# Tree species identity shapes the relationship between canopy cover and herb-layer species in temperate forests

Clément Vallé, Isabelle Le Viol, Jean Nabias, Karine Princé, Frédéric Gosselin

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Clément Vallé, Isabelle Le Viol, Jean Nabias, Karine Princé, Frédéric Gosselin. Tree species identity shapes the relationship between canopy cover and herb-layer species in temperate forests. Journal of Ecology, 2025, 113 (3), pp.582-597. 10.1111/1365-2745.14479. hal-05202955

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# Tree species identity shapes the relationship between canopy cover and herb-layer species in temperate forests

Clément Vallé<fnref n="1" /> ID | Isabelle Le Viol<fnref n="2" /> ID | Jean Nabias<fnref n="1" /> ID | Karine Princé<fnref n="1" /> ID | Frédéric Gosselin<fnref n="3" /> ID

**Correspondence**
Clément Vallé
Email: valle.clement@wanadoo.fr

**Funding information**
Agence Nationale de la Recherche, Grant/
Award Number: ANR-18-CE02-0025

Handling Editor: Frank Gilliam

## Abstract

1. The composition of canopy tree species is known to impact the distribution of herb-layer species through various factors, such as light availability, soil structure and composition. However, relationships between these strata have been rarely demonstrated empirically across a wide range of species and large spatial areas, which could help understand and spatially predict herb-layer species distribution patterns.

2. We examined the response of 85 herb-layer species occurrence (presence/absence) distribution to the cover of 105 tree species across 7255 sites in mainland France. By accounting for site characteristics and canopy diversity, we ensured that associations between herbaceous and tree species were not a reflection of these factors. We then predicted the occurrence distribution of herbaceous species by considering these environmental factors and comparing them with the addition of individual tree species cover.

3. Our models revealed that tree species identity cover relationships are important for all herb-layer species and with some strong associations between these two strata. While all herb-layer species had increased explanatory and predictive model performance from the information added by tree species identity cover, non-forest and open vegetation species showed the greatest increase. Non-forest generalist species are those with the most negative associations with these canopy tree species. Models also highlighted that tree species identity cover cannot be fully replaced by unidimensional functional tree groups based on trophic, dynamic, hydric or life form classification.

4. Our study emphasizes that incorporating tree species identity composition can enhance the prediction of herb-layer species occurrence distribution at new sites. Consequently, we provide an empirical demonstration of improved prediction leveraging on species associations, which synthesizes various factors challenging to measure in situ. This knowledge could guide future experiments to explore mechanisms involved, especially for the high-magnitude associations detected.

<footnote n="1">¹Centre d'Ecologie et des Sciences de la Conservation (CESCO), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, Paris, France</footnote>

<footnote n="2">²Centre d'Ecologie et des Sciences de la Conservation (CESCO), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, Quai de la Croix, Concarneau, France</footnote>

<footnote n="3">³INRAE, UR EFNO, Nogent-sur-Vernisson, France</footnote>
<figure><img src="image_2.png" /></figure>

5. Synthesis. Our study is an empirical demonstration of the importance of tree species identity cover in shaping the distribution of the herb layer, surpassing the effects of tree diversity alone. By considering the significance of tree species identity cover, we can inform forest management and conservation measures, improve spatial prediction of herb-layer species distribution and better understand herb layer and tree species relationships.

KEYWORDS

biotic interactions, herbaceous layer, macroecology, overstorey-understorey linkage, plant community, species distribution

1 | INTRODUCTION

In forest ecosystems, the understorey layer represents a huge pro-
portion of plant biodiversity, up to 90% (Gilliam & Roberts, 2014;
Thrippleton et al., 2016), and provides a variety of ecosystem ser-
vices, such as nutrient cycling, carbon sequestration and energy
flow (Landuyt et al., 2019; Mölder et al., 2008; Muller, 2014; Wang
et al., 2021). It shapes the distribution of other species by provid-
ing habitat not only for groups of species, such as mammals and
insects (Fayt et al., 2006; Marshall & Hawthorne, 2012), but also
for trees by regulating their regeneration through the resource
competition with germinating seedlings (Coomes & Grubb, 2000;
Gilliam, 2007) or facilitation processes (e.g. microclimate buff-
ering or exchange through mycorrhizal networks; Callaway &
Walker, 1997; Zilliox & Gosselin, 2014). Grasping the drivers of
herb-layer distribution is thus crucial to guide sustainable forest
management and biodiversity conservation programmes (Köhl
et al., 2020).

The herb-layer community responds to various factors rang-
ing from land-use history to abiotic and biotic factors across dif-
ferent scales (Barbier et al., 2008; Ellenberg & Leuschner, 2010;
Gilliam, 2007; Hermy & Verheyen, 2007). Climate influences its
broad distribution and temporal change. At local scale, the avail-
ability of suitable micro-environment in the forest floor is a strong
driver (Kelemen et al., 2014), including soil composition and con-
dition, water and light availability or microclimate temperature
(Depauw, 2020; Wei et al., 2020). Yet, these factors partly depend
on forest structure and tree composition (Augusto et al., 2003).
For instance, tree canopies intercept up to 99% of incoming light
(Coomes & Grubb, 2000), compete strongly for below-ground re-
sources (Coomes & Grubb, 2000; Germany et al., 2017) and modulate
the microclimate (Gottschall et al., 2019; Lembrechts et al., 2019).
Trees also can be associated with particular herbaceous species due
to differences in their nutrient cycling, rainfall partitioning, shade-
casting ability and quality of the light transmitted by their foliage
(Barbier, Balandier, & Gosselin, 2009; Rawlik et al., 2018). Tree spe-
cies identity can also lead to litter composition (e.g. allelopathic com-
pounds) and accumulation, affecting germination and performance
of herb-layer species (Bertin et al., 2003; Rodríguez-Calcerrada
et al., 2011). Thus, various overstorey composition characteristics

are well-known to impact understorey herb communities, but ques-
tions remain as to what extent they influence individual species
distribution (Ampoorter et al., 2015; Barbier et al., 2008; Germany
et al., 2021; Gilliam & Roberts, 2014).

The distribution, diversity and structure of forest tree species
communities are largely influenced by management practices (Tinya
et al., 2021), which also impact the herbaceous layer, such as in the
case of edge management (Govaert et al., 2020). The diversity of
tree species can positively impact its herb-layer counterpart by not
only increasing environmental heterogeneity (resource heterogene-
ity hypothesis, Ricklefs, 1977) but also creating new environmental
conditions through non-additive tree species richness effect (Ball
et al., 2008). Yet, recent studies found that inconsistent results about
species richness effect (Ampoorter et al., 2015; Both et al., 2011;
Pasion et al., 2018) and tree species identity (Coppi et al., 2019;
Corcket et al., 2020; Germany et al., 2021) or tree species abun-
dance (Barbier, Chevalier, et al., 2009), can be more important driv-
ers of herb-layer composition, biomass and phylogenetic diversity.
This can be explained by the fact that considering tree identity could
integrate their functional differences more directly than taxonomic
species richness, making it a better predictor of potential theoretical
ecological mechanisms influencing the distribution of the herb-layer
species. Tree identity could be a better predictor (as different tree
species capture a significant differential share of resources) of the
site's potential abiotic limiting factor for the growth of each herba-
ceous species (i.e. Liebig's Law of Limiting Factors, Austin, 2007) and
better capture potential niche heterogeneity by more accurately
describing the functional complementarity of canopy trees (i.e. re-
source heterogeneity hypothesis). Additionally, if certain tree spe-
cies exert greater competitive pressure on the herbaceous layer (e.g.
high-nutrient or light-uptake species), this may lead to the estab-
lishment of herb-layer species better adapted to stress resistance,
rather than those adapted to competition or disturbance (Grime's
CSR strategy; Grime, 2006). Therefore, certain functional groupings
of trees could also be more relevant for predicting the impact of the
canopy on the herb-layer species. For instance, many coniferous spe-
cies tend to accumulate litter, acidifying soils more than most broad-
leaf trees (Augusto et al., 2015; Díaz-Calafat et al., 2023), favouring
acid-tolerant herbaceous species and limiting germination and
survival of core-forest herbaceous species (Thomaes et al., 2013).
However, Barbier et al. (2008) cautioned against simplifying the effects of coniferous versus broadleaf trees on herbaceous species in particular because some species do not share the same characteristics that are predominantly found in their groups (e.g. beech broad-leaf trees that have thick litter that can acidify the soil). Moreover, alternative functional grouping based on tree identity could be more relevant for capturing the effect of this stratum on herbaceous species (e.g. Raunkiaer life form type, successional stage or Ellenberg affinity to soil acidity or humidity through competitive access to the substrate). Trees' Raunkiaer life form determines herb-layer species composition by modulating access to light, moisture, nutrients (Gilliam & Roberts, 2014; Maguire & Forman, 1983) and creating specific microsites (Glick & Matlack, 2021), such as tip-up mounds from taller trees (leading to specific herb-layer species composition; Spicer et al., 2018). Moreover, tree successional (or dynamic) stages can influence the composition and number of herbaceous species (Rybar et al., 2023) both directly (e.g. through changes in transmitted light quality and quantity), but also indirectly by impacting the abundance of organisms that influence this herb-layer, such as herbivorous insects (Hilmers et al., 2018). Nonetheless, Barbier et al. (2008) recommend exploring tree-understorey interactions as species-specific relationships, as the identity of tree species may influence herb-layer species through a combination of multidimensional functional characteristics and mechanisms not fully captured by the traits considered.

Species associations are challenging to retrieve due to datasets requirements, such as adequate resolution and high sample size (Vallé et al., 2023). Research focused on tree species identity is often restricted by a small sample size and limited spatial extent, resulting in the identification of only a few species-specific relationships (Bergès et al., 2017; Dölle et al., 2017; Nagel et al., 2019). Experimental studies exploring the underlying mechanisms of overstorey-understorey relationships can be time-consuming and require adequate designs (Both et al., 2011). As such, it is crucial to identify potential associations between a large pool of trees and herbaceous species. Several authors (Gilliam et al., 1995; Peet et al., 2014) also hypothesized that the overstorey-understorey linkage may only occur after a certain period of establishment (about 20 years of stand development), as the two layers primarily (and mainly) respond to distinct abiotic factors during early development (i.e. light availability for trees and water and nutrients availability for herb-layer species; Gilliam et al., 1995). Moreover, over time, the likelihood of new environmental conditions being created increases due to events, such as treefalls (Bartels & Chen, 2010). Additionally, the closure of the canopy from foliage growth leads to complementary crown architectures among species in the canopy space (Jucker et al., 2018; Williams et al., 2017). These findings could explain the difficulty of detecting the tree identity effect on understorey species through experimental studies (Germany et al., 2017, 2021). Moreover, several authors highlighted the lack of empirical evidence for positive associations (Carleton & Maycock, 1981; Ellenberg, 1988). This underscores the valuable contribution of large empirical data sets, including a great proportion of long-established stands.

Detecting potential associations between tree species and herb-layer species and assessing the magnitude of any tree species identity effects, can help us understand and predict spatial herb-layer distribution. In this study, we relied on a nation-wide dataset coupling overstorey-understorey sampling protocol describing tree canopy composition and herb-layer cover at fine resolution over mainland France (Vidal et al., 2005). This protocol benefits from detailed empirical environmental descriptions (abiotic and management parameters) of the sampling sites by skilled forest observers. This increases the likelihood that detected associations are not just due to species responding similarly to factors that are not considered in the analyses (Poggiato et al., 2021). Thus, we hypothesized that associations between herb-layer species and tree species cover identity can be detected (H1.a), while controlling for trees taxonomic species diversity effect and main measurable environmental factors known to influence the herb-layer distribution. We concurred with Barbier et al. (2008) and Barbier, Chevalier, et al. (2009) that the effect of tree identity cannot be sufficiently expressed through a simple binary classification of 'broadleaf' versus 'coniferous'. Given the intricate array of mechanisms that interconnect these two layers, we propose that alternative tree functional groupings may offer a more comprehensive representation of the trees' influence on herbaceous plants, although no classification can fully encompass the entirety of the tree identity effect (H1.b). We also hypothesized that herbaceous species can also share a common response to tree species identity cover depending on their habitat preferences (H1.c). For instance, non-forest herb-layer species were found to have negative responses of higher magnitude with the identity of dominant tree species or their abundances compared with forest herb-layer species, in local extent observational studies either based on controlled site type sampling (Barbier, Chevalier, et al., 2009) or taking site type variables into account in the statistical model (Zilliox & Gosselin, 2014).

While the monitoring of canopy trees is facilitated by advances in remote sensing allowing recognition of many species and identification of many individuals, the herb-layer does not directly benefit from this tool. Our second hypothesis proposes that using canopy tree species identity cover could improve predictions on individual distribution of numerous herb-layer species using these species associations (Vallé et al., 2023; Wilkinson et al., 2021), in new sites (H2). This could indirectly leverage remote sensing advances and represent a major advancement in the understanding and spatial prediction of this layer, which is difficult and time-consuming to survey.

# 2 | MATERIALS AND METHODS

## 2.1 | Biodiversity data

We used biodiversity data collected between 2009 and 2019 from the French National Forest Survey (NFI). This standardized monitoring survey consists of 25 m point sampled from a systematic sampling
<figure><img src="image_3.png" /></figure>

approach ensuring to represent the national forest area (Figure S1, more detailed protocol described in Vidal et al., 2005). Field observations are obtained from visual estimation from concentric plots of 15 m for herb-layer species abundance-dominance score (similar to Braun-Blanquet method; 1951) and 25 m radius for tree species canopy area (in %). Other sites characteristics (described in Section 2.3) are measured at the 25 m resolution.

To minimize the potential influence of phenology on the detected associations among the two layers, we limited our analysis to points that were surveyed during the spring season (April-May-June). Then, we removed survey points above 500 m altitude to exclude particular environments and communities. Subsequently, we eliminated the NFI's 'simplified plots', which refer to plots that are not fully sized, often due to the presence of a forest edge, road or other factor that decreases the plot's size. We ended up with a total of 7255 plots with this filtering procedure (more details on filtering surveys points protocol in Appendix S2).

As detecting species associations from herb-layer species required high sample size (Blanchet et al., 2020), we only retained herbaceous layer species with at least 200 occurrences. We truncated the abundance-dominance score (ranging from 0 to 5) for each herb species to occurrence (presence-absence). We ended up with the occurrence of 85 herb-layer species (from 388 initial), including ferns, grasses, heather and other herb-layer species (see list in Table S1 with their total occurrence).

Data on individual 'tree' species canopy area (in %) were available for 105 different species (see Table S2), defined as woody plants that can reach a height of 5 m at maturity in situ and have a diameter greater than 7.5 cm (measured at 1.30 m in height). This includes trees as well as some shrub species but excludes lianas (see Table S2). Species of trees and shrubs below these criteria could not be distinguished from those in the understorey communities in the protocol and are therefore not included in the analyses. This tree species canopy area is based on measurements from either 'free' (i.e. canopy cover with nothing above) or 'total' area (i.e. the entire area occupied by the tree species throughout the canopy). Given the strong correlation between the two variables (r=0.985), we opted to utilize the 'free' percentage area so that the sum of free individual tree species canopy areas would provide a reliable estimate of total canopy cover.

## 2.2 | Traits data

To evaluate the impact of canopy cover and diversity on herbaceous layers, and to compute the effect of tree species identity cover after accounting for these factors (H1.a), we calculated the total canopy area (by summing the canopy areas of each tree species) and Shannon trees diversity using the *vegan* R package. To explore underlying tree functional groups that may influence the impact of overstorey composition on the understorey layer (H1.b), we classified each tree into four distinct functional groups based on their respective traits, adapted from Rameau et al. (1989). These functional groups

were chosen to synthesize their preferences for soil moisture, soil acidity, dynamics (i.e. successional stage from early to late: pioneer, post-pioneer and dryad categories, with nomad species that can be at multiple successional stages according to site conditions, see Appendix S2 for their exact definitions), and developmental stage, based on the Raunkiaer (1934) classification (i.e. life form type with eight categories from nano-microphanerophytes to macrophanerophytes, Table S2). As a result, they may explain observed associations between trees and herb-layer species sharing common preferences for those soil characteristics, canopy height or successional stage.

As herbaceous layer species can also share common response to tree species cover identity depending on their own known preferences (H1.c), we grouped them by four main habitats preferences (i.e. where herb-layer species is mainly found) according to Heinken et al. (2022) (see Table S1). To simplify, we named them hereafter core-forest specialist (species of forest interiors), edge-forest specialist (species of forest edges and forest openings) and two other categories: non-forest species (species that can be found in forest as well as open vegetation) and open vegetation species (species that can be found partly in forest, but mainly in open vegetation). Since these four main habitat preferences only give qualitative information about the primary habitat of these species, we used a habitat specialization index calculated for the French flora (Mobaied et al., 2015). This index establishes a quantitative gradient of habitat specialization, with high values indicating generalist species found in multiple habitat types and low values indicating highly specialized species that primarily occupy a single preferred habitat.

## 2.3 | Environmental data

Local factors influence on vegetal communities' distribution were assessed using available in situ variables measured during biodiversity data surveys covering edaphic and topographic factors at the plot-resolution (25 m): maximum slope incline, slope aspect, four soil textures (sand, silt, clay and organic) and coarse element loading (i.e. proportion of coarse elements, including stones, boulders and gravel larger than 2 mm), six dominant topographic positions (flat terrain, lower slope, mid-slope, upper slope, narrow valley or depression), altitude, longitude and latitude (Gilliam et al., 2007; Leuschner & Lendzion, 2009). At the landscape-scale (1 km), we extracted five Available Water Storage Capacity categories (AWC; >15, >50, >75, >100, >150 mm/m s) and topsoil pH from the 1 km-remote sensing product Harmonized World Soil Database. At the regional-scale (10 km), we extracted seasonal mean temperature and total precipitation (May-July) from the European climate rasters (E-OBS) provided by ECA&D, using the R package climateExtract (Schmucki, 2021).

In summary, we used a total of 12 environmental explicative covariates: two edaphic local variables, three topographic local variables, three geospatial coordinates, two landscape edaphic variables, two regional climate variables and the year of the survey
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to account for potential temporal variations in the populations of each species over the studied period (see Table S3 for more detailed description). The combination of these variables represents the main environmental gradient in France. This ensures that the detected effect on tree species composition is not influenced by these controlled variables (see environmental variables maps in Appendix S3).

## 2.4 | Statistical analyses

We used generalized linear models (GLMs) to analyse the occurrence distribution (but also abundance-dominance score only presented in the Appendix S1 due to some diagnostic issue for 10 species and lack of appropriate family distribution) of each of the 85 herb-layer species. In each model, the response variable was the individual occurrence (or abundance-dominance score, Appendix S1) of a specific herb-layer species, followed a Bernoulli (resp., Poisson distribution with log function) distribution, and its modelled mean was related to the linear combination of environmental variables through a probit link function. Initially, we fitted a basal model ($M_{abiotic}$) that included all environmental variables described in Section 2.3, without information on the tree species layer. To explore the effect of canopy total area and tree diversity, we added total canopy area and Shannon tree diversity index ($M_{diversity}$). To test for the potential impact of tree species identity cover (H1.a), we further replaced total canopy area with individual tree species area cover, while controlling for other variables (i.e. environmental variables previously described and Shannon trees diversity index), in $M_{identity}$:

Regarding parsimony of this last model, we chose to only keep tree species which have a significant effect on the particular herb species by running a forward selection variable using stepAIC function of MASS package (Venables & Ripley, 2002). We chose to begin the procedure from $M_{abiotic}$ to control for all environmental variables to which we added Shannon tree diversity index. At the end of the procedure, to retrieve the total area canopy cover, we also included the sum of all non-significant individual tree species (see more detailed description in Appendix S2). Here, the pseudo-R syntax of these final most complex model:

$$ (M_{identity}): \text{Occurrence Herb Species} \\ \sim \text{Abiotic variables} + \text{Shannon Trees Diversity} \\ + \text{Individual area of each significant trees' species} \\ + \text{Total Area of non-significant tree species.} \quad (1) $$

We analysed the effect of different tree species identities on herb-layer occurrence by examining the odds ratios using the magnitude analysis framework proposed in ecology by Camp et al. (2008) and Barbier, Chevalier, et al. (2009). We assessed the impact of an increase in one standard deviation of each tree species identity cover

on herb-layer species occurrence at the linear combination level (on the log of odds ratios) according to guidelines adapted from Daniels et al. (1983). Based on the calculation of this effect magnitude, we classified each individual ‘tree on herb’ effect into nine categories: ‘High negative’ (–Inf, –0.5), ‘Medium negative’ [–0.5, –0.25), ‘Low negative’ [–0.25, –0.1), ‘Negligible or inconclusive negative’ [–0.1, 0), ‘Negligible or inconclusive positive’ [0, 0.1), ‘Low positive’ [0.1, 0.25), ‘Medium positive’ [0.25, 0.5) and ‘High positive’ [0.5, +Inf) when the value indicates the GLM regression coefficient of the associations with a standardization (mean=0, SD=1; further details provided in Appendix S2).

We also analysed whether the improvement in explanatory performance due to identity effect (based on $\Delta$(AIC) from $M_{diversity}$ to $M_{identity}$) can be explained by the occurrence (i.e. whether the species is common in the dataset or not, see Table S1), habitat specialization and habitat specialization index of herbaceous species (for the 75 species where all traits described in Section 2.2 were available), using a linear regression model. We normalized the independent variable ($\Delta$(AIC)), as the raw value did not follow a normal distribution (leading to issues of residual normality in the model diagnostics, more details in Appendix S2). We also only retained traits as explanatory variables if they improved the AIC of the linear model by at least 2 units. The final model followed the pseudo-R syntax:

$$ \begin{align} \text{normalized } \Delta(\text{AIC}) & \sim \text{Habitat preferences (4 categories)} \nonumber \\ & + \text{Habitat preferences: Specialization Index (SSI)} \tag{2} \\ & + \text{Species total occurrence.} \nonumber \end{align} $$

In a second step, we summed the cover of trees sharing the same categories (Table S2) within each of the four functional groups described in Section 2.2 (along with the coniferous/broadleaf dichotomy). We then analysed and compared how much of the variation in herb-layer species distribution occurrence can be explained by these four models ($M_{raunkier}$, $M_{hydric}$, $M_{trophic}$ and $M_{dynamic}$) in comparison with the tree species identity cover one ($M_{identity}$, H1.b). For each herb species, we calculated the explicative performance (i.e. the ability to explain the variation of the independent variables using the same data as those used to estimate the model) of these eight models according to their area under the curve (AUC) and Akaike information criterion (AIC).

To ensure that tree species identity cover effect can help to predict new sites (H2), we then calibrate $M_{identity}$ on 80% on the dataset (training dataset) and predict the occurrence probability of each herb species on the remaining 20% of the dataset (test or out-of-sample dataset). We also did that for $M_{abiotic}$ and $M_{diversity}$ and calculated their predictive performance according to their AUC.

We also assessed the predictive performance of $M_{identity}$ restricting the tree species to those currently identifiable by remote sensing technology in Immitzer et al. (2019) to illustrate future prospects (including 11 tree species; see Figure S2). As herb-layer
<figure><img src="image_5.png" /></figure>

species distribution can have non-linear response and as variable selection has some pitfalls (Derksen & Keselman, 1992), we also compared predictive performance between models using random forest procedure. Statistical testing was conducted to compare the significance of AIC and AUC variations between models, using pairwise Student t-test with Holm correction to adjust for multiple testing.

Finally, to ensure the statistical validity of each model, we employed several common diagnostic tests. We assessed the absence of multicollinearity between our explicative variables by checking the generalized variance inflation factor (GVIF < 2). We also conducted tests to check for outliers and overdispersion in the model residuals using the R package DHARMa, and to detect spatial autocorrelation using the Moran index and spatial correlogram from the correlog function in the R package ncf (see Appendix S2 for details). We did not detect any diagnostics problems with any of the models presented and thus retained them all for analysis and interpretation (except for 10 species for models using abundance-dominance herb-layer species data, which were therefore excluded from analyses using these data, see Appendix S1). We presented here the results from generalized models that offer better interpretation tools through their coefficients, but we also used random forest models that offered the same conclusions (procedure and results are detailed in Appendix S2). These analyses were conducted using the version 4.2.2 of R (R Core Team, 2022) on a high-performance computing (HPC) cluster.

## 3 | RESULTS

Our analysis revealed that including tree species identity (using cover data) led to significant improvements in both explicative and predictive performance of our species occurrence models (H1.a, H2; Figure 1). In terms of explicative performance, $M_{identity}$ achieved significantly improved ΔAIC (mean = -277, paired t-test $p$-value < 0.001) than $M_{diversity}$ (mean = -28), when setting $M_{abiotic}$ as the baseline (Figure 1a). Concerning predictive performance, $M_{identity}$ achieved significantly higher AUC (mean = 0.830) than $M_{diversity}$ (mean = 0.801, paired t-test $p$-value < 0.001) and, to a greater extent, $M_{abiotic}$ (mean = 0.796, Figure 1b). We found that all species benefited from these improvements in both explicative and predictive performance compared with $M_{abiotic}$ (Figure 2).

Regarding models with trees functional groups (H1.b), we found by decreasing order of explicative performance (in terms of AUC) $M_{raunkier} = M_{hydric} > M_{trophic} = M_{dynamic} > M_{coni/broad}$ (equals means no significant t-test, Figure 1a and see Figure 1b for predictive performance). However, all these groups perform better than $M_{diversity}$ (and to a larger extent than $M_{abiotic}$) but significantly less than $M_{identity}$ (H1.b). We also found that $M_{identity}$ has better explanatory performance (paired t-test < 0.001, mean(ΔAIC) = -102) than models with the respective best functional group of trees ($M_{raunkier}$, $M_{hydric}$, $M_{trophic}$ or $M_{dynamic}$) for each of the 85 herb-layer species, except for

four species (and 20 species in predictive performance; Figure 2; Figures S13-S15).

Focusing on $M_{identity}$ (Figure 3), we detected 452 positive relationships and 448 negative relationships between tree species identity cover and herbaceous species occurrence. In other words, some herbaceous species were more likely to be found associated with particular tree species, while other herb species were less likely to be found under the cover of potentially the same tree species. Each of the 85 herbaceous layer species responded significantly to at least two tree species (minimum = 2, mean = 10.5, maximum = 22). The number of positive associations compared with negative ones was strongly influenced by habitat preference with 1.70 times more positive associations for core-forest species (1.1 when only considering at least small magnitude effect) and 1.38 (resp. 0.62) for edge-forest species compared with 0.99 (resp. 0.42) for non-forest species and 0.16 (resp. 0.03) for open vegetation specialists. Based on the magnitude effect analysis on these significant associations (see methods Section 2.4), we found that the vast majority of the relationships were of negligible or small magnitude (326/96 for positives and 180/171 negatives ones, respectively), but 30 of the relationships were of medium to high magnitude and positive (23 and 7, respectively) and 97 were of medium to high magnitude and negative (72 and 25, respectively; see Figures S4 and S5). Using abundance-dominance data, we found similar patterns with slight differences (but see Figures S6 and S7 for details).

At species level (Figure B2 in Appendix S2), we found that common (i.e. higher occurrence) species benefited more (improved (Δ)AIC from $M_{identity}$ compared with $M_{diversity}$) from species tree identity effect (beta-effect = -0.06, $p$-value < 0.01; Figure B2 in Appendix S2). Then, we found that the increased explanatory performance through adding tree species identity cover effect into the model was greater (regression coefficient value = -0.40, $p$-value = 0.04) for non-forest species (species listed as b1 in Figure 3) than for forest habitat specialist species. Finally, we found that among non-forest species, those with higher SSI have the highest increased explanatory performance (Interactive regression coefficient for this group and SSI = -0.47, $p$-value = 0.002; Figure B2). Complete summary and detailed protocol of the linear regression is presented in Appendix S2 (Figure B2).

## 4 | DISCUSSION

Using a large-scale national survey (7255 sites) involving 85 herb species and 105 trees species, we found that knowing what tree species (using cover data) are present can help understand and spatially predict herbaceous species occurrence (i.e. presence-absence) beyond knowing the abiotic conditions of the site, the diversity of the overstorey and/or any particular functional grouping based on tree cover identity (H1.a; H1.b). Non-forest and open vegetation herbaceous layer species responded more negatively to a wider range of
<figure><img src="image_6.png" /></figure>
<figure><img src="image_7.png" /><figcaption>FIGURE 2 Explicative performance (based on -Δ(AIC) from $M_{abiotic}$) for each species using environmental variables with total canopy area and Shannon tree diversity index ($M_{diversity}$), with best group trees for each species ($M_{best.group}$), or with the tree species identity cover ($M_{identity}$). Names colours represent herb-layer species' main habitats preferences from Heinken et al. (2022). Results for predictive performance and random forest are available in Figures S13-S16.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>FIGURE 3 Magnitude effect of the 15 most prevalent (= total occurrence) tree species identity cover on herb-layer species occurrence grouped according to their main habitats preferences (a1) core-forest species, (a2) edge-forest species, (b1) non-forest species and (b2) open vegetation species. Names colours represent coniferous (green) and broadleaf (orange) trees. For herbaceous layer species; it represents grasses (yellow), fern (dark green) and heather (blue) and others herbaceous (green). Results with all tree species are available in Figures S3–S7.</figcaption></figure>

tree species compared with forest ones. Moreover, we also found that non-forest species are those that seem most sensitive to this relationship with tree identity and to a larger extent the more generalist ones among them (H1.c). Finally, we show that this tree species identity cover relationship can also be used to better predict the distribution of herbaceous layer communities at new sites (H2), opening new spatial prediction prospects in relation to this time-consuming sampling stratum.

## 4.1 | The explicative value of tree species identity cover

We provide empirical evidence of the tree identity importance on the distribution at fine resolution (15 m radius) of the herbaceous layer, at national scale (H1.a). This result is consistent with previous local experiments showing that tree species identity cover impacts the composition of the herbaceous layer. For instance,
<figure><img src="image_9.png" /></figure>

Ampoorter et al. (2015) found a slight dissimilarity in the composition of the herbaceous layer between four tree species after 6 years of establishment, with no effect of tree diversity. Their study used an adequate experimental design with 16 plots, each containing three subplots and 60 herbaceous species, to isolate these two effects. Similarly, Germany et al. (2017) found an effect of tree identity, but not tree diversity, on the composition and biomass of the herbaceous layer after 5 years of establishment. However, in a follow-up study by Germany et al. (2021), there was no stronger effect of tree identity observed 3 years later. We argue that this exploratory empirical analysis complements these experiments by providing an overview of 900 significant species-specific associations and their potential magnitude, with 394 showing non-negligible effects, between these two strata over an extended species pool. Such insights could be hard to achieve in an experimental context due to limitations like low sample size, a reduced pool of tree species, and a focus mainly on the early stages of forest succession. This study highlights which tree-understorey relationships could be useful to explore in future experimental designs to unravel their underlying mechanisms. Given the high number of tree species in our sampling design, future analyses could also consider other types of tree diversity beyond taxonomic richness, such as functional or phylogenetic diversity (Zheng et al., 2022), which can otherwise be challenging to analyse in local studies. Indeed, we also confirmed our initial hypothesis that none of the tree's functional group tested (based on dynamic, trophic, hydric and Raunkiaer's life form classification) explains more the distribution of herbaceous layer species than this tree's species identity effect (H1.b), highlighting the diversity of mechanisms that could be involved in these associations. We also agreed with Barbier et al. (2008) by warning against over-simplified dichotomy between coniferous and broadleaf effect on herbaceous layer not only because (i) it is the functional group that least improves the explanatory and predictive performances of the models compared with other functional groups (such as trophic or hydric grouping) but also because some coniferous/broadleaf tree species could have opposite effects on individual plant species (e.g. high positive effect of *Quercus pubescens* on *Rubia peregrina* but medium negative one of *Fagus sylvatica*). This point exacerbates one strength of this study, which is to detect the effect of tree species identity cover at the herb-layer species level, allowing to detect more detailed patterns. For instance, some species with strong conservation and management interest could have specific tree species identity cover relationships, which could be undetected either with analysis through community metrics often used (e.g. Ellenberg index and diversity index) or with experimental designs that are most often less representative than our large-scale empirical data.

We also emphasized that the detected species associations might have emerged due to missing environmental variables (Blanchet et al., 2020), despite the comprehensive variable sampling protocol used in this study. However, as tree species mainly impact the herbaceous layer by modifying the abiotic environment

(Barbier et al., 2008; Gilliam & Roberts, 2014), it is difficult for these taxa to distinguish between environmental filters and 'direct' biotic interactions. Nevertheless, we detected some strong positive associations between facultative vines (e.g. *Hedera helix*, *Lonicera periclymenum*) and tree species (such as *Quercus robur* and *Quercus petraea*) that align with expected facilitation interactions. We also confirmed strong positive associations previously detected at a local scale (e.g. *Ruscus aculeatus* with *Quercus pubescens*: Debussche et al., 2001). In contrast, some specialized species found in specific habitats (such as *Calluna vulgaris*, *Molinia caerulea*, *Rumex sanguineus* and *Rubus idaeus*) showed predominantly negative associations with tree species that could suggest missing environmental factors characterizing their specific less common habitats (e.g. heath, moorland, inner forest edges and clearings) that may have contributed to these observed negative relationships. Moreover, missing environmental variables modulated or not by the tree species could be difficult to measure in the field but are important for herbaceous layer communities, such as microclimate and microhabitat (De Frenne et al., 2019), historical factors, including historical land-use, past anthropogenic pressures and nitrogen/sulphur/acid deposition (Depauw et al., 2020; Hermy & Verheyen, 2007), management (Durak, 2012) and even other biotic components like soil biodiversity and herbivore pressures (Sabo et al., 2017). These detected associations with tree species can serve as an important asset for indirectly measuring the missing environmental factors, thereby improving spatial predicting at new sites and as useful drivers for species distribution models (Poggiato et al., 2021; Vallé et al., 2023).

## 4.2 | Pattern of herb-layer species response to tree species identity cover

If it is difficult to interpret which mechanisms involve these detected species associations with our large-scale empirical analysis, the large species pool could however highlight some general response patterns. Notably, we confirmed the hypotheses of Ellenberg (1988) and Carleton and Maycock (1981) concerning the rarity of high-magnitude positive associations between these herb-layer and tree species. Nonetheless, our study demonstrates the overall preponderance of low-magnitude associations, regardless of direction. However, we observed a higher rate of negative associations than positive ones (except in core-forest species), particularly among non-forest and open vegetation species that respond to a wider range of tree species (both in terms of magnitude and number of associations detected). This pattern is even stronger when considering only associations of at least small magnitude. These results are in line with previous findings from Zilliox and Gosselin (2014) and Barbier, Chevalier, et al. (2009). More specifically, we found that within them, non-forest species show the better increase in explicative performance with this tree species identity cover (i.e. probably highlighting that species pools are the most sensitive to the tree composition). Moreover, we also found that among these non-forest species, the
<figure><img src="image_10.png" /></figure>

most generalist species showed the greatest improvement of expli-
cative performance from this tree species relationship. These results
might be explained by various reasons. First, as generalist species
are defined as species using a wider range of resources and micro-
habitats than specialists (Clavel et al., 2011), they have a higher prob-
ability to encounter a wider range of tree species (i.e. more potential
associations but also probably statistical power). In addition to being
able to exploit a wider resources gradient, generalists also often
have a wider geographic range, leading to a reinforcement of this
mechanism (even if we have here controlled by the occurrence of
species which may be a proxy for these range differences). Second,
forest specialist species could have evolved to be less impacted by
competition from tree species that share their habitats through ad-
aptation processes (Pandit et al., 2009) and thus less sensitive to the
tree composition encountered (e.g. through allelopathy resistance
compounds). On the contrary, the generalist species that are found
in forest and open vegetation have been able to adapt to be more
selective in the pool of species encountered and avoiding the spe-
cies of trees most typical of mature forests as evidenced by the high
proportion of this category responding negatively (or less positively
compared with forest herb species) to late successional species (i.e.
post-pioneer and dryad tree species, such as hornbeam, beech, holm
and sessile oak) (Rameau et al., 1989). Third, the distribution of core
and edge-forest species was better explained by abiotic environ-
mental factors than non-forest species, likely due to the detailed
description of forest abiotic environmental factors within the pro-
tocol. This allowed for greater potential improvement in explaining
the distribution of non-forest species by this identity effect (with
also trees species potentially synthesizing abiotic information less
specific to forest environment, especially important for these non-
forest species).

Among the tree species, we distinguish three categories with
important ecological implications for the conservation and man-
agement of forest communities, based on multiple moderate to
high-magnitude associations with herb-layer species: (1) tree spe-
cies that increase the occurrence of forest herb-layer species while
reducing that of non-forest species, leading to more distinct forest
communities (mostly post-pioneer and dryad tree species, such as
*Carpinus betulus*, *Fagus sylvatica* or *Quercus petraea*); (2) tree species
with overall positive associations with both forest and non-forest
herb-layer species, increasing species richness without enhancing
community specificity (e.g. *Quercus robur*); and (3) tree species that
negatively affect both types of herb-layer species, reducing overall
species richness of this understorey strata (e.g. *Pinus sylvestris* and
*Pinus pinaster*).

## 4.3 | Perspectives from conservation and spatial predicting of herb-layer community

Our study also highlights some interesting perspectives to leverage
on tree canopy composition for conservation and spatial prediction
of the herb layer. As our study focused on the species level, we

provide more precise recommendations to protect some herb-layer
species by providing an overview of their most favourable tree
species composition. Moreover, this approach allows us to warn
against recommendations at the community level. For instance,
Früchtenicht et al. (2018) concluded that *Quercus ilex* can be
used to preserve herb-layer diversity instead of *Q. robur* which is
more threatened by global changes staying with the idea that this
two tree species have the same impact on herb-layer (van Oijen
et al., 2005). However, we found that *Q. ilex* is not associated with
the same herb-layer species and negatively affects a wider range
of forest herb-layer species probably due to litter difference (e.g.
more allelopathic effect or lower decomposition rate of *Q. ilex*
litter). If we provide a wide overview of species association of all
tree species at the national scale, we emphasize they can probably
fluctuate in ecological space and have interactive effects with
environmental context (e.g. different effects of tree abundance with
slope direction, Zilliox & Gosselin, 2014; Heinrichs et al., 2019). One
perspective would be to explore whether these associations found
in France are also found in other parts of the distribution range of
herbaceous species. There are a number of reasons why this might
not be the case: (i) herbaceous-tree associations are modified by
the environmental context, and (ii) since associations with trees can
 synthesize missing environmental factors, particularly local ones,
this part of the information could be spatially heterogeneous (in
this respect, a classification by functional group and not by identity
may be more stable). Considering interaction between different
tree effects as well is also another interesting perspective, as this
can synthesize the additive effect that some studies explore with
diversity metrics and that consist of some tree species creating
specific habitats when mixed together ('complementary effect';
Ellenberg & Leuschner, 2010). We emphasized that while GLMs do
not include such interactions, the random forest model does, which
may partly explain its greater predictive performance. Finally, we
also emphasized that we focus on the species association at plot-
level scale (25 m) but management should also consider that tree
species identity cover relationships are probably variable across
scales (notably at plot level versus forest level; Heinrichs et al., 2019).

Our findings highlight that the identity of tree species can en-
hance predictions of the herb layer at new sites (H2) compared with
diversity or functional tree groups. This confirms that the enhance-
ment in explicative metrics from species associations was not due
to overfitting or random noise (Tikhonov, 2018). These predictions
using species associations can have various applications, such as im-
proving the prediction of the spatial distribution of herb-layer com-
munities in the same spatial domain as the one of the statistical fit.
For example, this could enable the development of kriging tools to
spatially predict the distribution of herb-layer species based on spa-
tially referenced information—partly from remote sensing—regard-
ing site type and tree species abundance.

From a statistical point of view, we have also shown that random
forest seems better suited to improving these predictions for several
potential reasons, such as considering interactions between vari-
ables and the potential non-linearity of these associations (Godeau
et al., 2020), even if the inclusion of longitude and latitude as potentially non-linear factors may have overestimated the predictive performance (given the random sampling between calibration and test data). Finally, although we compared abundance-dominance data with occurrence data, highlighting the little difference (as Vallé et al., 2023 between abundance and occurrence in the associations of an avian community), statistical methodologies more suited to this type of data could be considered in the future (Herpigny & Gosselin, 2015). On the inference side, our correlative models represent an initial step that provide initial hypotheses to guide more advanced statistical approaches, which can explore mechanisms more directly (Austin, 2007). An interesting next step would thus be to investigate the causal structures among our explanatory variables using models, such as structural equation models (SEM), which can capture more complex relationships than we have identified (e.g. indirect effects and feedback loops). This framework could be suitable for extending our approach to a non-stationary climate change context, incorporating both ecological determinants of tree species abundance and distribution, as well as their influence on more proximal mechanisms, such as microclimate (Richter et al., 2022; Vanwalleghem & Meentemeyer, 2009) and related to microrefugia (defined as microsites enabling the survival of species populations outside their main range limits; Finocchiaro et al., 2023). This approach should be more robust over time with respect to climate change than our GLM regression models, yet would still require a dynamic version to integrate different demographic features, such as lifespan and dispersal, between tree and herbaceous species.

Moreover, leveraging on trees species identity to predict herbaceous layers is also particularly crucial with the advances in remote sensing to monitor canopy composition (e.g. 100 individual millions of trees identified and numerous species; Fassnacht et al., 2016; Weinstein et al., 2021). Our study demonstrates these benefits using tree species currently identified in Europe through these remote sensing advances (11 species in Immitzer et al., 2019; Figure S3). We thus emphasize that our understanding of the associations between these two layers can yield significant benefits (+0.03 AUC predictive performance in our case with 0.83 average AUC). For instance, by improving predictions between herb-layer survey points, indirectly leveraging on advances from identifying tree canopies over large areas with remote sensing. However, this perspective will require considering the uncertainty propagation of tree species identification (e.g. from 76% to 100% accuracy in Immitzer et al., 2019), which we did not have to address here. Additionally, our study provides rare empirical evidence highlighting the importance of using species associations to improve predictions, which has previously been shown to have theoretical benefits (Poggiato et al., 2021; Vallé et al., 2023). In particular, the predictions of our study show that the predictive contribution can be made by conditioning a taxonomic group with a more easily monitored one, which opens new prospects for other communities with similar linkages (e.g. corals-fish; trees-fungi communities; Messmer et al., 2011; Kernaghan & Patriquin, 2011). This can also have applications for better predicting rare or invasive plant species in the initial expansion phase (Briscoe Runquist et al., 2021; Zhang et al., 2020).

## AUTHOR CONTRIBUTIONS

Clément Vallé, Frédéric Gosselin and Isabelle Le Viol designed the research. Clément Vallé analysed the data and Clément Vallé prepared figures and tables and drafted the manuscript. All authors revised and contributed to the paper.

## ACKNOWLEDGEMENTS

We thank Jeanne Vallet for the insightful discussions throughout this work. We would also like to thank Lily Morlot for her support. This work was granted access to the HPC resources of the MeSU platform at Sorbonne Université. This work is part of the PhD thesis of the corresponding author, supported by the GAMBAS project funded by the Agence Nationale pour la Recherche (ANR-18-CE02-0025) and doctoral school 'Sciences de la nature et de l'Homme: évolution et écologie' (ED227 MNHN-SU).

## CONFLICT OF INTEREST STATEMENT

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.

## PEER REVIEW

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/1365-2745.14479.

## DATA AVAILABILITY STATEMENT

The data and main code that support the findings of this study are openly available in Dryad: https://doi.org/10.5061/dryad.7wm37pw1j (Vallé et al., 2024). One specific explicative variable (altitude), which is mainly used to filter the points as described in the article, is however not shared due to a specific non-disclosure clause; nevertheless, it is available upon request to the French National Forest Inventory at https://inventaire-forestier.ign.fr/dataifn/DonnesBrutes/requetage or contact the corresponding author directly.

## ORCID

Clément Vallé https://orcid.org/0000-0003-3814-7975

Isabelle Le Viol https://orcid.org/0000-0003-3475-5615

Jean Nabias https://orcid.org/0009-0000-8864-0165

Karine Princé https://orcid.org/0000-0001-7739-7438

Frédéric Gosselin https://orcid.org/0000-0003-3737-106X

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the
Supporting Information section at the end of this article.

**Appendix S1:** Supporting information, Appendix A.

**Appendix S2:** Supporting information, Appendix B.

**Appendix S3:** Supporting information, Appendix C.

**How to cite this article:** Vallé, C., Le Viol, I., Nabias, J., Princé, K., & Gosselin, F. (2025). Tree species identity shapes the relationship between canopy cover and herb-layer species in temperate forests. *Journal of Ecology*, 113, 582–597. https://doi.org/10.1111/1365-2745.14479