhance both total and forest-specialist bryophyte diversity. We also found threshold effects that could be efficient tools for forest managers. Although we were not able to estimate the threshold values precisely, our results suggest a threshold value of (maximum) $25.6 \text{ m}^3 \text{ ha}^{-1}$ for large-diameter deadwood pieces. However, this level may be unachievable in managed forests due to technical difficulties and associated economic costs. That is why setting aside forest stands remains an essential strategy for forest-specialist
species conservation since these values are more likely observed in un-managed forests (a result similar to e.g. Bouvet et al. (2016) for birds and bats). Bryophyte diversity conservation needs several complementary measures: a network of strict reserves to achieve sufficient amounts of deadwood for a strong increase in bryophyte richness on the one hand, and intermediate measures integrated into the managed forest stands on the other hand to enhance bryophyte richness in managed forests, albeit at weaker levels. Integrated measures such as deliberate retention of dead and living trees, unharvested patches or deadwood pieces may be interesting solutions and, moreover, they are broadly applicable and sustainable for forest managers.

We also observed that the diversity of deadwood types and other manageable variables like total basal area and mean quadratic diameter had positive effects. This is a good news, since here foresters have tools to strengthen their conservation strategy and enhance forest biodiversity in managed stands.

The beneficial effects of LVL deadwood volume may be reinforced under conditions of high solar radiation. These results indicate that maintaining high amounts of deadwood in managed as well as unman-aged forests will be particularly important in the context of ongoing climate change, which is likely to increase temperatures and could modify solar radiation.

## CRediT authorship contribution statement

**M. Gosselin:** Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Y. Dumas:** Writing – review & editing, Investigation, Data curation, Conceptualization. **S. Cadet:** Writing – review & editing, Project administration. **E. Cateau:** Writing – review & editing, Project administration. **Y. Paillet:** Writing – review & editing, Project administration, Investigation, Funding acquisition, Conceptualization. **F. Gosselin:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Software, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

Data will be made available on request.

## Acknowledgements

This research carried out as a part of the GNB (Gestion, Naturalité, Biodiversité) project which was managed by the INRAE. It was supported by the French Ministry in charge of the Environment through the DEB-Irstea convention (GNB-Valo; Action n°7) and the BGF program (convention GNB 10-MBGD-BGF-1-CVS-092, no. CHORUS 2100 214 651) and, by the French National Forest Office (ONF).

We especially thank Jean-Christophe Gattus (ONF), Frédéric Ritz (ONF), Denis Cartier (ONF), Thierry Gautrot (ONF), Christian Marck (ONF), Mirham Blin (ONF), Johann Keller (ONF) and Mickaël Kaczmar (ONF) for their help in collecting field data; Thomas Perot (INRAE) for his help in preparing SAFRAN Data; Vincent Boulanger (ONF) and Nicolas Debaive (RNF) for their coordination work; Camille Fabbri (INRAE) for her work on parametric models; and Victoria Moore for her help in improving the English style of the manuscript.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jenvman.2024.121840.

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