# Differential responses of taxonomic, functional and phylogenetic multi-taxa diversity to environmental factors in temperate forest ecosystems

Jan Vigués Jorba, Daniel Scherrer, François Duchenne, Florian Zellweger, Martin M Gossner, Kurt Bollmann

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Jan Vigués Jorba, Daniel Scherrer, François Duchenne, Florian Zellweger, Martin M Gossner, et al. Differential responses of taxonomic, functional and phylogenetic multi-taxa diversity to environmental factors in temperate forest ecosystems. Ecological Indicators, 2025, 178, pp.113855. 10.1016/j.ecolind.2025.113855. hal-05200623

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Original Articles

# Differential responses of taxonomic, functional and phylogenetic multi-taxa diversity to environmental factors in temperate forest ecosystems

Jan Vigués Jorba<sup>a,b,*</sup> <sup>ID</sup>, Daniel Scherrer<sup>c</sup> <sup>ID</sup>, François Duchenne<sup>a,d ID</sup>, Florian Zellweger<sup>c ID</sup>, Martin M. Gossner<sup>b,e ID</sup>, Kurt Bollmann<sup>a ID</sup>

ARTICLE INFO

Keywords:

Biodiversity

Environmental drivers

Forest ecosystems

Functional diversity

Multi-taxon

Phylogenetic diversity

Taxonomic diversity

A B S T R A C T

Accelerated global biodiversity loss critically threatens forest ecosystem multifunctionality and service provision. Understanding environmental drivers across taxonomic (TD), functional (FD), and phylogenetic (PD) biodiver- sity facets is essential for effective conservation. However, the multi-dimensional nature of biodiversity is difficult to assess, and many studies overlook the interplay between functional traits and evolutionary history within a community. Here, we use Hill numbers to integrate TD, FD, and PD across five ecologically distinct taxa (birds, butterflies, snails, vascular plants, and mosses) to better understand environmental factors driving forest biodiversity in Switzerland. We included micro- and macroclimatic conditions, soil properties, topography, and vegetation structure and diversity. Our results highlight the intricate, taxon-specific nature of environmental effects on biodiversity. Across taxa, vegetation structure and diversity, and climatic factors emerged as key drivers of biodiversity facets, while soil characteristics mostly influenced less-mobile taxa. Vegetation structure and diversity acted as strong ecological filters shaping species richness and traits, reflecting responsiveness to short-term dynamics like disturbance or management, but were weak predictors of PD. Conversely, more temporally stable abiotic factors such as climate and soil conditions were consistent drivers across all facets, highlighting their broad impact on biodiversity. We show that FD and PD metrics complement TD by revealing additional insights into ecosystem functionality and evolutionary history. Given the differential responses of biodiversity indicators to environmental drivers, especially climate, maintaining ecosystem functionality and resilience under climate change requires assessments that go beyond taxonomic diversity and include the functional and phylogenetic dimensions.

1. Introduction

Accelerated loss of global biodiversity is evident across all ecosystems and largely attributed to global change drivers (Cardinale et al. 2012; Scheffers et al. 2016). Massive exploitation of resources, land-use change and intensification as well as climate change are threatening forest habitats (Potapov et al. 2017), which play a vital role in maintaining terrestrial biodiversity and multifunctionality (Aerts and Honay 2011; van der Plas et al. 2018). The resistance and resilience of

forest ecosystems are thus increasingly challenged and a comprehensive understanding of the drivers of forest biodiversity across scales is required to develop effective strategies to mitigate further losses.

The interplay of biotic and abiotic factors in shaping diversity patterns is a pivotal focus in ecology and conservation, demanding comprehensive exploration. While there is extensive support for the effect of spatial-environmental heterogeneity on species diversity (MacArthur and MacArthur 1961; Tews et al. 2004; Stein et al. 2014), there is still a lack of understanding of the combined influence of biotic

<footnote><sup>a</sup> Biodiversity and Conservation Biology, Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland</footnote>

<footnote><sup>b</sup> Department of Environmental Systems Science, ETHZ, Zürich, Switzerland</footnote>

<footnote><sup>c</sup> Forest Resources and Management, Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland</footnote>

<footnote><sup>d</sup> Biological Station of Doñana (EBD-CSIC), Sevilla, Spain</footnote>

<footnote><sup>e</sup> Forest Health and Biotic Interactions, Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland</footnote>

<footnote>* Corresponding author at: Biodiversity and Conservation Biology, Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland.</footnote>

<footnote>E-mail addresses: jan.vigues@wsl.ch (J. Vigués Jorba), daniel.scherrer@wsl.ch (D. Scherrer), francois.duchenne@wsl.ch (F. Duchenne), florian.zellweger@wsl.ch (F. Zellweger), martin.gossner@wsl.ch (M.M. Gossner), kurt.bollmann@wsl.ch (K. Bollmann).</footnote>

<footnote>https://doi.org/10.1016/j.ecolind.2025.113855</footnote>

<footnote>Received 9 May 2025; Received in revised form 19 June 2025; Accepted 3 July 2025</footnote>

<footnote>Available online 7 July 2025</footnote>

<footnote>1470-160X/© 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).</footnote>
and abiotic variables as drivers of variation in species distributions (King et al. 2021; Bazzato et al. 2023). Such interactions are particularly relevant in structurally complex systems like forests, where fine-scale variation in environmental conditions is common (Zellweger et al. 2024). This relationship can be exemplified by the changes in microclimatic conditions resulting from differences in vegetation structure, which in turn impact forest species diversity and multifunctionality (De Frenne et al. 2021). Nevertheless, despite being substantially studied, a comprehensive understanding of how biotic–abiotic interactions jointly influence forest biodiversity remains elusive (Gossner et al. 2013; Heidrich et al. 2020).

Biodiversity has many facets, including its functional and phyloge-
netic dimensions, yet it has traditionally been associated only with
species (taxonomic) richness (Pavoine et al. 2011). The sole use of
taxonomic diversity (TD) as a biodiversity indicator falls short in
capturing the intricate interplay of functional traits and evolutionary
history within a community. Furthermore, the inclusion of functional
(FD) and phylogenetic diversity (PD) in biodiversity studies offers a
more holistic perspective and is now recognized essential for effective
conservation efforts to maintain gene pools and ecosystem multi-
functionality (Devictor et al. 2010; Diniz-Filho et al. 2013; Cadotte and
Tucker 2018). (Diniz-Filho et al. 2013) For instance, FD considers the
variation of species' ecological traits, providing insights into ecosystem
functioning and their capacity to adapt to a changing environment (Díaz
et al. 2007; Naeem et al. 2012). In addition, PD considers the variation in
evolutionary relationships between species in a community (Winter
et al. 2013), and therefore reflects its potential for resilience, as evolu-
tionary distinctiveness can enhance adaptability and persistence in the
face of change (Faith 2008). Finally, TD provides a simple and thor-
oughly studied indicator of diversity which can be used to easily
compare values across spatial and temporal scales (Fleishman et al.
2006). Previous studies investigating the relationship between the three
facets have uncovered complex relationships and disparities between
them (Devictor et al. 2010; Le Bagousse-Pinguet et al. 2019). The
anticipated positive associations between TD, FD or PD may be more
complex than presumed, with communities exhibiting identical species
richness differing in their representation of traits and evolutionary his-
tory (Tucker et al. 2017; Aguirre-Gutiérrez et al., 2017). Although many
studies have investigated the effects of environmental factors on a
combination of the different diversity facets (Devictor et al. 2010; De
Pauw et al. 2021; Gheoca et al. 2023; Lazarina et al. 2023), the ensemble
of all three across multiple taxa remains largely understudied
(Burrascano et al. 2023), particularly at landscape scales.

The quantification of environmental heterogeneity in three-
dimensional habitats such as forests has greatly benefitted from recent
advances in remote sensing techniques, facilitating the measure of
vegetation structure (Seidel et al. 2016) and microclimate (Zellweger
et al. 2024). For instance, quantification of vegetation structure, which
correlates with other habitat characteristics such as air and ground
temperature, light transmissivity, and humidity (Brüllhardt et al. 2020),
improved species distribution predictions (Zellweger et al. 2015).
Furthermore, changes in microclimate and niche spaces, caused by
varying structural characteristics, impact diversity across different taxa
in temperate forest ecosystems (Tinya et al. 2021). Bird diversity has
been shown to respond directly to vegetation structure in forests (Bouvet
et al. 2016), with similar relationships found in butterflies (Aguirre-
Gutiérrez et al., 2017) and mosses (Vitt et al. 2019). Conversely, the
distribution of other taxa can be driven by factors indirectly affected by
vegetation structure, such as light availability (vascular plants, Ellen-
berg 1988), or ground moisture content (snails, Hettenbergerová et al.
2013). The increasing availability of highly resolved nation-wide envi-
ronmental information significantly contributes to the knowledge about
biodiversity drivers.

In this study, we use environmental variables of forests across
Switzerland, in combination with species survey data from a national
biodiversity monitoring scheme (Weber et al. 2004). The aims of this

study are to 1) assess the differences in environmental drivers of di-
versity across multiple taxa, i.e., birds, butterflies, snails, vascular
plants, and mosses, 2) elucidate differences in how environmental
drivers affect the different diversity facets, i.e., TD, FD and PD and 3)
evaluate the relationships between the different diversity facets. In
terms of environmental drivers, we expect all diversity facets of more
mobile taxa (birds and butterflies) to be strongly driven by vegetation
structure and diversity (Bouvet et al. 2016; Aguirre-Gutiérrez et al.,
2017), while diversity of less mobile taxa (snails, plants, and mosses)
should be more affected by macro and microclimatic characteristics as
well as topographic and soil variables (Zellweger et al. 2015). Further,
we expect warmer microclimates to generally harbor greater diversity
(Brown et al. 2004), with differing effect strengths across taxa
(Zellweger et al. 2016), and diversity facets (De Pauw et al. 2021).
Nevertheless, we anticipate that environmental effects on different di-
versity facets will be taxon-specific and complex to disentangle (De
Pauw et al. 2021; Hacala et al. 2021). Considering that the different
diversity facets measure different aspects of diversity, we do not expect
clear correlations between them and suspect different mechanisms
shaping each dimension (Devictor et al. 2010; Cadotte and Tucker
2018). Nevertheless, considering the close relationships between func-
tional traits and phylogenetic distance, a positive relationship between
FD and PD can also be expected (Winter et al. 2013). By broadening our
perspective beyond taxonomic diversity (i.e. species richness), we aim to
elucidate the effects of environmental factors also on the functional and
phylogenetic facets of temperate forest biodiversity.

## 2. Methods

2.1. Study area

The study was conducted in Switzerland, extending over 41,285 km² in Central Europe (45°490–47°480 N, 5°570–10°300E). Forests cover 32 % of the landscape, with a higher coverage in the mountainous terrain to the South and Northwest of the country (Brändli et al. 2020). The elevation ranges from 193 to 4634 m.a.s.l., with a mean elevation of 1300 m.a.s.l. and a maximum tree line of 2414 m.a.s.l. (Gehrig-Fasel et al., 2007). The mean annual temperature ranges from –10.5 to 12.5 °C and the mean annual precipitation from 438 to 2950 mm (Zimmermann and Kienast 1999).

## 2.2. Species data

Data used to infer species occurrence and abundance was obtained
from the Swiss Biodiversity Monitoring Program (BDM) for the years
2017–2021 (Weber et al. 2004). In this program, birds, butterflies,
vascular plants, snails, and mosses are surveyed in five-year intervals
across all land-use types. We accessed species data from two different
sampling grids of the national monitoring program, both distributed
along regular sampling grids but varying in scale and taxa. Sample
squares of 1 km² allocated along a regular grid of 5 km × 7 km, are used
for birds, butterflies and vascular plants (Fig. 1). To sample this area,
territory sampling was done for birds, while transects were done for
butterflies and plants (see Appendix S1.1 for more information on
sampling protocols). Considering the focus of this study on forest habi-
tats, we only retained species data from squares with at least 50 % of
forest coverage within the 1 km² squares, to avoid negative effects of
habitat fragmentation on species richness and density (Andrén and
Andren 1994). Further, we only counted species occurrence points
within a 30 m distance from the forest edge, this allowed us to account
for potential forest edge effects. Since no coordinate data was available
for plant surveys in sample squares, we performed a post-hoc filtering
procedure to select plant species linked to forest habitats only, according
to the ecological groups defined in (Landolt et al. 2010). We used cir-
cular plots with a size of 10 m² distributed along a grid of 6 km × 4 km
for snails, mosses, and vascular plants (Fig. 1). Also here, we only
<figure><img src="image_3.png" /><figcaption>Fig. 1. Study area of Switzerland depicting the grid-based sampling design of the Swiss Biodiversity Monitoring scheme, based around circular plots and sample squares. An example sample square at the landscape scale is shown with the 30 m forest edge buffer (light green) and the sampled forested area (dark green). The vascular plant and butterfly transects within this forested area are shown as a bright green line. At the local scale, the circular plot shows the snail sample location around the outside of the moss and vascular plant 10 m² survey area. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

retained plots within the forest, with a minimum distance of 30 m from the forest edge. We used a forest cover layer obtained from the Federal Office of Topography (swisstopo 2017) for this purpose. Vascular plants were therefore the only taxon measured both at the landscape scale (sample square) and local scale (circular plot). For more details on species data collection protocols refer to Appendix S1.1. The filtering process resulted in a total of 139 squares for birds, 107 transects for butterflies, 138 transects for plants (landscape) and 277 circular plots for snails, 290 for mosses, and 291 for plants (local) fulfilling all conditions (Fig. 1).

## 2.3. Environmental data

We selected a suite of predictor variables for the occurrence of our target taxa, with the aim to comprehensively represent the environmental variability within our sites (Table 1). These variables were divided into four categories: climate, vegetation structure, biotic, and soil/topography.

As climatic variables, we selected microclimatic mean air temperature (at 1 m above the ground, Zellweger et al. 2024) and sum of precipitation of the growing season (April to October), provided by the Swiss Federal Office of Meteorology and Climatology. To represent vegetation structure, we used an airborne LiDAR-derived vegetation height model at 10 m resolution from the Swiss Federal Office for the Environment, FOEN (Ginzler 2021). From this, we extracted mean canopy height, canopy height heterogeneity and a canopy transmissivity index. The proportion of deciduous to coniferous trees (forest mix rate) was used as a biotic variable and extracted from the FOEN (Waser et al. 2017). To evaluate soil characteristics such as nutrient availability, microbial activity and moisture retention, we obtained soil pH data and the topographic wetness index (TWI) from freely available layers for Swiss forests (Baltensweiler et al. 2021). For more details on environmental variable extraction and calculations refer to Appendix S1.2.

## 2.4. Analysis

### 2.4.1. Geographical analysis

For the 1 km² sample squares, we did calculations for each environmental variable only for the area within the forest and at 30 m from the forest edge, using the zonal statistics tool in QGIS (QGIS Development Team 2022). These filtered environmental values were used as predictor variables for bird diversities. For butterflies and plants (landscape), we extracted environmental values using a buffer of 10 m on each side of the transects, filtering for transects within the forest and beyond 30 m from the forest edge. For circular plots, we calculated values for environmental variables within a radius of 15 m from the plot center, using the zonal statistics tool.

### 2.4.2. Species data analysis

We treated all species data equally across the different taxa and diversity facet: aggregates, complexes and subspecies were treated as single species, while hybrids were removed from the datasets. Considering the breadth of datasets used for the analyses, we had to standardize species synonyms across all databases, this was realized using the GBIF backbone taxonomy (GBIF Secretariat 2023).

### 2.4.3. Phylogenetic tree management

To calculate phylogenetic diversity, we used open-source trees with branch lengths for each taxon. Nevertheless, for snails and mosses, we found no compiled phylogenetic backbone that included all species, we therefore excluded these taxa from the phylogenetic analyses. Refer to Appendix S1.3. for more information on the phylogenetic tree procurement.

### 2.4.4. Functional trait management

We selected animal and plant functional traits with the aim of capturing the main life history trait categories known to respond to environmental pressures for each taxon (refer to Appendix S1.4. for
<caption>**Table 1**</caption>

Environmental predictor variables to model diversity facets in birds, butterflies, vascular plants, snails, and mosses in forest landscapes. Growing season refers to the period from April to October.

<table><thead><tr><th>Variable name</th><th>Description</th><th>Resolution</th><th>Source</th></tr></thead><tbody><tr><td><strong>Climate</strong></td><td></td><td></td><td></td></tr><tr><td>temp1m_m</td><td>Mean monthly air temperature at 1 m above ground, averaged over the growing season during the years 2012 – 2021</td><td>10 m</td><td>Zellweger et al. 2024</td></tr><tr><td>ppt_m</td><td>Mean monthly precipitation sum, averaged over the growing season during the years 2012 – 2021</td><td>1 km</td><td>https://www.meteoswiss.ch</td></tr><tr><td><strong>Vegetation structure</strong></td><td></td><td></td><td></td></tr><tr><td>transpot_m</td><td>Mean canopy light transmissivity in June</td><td>10 m</td><td>Webster et al. 2023</td></tr><tr><td>vhm_m</td><td>Mean vegetation height above 1 m (m)</td><td>10 m</td><td>www.opendata.swiss</td></tr><tr><td>vhm_sd</td><td>Vegetation height heterogeneity (m), ie. SD of canopy height model</td><td>10 m</td><td>www.opendata.swiss</td></tr><tr><td><strong>Biotic</strong></td><td></td><td></td><td></td></tr><tr><td>forestmix_sd</td><td>Standard deviation of the ratio of the visible tree crowns of deciduous and coniferous tree proportions</td><td>10 m</td><td>www.opendata.swiss</td></tr><tr><td>plant_tq0</td><td>Plant q = 0 taxonomic diversity per sample square or circular plot</td><td>—</td><td>https://www.biodiversitymonitoring.ch</td></tr><tr><td>plant_pq0_ses</td><td>Plant q = 0 phylogenetic diversity per sample square or circular plot, corrected by TD by using SES</td><td>—</td><td>https://www.biodiversitymonitoring.ch</td></tr><tr><td>plant_fq0_ses</td><td>Plant q = 0 functional diversity per sample square or circular plot, corrected by TD by using SES</td><td>—</td><td>https://www.biodiversitymonitoring.ch</td></tr><tr><td><strong>Soil / Topography</strong></td><td></td><td></td><td></td></tr><tr><td>twi_m</td><td>Mean topographical wetness index</td><td>10 m</td><td>Baltensweiler et al. 2021</td></tr><tr><td>twi_sd</td><td>SD of topographical wetness index</td><td>10 m</td><td>Baltensweiler et al. 2021</td></tr><tr><td>ph_m</td><td>Mean topsoil pH</td><td>25 m</td><td>Baltensweiler et al. 2021</td></tr></tbody></table>

more details). For animals, we chose traits following the approach of Hahs et al. (2023) through measures of mobility, reproduction, diet, and body size (Appendix S2, Table S1). We chose vascular plant traits according to trait completeness and following (Díaz et al. 2016), with moss trait selection emulating this whenever possible, while representing the scope of moss trait diversity regardless (Appendix S2, Table S1).

### 2.4.5. Diversity facet quantification

We used Hill numbers to quantify alpha diversity for the taxonomic, phylogenetic, and functional dimensions (Chao et al. 2014) providing a unified framework for measuring biodiversity. Accordingly, we selected the first Hill number across diversity facets: $q = 0$ (species richness), using the “hillR” package (Li 2018). Considering that the outputs of Hill number $q = 0$ are inherently richness-dependent, for FD and PD, we used standard effect sizes (SES) to extract the component that is not just a function of species richness (refer to Appendix S1.5. for more details on this procedure).

### 2.4.6. Analysis of the drivers of biodiversity facets

We selected the same predictor variables for all taxa and diversity facets, to assess environmental effects on diversities. Additionally, we used forest plant diversity (i.e., species richness, SES FD and SES PD, $q = 0$) at each sample square and circular plot as biotic explanatory variables for taxa measured at the landscape and local scale respectively. This was

not done for vascular plant models. Considering that not all sample squares had the same area of forest nor transect length, we added these as predictors, to account for the sample-area effect (Preston 1962). We used the total forested area within the 30 m edge buffer per square as an explanatory variable for birds, while the total forested transect length per square was used for butterflies and plants (landscape). Furthermore, we scaled all continuous variables (mean = 0, SD = 1) to ensure comparison of their coefficients as measures of relative importance (Schielzeth 2010).

Before final variable selection, we checked explanatory variables for bivariate correlations (Appendix S2, Fig. S2-S7). We retained the most ecologically plausible variable from highly correlated pairs (Pearson's $r > 0.7$), according to expert knowledge. For transects in square samples, functional plant diversity was positively correlated with mean temperature one meter above ground (Pearson's $r = 0.71$, Appendix S2, Fig. S3), in this case, the temperature variable was kept (Table 2). For the entire square samples, the mean forest mix rate also showed strong correlations with temperature one meter above ground (Pearson's $r = 0.72$, Appendix S2, Fig. S2-S4) and was therefore discarded in the variable selection process for all plot types, with the purpose of keeping explanatory variables the same across taxa. Other variables were not significantly correlated with each other (Appendix S2, Fig. S2-S7) and therefore were kept in all global models across taxa (Table 2).

We used generalized additive models to evaluate the relationship between diversity facets and the effect of the environmental variables across taxa and diversity facets, using the R package “mgcv” and the gam function (Wood 2011). For the evaluation between facets, we first scaled all diversities (mean = 0, SD = 1) and considered differences across years by including year as a random effect in our models. Furthermore, to account for any potential spatial autocorrelation, we fitted an exponential covariate structure for all modelled comparisons. We fitted the models using maximum likelihood with a Gaussian error distribution and an identity link function. To assess the effect of environmental variables across taxa and facets, we constructed global models including all selected predictor variables, with Hill number $q = 0$ for each diversity facet as the response variable (in the case of PD and FD; the SES of the $q = 0$). Again, we included year as a random effect in our models, while all other variables were kept as fixed predictors without interactions (Table 2). We accounted for spatial autocorrelation as described above for diversity facet comparisons. We fitted the models using maximum likelihood with a Gaussian error distribution and an identity link function for phylogenetic and functional diversity, while for taxonomic diversity, we used a negative binomial distribution and a log link function, due to a better distribution fit with count data. We chose the best fitting models using the dredge function in the “MuMln” package (Barton 2010). We performed a weighted average of parameter estimates for the models with $\Delta AICc < 2$, using the model.avg function in “MuMln”. The weights obtained for each model were then used to calculate an averaged $R^2$ value for the final averaged model, and the standard deviation of this value. Visual inspection of standardized residuals for all models did neither suggest conflicts with model assumptions nor signs of over- or under-dispersion. Furthermore, we used Moran's I to check for spatial autocorrelation in model residuals, which proved non-significant. We inspected residuals with the “DHARMA” package (Hartig 2016). To test significant differences of averaged $R^2$ values between model groups, we used two-sample t-Tests (for landscape vs. local and more mobile vs. less mobile) and one-way ANOVA (TD vs. SES PD vs. SES FD). All statistical analyses were performed in the R environment (version 4.3.2, R Core Team 2022).

## 3. Results

At the landscape scale, the average forested area for square samples, after excluding the edge buffer, was $0.53 \text{ km}^2$ (SD = 0.19), while mean forest transect length was 1.18 km (SD = 0.62) (summary information found in Table 3). Note that the lower total number of species for plants
<caption>**Table 2**</caption>

GAM global models before dredging, for the different taxa with the included environmental variables, random effect, and covariance structure. Also noted is the averaged R² and its standard deviation (SD), obtained from the weighted models selected for after the model averaging procedure (those with a ΔAICc < 2).

<table><caption>**Table 3**</caption><thead><tr><th>Taxon</th><th>Global model</th><th>Diversity Attribute</th><th>μR<sup>2</sup></th><th>μR<sup>2</sup> SD</th></tr></thead><tbody><tr><td rowspan="2">Birds</td><td rowspan="2">q0 ~ forest_area + forestmix_sd + temp1m_m + transpot_m + vhm_m + vhm_sd + plant_tq0 + plant_fq0_ses + plant_pq0_ses + twi_m + twi_sd + pH + s(year) + s(x,y)</td><td>Taxonomic Diversity</td><td>0.488</td><td>0.002</td></tr><tr><td>Phylogenetic Diversity</td><td>0.141</td><td>0.011</td></tr><tr><td rowspan="2">Butterflies</td><td rowspan="2">q0 ~ trans_leng + forestmix_sd + temp1m_m + transpot_m + vhm_m + vhm_sd + plant_tq0 + plant_fq0_ses + twi_m + twi_sd + pH + s(year) + s(x,y)</td><td>Functional Diversity</td><td>0.190</td><td>0.009</td></tr><tr><td>Taxonomic Diversity</td><td>0.622</td><td>0.002</td></tr><tr><td rowspan="2">Plants (landscape)</td><td rowspan="2">q0 ~ trans_leng + forestmix_sd + temp1m_m + transpot_m + vhm_m + vhm_sd + twi_m + twi_sd + pH + s(year) + s(x,y)</td><td>Phylogenetic Diversity</td><td>0.206</td><td>0.012</td></tr><tr><td>Functional Diversity</td><td>0.488</td><td>0.004</td></tr><tr><td rowspan="2">Snails</td><td rowspan="2">q0 ~ forestmix_sd + temp1m_m + transpot_m + vhm_m + vhm_sd + plant_tq0 + plant_fq0_ses + plant_pq0_ses + twi_m + twi_sd + pH + s(year) + s(x,y)</td><td>Functional Diversity</td><td>0.786</td><td>0.002</td></tr><tr><td>Taxonomic Diversity</td><td>0.575</td><td>0.016</td></tr><tr><td rowspan="2">Mosses</td><td rowspan="2">q0 ~ forestmix_sd + temp1m_m + transpot_m + vhm_m + vhm_sd + plant_tq0 + plant_fq0_ses + plant_pq0_ses + twi_m + twi_sd + pH + s(year) + s(x,y)</td><td>Phylogenetic Diversity</td><td>0.601</td><td>0.004</td></tr><tr><td>Functional Diversity</td><td>0.548</td><td>0.002</td></tr><tr><td rowspan="2">Plants (local)</td><td rowspan="2">q0 ~ forestmix_sd + temp1m_m + transpot_m + vhm_m + vhm_sd + twi_m + twi_sd + pH + s(year) + s(x,y)</td><td>Functional Diversity</td><td>0.409</td><td>0.002</td></tr><tr><td>Taxonomic Diversity</td><td>0.064</td><td>0.004</td></tr><tr><td rowspan="2">Snails</td><td rowspan="2">q0 ~ forestmix_sd + temp1m_m + transpot_m + vhm_m + vhm_sd + plant_tq0 + plant_fq0_ses + plant_pq0_ses + twi_m + twi_sd + pH + s(year) + s(x,y)</td><td>Functional Diversity</td><td>0.165</td><td>0.004</td></tr><tr><td>Taxonomic Diversity</td><td>0.116</td><td>0.007</td></tr><tr><td rowspan="2">Mosses</td><td rowspan="2">q0 ~ forestmix_sd + temp1m_m + transpot_m + vhm_m + vhm_sd + plant_tq0 + plant_fq0_ses + plant_pq0_ses + twi_m + twi_sd + pH + s(year) + s(x,y)</td><td>Phylogenetic Diversity</td><td>0.158</td><td>0.004</td></tr><tr><td>Functional Diversity</td><td>0.158</td><td>0.004</td></tr></tbody></table>

Summary data for the species richness (SR) found for each taxon across all sample squares and circular plots. Shown are the total species per taxon considered (Total SR), and the minimum, maximum and mean species found across all plots (Min SR, Max SR and Mean SR). The lower total species richness for plants at the landscape scale is likely due to the post-hoc exclusion of non-forest plants in square samples.

<table><thead><tr><th>Taxon</th><th>Total SR</th><th>Min SR</th><th>Max SR</th><th>Mean SR</th></tr></thead><tbody><tr><td>Birds</td><td>91</td><td>10</td><td>42</td><td>26</td></tr><tr><td>Butterflies</td><td>123</td><td>2</td><td>55</td><td>22</td></tr><tr><td>Plants (landscape)</td><td>372</td><td>25</td><td>135</td><td>97</td></tr><tr><td>Snails</td><td>103</td><td>1</td><td>36</td><td>10</td></tr><tr><td>Mosses</td><td>281</td><td>1</td><td>39</td><td>14</td></tr><tr><td>Plants (local)</td><td>500</td><td>1</td><td>55</td><td>17</td></tr></tbody></table>

at the landscape scale in comparison to those at the local scale is likely a methodological artefact resulting from the post-hoc exclusion of non-forest plants in square samples.

### 3.1. Variance explained by the models

The averaged conditional R² values for models of taxa evaluated at the landscape scale were significantly higher than those at the local scale (μR² = 0.46 for landscape scale and μR² = 0.21 for local scale, T = 2.32, p = 0.04). However, there were no significant differences between the averaged R² values of more mobile vs. less mobile taxa (μR² = 0.36 for more mobile taxa and μR² = 0.35 for less mobile taxa, T = -0.09, p = 0.93). The variance explained by the models did not vary significantly between diversity facets (μR² = 0.50 for TD, μR² = 0.26 for SES FD and μR² = 0.26 for SES PD, ANOVA (2,13), F = 2.34, p = 0.14), although TD showed a consistently higher R² value throughout all models. The individual R² values per taxon and diversity facet are shown in Table 2.

### 3.2. Effects of environmental variables on diversity facets across taxa

To study the effects of environmental variables on diversity facets, we considered only the full models obtained from the averaging procedure of the dredged models (Table 2). Several consistent patterns emerged across taxa: generally, biotic variables positively influenced diversities (mostly TD) of birds, butterflies, mosses, and snails, while structural attributes affected all taxa (except snails) both positively and negatively. Climatic variables showed contrasting effects and appeared across all taxa (except mosses). Soil variables (pH) also showed strong significance for butterflies, snails and plants. Significant results are summarized in Fig. 2; detailed and non-significant results are provided in Appendix S2, Tables S3–S8, with partial dependence plots in Appendix S2, Figs. S8–S10.

### 3.3. Relationships between diversity facets

The comparison of TD with SES FD and SES PD showed no significant relationships across taxa, except for a negative correlation in plants at the local scale (p < 0.001, Fig. 3). For SES FD and SES PD, the relationship proved to be significantly positive for birds (p = 0.002), butterflies (p = 0.002) and plants at the local scale (p < 0.001, Fig. 3).

## 4. Discussion

In this study we found that climatic variables, vegetation structure and diversity as well as soil conditions are important drivers of biodiversity, but direction and strength largely differed between taxa. Abiotic factors (temperature, precipitation and pH) had pervasive impacts across all facets, while TD and SES FD were more affected by vegetation structure and diversity. Furthermore, SES FD and SES PD correlated positively across taxa, in line with our hypothesis and suggesting that
Birds

<figure><img src="image_4.png" /></figure>

Butterflies

<figure><img src="image_5.png" /></figure>

Mosses

<figure><img src="image_6.png" /></figure>

Snails

<figure><img src="image_7.png" /></figure>

Plants (landscape)

<figure><img src="image_8.png" /></figure>

Plants (local)

<figure><img src="image_9.png" /><figcaption>**Fig. 2.** Schematic view of significant effects (p < 0.05) on the different diversity facets (TD, SES FD and SES PD) on each studied taxon (birds, butterflies, snails, mosses and plants, at the landscape and local scale). The size of the arrow indicates the magnitude of the effect. Black arrows represent positive relationships while red arrows indicate negative relationships. Differently colored boxes represent the different classification of predictor variables (green for biotic, yellow for climatic, purple for sampled area, blue for vegetation structure and pink for soil). The conditional R² value for each model can be seen within the circle for each diversity facet. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

both metrics capture similar aspects of the communities. By simulta-
neously assessing TD, FD and PD across multiple taxa, this study pro-
vides novel insights into the generality and context-dependence of
biodiversity patterns, advancing our understanding of the mechanisms
shaping the different facets of biodiversity.

4.1. *Explanatory power for predicting different diversity facets*

The consistently higher explanatory power of TD models compared
to SES FD and SES PD models suggests that the environmental factors
investigated in our study are better suited to explain the variability in
<figure><img src="image_10.png" /><figcaption>**Fig. 3.** Generalized additive model plots showing the correlations between diversity facets (TD vs. SES FD vs SES PD) across the different taxa. Although TD was scaled for the model fitting procedure, it is represented here as conventional species richness for ease of interpretation. The lines show model predictions for significant parameter estimates based on GAMs and shading corresponds to 95% confidence intervals. SES: standard effect size.</figcaption></figure>

TD. The additional dimensions introduced by FD and PD metrics may not be as directly linked to the environmental factors included here. This points toward unmeasured drivers such as species interactions or fine-scale habitat conditions, possibly explaining the lower number of significant predictors for these facets, exemplified by the lower R² values. However, the lack of significant differences in explanatory power across diversity facets may reflect shared drivers across facets. Furthermore, models for taxa assessed at the landscape level (birds, butterflies, and plants) showed higher R² values than those at the local level (snails, mosses, and plants). This suggests that either spatial patterns at the landscape scale more effectively capture environmental drivers of diversities or that the spatial resolution of our predictor variables are not as suitable at the local scale.

## 4.2. Commonalities and differences of drivers across taxa

As predicted, the diversity of more mobile taxa was explained by

vegetation structure and plant diversity, but climatic factors also played an important role. For less mobile taxa, diversity was also driven by these variables, but soil characteristics (pH) also showed important effects. In accordance with our hypothesis, temperature positively affected the different diversity facets across taxa. This is in line with large-scale climate-richness relationships along latitudinal gradients, since higher temperatures increase energy availability and metabolic rates, enhancing diversity (Hawkins et al. 2003). This positive association is in accordance with earlier studies with similar conditions, where species richness, phylogenetic filtering and trait composition were strongly mediated by altitudinal gradients (Pellissier et al. 2013; Leingärtner et al. 2014; Zellweger et al. 2016). The observed negative effect of temperature on plant TD (local) could be linked to increased evapotranspiration and water stress at the local scale (Frei et al. 2022). This effect might be particularly strong in our study since the species data collection spanned over two drought years in 2018 and 2020 (Von Matt et al. 2024).
Precipitation was not as pervasive in our models as temperature and showed both negative and positive effects depending on taxon and diversity facet. The positive relationship between precipitation and TD in plants is well studied, especially when interacting with higher temperatures (Gu et al. 2020). This was further supported by our results for plants at the landscape and local scale. In combination with temperature affecting plant growth rates, precipitation impacts resource availability essential for plant survival (Gu et al. 2020). Surprisingly, a negative effect of precipitation was found for plant SES FD at the landscape scale and bird TD. For plant SES FD, this could be explained by the higher precipitation regime in the south of our study area (southern side of the Alps) coinciding with the higher frequency of fire events in this area, which may act as a selective pressure shaping trait distributions (Nóbrega et al. 2019). This area has also been found to harbor low bird species richness (Zellweger et al. 2016), which is also impacted in mountain regions with broad climatic gradients, where precipitation increases at higher elevations and could indirectly affect resource availability (Zellweger et al. 2016). Other studies in temperate forests have shown that vegetation structure and productivity have contrasting effects on bird diversities (Bae et al. 2018), the interaction between these variables could have led to our results.

The selected edaphic factor, pH, showed significant effects on butterfly, snail and plant diversities, but with mixed directions depending on diversity facet. Notably, the effect of pH was significant and positive in butterflies, most likely through filtering via the plant communities (Oostermeijer and van Swaay 1998). Snail TD also showed a strong positive relationship with pH, which has long been documented and likely due to the correlation of pH with calcium-rich environments, a critical resource for shell building and maintenance in snails (Martin and Sommer 2004). The reported increase in plant TD may be linked to decreasing soil toxicity at higher pH values (Falkengren-Grerup and Tyler 1993). On the other hand, the decrease in SES PD and SES FD with increasing pH could be a result of environmental filtering for acid-ophilous species, as found in recent studies (Piwczyński et al. 2016; De Pauw et al. 2021), especially when no highly alkaline soils were observed in our study region (Appendix S2, Table S2).

Vegetation structure exerted persistent and widespread effects across all taxa, extending beyond the more mobile taxa as initially anticipated, underlining the crucial role of vegetation structure as a key ecological variable influencing forest communities (Toivonen et al. 2023). Canopy height had consistently negative effects, while canopy heterogeneity always showed positive effects. In contrast, canopy openness had positive effects on more mobile taxa, but negative for less mobile taxa. Dense canopy cover is known to significantly buffer microclimates (Frey et al. 2016), benefitting moisture-dependant taxa such as mosses (Niinemets and Tobias 2019). However, the joint negative effect of increasing canopy height and decreasing openness on plant TD and SES FD is likely related to early successional stages harboring greater plant diversity in our study area, as seen in Stefańska-Krzaczek et al. (2019). Young forest stands with higher light availability and more diverse microhabitats can promote rich understory communities through a more dynamic ecological environment (Baeten et al. 2010). In these cases, denser canopy cover may not reduce the light availability at the forest floor due to lower canopy height. On the other hand, considering our selection for sample squares with more than 50 % forest cover, canopy openness at the landscape scale could lead to an increase in heterogeneity of understory vegetation structures. This could support higher diversity for more mobile species (birds and butterflies), through increased resource and niche availability and by favoring more light-dependent hostplants for butterflies (Zellweger et al. 2016). Canopy heterogeneity showed consistent positive effects across taxa by supporting a range of light and moisture gradients, leading to complex vegetation structures, which in turn drive butterfly richness (van Schalkwyk et al. 2021) and functional adaptations for mosses (Jonsson et al. 2015). For many butterfly species, these conditions facilitate dispersal among habitats, provide food resources, roosting sites and refugia from unfavorable weather conditions

and disturbances (Dennis 2004).

When included in the models, the effect of biotic factors on diversities proved significant across all taxa with mostly positive effects. Greater plant diversity results in more heterogeneous resources and niches and therefore can support higher species numbers, in accordance with the habitat-heterogeneity hypothesis (MacArthur and MacArthur 1961) and the ‘diversity-begets-diversity’ hypothesis (Whittaker 1972). This can occur directly through the provision of more diverse food resources for consumers (Muiruri et al. 2019; Ampoorter et al. 2020) or indirectly through the provision of prey items (Ebeling et al. 2018). In addition, effects of abiotic variables may affect diversity often indirectly through their effect on the vascular plant community (Jonsson et al. 2015). Higher plant diversity can lead to higher diversities in canopy structure, moisture retention, organic matter inputs, and substrate types, creating varied microhabitats and niches, particularly important for moisture-dependent taxa such as snails (Astor et al. 2017) and mosses (Vuidot et al. 2011). The surprising negative effects of plant functional diversity on snail TD and plant richness on butterfly SES FD underline the complexity of these facets. This could be due to the creation of more heterogeneous microhabitats, altering moisture conditions critical for snails or reducing the dominance of preferred plant species, disadvantaged butterfly species with specialized traits. These findings highlight the need for further research into indirect and context-dependent interactions between plant diversity and animal taxa.

### 4.3. Commonalities and differences of drivers across diversity facets

The responses of the different diversity dimensions to the environmental variables selected in this study varied in magnitude and direction. We anticipated that environmental effects on different diversity types would be species-specific and complex to disentangle. Nevertheless, by grouping variables into four distinct groups (biotic, soil/topographic, vegetation structure and climatic), some interesting patterns could be identified.

Sampled area (forest area and transect length), showed a stronger positive effect on TD than SES FD or SES PD across all taxa at the landscape scale. This suggests that species richness is more sensitive to variations in sampling effort and spatial scale, reflecting its dependence on comprehensive sampling to capture it adequately (Gotelli and Colwell 2001). Nevertheless, these effects were relatively weak, suggesting that the sampled area was sufficient across sites.

Climatic variables (characterized by precipitation and temperature), along with soil pH, were consistently found to be significant predictors across all diversity types, underscoring the fundamental role of abiotic factors in shaping biodiversity patterns (Zellweger et al. 2016; De Frenne et al. 2021). Their consistent significance across the diversity facets highlights their pervasive impact on biodiversity at multiple ecological and evolutionary levels. These variables, for example, influence physiological tolerances, species distributions, and ecosystem processes (van der Plas et al. 2018). Notably, SES PD was mostly only explained by abiotic variables, reflecting the evolutionary constraints imposed by long-term climatic and soil conditions. Furthermore, although pH appeared across all diversity facets, it showed contrasting effects, with a positive relationship to TD but negative to SES FD and SES PD. This highlights the connection between the latter two, suggesting that for some taxa, certain environmental constraints may shape functional traits and evolutionary lineages in similar ways. These patterns highlight the importance of incorporating multiple diversity facets in ecological studies, as TD alone may overlook deeper functional and phylogenetic responses to environmental gradients. Temperature had a consistently positive effect across diversity facets, reinforcing its role as a key driver of biodiversity, since warmer conditions enhance metabolic rates, ecosystem productivity, and species diversification, contributing to higher overall diversity (Brown et al. 2004).

In contrast, variables related to biotic factors and vegetation structure played a more pronounced role in explaining TD and SES FD as
opposed to SES PD. The positive relationship between heterogenous vegetation structure and plant diversity has been well established (Coverdale and Davies 2023), and known to strongly drive cross-taxon diversity through trophic and non-trophic bottom-up effects (Bazzato et al. 2023). The microclimatic conditions indirectly created by these interactions, together with direct resource availability constraints can lead to increased TD and SES FD. Furthermore, plant diversity as a biotic variable, generally showed a positive relationship across TD and SES FD of higher trophic levels, in line with the diversity-begets-diversity hypothesis (Whittaker 1972). The absent relationship of these variables with SES PD may reflect evolutionary relationships among taxa being less directly tied to temporally transient biotic and structural characteristics, emphasizing the longer temporal scales over which PD is shaped (Winter et al. 2013).

Lastly, topographic wetness indices (TWI) were notably absent from all our final models, despite influencing diversities of low-mobility, moisture-dependent taxa in other studies (Moeslund et al. 2013; Zellweger et al. 2015). The lack of association could be explained by the inclusion of more important biotic factors overshadowing the influence of TWI, together with the buffering action of forest soils to counteract water deficits (Meusburger et al. 2022). Moreover, the soil in our study region is characterized by a gradient of water storage capacity that increases as mean annual precipitation decreases (Appendix S2, Fig. S11), further modulating conditions of water stress. Notwithstanding, considering the slight mismatch between resolutions of our species and environmental data at the local scale, and the lower explanatory values of our models, interpretation of these results remains difficult, and we do not disregard the potential role of topographic factors in shaping biodiversity patterns.

### 4.4. Importance of considering different diversity facets

Functional and phylogenetic diversity are known to be influenced by species richness. To test for complementary effects of abiotic and biotic factors on the eco-evolutionary dimension of diversity we corrected FD and PD for differences in species richness. The lack of correlations between TD and both SES FD and SES PD reinforces the idea that relying on species richness as a single diversity dimension may overlook critical aspects of biodiversity that influence ecosystem resilience and function. This finding supports the complementary use of the different diversity facets, given that each represents a distinct aspect of biodiversity and is influenced by different ecological and/or evolutionary processes (Swenson 2011).

Moreover, the positive correlation between SES FD and SES PD metrics for birds, butterflies and plants (local), highlights the connection between evolutionary relatedness and functional similarity (Winter et al. 2013). Functional trait data were more accessible than comprehensive phylogenies, particularly for less-studied taxa, and FD models performed slightly better (Table 2). While PD includes unmeasured traits not captured by FD, it comes with a loss of interpretative power as the specific traits driving ecosystem processes remain unknown (Flynn et al. 2011). Identifying the traits that directly influence ecosystem functioning should therefore be given priority in future studies. A more complete availability of relevant traits can improve our understanding of the consequences of species loss and the mechanisms shaping ecosystem processes, such as niche complementarity, while also clarifying the extent to which evolutionary history can serve as a proxy for trait-based measurements (Flynn et al. 2011). In conclusion, FD metrics may provide a more practical and interpretable indicator of biodiversity than PD metrics, especially in contexts where trait data are more readily available, and where the ecological functions of species are better understood.

Nevertheless, while SES FD and SES PD were positively correlated, they often responded to different environmental drivers, meaning that selecting only one may not fully capture the ecological processes structuring communities. In addition, trait selection and coverage can

affect these relationships, which is an inherent caveat of FD metrics (Rocha-Santos et al. 2023). Recognizing these differences is essential for appropriately interpreting biodiversity-functioning relationships and ensuring that key ecological mechanisms are not overlooked (Mazel et al. 2018).

## 5. Conclusions

The combined application of taxonomic, functional, and phylogenetic diversity facets provided unique and comprehensive insights into the relationship with each other and the environmental factors influencing these diversity indicators, across various temperate forest taxa. Our findings have important implications for biodiversity research and conservation, emphasizing the need to quantify taxonomic diversity concurrently with at least one other facet. This provides a more comprehensive understanding of community assembly processes and strengthens our ability to predict ecosystem functioning and responses to environmental change. Vegetation structure, climatic effects and biotic factors were important drivers across taxa and diversity facets. However, both taxonomic and functional diversities were more strongly affected by structure and biotic factors than phylogenetic attributes, highlighting the temporally dynamic aspect of these habitat features. Temporally stable abiotic conditions were consistently influential across all diversity facets, emphasizing their widespread impact on biodiversity at both ecological and evolutionary scales. The complex differences across taxa and diversity facets suggest that environmental effects are often taxon- and facet-specific, underscoring the need for multi-faceted approaches in biodiversity research. Further studies should also explore how spatial and temporal scales influence these indices, particularly under the destabilizing dynamics of climate change.

## Funding sources

Jan Vigués Jorba was funded by the Swiss National Science Foundation (SNSF, grant number 204720).

## CRediT authorship contribution statement

Jan Vigués Jorba: Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Daniel Scherrer: Writing – review & editing, Methodology, Conceptualization.

François Duchenne: Writing – original draft, Formal analysis. Florian Zellweger: Writing – review & editing, Data curation. Martin M. Gossner: Writing – review & editing, Methodology, Conceptualization.

Kurt Bollmann: Writing – review & editing, Resources, Methodology, 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.

## Acknowledgements

We are grateful to the Swiss Federal Office for the Environment (FOEN) for providing the biodiversity data and the many volunteers and professionals that participated in the data collection and curation within this scheme. We also want to acknowledge Tobias Roth and Roman Bühler for providing invaluable data and insights on the monitoring schemes. We thank Andri Baltensweiler for providing data and support in predictor variable selection, as well as Marco Moretti and Ariel Bergamini for their help in selecting suitable traits across the different taxa. We also thank Janneke Hille Ris Lambers, Elisa Barreto Pereira and Lucía Villarroya-Villalba for their useful comments and suggestions throughout the manuscript preparation. Finally, we would like to thank
two anonymous reviewers who provided suggestions that improved our manuscript.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ecolind.2025.113855.

### Data availability

Species data are available from Hintermann & Weber AG with restriction and are not accessible to the public with spatial coordinates, which have therefore been modified. The raw data for species can be requested by contacting reinach@hintermannweber.ch at https://www.biodiversitymonitoring.ch/index.php/en/data/data-orders. Non-restricted data and code used for the manuscript analyses are publicly available on GitHub at https://github.com/jvigues/Environmental-forest-diversity-drivers. Environmental variable datasets are already published and publicly available (Waser et al. 2017; Ginzler 2021; Baltensweiler et al. 2021; Zellweger et al. 2024).

### References

Aerts, R., Honnay, O., 2011. Forest restoration, biodiversity and ecosystem functioning. BMC Ecol. 11, 29. https://doi.org/10.1186/1472-6785-11-29.

Aguirre-Gutiérrez, J., WallisDeVries, M.F., Marshall, L., et al., 2017. Butterflies show different functional and species diversity in relationship to vegetation structure and land use. Glob. Ecol. Biogeogr. 26, 1126–1137. https://doi.org/10.1111/geb.12622.

Ampoorter, E., Barbaro, L., Jactel, H., et al., 2020. Tree diversity is key for promoting the diversity and abundance of forest-associated taxa in Europe. Oikos 129, 133–146. https://doi.org/10.1111/oik.06290.

Andrén, H., Andren, H., 1994. Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos 71, 355. https://doi.org/10.2307/3545823.

Astor, T., von Proschwitz, T., Strengbom, J., et al., 2017. Importance of environmental and spatial components for species and trait composition in terrestrial snail communities. J. Biogeogr. 44, 1362–1372. https://doi.org/10.1111/jbi.12946.

Bae, S., Müller, J., Lee, D., et al., 2018. Taxonomic, functional, and phylogenetic diversity of bird assemblages are oppositely associated to productivity and heterogeneity in temperate forests. Remote Sens. Environ. 215, 145–156. https://doi.org/10.1016/j.rse.2018.05.031.

Baeten, L., Hermy, M., Van Daele, S., Verheyen, K., 2010. Unexpected understory community development after 30 years in ancient and post-agricultural forests. J. Ecol. 98, 1447–1453. https://doi.org/10.1111/j.1365-2745.2010.01711.x.

Baltenweiler, A., Walthert, L., Hanewinkel, M., et al., 2021. Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland. Geoderma Reg. 27, e00437. https://doi.org/10.1016/j.geodrs.2021.e00437.

Bartó K (2010) MuMIn: Multi-Model Inference. CRAN: Contributed Packages.

Bazzato, E., Lallai, E., Caria, M., et al., 2023. Focusing on the role of abiotic and biotic drivers on cross-taxon congruence. Ecol. Indic. 151. https://doi.org/10.1016/j.ecolind.2023.110323.

Bouvet, A., Paillet, Y., Archaux, F., et al., 2016. Effects of forest structure, management and landscape on bird and bat communities. Environ. Conserv. 43, 148–160. https://doi.org/10.1017/S0376892915000363.

Brändli U, Abegg M, Allgaier Leuch B (2020) Schweizerisches Landesforinstentar. Ergebnisse der vierten Erhebung 2009–2017. Birmensdorf.

Brown, J.H., Gillooly, J.F., Allen, A.P., et al., 2004. Toward a metabolic theory of ecology. Ecology 85, 1771–1789. https://doi.org/10.1890/03-9000.

Brüllhardt, M., Rotach, P., Schleppi, P., Bugmann, H., 2020. Vertical light transmission profiles in structured mixed deciduous forest canopies assessed by UAV-based hemispherical photography and photogrammetric vegetation height models. Agric. For. Meteorol. 281, 107843. https://doi.org/10.1016/j.agrformet.2019.107843.

Burrascano, S., Chianucci, F., Trentanovi, G., et al., 2023. Where are we now with European forest multi-taxon biodiversity and where can we head to? Biol. Conserv. 284, 110176. https://doi.org/10.1016/j.biocon.2023.110176.

Cadotte, M.W., Tucker, C.M., 2018. Difficult decisions: strategies for conservation prioritization when taxonomic, phylogenetic and functional diversity are not spatially congruent. Biol. Conserv. 225, 128–133. https://doi.org/10.1016/j.biocon.2018.06.014.

Cardinale, B.J., Duffy, J.E., Gonzalez, A., et al., 2012. Biodiversity loss and its impact on humanity. Nature 486, 59–67. https://doi.org/10.1038/nature11148.

Chao A, Gotelli NJ, Hsieh TC, et al (2014) Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies.

Coverdale, T.C., Davies, A.B., 2023. Unravelling the relationship between plant diversity and vegetation structural complexity: a review and theoretical framework. J. Ecol. 111, 1378–1395. https://doi.org/10.1111/1365-2745.14068.

De Frenne, P., Lenoir, J., Luoto, M., et al., 2021. Forest microclimates and climate change: Importance, drivers and future research agenda. Glob. Chang. Biol. 27, 2279–2297. https://doi.org/10.1111/gcb.15569.

De Pauw, K., Meeussen, C., Govaert, S., et al., 2021. Taxonomic, phylogenetic and functional diversity of understory plants respond differently to environmental conditions in European forest edges. J. Ecol. 109, 2629–2648. https://doi.org/10.1111/1365-2745.13671.

Dennis, R.L.H., 2004. Butterfly habitats, broad-scale biotope affiliations, and structural exploitation of vegetation at finer scales: the matrix revisited. Ecol. Entomol. 29, 744–752. https://doi.org/10.1111/j.0307-6946.2004.00646.x.

Devictor, V., Mouillot, D., Meynard, C., et al., 2010. Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: the need for integrative conservation strategies in a changing world. Ecol. Lett. 13, 1030–1040. https://doi.org/10.1111/j.1461-0248.2010.01493.x.

Díaz, S., Kattge, J., Cornelissen, J.H.C., et al., 2016. The global spectrum of plant form and function. Nature 529, 167–171. https://doi.org/10.1038/nature16489.

Díaz, S., Lavorel, S., de Bello, F., et al., 2007. Incorporating plant functional diversity effects in ecosystem service assessments. Proc. Natl. Acad. Sci. 104, 20684–20689. https://doi.org/10.1073/pnas.0704716104.

Diniz-Filho, J.A.F., Loyola, R.D., Raia, P., et al., 2013. Darwinian shortfalls in biodiversity conservation. Trends Ecol. Evol. 28, 689–695. https://doi.org/10.1016/j.tree.2013.09.003.

Ebeling, A., Hines, J., Hertzog, L.R., et al., 2018. Plant diversity effects on arthropods and arthropod-dependent ecosystem functions in a biodiversity experiment. Basic Appl. Ecol. 26, 50–63. https://doi.org/10.1016/j.bae.2017.09.014.

Ellenberg H (1988) Vegetation Ecology of Central Europe, 4th edn. Cambridge University Press, Cambridge.

Faith, D.P., 2008. Threatened species and the potential loss of phylogenetic diversity: conservation scenarios based on estimated extinction probabilities and phylogenetic risk analysis. Conserv. Biol. 22, 1461–1470. https://doi.org/10.1111/j.1523-1739.2008.01068.x.

Falkengren-Grerup, U., Tyler, G., 1993. Experimental evidence for the relative sensitivity of deciduous forest plants to high soil acidity. For. Ecol. Manage. 60, 311–326. https://doi.org/10.1016/0378-1127(93)90086-3.

Fleishman, E., Noss, R.F., Noon, B.R., 2006. Utility and limitations of species richness metrics for conservation planning. Ecol. Indic. 6, 543–553. https://doi.org/10.1016/j.ecolind.2005.07.005.

Flynn, D.F.B., Mirotnick, N., Jain, M., et al., 2011. Functional and phylogenetic diversity as predictors of biodiversity-ecosystem-function relationships. Ecology 92, 1573–1581. https://doi.org/10.1890/10-1245.1.

Frei, E.R., Gossner, M.M., Vitasse, Y., et al., 2022. European beech dieback after premature leaf senescence during the 2018 drought in northern Switzerland. Plant Biol. 24, 1132–1145. https://doi.org/10.1111/plb.13467.

Frey, S.J.K., Hadley, A.S., Johnson, S.L., et al., 2016. Spatial models reveal the microclimatic buffering capacity of old-growth forests. Sci. Adv. 2. https://doi.org/10.1126/sciadv.1501392.

GBIF Secretariat (2023) GBIF Backbone Taxonomy. Checklist dataset. https://doi.org/10.15468/39omei. Accessed 5 Nov 2023.

Gehrig-Fasel, J., Guisan, A., Zimmermann, N.E., 2007. Tree line shifts in the Swiss Alps: climate change or land abandonment? J. Veg. Sci. 18, 571–582. https://doi.org/10.1111/j.1654-1103.2007.tb02571.x.

Gheoca, V., Benedek, A.M., Schneider, E., 2023. Taxonomic and functional diversity of land snails reflects habitat complexity in riparian forests. Sci. Rep. 13, 9808. https://doi.org/10.1038/s41598-023-36896-6.

Ginzler C (2021) Vegetation Height Model NFI.

Gossner, M.M., Getzin, S., Lange, M., et al., 2013. The importance of heterogeneity revisited from a multiscale and multitaxa approach. Biol. Conserv. 166, 212–220. https://doi.org/10.1016/j.biocon.2013.06.033.

Gotelli, N.J., Colwell, R.K., 2001. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol. Lett. 4, 379–391. https://doi.org/10.1046/j.1461-0248.2001.00230.x.

Gu, Y., Han, S., Zhang, J., et al., 2020. Temperature-dominated driving mechanisms of the plant diversity in temperate forests. Northeast China Forests 11, 227. https://doi.org/10.3390/f11020227.

Hacala, A., Lafage, D., Prinzing, A., et al., 2021. Drivers of taxonomic, functional and phylogenetic diversities in dominant ground-dwelling arthropods of coastal heathlands. Oecologia 197, 511–522. https://doi.org/10.1007/s00442-021-05032-4.

Hahs, A.K., Fournier, B., Aronson, M.F.J., et al., 2023. Urbanisation generates multiple trait syndromes for terrestrial animal taxa worldwide. Nat. Commun. 14, 4751. https://doi.org/10.1038/s41467-023-39746-1.

Hartig F (2016) DHARMA: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. CRAN: Contributed Packages.

Hawkins, B.A., Field, R., Cornell, H.V., et al., 2003. Energy, water and broad-scale geographic patterns of species richness. Ecology 84, 3105–3117. https://doi.org/10.1890/03-8006.

Heidrich, L., Bae, S., Levick, S., et al., 2020. Heterogeneity-diversity relationships differ between and within trophic levels in temperate forests. Nat. Ecol. Evol. 4, 1204–1212. https://doi.org/10.1038/s41559-020-1245-z.

Hettenbergerová, E., Horsák, M., Chandran, R., et al., 2013. Patterns of land snail assemblages along a fine-scale moisture gradient. Malacologia 56, 31–42. https://doi.org/10.4002/040.056.0227.

Jonsson, M., Kardol, P., Gundale, M.J., et al., 2015. Direct and indirect drivers of moss community structure, function, and associated microfauna across a successional gradient. Ecosystems 18, 154–169. https://doi.org/10.1007/s10021-014-9819-8.

King, T.W., Vynne, C., Miller, D., et al., 2021. The influence of spatial and temporal scale on the relative importance of biotic vs. abiotic factors for species distributions. Divers. Distrib. 27, 327–343. https://doi.org/10.1111/ddi.13182.
Landolt, E., Bäumler, B., Erhardt, A., et al., 2010. Flora Indicativa. Ökologische Zeigerwerte Und Biologische Kennzeichen Zur Flora Der Schweiz Und Der Alpen, 2nd edn. Haupt, Bern.

Lazarina, M., Michailidou, D.-E., Tsianou, M., Kallimanis, A.S., 2023. How biodiversity, climate and landscape drive functional redundancy of British butterflies. Insects 14, 722. https://doi.org/10.3390/insects14090722.

Le Bagousse-Pinguet, Y., Soliveres, S., Gross, N., et al., 2019. Phylogenetic, functional, and taxonomic richness have both positive and negative effects on ecosystem multifunctionality. Proc. Natl. Acad. Sci. U. S. A. 116, 8419–8424. https://doi.org/10.1073/pnas.1815727116.

Leingärtner, A., Krauss, J., Steffan-Dewenter, I., 2014. Species richness and trait composition of butterfly assemblages change along an altitudinal gradient. Oecologia 175, 613–623. https://doi.org/10.1007/s00442-014-2917-7.

Li, D., 2018. hillR: taxonomic, functional, and phylogenetic diversity and similarity through Hill Numbers. J. Open Source. Softw. 3, 1041. https://doi.org/10.21105/joss.01041.

MacArthur, R.H., MacArthur, J.W., 1961. On bird species diversity. Ecology 42, 594–598.

Martin, K., Sommer, M., 2004. Relationships between land snail assemblage patterns and soil properties in temperate-humid forest ecosystems. J. Biogeogr. 31, 531–545. https://doi.org/10.1046/j.1365-2699.2003.01005.x.

Mazel, F., Pennell, M.W., Cadotte, M.W., et al., 2018. Prioritizing phylogenetic diversity captures functional diversity unreliably. Nat. Commun. 9, 2888. https://doi.org/10.1038/s41467-018-05126-3.

Meusburger, K., Trotsiuk, V., Schmidt-Walter, P., et al., 2022. Soil–plant interactions modulated water availability of swiss forests during the 2015 and 2018 droughts. Glob. Chang. Biol. 28, 5928–5944. https://doi.org/10.1111/gcb.16332.

Moeslund, J.E., Arge, L., Bocher, P.K., et al., 2013. Topography as a driver of local terrestrial vascular plant diversity patterns. Nord. J. Bot. 31, 129–144. https://doi.org/10.1111/j.1756-1051.2013.00082.x.

Naeem, S., Duffy, J.E., Zavaleta, R., 2012. The functions of biological diversity in an age of extinction. Science 336, 1401–1406. https://doi.org/10.1126/science.1215855.

Niinemets, Ü., Tobias, M., 2019. Canopy leaf area index at its higher end: dissection of structural controls from leaf to canopy scales in bryophytes. New Phytol. 223, 118–133. https://doi.org/10.1111/nph.15767.

Nóbrega, C.C., Brando, P.M., Silvério, D.V., et al., 2019. Effects of experimental fires on the phylogenetic and functional diversity of woody species in a neotropical forest. For. Ecol. Manage. 450, 117497. https://doi.org/10.1016/j.foreco.2019.117497.

Oostermeijer, J.G.B., van Swaay, C.A.M., 1998. The relationship between butterflies and environmental indicator values: a tool for conservation in a changing landscape. Biol. Conserv. 86, 271–280. https://doi.org/10.1016/S0006-3207(98)00040-8.

Pavoine, S., Vela, E., Gachet, S., et al., 2011. Linking patterns in phylogeny, traits, abiotic variables and space: a novel approach to linking environmental filtering and plant community assembly. J. Ecol. 99, 165–175. https://doi.org/10.1111/j.1365-2745.2010.01743.x.

Pellissier, L., Alvarez, N., Espíndola, A., et al., 2013. Phylogenetic alpha and beta diversities of butterfly communities correlate with climate in the western Swiss Alps. Ecography 36, 541–550. https://doi.org/10.1111/j.1600-0587.2012.07716.x.

Piwczyński, M., Puchałka, R., Ulrich, W., 2016. Influence of tree plantations on the phylogenetic structure of understory plant communities. For. Ecol. Manage. 376, 231–237. https://doi.org/10.1016/j.foreco.2016.06.011.

Potapov, P., Hansen, M.C., Laestadius, L., et al., 2017. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3. https://doi.org/10.1126/sciadv.1600821.

Preston, F.W., 1962. The canonical distribution of commonness and rarity: part I. Ecology 43, 185. https://doi.org/10.2307/1931976.

QGIS Development Team (2022) QGIS Geographic Information System.

R Core Team (2022) R: A language and environment for statistical computing.

Rocha-Santos, L., Faria, D., Mariano-Neto, E., et al., 2023. Taxonomic, phylogenetic and functional responses of plant communities in different life-stages to forest cover loss. Perspect. Ecol. Conserv. 21, 136–142. https://doi.org/10.1016/j.pecon.2023.03.001.

Scheffers, B.R., De Meester, L., Bridge, T.C.L., et al., 2016. The broad footprint of climate change from genes to biomes to people. Science (1979) 354. https://doi.org/10.1126/science.aaf7671.

Schielzeth, H., 2010. Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol. 1, 103–113. https://doi.org/10.1111/j.2041-210x.2010.00012.x.

Seidel, D., Ehbrecht, M., Puettmann, K., 2016. Assessing different components of three-dimensional forest structure with single-scan terrestrial laser scanning: a case study. For. Ecol. Manage. 381, 196–208. https://doi.org/10.1016/j.foreco.2016.09.036.

Stefańska-Krzaczek, E., Staniszek-Kik, M., Szczepańska, K., Szymura, T.H., 2019. Species diversity patterns in managed Scots pine stands in ancient forest sites. PLoS One 14, e0219620. https://doi.org/10.1371/journal.pone.0219620.

Stein, A., Gerstner, K., Kreft, H., 2014. Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales. Ecol. Lett. 17, 866–880. https://doi.org/10.1111/ele.12277.

Swenson, N.G., 2011. The role of evolutionary processes in producing biodiversity patterns, and the interrelationships between taxonomic, functional and phylogenetic biodiversity. Am. J. Bot. 98, 472–480. https://doi.org/10.3732/ajb.1000289.

swisstopo (2017) Nationales ökologisches Netzwerk REN, Lebensraum Wald. https://www.swisstopo.admin.ch/en/geodata/landscape/tlm3d.html. Accessed 4 Aug 2023.

Tews, J., Brose, U., Grimm, V., et al., 2004. Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures. J. Biogeogr. 31, 79–92. https://doi.org/10.1046/j.0305-0270.2003.00994.x.

Tinya, F., Kovács, B., Bidló, A., et al., 2021. Environmental drivers of forest biodiversity in temperate mixed forests – a multi-taxon approach. Sci. Total Environ. 795, 148720. https://doi.org/10.1016/j.scitotenv.2021.148720.

Toivonen, J., Kangas, A., Maltamo, M., et al., 2023. Assessing biodiversity using forest structure indicators based on airborne laser scanning data. For. Ecol. Manage. 546, 121376. https://doi.org/10.1016/j.foreco.2023.121376.

Tucker, C.M., Cadotte, M.W., Carvalho, S.B., et al., 2017. A guide to phylogenetic metrics for conservation, community ecology and macroecology. Biol. Rev. 92, 698–715. https://doi.org/10.1111/brv.12252.

van der Plas, F., Ratcliffe, S., Ruiz-Benito, P., et al., 2018. Continental mapping of forest ecosystem functions reveals a high but unrealised potential for forest multifunctionality. Ecol. Lett. 21, 31–42. https://doi.org/10.1111/ele.12868.

van Schalkwyk, J., Gaigher, R., Pryke, J.S., Samways, M.J., 2021. Within-corridor heterogeneity is more important than corridor design for maintaining butterfly functional and taxonomic diversity. J. Appl. Ecol. 58, 2734–2746. https://doi.org/10.1111/1365-2664.14006.

Vitt, D.H., Finnegan, L., House, M., 2019. Terrestrial bryophyte and lichen responses to canopy opening in pine-moss-lichen forests. Forests 10, 233. https://doi.org/10.3390/f10030233.

Von Matt, C.N., Muelchi, R., Gudmundsson, L., Martius, O., 2024. Compound droughts under climate change in Switzerland. Nat. Hazards Earth Syst. Sci. 24, 1975–2001. https://doi.org/10.5194/nhess-24-1975-2024.

Vuidot, A., Paillet, Y., Archaux, F., Gosselin, F., 2011. Influence of tree characteristics and forest management on tree microhabitats. Biol. Conserv. 144, 441–450. https://doi.org/10.1016/j.biocon.2010.09.030.

Waser, L., Ginzler, C., Rehush, N., 2017. Wall-to-wall tree type mapping from countrywide airborne remote sensing surveys. Remote Sens (Basel) 9, 766. https://doi.org/10.3390/rs9080766.

Weber, D., Hintermann, U., Zangger, A., 2004. Scale and trends in species richness: considerations for monitoring biological diversity for political purposes. Global Ecol. Biogeogr. 13, 97–104. https://doi.org/10.1111/j.1466-882X.2004.00078.x.

Whittaker, R.H., 1972. Evolution and measurement of species diversity. Taxon 21, 213–251. https://doi.org/10.2307/1218190.

Winter, M., Devictor, V., Schweiger, O., 2013. Phylogenetic diversity and nature conservation: where are we? Trends Ecol. Evol. 28, 199–204. https://doi.org/10.1016/j.tree.2012.10.015.

Wood, S.N., 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Statist. Soc. B 73, 3–36.

Zellweger, F., Baltensweiler, A., Ginzler, C., et al., 2016. Environmental predictors of species richness in forest landscapes: abiotic factors versus vegetation structure. J. Biogeogr. 43, 1080–1090. https://doi.org/10.1111/jbi.12696.

Zellweger, F., Braunisch, V., Morsdorf, F., et al., 2015. Disentangling the effects of climate, topography, soil and vegetation on stand-scale species richness in temperate forests. For. Ecol. Manage. 349, 36–44. https://doi.org/10.1016/j.foreco.2015.04.008.

Zellweger, F., Sulmoni, E., Malle, J.T., et al., 2024. Microclimate mapping using novel radiative transfer modelling. Biogeosciences 21, 605–623. https://doi.org/10.5194/bg-21-605-2024.

Zimmermann, N.E., Kienast, F., 1999. Predictive mapping of alpine grasslands in Switzerland: species versus community approach. J. Veg. Sci. 10, 469–482. https://doi.org/10.2307/3237182.