# The Limited Legacy of Post-Glacial Recolonization in the Floristic Patterns of the European Alps

Lara M Wootton, Florian C Boucher, Julien Renaud, Pierre G Valla, Gabriele

Midolo, Zdeňka Lososová, Wilfried Thuiller, Sébastien Lavergne

► To cite this version:

Lara M Wootton, Florian C Boucher, Julien Renaud, Pierre G Valla, Gabriele Midolo, et al.. The Lim- ited Legacy of Post-Glacial Recolonization in the Floristic Patterns of the European Alps. Systematic Botany, 2025, 50 (1), pp.83-98. 10.1600/036364425X17466502618876 . hal-05351338

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Accepted manuscript / DOI: 10.1600/036364425X17466502618876

WOOTTON ET AL.: LEGACY OF POST-GLACIAL RECOLONIZATION IN THE ALPS

# The Limited Legacy of Post-glacial Recolonization in the Floristic Patterns of the European Alps

Lara M Wootton¹,⁵, Florian C Boucher¹, Julien Renaud¹, Pierre G Valla², Gabriele Midolo³, Zdeňka Lososová⁴, Wilfried Thuiller¹, and Sébastien Lavergne¹

¹University Grenoble Alpes, University Savoie Mont Blanc, CNRS, LECA, F-38000, Grenoble, France

²University Grenoble Alpes, University Savoie Mont Blanc, CNRS, IRD, University Gustave Eiffel, ISTerre, F-38000, Grenoble, France

³Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha-Suchdol, Czech Republic

⁴Department of Botany and Zoology, Faculty of Science, Masaryk University, Brno, Czech Republic

⁵Author for correspondence: Lara M Wootton (larawootton@gmail.com)
**Abstract**– Past glacial periods were highly disruptive to plant species distributions in mid-latitude mountain belts, such as the European Alps, and drove many species to survive in peripheral or nunatak refugia. Progressive glacial retreat during the late Pleistocene and Holocene is thought to have triggered a mass recolonization event in the European Alps. Here, we asked whether this recolonization event has left a spatial legacy in the floristic patterns of the extant Alpine flora, and if so, how strongly this determines floristic patterns in comparison to present-day landscape and climatic drivers. We built on approximately 6 million data points sourced from the Global Biodiversity Information Facility and herbaria to build plant species ranges within a 10 x 10 km grid system laid across the European Alps, and then calculated species assemblage turnover across the grid. Additionally, we used a recent species-level molecular phylogeny encompassing over 80% of the Alpine flora to calculate standardized phylogenetic diversity, phylogenetic endemism and phylogenetic turnover between grid cells. Using spatial autoregressive and generalised dissimilarity modelling, we showed that all floristic measures vary significantly, albeit weakly, with three key post-glacial variables: climate change velocity, time since deglaciation, and distance from the nearest refugium. Unexpectedly, differences in species' dispersal ability did not explain these patterns. The post-glacial variables have little explanatory power relative to contemporary climate and landscape drivers. We conclude that postglacial recolonization of the Alps is largely complete for the flora as a whole, and
that the spatial structure of the extant flora is primarily driven by the contemporary
environmental conditions.

**Keywords**– Alpine, dispersal, GDM, Pleistocene, refugia, regional flora, spatial phylogenetics.

The Quaternary era was a time of turmoil for plant diversity in the European Alps (hereafter ‘the Alps’). During this time, strong climate fluctuations occuring over scales of 10-100k years led to the repeated formation and retreat of vast valley glaciers (Rutherford and D’Hondt 2000). These glaciers frequently covered much of the Alpine range and occasionally extended into the adjacent forelands (Seguinot et al. 2018; Valla et al. 2021). Paleo-glacier fluctuations and erosion continuously altered the extent and type of habitat available to Alpine plants. As glaciers advanced, alpine species were forced to migrate and survive either in peripheral refugia (disjoint patches of suitable habitat located around the border of the Alps, Schonswetter et al. 2005), in nunatak refugia (ice-free, high-elevation areas located on peaks protruding from the glaciers), or through a combination of these strategies (Bettin et al. 2007; Holderegger and Thiel-Egenter 2009; Escobar García et al. 2012; Rota et al. 2023). The glacial retreat during the late Pleistocene to Holocene has since triggered a mass recolonization of the Alps, with many species expanding from their respective refugia to newly accessible habitats (Parisod 2008; Schonswetter et al. 2005). The extent to
which the contemporary spatial configuration of the Alpine flora is a result of post-
glacial recolonization patterns, as opposed to spatial variability in contemporary
environmental drivers, such as temperature and precipitation, is not fully known.

The post-glacial range dynamics of individual mountain species have been highly
idiosyncratic (Carnicero et al. 2022). Many species, such as *Hypochaeris uniflora* and
*Arabis alpina*, have successfully recolonised the entire Alps (Mráz et al. 2007;
Rogivue et al. 2018), while others have remained confined to their refugia (Kropf et
al. 2002; Tribsch 2004; Boucher et al. 2021; Voisin et al. 2023). Whether species'
ranges have expanded or contracted during the late Pleistocene-Holocene depends
largely on their elevational niche, as cold-adapted high-elevation species have tended
to either retreat upslope into warm-stage refugia (e.g., *Berardia subacaulis*, Guerrina
et al. 2022), or maintain relatively stable range structures (e.g., *Androsace lactea*,
Schneeweiss and Schönswetter 2010). Nonetheless, exceptions exist, such as the
range expansion of the high-elevation species *Primula farinosa* during the late
Quaternary (Theodoridis et al. 2017). A species' ability to recolonize the Alps may
also be influenced by historical contingency, including the type and location of
refugia (i.e., peripheral vs nunatak) where the species survived successive glaciations
(Windmaißer et al. 2016; Schönswetter and Schneeweiss 2019). Variation in dispersal
ability, however, is likely to be one of the more influential variables in determining
post-glacial range dynamics (Baselga et al. 2012; Malanson et al. 2022; Guerrina et al. 2024). For example, Zimmer et al. (2018) found that dispersal limitation was a major constraint for plant establishment in alpine communities, which drove an over-representation of wind-dispersed species in recently deglaciated areas.

Despite idiosyncrasy in the movement of individual species, there is some indication that broad patterns in recolonization history exist, and have contributed to shaping the spatial configuration of the modern Alpine flora. Evidence suggests that species often followed similar migratory pathways during recolonization (Parisod 2008; Alvarez et al. 2009; Rogivue et al. 2018), and these pathways are reflected in the floristic patterns of Alpine assemblages (Jiménez-Alfaro et al. 2021). Suture zones in species composition coincide with recolonization barriers or meeting points from refugial expansion (Thiel-Egenter et al. 2011). In addition, local species assemblages in long-term peripheral refugia exhibit high endemism and an abundance of evolutionarily isolated lineages, resulting in the phylogenetic overdispersion of species assemblages (Tribsch 2004; Casazza et al. 2008; Smyčka et al. 2018). This pattern suggests that many species surviving glacial cycles in the ice-free refugia have struggled to recolonize during post-glacial periods, resulting in a decrease in species range-filling with distance from peripheral refugia (Dullinger et al. 2012).
Given the above evidence, we would expect that the effect of the Pleistocene
glaciations on Alpine landscapes translates into specific spatial signatures of floristic
structure. Such an expectation implies differences in plant assemblage characteristics
between areas that have been ice-free for much of the last glacial cycle, and areas that
were more recently deglaciated or whose climate has changed particularly rapidly
since the LGM, i.e., areas with high climate change velocity (Loarie et al. 2009).

Indices such as species richness, phylogenetic diversity and phylogenetic endemism
should be high in refugial areas due to the long term accumulation of species and
lineages that predate the Pleistocene cycles (Casazza et al. 2008), such as the late
Tertiary relicts *Berardia subacaulis* and *Saxifraga florulenta* (Patsiou et al. 2014).

Conversely, these indices should decrease away from refugia and in areas with high
climate velocity, due to both lags in range filling and environmental filtering for
species with greater dispersal ability, which can better track environmental change
(Sandel et al. 2011). The latter factors should also drive differences in turnover of
species and phylogenetic composition. We hypothesize that differences in dispersal
ability are the predominant drivers of these patterns, leading to assemblages in
recently deglaciated and high velocity areas being composed of better dispersers
(Sandel et al. 2011; Smyčka et al. 2018).
To date, studies on the glacial legacy of floristic patterns in the Alps have primarily focused on vegetation above the treeline, with emphasis on its endemic component (Tribsch 2004; Thiel-Egenter et al. 2011; Essl et al. 2011; Smyčka et al. 2018; Parisod 2022). However, lower-elevation montane species were equally, if not more so, impacted by glaciations (Kropf et al. 2003; Birks and Willis 2008) and currently contribute to a large proportion of the Alpine flora (Aeschimann et al. 2004; Wootton et al 2024). Including both high-elevation and lower-elevation species may provide a more comprehensive signal of late-Pleistocene and Holocene effects in the spatial configuration of the contemporary flora. Although it is possible that the Alpine flora has tracked its environment in the ca. 21000 years since the LGM, and that contemporary environmental factors such as temperature and precipitation form the most impactful drivers of distribution, we believe that there is sufficient evidence of a post-glacial recolonization impact to detect its legacy in contemporary spatial patterns.

This study aims to address two main questions. First, we ask whether floristic patterns in the Alps can be attributed to post-glacial variables, i.e., whether a spatial legacy of recolonization still exists in the general, spatial structure of the Alpine flora. Second, we ask how strongly these post-glacial variables determine floristic patterns in comparison to present-day landscape and climatic drivers. We focus on the most
recent glacial cycle as being the one that had the largest imprint of all Pleistocene
climatic cycles, although older glacial extents may have differed in geometry or size
(Ehlers et al. 2011). Dispersal characteristics and phylogenetic information were
extracted for approximately 2700 Alpine species from recently published databases
(Lososová et al. 2023; Lavergne et al. 2024), and we incorporated nearly 6 million
occurrence records sourced from Global Biodiversity Information Facility (GBIF) and
herbaria to determine species ranges across a grid system. Using spatial
autoregressive and generalised dissimilarity modelling, we analyzed how species
assemblages vary across the Alps as a result of post-glacial environmental change.

MATERIALS AND METHODS

***Study area***– We focus on the Alps, a mountain range spanning over 200,000 km² from south-eastern France to Slovenia. The Alps are a well-known hotspot of plant biodiversity in Europe (Väre et al. 2003), as they are relatively species rich (approximately 4350 angiosperm taxa, Aeschimann et al. 2004), with a fairly high rate of plant endemism (about 13%; Aeschimann et al. 2004). Current climate in the Alps varies from temperate in the low-elevation valleys, to some of the coldest climates experienced by angiosperms on Earth towards the highest elevations (Körner 2011, see also Ozenda 1985 for detailed climatic information). These extreme conditions would have been even harsher during the glacial periods of the
Pleistocene, when, at its coldest, large glaciers extended into the forelands of the Alps
(Ehlers et al. 2011; Monegato et al. 2017).

***Occurrence data and grid system***– The extent of the Alps was defined following
the delineation of the Global Mountain Biodiversity Assessment (GMBA) v2
(Snethlage et al. 2022a; Snethlage et al. 2022b). We divided the whole Alpine range
into a grid of 10 x 10 km cells (n = 1611), which is of a finer resolution than most
grid-based studies of the Alps (Gugerli et al. 2008; Thiel-Egenter et al. 2011; Smyčka
et al. 2022). Grid cells partially within the Alps were retained if more than 50% of
their area fell within the Alpine boundary.

We then downloaded all georeferenced occurrence records between 1980 and 2023 for
indigenous Alpine angiosperm species from the online database Global Biodiversity
Information Facility (GBIF, http://www.gbif.org/) with an accuracy of 10 km or better. The
cut-off of 1980 was chosen to ensure that the precision of the occurrence data was in-line
with the spatial scale of our study. We retrieved nearly 6 million occurrence records from
across the Alps. Duplicate and inaccurate occurrence records were removed using the R
package *CoordinateCleaner* v2.0-20 (Zizka et al. 2019). These data were supplemented by
relevé survey data from an in-house database. Species taxonomy was updated using the
Euro+Med PlantBase (http://www.europlusmed.org). These data were then used to create
species occurrence lists for each grid cell. There was a strong difference in available data
between countries, and therefore the majority of these data originated from France and
Switzerland.
**Indices of floristic structure**– The density of occurrence points, or the sampling effort, varied considerably across the Alps (Fig. 1.A). As a consequence, the direct comparison of floristic measures, such as species richness, between grid cells would be statistically unsound. Therefore, we filtered the grid to retain only well-sampled cells, considered representative of true plant biodiversity. Sampling completeness of each grid cell was calculated using the formula 1 – *f*₁/*n* (Chao and Jost 2012), where *f*₁ is the number of species represented by exactly one observation in the grid cell *n*. We used this measure to retain only grid cells at a 95% or greater sampling completeness. In addition, we removed grid cells that had fewer than 250 observations in total, as the calculation of sampling completeness needs a reasonable sample size to be accurate (Chao and Jost 2012). These filtering steps resulted in the removal of 1088 grid cells. Due to the spatial bias of the occurrence records, most of the grid cells that needed to be removed due to low sampling coverage originated from the eastern and southern Alps. After filtering, our data set contained distribution information for more than 2600 species.

We then accounted for remaining differences in sampling completeness by estimating
species richness at 95% sampling completeness in any given grid cell. This method is
similar to sample-based rarefaction, but the interpolation is based on sampling
completeness rather than sampling effort. As a result, the total number of observations in
two given grid cells may be different, but how well these observations capture the true
biodiversity within the cells should be equivalent (Chao and Jost 2012). The interpolation
was performed using the estimateD() function in the R package iNEXT v3.0.0 (Hsieh et al.
2022). The results of this analysis should not be interpreted as representing the absolute
species richness in each grid cell, but rather as relative, and comparable, estimates of
species richness.

Next, we used an existing dated species-level megaphylogeny (Lavergne et al. 2025; Wootton et al 2025), comprising 82% of the Alpine angiosperm flora to calculate phylogenetic diversity standardized for species richness effects (Faith 1992) for the species assemblage within each grid cell (there was phylogenetic information for over 90% of species per grid cell). The standardization accounts for the dependence between species richness and phylogenetic diversity by comparing the observed phylogenetic diversity to that of a null distribution of randomised communities. We used the ses.pd() function from the R package *picante* v1.8.2 (Kembel et al. 2010), with a distribution of 999 null communities created by randomly shuffling tips across the phylogeny. In addition, we calculated phylogenetic endemism using the R package *phyregion* (Daru et al. 2020). Phylogenetic endemism is the degree to which phylogenetic diversity is restricted to each part of a study area (Rosauer et al. 2009; Mishler 2023), where the study area here is all grid cells retained after filtering. Regions of high phylogenetic endemism are characterised by an abundance of evolutionarily isolated and range restricted lineages.
The maximum dispersal distance of each species was estimated following the trait-
based method proposed by Tamme et al. (2014). We obtained data for four key
dispersal traits, namely dispersal syndrome, growth form, plant height and seed mass,
from the database of European Vegetation, Habitats and Flora (www.floraveg.eu,
Lososová et al. 2023). Dispersal data was available for at least 82% of species per
grid cell, with the vast majority of cells having dispersal data for over 90% of species.
Maximum dispersal distances were calculated using the dispeRsal() function provided
in Tamme et al. 2014, which fits mixed effect models based on species traits and
taxonomy. For each species, we ran dispeRsal() four times using different
combinations of the dispersal traits, while using species taxonomy (either as family or
order) as a random variable, following the models outlined in Lososová et al. 2023.
The dispersal distance for each species was determined to be the average of dispersal
distances obtained from each of the models. Estimated species dispersal distances
ranged from 0.2 m (*Anagallis minima*) to 1854 m (*Larex decidua*, mean = 4.7, sd =
7.3, n = 2742). Finally, the dispersal ability of the assemblage within each grid cell
was calculated as the mean dispersal distance of all species occurring in a given grid
cell.
***Environmental data***– We grouped our set of predictor variables into three categories: postglacial (time since deglaciation, distance from the nearest glacial refugium, and climate change velocity), contemporary climate (mean annual temperature, annual precipitation amount and potential evapotranspiration (PET)) and landscape (bedrock type and terrain ruggedness, as a measured by the standard deviation of elevation in each grid cell).

We extracted the average time since the most recent deglaciation event (hereafter
time since deglaciation) for each grid cell from a model of the glacial dynamics of the
last glacial cycle in the Alps at a temporal resolution of 1,000 years, accessed from
Seguinot et al. (2018 (Fig. 1.B). In order to include time since deglaciation as a
predictor variable in our models, we needed to remove an additional 14 grid cells that
had not been glaciated during the last glacial cycle from our data set.

We adopted a definition of peripheral refugia from Schonswetter et al. (2005), who
delimited refugia using geological and paleo-environmental information together with
phylogeographic patterns. The exact position and extent of nunatak refugia in the
Alps, however, has not yet been clearly circumscribed. We defined them here using a
spatial reconstruction of Alpine areas that extended above the LGM glaciers, as
determined by geomorphological inference of trimlines and bedrock geochronology
across the entire Alpine range (Wirsig et al. 2016; Ehlers and Gibbard 2004). We
calculated the area of each potential nunatak across the Alps, and retained only those
that were in the 99% size quantile (Fig. 1.C). While this may seem a harsh cutoff, we
chose it because a) the spatial reconstruction is known to underestimate glacial
thickness and, therefore, largely overestimate the size of the nunataks (Jouvet et al.
2023), and b) we wanted the retained nunataks to be of sufficient spatial extent to
have supported enough species to make a measurable impact on the spatial patterns of
the flora. The R package *sf* 1.0-9 (Pebesma 2018) was used to calculate the
geographic distance between each grid cell and its closest refugium, whether nunatak
or peripheral.

Gradient-based climate change velocity was calculated as the ratio between the
magnitude of temporal climate change and the spatial gradient of climate in the
surrounding areas (Loarie et al. 2009). Climate change velocity therefore tends to be
lower in regions with strong elevational gradients. We calculated the local climate
velocity in temperature using the *VoCC* R package v1.0.0 (García Molinos et al.
2019; Loarie et al. 2009). Rasters of modern mean annual temperatures and the mean
annual temperatures at the LGM were obtained from the CHELSA climate database
(https://chelsa-climate.org/, Karger et al. 2017) at a resolution of 30 arc seconds
(approximately 1 km). The TempTrend() function in the VoCC package calculates the long-term temporal trend by performing linear regressions of temperature against time for each individual raster cell. Next, the SpatGrad() function calculates the local spatial gradients for each cell by determining the magnitude of the differences in temperature over its neighbouring (3 × 3) cells (here, these are 1km² raster cells, not grid cells). Climate change velocity across the Alps was then calculated by dividing the temporal trend by the spatial gradient. We used this information to extract the average climate change velocity since the LGM for each of our grid cells (Fig. 1.D).

Data for the current annual temperature, annual precipitation amount and the average monthly potential evapotranspiration (PET) were obtained from the CHELSA climate database (https://chelsa-climate.org/, Karger et al. 2017), as these variables are known to be highly influential in determining species distributions in the Alps (Testolin et al. 2021; Moser et al. 2005; Tordoni et al. 2020). While many other environmental variables are known to affect modern plant distributions, it is not our intention here to make an exhaustive model of Alpine plant biodiversity (instead see Chauvier et al. 2021). We used the R package *elevatr* v0.99.0 (Hollister et al. 2023) to download a digital elevation model from Amazon Web Services Terrain Tiles (https://registry.opendata.awss/terrain-tiles) at a resolution of approximately 500m. This was used to calculate the standard deviation in elevation for each grid cell, as a
proxy for available niche space. Finally, we coded each grid cell as calcareous or
siliceous based on the bedrock type found most frequently in the grid cell. We used
bedrock data sourced from the European Soil Database distribution version 2.0
(Panagos et al. 2012, https://esdac.jrc.ec.europa.eu/) and from the French Geological
Survey (https://www.brgm.fr/, Référentiel géologique de la France, cartes
harmonisées au 1/50 000 - BRGM 2009).

***Detecting the legacy of post-glacial recolonization on the Alpine floristic structure***– We examined whether there is a signal of post-glacial recolonization in the spatial structure of the Alpine flora by regressing dispersal ability, standardized species richness, standardized phylogenetic diversity and phylogenetic endemism against all combinations of our three post-glacial variables: climate velocity, time since deglaciation, and distance from the nearest refugium. The best-fit model was chosen using AIC. Pearson’s correlation was used to test for multicollinearity (r < 0.6). A post-hoc Moran’s I test showed that the residuals of these models were spatially correlated, which could create artificially significant relationships. We, therefore, used a spatial simultaneous autoregressive error modelling approach (SAR) on the same model sets using the lagsarlm() function from the R package *spatialreg* v1.2-8 (Bivand et al. 2013), to confirm the significance of these relationships. The spatial weights matrices in the SAR models were built from all neighbours of each
grid cell. All analyses were conducted on the 509 grid cells retained after filtering,
except those involving phylogenetic turnover (see below).

To quantify how the rate of species and phylogenetic turnover varied with the post-
glacial predictor variables, we used generalised dissimilarity modeling (GDM)
(Ferrier et al. 2007). GDM is an extension of generalised linear modelling designed to
analyse spatial patterns of turnover in assemblage composition across large regions in
relation to ecological or geographic distance. GDMs were fitted using the function
gdm() in the R package gdm (Fitzpatrick et al. 2022), with geographic distance
included as an additional predictor together with the post-glacial variables. We used
Sorenson's index to calculate the pairwise distance matrix for species turnover, while
we used the phylogenetic Sorenson's index as implemented by the function
phylosor() in the R package picante to calculate the pairwise distance matrix for
phylogenetic turnover. Three further grid cells were removed from the dataset for
phylogenetic turnover, as they were clear and extreme outliers in our models.

We then used the function *gdm.varImp()* to test the significance of the post-glacial
variables on turnover using permutation tests. This analysis compares the deviance
explained from the fitted model to that derived when all predictors are randomly
permuted between grid cells (Fitzpatrick et al. 2013; Mokany et al. 2022). The
resulting p-value is the proportion of models where the randomized environmental
data perform better in explaining deviance than the actual environmental data. We
performed the analysis with 50 permutations and sampled 100% of sites.

***Quantifying the relative importance of postglacial vs contemporary drivers in
explaining the Alpine floristic structure***– After focusing on the effects of post-glacial
variables only, we compared their importance to that of contemporary variables in
explaining the diversity of the Alpine flora. To do so, our predictor variables were
grouped into three categories: post-glacial, contemporary climate, and landscape. We
conducted a variation partitioning analysis with these three categories using varpart()
in the R package vegan v2.5-7 (Oksanen et al. 2020), which uses partial regression to
partition the variation of the response variable, in our case one of dispersal ability,
standardized species richness, standardized phylogenetic diversity or phylogenetic
endemism. This test is not sensitive to correlated explanatory variables.

For species and phylogenetic turnover, we partitioned deviance explained by a
GDM comprising the post-glacial and contemporary climate variables, together with
geographic distance. We were, however, unable to include the landscape category in
this analysis. GDMs are currently unable to incorporate categorical data (Mokany et
al. 2022), which necessitated the removal of bedrock type information. All analyses
were conducted in R v4.1.2 (R Core Team 2021).
## RESULTS

**Floristic spatial structure**– All floristic measures showed strong spatial patterns (Fig. 2). In general, mean dispersal ability and standardized phylogenetic diversity were greater at the periphery of the Alps and decreased towards the interior.

Standardized species richness peaked in the west, gradually decreasing towards the east. Both phylogenetic endemism and standardized species richness tended to be higher in Alpine valleys, such as the upper Rhône valley in Switzerland. Predicted species and phylogenetic turnover followed very similar patterns, with a cluster of assemblages with a similar composition in the south-western Alps that gradually changes in identity towards the eastern Alps.

**Post-glacial drivers of floristic structure**– We used both linear regression and SAR models to determine the relationship between post-glacial environmental drivers and the spatial patterning of the Alpine flora. The best ranked linear regression models consistently included the same predictor variables as one of the top two SAR models, despite not accounting for spatial auto-correlation (Tables S1, S2, S3, S4). Significant coefficients in the linear regressions were generally also significant in the SAR models (Tables 1, S5).
All three post-glacial environmental variables (climate velocity, distance from the closest refugium, and time since deglaciation) were included in the best linear regression models for mean assemblage dispersal ability, phylogenetic endemism and standardized phylogenetic diversity. However, for standardized phylogenetic diversity, time since deglaciation was not selected in the best SAR model. Only distance from the closest refugium and time since deglaciation were important predictors for species richness. The post-glacial environmental variables explained a substantial proportion of the variance in dispersal ability, standardized phylogenetic diversity and phylogenetic endemism (adj R² = 0.34, adj R² = 0.45, and adj R² = 0.31, respectively), though these values may be inflated as linear regression does not account for spatial autocorrelation. Conversely, these variables explained only 9% of the variance in species richness.

There was mixed support for the hypothesized relationships between the post-glacial environmental variables and floristic structure. As expected, assemblage dispersal ability increased with climate velocity. However, dispersal ability unexpectedly showed a negative relationship with distance from the closest refugium and was higher in areas that had been deglaciated for longer period (Tables 1, S5; Fig. S1). Species richness declined with distance from the closest refugium, increased with climate velocity, and showed no relationship with time since deglaciation (Tables 1,
S5; Fig. S2). Both standardized phylogenetic diversity and phylogenetic endemism increased with time since deglaciation and decreased with distance from refugia, aligning with the hypothesized relationships (Tables 1, S5; Figs. S3, S4).

Similarly, all post-glacial environmental variables were significant predictors of floristic turnover, but explained little deviance overall (Table 2). Climate velocity and time since deglaciation were of similar importance for species turnover (9.10% and 9.05%) and for phylogenetic turnover (10.95%, 12.67%, Table 2; Figs. S5, S7). Distance from the nearest refugium had lower importance for both species and phylogenetic turnover (0.52% and 1.33% respectively, Table 2; Fig. S7). In contrast, the geographic distance between grid cells explained 64.55% of the deviance in species turnover and 57.58% of the deviance in phylogenetic turnover (Table 2).

**Relative contribution of post-glacial and contemporary variables to floristic patterns**– Variation partitioning allowed us to quantify the relative importance of the post-glacial environmental variables in explaining floristic patterns in comparison to landscape (bedrock type, slope and terrain ruggedness) and contemporary climate variables (mean annual temperature, mean monthly precipitation, PET). Post-glacial variables alone explained essentially none of the variation in the spatial patterning of the flora, when the other variables are accounted for (Fig. 3). In conjunction with contemporary climate and landscape, however, post-glacial variables explained a
large proportion of variance for dispersal ability and standardized phylogenetic diversity. The inability of the model to tease apart the effects of post-glacial, landscape, and contemporary variables is likely due to a strong correlation between mean annual temperature, slope, and time since deglaciation (r > 0.67).

A similar pattern was observed for floristic turnover (Fig. 4). Contemporary climate alone explained 26% and 30% of the deviance for species turnover and phylogenetic turnover respectively, while the post-glacial variables alone explain 0% of the deviance. Notably, contemporary climate had a particularly strong effect on structuring phylogenetic turnover and accounted for nearly double the deviance explained by geographic distance (Fig. 4; Fig. S8).
DISCUSSION

In this study, we aimed to detect the spatial legacy of recolonization dynamics in the contemporary Alpine flora and determine the relative importance of post-glacial physical scenarios in shaping spatial community patterns. Our findings indicate the spatial pattern of all examined floristic indices showed a significant relationship with at least two post-glacial environmental variables, although in the case of mean assemblage dispersal ability, the direction of the relationship was opposite to that hypothesized. Relative to contemporary climate, however, the impact of post-glacial recolonization history was weak, despite our analyses including only a limited set of contemporary climate variables. Overall, our results suggest that the effect of post-glacial recolonization history on the spatial structure of the modern Alpine flora is, at best, very much confounded by the contemporary climatic and landscape features of the Alps.

Post-glacial recolonization has left a faint signal in the spatial configuration of the Alpine flora. Species richness declined with distance from the closest refugium, whether peripheral or nunatak, hinting that there are ongoing lags in range expansion from glacial refugia. Although the relationship was not strong, this is a remarkable result given that geographic distance is a very simplistic measure of landscape permeability (Normand et al. 2011), especially in mountain systems where peaks and valleys can both act as barriers to dispersal. We also show that phylogenetic diversity and phylogenetic endemism are higher in areas that remained
glaciated for longer periods following the LGM, and that the rate of phylogenetic turnover
increased with time since deglaciation, which is an indication of higher survival of late
Tertiary relicts and other species in areas less heavily impacted by the Pleistocene glaciations
(Tribsch 2004; Smyčka et al. 2018).

We hypothesized that differences in dispersal ability would be the major driver behind post-
glacial recolonization patterns. However, mean assemblage dispersal ability was higher in
refugia and in areas long deglaciated, contrary to expectations if purely dispersal differences
were driving lags in species expansion from refugia. This counter-intuitive result may be caused
by the decline in plant height with elevation that is commonly observed in temperate mountain
systems, due to changes in environmental conditions along mountain slopes (Körner et al.
1989). For example, valleys and hills in the Alps tend to be dominated by broadleaf deciduous
forests, which contain tree species, such as *Fagus sylvatica* and *Quercus robur*, that can grow
over 30 m in height. Conversely, high-elevation Alpine vegetation typically comprises either
shrubland or graminoid-dominant alpine meadow. Plant height is one of the strongest predictors
of seed dispersal distance, regardless of dispersal syndrome (Thomson et al. 2011; Tamme et al.
2014, Thomson et al. 2018). Thus, the elevational gradient in plant height is likely why we find
that, on average, dispersal ability is greater at low elevations than at high elevations (Fig. S9).
In turn, as refugia and areas long deglaciated tend to be located in low-elevation areas, we find a
negative relationship between these variables and dispersal ability.
Nonetheless, the decline of species richness with distance from the closest refugium could still be attributed to recolonization history, if our measure of seed dispersal ability is decoupled from the rate of species colonization. For example, the presence or absence of microbial symbionts, such as mycorrhizal fungi, is known to determine migration rate in many Northern Hemisphere tree species (Nuñez et al. 2009; Van Nuland et al. 2024), although the extent to which glaciations drove the extirpation of mycorrhizal fungi is debated (Pither and Pickles, 2017). Similarly, dispersal ability does not necessarily predict species establishment. Species establishment depends on a seedling survival rate that is sufficient to maintain a population (Alexander et al. 2018). The ability of a species to establish in a given area depends on a myriad of intrinsic factors, such as generation time, resource-use strategy, fecundity, and habitat specialization (e.g., Marchand and Roach 1980; Forbis and Doak 2004; Kroiss and HilleRisLambers 2015; Estrada et al. 2016; Midolo 2024). On glacial forelands, the rate of species establishment can also be constrained by harsh abiotic factors (e.g., summer frosts and poor soil development, Marcante et al. 2012; Henne et al. 2011), as well as opportunities for facilitation within emerging plant communities (Schlag and Erschbamer 2000; Anthelme et al. 2014; Erschbamer et al. 2023). Therefore, although we show that differences in dispersal ability are not behind patterns of species richness, we cannot conclusively rule out a role of recolonization history in driving the spatial structure of the Alpine flora.
The magnitude of climate change velocity since the LGM does not appear to have been a
major obstacle to post-glacial recolonization. Climate change velocity was generally low with
little variability across the Alps. Only broad, low-elevation valleys had somewhat elevated
magnitudes. We expected that high velocities would have caused local extinction of
populations of Alpine species that were unable to tolerate changing climatic conditions,
leading to lower biodiversity. Instead, greater species richness, phylogenetic diversity and
phylogenetic endemism were located in areas with higher velocities. Many Alpine species are
able to tolerate extensive fluctuations in climate, and may persist in areas even after conditions
are no longer suitable for recruitment or long-term survival, resulting in what is known as
extinction debt (De Witte et al. 2012; Alexander et al. 2018; Rumpf et al. 2019).

Simultaneously, changing climate can enable an influx of species that may be better suited to
the new conditions (Lamprecht et al. 2018). The combination of extinction debt and species
colonization can drive up species richness, as has already been recorded in the Alps with
respect to anthropogenic climate change (Holzinger et al. 2008; Steinbauer et al. 2018;
Dentant et al. 2024).

We found that the relative importance of post-glacial environmental variables in explaining
floristic patterns was dwarfed by the explanatory power of contemporary climate and
landscape variables. We were expecting the effect of post-glacial history to be less important
than contemporary climate overall, but the fact that history alone explained nearly zero
variation across all floristic measures after accounting for contemporary conditions was
surprising, given a wealth of evidence that historical processes can be influential in explaining
species distributions and largescale biodiversity patterns (Svenning and Skov 2007; Normand
et al. 2011; Svenning et al. 2015). We included relatively few contemporary climate and
landscape variables in our analyses, as it was not an objective of this study to make an
exhaustive model of Alpine plant biodiversity (instead see Chauvier et al. 2021). Rather, we
wanted to quantify the relative importance of past and present variables, and even without the
inclusion of additional contemporary variables, the importance of post-glacial history is
minimal. Post-glacial variables did, however, conjointly explain over 40% of the variation in
phylogenetic diversity, together with contemporary climate and landscape. This is likely
because time since deglaciation and mean annual temperature are highly correlated (Pearson's
r = 0.77), making it difficult for the analysis to tease their effects apart. Temperature is
probably the more influential driver of the two, as low phylogenetic diversity in our study was
primarily driven by an abundance of species from a few cold-tolerant genera (e.g., *Carex*,
*Gentiana* and *Saxifraga*). This is corroborated by the GDMs, which showed that temperature
was the major ecological gradient behind phylogenetic turnover. Overall, these results support
growing evidence that environmental filtering and differential clade diversification across
environmental gradients are the primary drivers of current Alpine phylogenetic diversity
patterns, resulting in more closely related lineages in colder regions (Smyčka et al. 2022;
Padullés Cubino et al. 2022; Qian et al. 2024).
The lack of variation in spatial structure that can be attributed solely to historical variables suggests that the post-glacial recolonization of the Alps is largely complete, from a species assemblage point-of-view. While our data do not allow us to speculate about the timing or pace of the establishment of the flora, pollen records that suggest that several tree species had reached elevations equivalent to the modern tree line by 11.5 - 9.5 ka, e.g., *Larex decidua* and *Pinus cembra* (Wagner et al. 2015). This implies that at least some Alpine assemblages had reached late-stage succession within 10 thousand years of the LGM. Similarly, recently observed succession patterns within glacial forelands show that established shrub and grassland communities can develop within just 100-200 years (e.g., Fickert 2020; Bayle et al. 2023) and that forest vegetation can establish within 150-700 years after deglaciation (Ravazzi et al. 2014). There is also evidence that many species were able to survive in microrefugia or nunataks within the boundaries of the Alps during the Pleistocene (Bettin et al. 2007; Parisod and Besnard 2007; Escobar García et al. 2012; Patsiou et al. 2014; Pan et al. 2020). Since the rate of community succession can be increased by proximity of source populations (Bayle et al. 2023), these internal refugia may have facilitated quick recolonization after glacial retreat.

By finding little evidence for post-glacial recolonization as an influential structuring force in the Alpine flora, our results apparently contradict prior studies that have documented an abundance of species that display distributional stasis or incomplete range filling in the Alps
after the glaciations (e.g., Kropf et al. 2002; Schneeweiss and Schönswetter 2010; Essl et al. 2011; Dullinger et al. 2012). We, therefore, believe this discrepancy to be primarily driven by differences in species choice. Past studies have focused on range-restricted Alpine endemics or habitat specialists, which are a collection of species perhaps most likely to display strong migration lags (Carnicero et al. 2022). Endemics and habitat specialists are included within our dataset, but the overall lack of strong signal suggests that these species do not represent the post-glacial history of the flora as a whole. However, while we consider broad species sampling to be an advantage in understanding the flora-scale dynamics, our approach may mask the fine-scale patterns found between or within Alpine communities that have been highlighted by previous studies. For example, the fragmented distribution of calcareous bedrock around the periphery of the Alps likely constrained the recolonization of many calcicolous Alpine species (e.g., *Anthyllis montana*, and *Androsace lactea*, Kropf et al. 2002; Schneeweiss and Schönswetter 2010), whereas the continuous siliceous bedrock in the center of the Alps may have facilitated the post-glacial movement of siliceous-preferring species (Alvarez et al. 2009; Dullinger et al. 2012).

In addition, our data are concentrated in the western and central Alps, where most areas are within 40 km of a peripheral or nunatak refugium. Conversely, areas in the core of the eastern Alps may be up to 80 km of the nearest refugium (Fig. 1.C). The inclusion of more data from the eastern Alps may, therefore, result in a stronger overall gradient in species richness with distance from the closest refugium than we currently find, although it would not alter the weak
post-glacial legacy in the western and central Alps. We also note that, due to the spatial
configuration of the Alps, the closest refugium for most grid cells lies either to the north or
south (Fig. 1.C). Consequently, we may have missed lags in post-glacial recolonization along
the east-west axis, which due to the greater distances involved, might occur at a slower pace
than the recolonization of elevational belts from local peripheral or nunatak refugia along the
north-south axis.

It is important to note that our findings do not exclude other historical processes from being
important drivers of spatial floristic patterns in the Alps. Variation in plant assemblages can be
driven by historical factors that are not considered in our study, such as the location of suitable
climate in the past (Svenning et al. 2015; Kadereit 2024). Long-term landscape dynamics are
also highly influential in the formation of mountain floras (Rahbek et al. 2019; Ding et al.
2020; Wootton et al 2024) and can alter assemblage composition by causing speciation to
occur across topographic barriers or by barring species range expansion (Thiel-Egenter et al.
2011; Boucher et al. 2016). Expansion and contraction of species ranges throughout the
Pleistocene drove the sorting and survival of species into different refugia (Ley et al. 2018;
Voisin et al. 2023), therefore determining their post-glacial distributions and potentially
driving floristic turnover. Over time, the contribution of regional species pools with different
floristic affinities (e.g., Mediterranean or temperate European) to different areas of the Alps
could also promote species and phylogenetic turnover across the range.
In summary, the legacy of post-glacial recolonization plays only a minor role in structuring the spatial patterns of the modern flora of the Alps, with the caveat that a strong correlation between temperature and the timing of deglaciation makes it difficult to assess their relative contributions to floristic structure through time. On a broad scale, post-glacial recolonization appears to be predominantly complete. While we do not know how long it took for recolonization to occur after the LGM, palynological records suggest that the succession of the flora occurred relatively fast, on a scale of hundreds to thousands of years (Ravazzi et al. 2014; Wagner et al. 2015). The Alpine flora as a whole, therefore, appears to have been able to rapidly track the warming conditions of the late-Pleistocene and Holocene, quickly expanding into recently deglaciated areas from peripheral and nunatak refugia. Since then, the spatial signal of recolonization in the flora has largely been eroded by the influence of contemporary climate (Chauvier et al. 2021; Padullés Cubino et al. 2022). Our conclusions should not be taken as an invitation to disregard the importance of the past in driving macroecological patterns, however, as post-glacial recolonization is just one facet of many different historical processes that may have influenced the assembly of the Alpine flora.

## ACKNOWLEDGEMENTS

The collation of floristic data was performed thanks to the joint ANR-SNF project Origin-Alps (ANR-16-CE93-0004, SNF-310030L_170059). The authors would like to thank Laurent
Husson, Matthias Bernet, Suzette Flantua, and Julien Seguinot for helpful discussions at earlier stages of the project.

## AUTHOR CONTRIBUTIONS

LMW, FCB, SL, PV, and WT conceptualized the study. LMW, FCB, and SL wrote the manuscript. LMW and GM conducted the analyses. JR curated the data. ZL and GM contributed data. All authors reviewed and edited the manuscript.

## LITERATURE CITED

Aeschimann, D., K. Lauber, D. M. Moser, and J.-P. Theurillat, 2004. *Flora Alpina*. Vol. 1-3.
Bern, Stuttgart, Wien: Haupt Verlag.

Alexander, J. M., L. Chalmandrier, J. Lenoir, T. I. Burgess, F. Essl, S. Haider, C. Kueffer, K. McDougall, A. Milbau, M. A. Nuñez, A. Pauchard, W. Rabitsch, L. J. Rew, N. J. Sanders, and L. Pellissier, 2018. Lags in the response of mountain plant communities to climate change. *Global change biology* 24: 563–579.

Alvarez, N., C. Thiel-Egenter, A. Tribsch, R. Holderegger, S. Manel, P. Schönswetter, P. Taberlet, S. Brodbeck, M. Gaudeul, L. Gielly, P. Küpfer, G. Mansion, R. Negrini, O. Paun, M. Pellecchia, D. Rioux, F. Schüpfer, M. Van Loo, M. Winkler, F. Gugerli, and IntraBioDiv Consortium, 2009. History or ecology? Substrate type as a major driver of spatial genetic structure in Alpine plants. *Ecology Letters* 12: 632–640.
Anthelme, F., L. A. Cavieres, and O. Dangles, 2014. Facilitation among plants in alpine environments in the face of climate change. *Frontiers in plant science* 5: 98257.

Baselga, A., J. M. Lobo, J.-C. Svenning, and M. B. Araujo, 2012. Global patterns in the shape of species geographical ranges reveal range determinants. *Journal of Biogeography* 39: 760–771.

Bayle, A., B. Z. Carlson, A. Zimmer, S. Vallée, A. Rabatel, E. Cremonese, G. Filippa, C. Dentant, C. Randin, A. Mainetti, et al., 2023. Local environmental context drives heterogeneity of early succession dynamics in alpine glacier forefields.

*Biogeosciences* 20: 1649–1669.

Bettin, O., C. Cornejo, P. Edwards, and R. Holderegger, 2007. Phylogeography of the high alpine plant *Senecio halleri* (Asteraceae) in the European Alps: *In situ* glacial survival with postglacial stepwise dispersal into peripheral areas. *Molecular Ecology* 16: 2517–2524.

Birks, H. J. B. and K. J. Willis, 2008. Alpines, trees, and refugia in Europe. *Plant Ecology & Diversity* 1: 147–160.

Bivand, R. S., E. Pebesma, and V. Gómez-Rubio, 2013. *Applied spatial data analysis with R*. 2nd ed. Springer, NY.

Boucher, F. C., C. Dentant, S. Ibanez, T. Capblancq, M. Boleda, L. Boulangeat, J. Smyčka, C. Roquet, and S. Lavergne, 2021. Discovery of cryptic plant diversity on the rooftops of the Alps. *Scientific Reports* 11: 11128.
Boucher, F. C., N. E. Zimmermann, and E. Conti, 2016. Allopatric speciation with little niche divergence is common among alpine Primulaceae. *Journal of Biogeography* 43: 591–602.

Carnicero, P., J. Wessely, D. Moser, X. Font, S. Dullinger, and P. Schönswetter, 2022. Postglacial range expansion of high elevation plants is restricted by dispersal ability and habitat specialization. *Journal of Biogeography* 49: 1739–1752.

Casazza, G., E. Zappa, M. G. Mariotti, F. Médail, and L. Minuto, 2008. Ecological and historical factors affecting distribution pattern and richness of endemic plant species: the case of the Maritime and Ligurian Alps hotspot. *Diversity and Distributions* 14: 47–58.

Chao, A. and L. Jost, 2012. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. *Ecology* 93: 2533–2547.

Chauvier, Y., W. Thuiller, P. Brun, S. Lavergne, P. Descombes, D. N. Karger, J. Renaud, and N. E. Zimmermann, 2021. Influence of climate, soil, and land cover on plant species distribution in the European Alps. *Ecological monographs* 91: e01433.

Daru, B. H., P. Karunarathne, and K. Schliep, 2020. phyloregion: R package for biogeographic regionalization and macroecology. *Methods in Ecology and Evolution* 11: 1483–1491.
De Witte, L. C., G. F. Armbruster, L. Gielly, P. Taberlet, and J. Stoecklin, 2012. AFLP markers reveal high clonal diversity and extreme longevity in four key arctic-alpine species. *Molecular Ecology* 21: 1081–1097.

Dentant, C., B. Z. Carlson, N. Bartalucci, A. Bayle, and S. Lavergne, 2024. Anthropocene trajectories of high alpine vegetation on Mont-Blanc nunataks. *Botany Letters* 171: 65–79.

Ding, W.-N., R. H. Ree, R. A. Spicer, and Y.-W. Xing, 2020. Ancient orogenic and monsoon-driven assembly of the world's richest temperate alpine flora. *Science* 369: 578–581.

Dullinger, S., W. Willner, C. Plutzar, T. Englisch, L. Schratt-Ehrendorfer, D. Moser, S. Ertl, F. Essl, and H. Niklfeld, 2012. Post-glacial migration lag restricts range filling of plants in the European Alps. *Global Ecology and Biogeography* 21: 829–840.

Ehlers, J. and P. L. Gibbard, 2004. *Quaternary glaciations—extent and chronology: Part I: Europe*. Amsterdam: Elsevier.

Ehlers, J., P. Gibbard, and P. Hughes, 2011. *Quaternary glaciations—extent and chronology: A closer look*. Amsterdam: Elsevier.

Erschbamer, B., R. Niederfriniger Schlag, P. Carnicero, and R. Kaufmann, 2023. Long-term monitoring confirms limitations of recruitment and facilitation and reveals unexpected changes of the successional pathways in a glacier foreland of the Central Austrian Alps. *Plant Ecology* 224: 373–386.
Escobar García, P., M. Winkler, R. Flatscher, M. Sonnleitner, J. Krejčíková, J. Suda, K.

Huelber, G. M. Schneeweiss, and P. Schoenswetter, 2012. Extensive range persistence in peripheral and interior refugia characterizes Pleistocene range dynamics in a widespread Alpine plant species (*Senecio carniolicus*, Asteraceae). *Molecular Ecology* 21: 1255–1270.

Essl, F., S. Dullinger, C. Plutzar, W. Willner, and W. Rabitsch, 2011. Imprints of glacial history and current environment on correlations between endemic plant and invertebrate species richness. *Journal of Biogeography* 38: 604–614.

Estrada, A., I. Morales-Castilla, P. Caplat, and R. Early, 2016. Usefulness of species traits in predicting range shifts. *Trends in Ecology & Evolution* 31: 190–203.

Faith, D. P., 1992. Conservation evaluation and phylogenetic diversity. *Biological conservation* 61: 1–10.

Ferrier, S., G. Manion, J. Elith, and K. Richardson, 2007. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. *Diversity and distributions* 13: 252–264.

Fickert, T., 2020. Common patterns and diverging trajectories in primary succession of plants in Eastern Alpine glacier forelands. *Diversity* 12: 191.

Fitzpatrick, M., K. Mokany, G. Manion, D. Nieto-Lugilde, and S. Ferrier, 2022. *gdm*: *Generalized Dissimilarity Modeling*. R package version 1.5.0-9.1.
Fitzpatrick, M. C., N. J. Sanders, S. Normand, J.-C. Svenning, S. Ferrier, A. D. Gove, and R. R. Dunn, 2013. Environmental and historical imprints on beta diversity: Insights from variation in rates of species turnover along gradients. *Proceedings of the Royal Society B: Biological Sciences* 280: 20131201.

Forbis, T. A. and D. F. Doak, 2004. Seedling establishment and life history trade-offs in alpine plants. *American Journal of Botany* 91: 1147–1153.

García Molinos, J., D. S. Schoeman, C. J. Brown, and M. T. Burrows, 2019. *VoCC: The Velocity of Climate Change and related climatic metrics*. R package version 1.0.0.

Guerrina, M., D. Dagnino, L. Minuto, F. Médail, and G. Casazza, 2024. Unveiling the hypotheses of endemic richness: A study case in the Southwestern Alps. *Perspectives in Plant Ecology, Evolution and Systematics* 63: 125792.

Guerrina, M., S. Theodoridis, L. Minuto, E. Conti, and G. Casazza, 2022. First evidence of post-glacial contraction of Alpine endemics: Insights from *Berardia subacaulis* in the European Alps. *Journal of Biogeography* 49: 79–93.

Gugerli, F., T. Englisch, H. Niklfeld, A. Tribsch, Z. Mirek, M. Ronikier, N. E. Zimmermann, R. Holderegger, P. Taberlet, and IntraBioDiv Consortium., 2008. Relationships among levels of biodiversity and the relevance of intraspecific diversity in conservation – a project synopsis. *Perspectives in Plant Ecology, Evolution and Systematics* 10: 259–281.
Henne, P. D., C. M. Elkin, B. Reineking, H. Bugmann, and W. Tinner, 2011. Did soil development limit spruce (*Picea abies*) expansion in the Central Alps during the Holocene? Testing a palaeobotanical hypothesis with a dynamic landscape model. *Journal of Biogeography* 38: 933–949.

Holderegger, R. and C. Thiel-Egenter, 2009. A discussion of different types of glacial refugia used in mountain biogeography and phylogeography. *Journal of Biogeography* 36: 476–480.

Hollister, J., T. Shah, J. Nowosad, A. L. Robitaille, M. W. Beck, and M. Johnson, 2023. *elevatr: Access Elevation Data from Various APIs*. R package version 0.99.0.

Holzinger, B., K. Hülber, M. Camenisch, and G. Grabherr, 2008. Changes in plant species richness over the last century in the eastern Swiss Alps: Elevational gradient, bedrock effects and migration rates. *Plant Ecology* 195: 179–196.

Hsieh, T. C., K. H. Ma, and A. Chao, 2022. *iNEXT: Interpolation and Extrapolation for Species Diversity*. R package version 3.0.0.

Jiménez-Alfaro, B., S. Abdulhak, F. Attorre, A. Bergamini, M. L. Carranza, A. Chiarucci, R. Ćušterevska, S. Dullinger, R. G. Gavilán, G. Giusso del Galdo, N. Kuzmanović, P. Laiolo, J. Loidi, G. P. Malanson, C. Marcenó, D. Milanović, E. R. Pansing, J. V. Roces-Díaz, E. Ruprecht, J. Šibik, A. Stanisci, R. Testolin, J.-P. Theurillat, K. Vassilev, W. Willner, and M. Winkler, 2021. Post-glacial determinants of regional species pools in alpine grasslands. *Global Ecology and Biogeography* 30: 1101–1115.
Jouvet, G., D. Cohen, E. Russo, J. Buzan, C. C. Raible, W. Haeberli, S. Kamleitner, S. Ivy-Ochs, M. A. Imhof, J. K. Becker, A. Landgraf and U. H. Fischer, 2023. Coupled climate-glacier modelling of the last glaciation in the Alps. *Journal of Glaciology*: 1–15.

Kadereit, J. W., 2024. The uneven distribution of refugial endemics across the European Alps suggests a threefold role of climate in speciation of refugial populations. *Alpine Botany*: 1–22.

Karger, D. N., O. Conrad, J. Böhner, T. Kawohl, H. Kreft, R. W. Soria-Auza, N. E. Zimmermann, H. P. Linder, and M. Kessler, 2017. Climatologies at high resolution for the earth's land surface areas. *Scientific data* 4: 1–20.

Kembel, S., P. Cowan, M. Helmus, W. Cornwell, H. Morlon, D. Ackerly, S. Blomberg, and C. Webb, 2010. Picante: R tools for integrating phylogenies and ecology. *Bioinformatics* 26: 1463–1464.

Körner, C., 2011. Coldest places on earth with angiosperm plant life. *Alpine Botany* 121: 11–22.

Körner, C., M. Neumayer, S. P. Menendez-Riedl, and A. Smeets-Scheel, 1989. Functional-morphology of mountain plants. *Flora* 182: 353–383.

Kroiss, S. J. and J. HilleRisLambers, 2015. Recruitment limitation of long-lived conifers: implications for climate change responses. *Ecology* 96: 1286–1297.
Kropf, M., J. W. Kadereit, and H. P. Comes, 2002. Late Quaternary distributional stasis in the submediterranean mountain plant *Anthyllis montana* L.(Fabaceae) inferred from ITS sequences and amplified fragment length polymorphism markers. *Molecular Ecology* 11: 447-463.

Kropf, M., J. W. Kadereit, and H. P. Comes, 2003. Differential cycles of range contraction and expansion in European high mountain plants during the Late Quaternary: Insights from *Pritzelago alpina* (L.) O. Kuntze (Brassicaceae). *Molecular Ecology* 12: 931-949.

Lamprecht, A., P. R. Semenchuk, K. Steinbauer, M. Winkler, and H. Pauli, 2018. Climate change leads to accelerated transformation of high-elevation vegetation in the central Alps. *New Phytologist* 220: 447-459.

Lavergne, S., C. Pouchon, C. Roquet, M. Boleda, E. Coissac, J. Renaud, B. Bzeznik, C. Perrier, M. Rome, R. Douzet, J. Chave, A. Alberti, F. Donoeud, the PhyloNorway consortium, the PhyloAlps Consortium, J.-G. Valay, P. Wincker, N. E. Zimmermann, and W. Thuiller, 2025. Towards a comprehensive barcoding and phylogenomic reference for the European arctic-alpine flora. in prep.

Ley, A. C., J. Nissen, A. Wölk, and M. Röser, 2018. Glacial refugia and speciation in a group of wind-pollinated and-dispersed, endemic Alpine species of *Helictotrichon* (Poaceae). *Plos one* 13: e0205354.
Loarie, S. R., P. B. Duffy, H. Hamilton, G. P. Asner, C. B. Field, and D. D. Ackerly, 2009.
The velocity of climate change. *Nature* 462: 1052–1055.

Lososová, Z., I. Axmanová, M. Chytrý, G. Midolo, S. Abdulhak, D. N. Karger, J. Renaud, J.
Van Es, P. Vittoz, and W. Thuiller, 2023. Seed dispersal distance classes and dispersal
modes for the European flora. *Global Ecology and Biogeography* 32: 1485–1494.

Malanson, G. P., E. R. Pansing, R. Testolin, S. Abdulhak, A. Bergamini, R. Ćušterevska, C.
Marcenò, N. Kuzmanović, Đ. Milanović, E. Ruprecht, K. Vassilev, W. Willner, and
B. Jiménez-Alfaro, 2022. Explanation of beta diversity in European alpine grasslands
changes with scale. *Ecosphere* 13: e4066.

Marcante, S., A. Sierra-Almeida, J. P. Spindelböck, B. Erschbamer, and G. Neuner, 2012.
Frost as a limiting factor for recruitment and establishment of early development
stages in an alpine glacier foreland? *Journal of Vegetation Science* 23: 858–868.

Marchand, P. J. and D. A. Roach, 1980. Reproductive strategies of pioneering alpine species:
seed production, dispersal, and germination. *Arctic and Alpine research* 12: 137–146.

Midolo, G., 2024. Plant functional traits couple with range size and shape in European trees.
*Global Ecology and Biogeography*: e13838.

Mishler, B. D., 2023. Spatial phylogenetics. *Journal of Biogeography* 50: 1454–1463.

Mokany, K., C. Ware, S. N. Woolley, S. Ferrier, and M. C. Fitzpatrick, 2022. A working
guide to harnessing generalized dissimilarity modelling for biodiversity analysis and
conservation assessment. *Global Ecology and Biogeography* 31: 802–821.
Monegato, G., G. Scardia, I. Hajdas, F. Rizzini, and A. Piccin, 2017. The Alpine LGM in the boreal ice-sheets game. *Scientific reports* 7: 2078.

Moser, D., S. Dullinger, T. Englisch, H. Niklfeld, C. Plutzar, N. Sauberer, H. G. Zechmeister, and G. Grabherr, 2005. Environmental determinants of vascular plant species richness in the Austrian Alps. *Journal of Biogeography* 32: 1117–1127.

Mráz, P., M. Gaudeul, D. Rioux, L. Gielly, P. Choler, P. Taberlet, and IntraBioDiv Consortium, 2007. Genetic structure of *Hypochaeris uniflora* (Asteraceae) suggests vicariance in the Carpathians and rapid post-glacial colonization of the Alps from an eastern Alpine refugium. *Journal of Biogeography* 34: 2100–2114.

Normand, S., R. E. Ricklefs, F. Skov, J. Bladt, O. Tackenberg, and J.-C. Svenning, 2011. Postglacial migration supplements climate in determining plant species ranges in Europe. *Proceedings of the Royal Society B: Biological Sciences* 278: 3644–3653.

Nuñez M. A., T. R. Horton, D. Simberloff, 2009. Lack of belowground mutualisms hinders Pinaceae invasions. *Ecology* 90: 2352–2359.

Oksanen, J., F. G. Blanchet, M. Friendly, R. Kindt, P. Legendre, D. McGlinn, P. R. Minchin, R. B. O’Hara, G. L. Simpson, P. Solymos, M. H. H. Stevens, E. Szoecs, and H. Wagner, 2020. *vegan: Community Ecology Package*. R package version 2.5-7.

Ozenda, P., 1985. *La végétation de la chaîne alpine dans l'espace montagnard européen*. Paris:Masson.
Padullés Cubino, J., M. Chytrý, J. Divíšek, and B. Jiménez-Alfaro, 2022. Climatic filtering and temporal instability shape the phylogenetic diversity of European alpine floras. *Ecography* 2022.

Pan, D., K. Hülber, W. Willner, and G. M. Schneeweiss, 2020. An explicit test of Pleistocene survival in peripheral versus nunatak refugia in two high mountain plant species. *Molecular Ecology* 29: 172–183.

Panagos, P., M. Van Liedekerke, A. Jones, and L. Montanarella, 2012. European Soil Data Centre: Response to European policy support and public data requirements. *Land use policy* 29: 329–338.

Parisod, C., 2022. Plant speciation in the face of recurrent climate changes in the Alps. *Alpine Botany* 132: 21–28.

Parisod, C., 2008. Postglacial recolonization of plants in the western Alps of Switzerland. *Botanica Helvetica* 118: 1–12.

Parisod, C. and G. Besnard, 2007. Glacial *in situ* survival in the Western Alps and polytopic autopolyploidy in *Biscutella laevigata* L.(Brassicaceae). *Molecular Ecology* 16: 2755–2767.

Patsiou, T. S., E. Conti, N. E. Zimmermann, S. Theodoridis, and C. F. Randin, 2014. Topoclimatic microrefugia explain the persistence of a rare endemic plant in the Alps during the last 21 millennia. *Global Change Biology* 20: 2286–2300.
Pebesma, E., 2018. Simple features for R: Standardized support for spatial vector data. *The R Journal* 10: 439–446.

Pither, J. and B. J. Pickles, 2017. The paleosymbiosis hypothesis: Host plants can be colonised by root symbionts that have been inactive for centuries to millenia. *FEMS Microbiology Ecology* 93: fix061.

Qian, H., S. Qian, J. Zhang, and M. Kessler, 2024. Effects of climate and environmental heterogeneity on the phylogenetic structure of regional angiosperm floras worldwide. *Nature Communications* 15: 1079.

R Core Team, 2021. *R: A Language and Environment for Statistical Computing*. R Foundation for Statistical Computing. Vienna, Austria.

Rahbek, C., M. K. Borregaard, A. Antonelli, R. K. Colwell, B. G. Holt, D. Nogues-Bravo, C. M. Ø. Rasmussen, K. Richardson, M. T. Rosing, R. J. Whittaker, and J. Fjeldså, 2019. Building mountain biodiversity: Geological and evolutionary processes. *Science* 365: 1114–1119.

Ravazzi, C., R. Pini, F. Badino, M. De Amicis, L. Londeix, and P. J. Reimer, 2014. The latest LGM culmination of the Garda Glacier (Italian Alps) and the onset of glacial termination. Age of glacial collapse and vegetation chronosequence. *Quaternary Science Reviews* 105: 26–47.
Rogivue, A., R. Graf, C. Parisod, R. Holderegger, and F. Gugerli, 2018. The phylogeographic structure of *Arabis alpina* in the Alps shows consistent patterns across different types of molecular markers and geographic scales. *Alpine Botany* 128: 35–45.

Rosauer, D., S. W. Laffan, M. D. Crisp, S. C. Donnellan, and L. G. Cook, 2009. Phylogenetic endemism: a new approach for identifying geographical concentrations of evolutionary history. *Molecular ecology* 18: 4061–4072.

Rota, F., P. C. Campmany, G. Casazza, J. Nascimbene, P. Schönswetter, and C. Wellstein, 2023. Altitudinal segregation partially predicts survival of three alpine plant species across nunatak and peripheral glacial refugia. *Authorea Preprints*.

Rumpf, S. B., K. Hülber, J. Wessely, W. Willner, D. Moser, A. Gattringer, G. Klonner, N. E. Zimmermann, and S. Dullinger, 2019. Extinction debts and colonization credits of non-forest plants in the European Alps. *Nature Communications* 10: 4293.

Rutherford, S. and S. D’Hondt, 2000. Early onset and tropical forcing of 100,000-year Pleistocene glacial cycles. *Nature* 408: 72–75.

Sandel, B., L. Arge, B. Dalsgaard, R. Davies, K. Gaston, W. Sutherland, and J.-C. Svenning, 2011. The influence of Late Quaternary climate-change velocity on species endemism. *Science* 334: 660–664.

Schlag, R. N. and B. Erschbamer, 2000. Germination and establishment of seedlings on a glacier foreland in the Central Alps, Austria. *Arctic, Antarctic, and Alpine Research* 32: 270–277.
Schneeweiss, G. and P. Schönswetter, 2010. The wide but disjunct range of the European mountain plant *Androsace lactea* L.(Primulaceae) reflects Late Pleistocene range fragmentation and post-glacial distributional stasis. *Journal of Biogeography* 37: 2016–2025.

Schonswetter, P., I. Stehlik, R. Holderegger, and A. Tribsch, 2005. Molecular evidence for glacial refugia of mountain plants in the European Alps. *Molecular Ecology* 14: 3547–3555.

Schönswetter, P. and G. M. Schneeweiss, 2019. Is the incidence of survival in interior Pleistocene refugia (nunataks) underestimated? Phylogeography of the high mountain plant *Androsace alpina* (Primulaceae) in the European Alps revisited. *Ecology and Evolution* 9: 4078–4086.

Seguinot, J., S. Ivy-Ochs, G. Jouvet, M. Huss, M. Funk, and F. Preusser, 2018. Modelling last glacial cycle ice dynamics in the Alps. *The Cryosphere* 12: 3265–3285.

Smyčka, J., C. Roquet, M. Boleda, A. Alberti, F. Boyer, R. Douzet, C. Perrier, M. Rome, J.-G. Valay, F. Denoeud, K. Šem berová, N. E. Zimmermann, W. Thuiller, P. Wincker, I. G. Alsos, E. Coissac, the PhyloAlps consortium, C. Roquet, M. Boleda, A. Alberti, F. Boyer, R. Douzet, C. Perrier, M. Rome, J.-G. Valay, F. Denoeud, N. E. Zimmermann, W. Thuiller, P. Wincker, I. G. Alsos, E. Coissac, S. Lavergne, and S. Lavergne, 2022. Tempo and drivers of plant diversification in the European mountain system. *Nature Communications* 13: 2750.
Smyčka, J., C. Roquet, J. Renaud, W. Thuiller, N. E. Zimmermann, and S. Lavergne, 2018.

Reprint of: Disentangling drivers of plant endemism and diversification in the European Alps-a phylogenetic and spatially explicit approach. *Perspectives in plant ecology, evolution and systematics* 30: 31–40.

Snethlage, M. A., J. Geschke, A. Ranipeta, W. Jetz, N. G. Yoccoz, C. Körner, E. M. Spehn, M.

Fischer, and D. Urbach, 2022a. A hierarchical inventory of the world’s mountains for global comparative mountain science. *Scientific Data* 9: 149.

Snethlage, M. A., J. Geschke, E. M. Spehn, A. Ranipeta, N. G. Yoccoz, C. Körner, W. Jetz, M.

Fischer, and D. Urbach, 2022b. *GMBA Mountain Inventory v2*.

Steinbauer, M. J., J.-A. Grytnes, G. Jurasinski, A. Kulonen, J. Lenoir, H. Pauli, C. Rixen, M.

Winkler, M. Bardy-Durchhalter, E. Barni, A. D. Bjorkman, F. T. Breiner, S. Burg, P.

Czortek, M. A. Dawes, A. Delimat, S. Dullinger, B. Erschbamer, V. A. Felde, O.

Fernández-Arberas, K. F. Fossheim, D. Gómez-García, D. Georges, E. T. Grindrud, S.

Haider, S. V. Haugum, H. Henriksen, M. J. Herreros, B. Jaroszewicz, F. Jaroszynska,

R. Kanka, J. Kapfer, K. Klanderud, I. Kühn, A. Lamprecht, M. Matteodo, U. Morra di

Cella, S. Normand, A. Odland, S. L. Olsen, S. Palacio, M. Petey, V. Piscová, B.

Sedlakova, K. Steinbauer, V. Stöckli, J.-C. Svenning, G. Teppa, J.-P. Theurillat, P.

Vittoz, S. J. Woodin, N. E. Zimmermann and S. Wipf, 2018. Accelerated increase in plant species richness on mountain summits is linked to warming. *Nature* 556: 231–234.
Svenning, J.-C., W. L. Eiserhardt, S. Normand, A. Ordonez, and B. Sandel, 2015. The influence of paleoclimate on present-day patterns in biodiversity and ecosystems.
*Annual Review of Ecology, Evolution, and Systematics* 46: 551–572.

Svenning, J.-C. and F. Skov, 2007. Could the tree diversity pattern in Europe be generated by postglacial dispersal limitation? *Ecology letters* 10: 453–460.

Tamme, R., L. Götzenberger, M. Zobel, J. M. Bullock, D. A. Hooftman, A. Kaasik, and M. Pärtel, 2014. Predicting species’ maximum dispersal distances from simple plant traits. *Ecology* 95: 505–513.

Testolin, R., F. Attorre, P. Borchardt, R. F. Brand, H. Bruelheide, M. Chytrý, M. De Sanctis, J. Dolezal, M. Finckh, S. Haider, A. Hemp, U. Jandt, M. Kessler, A. Y. Korolyuk, J. Lenoir, N. Makunina, G. P. Malanson, D. B. Montesinos-Tubée, J. Noroozi, A. Nowak, R. K. Peet, G. Peyre, F. M. Sabatini, J. Šibík, P. Sklenář, S. P. Sylvester, K. Vassilev, R. Virtanen, W. Willner, S. K. Wiser, E. G. Zibzeev, and B. Jiménez Alfaro, 2021. Global patterns and drivers of alpine plant species richness. *Global Ecology and Biogeography* 30: 1218–1231.

Theodoridis, S., C. Randin, P. Szövényi, F. C. Boucher, T. S. Patsiou, and E. Conti, 2017.
How do cold-adapted plants respond to climatic cycles? Interglacial expansion
explains current distribution and genomic Diversity in *Primula farinosa* L. *Systematic
Biology* 66: 715–736.
Thiel-Egenter, C., N. Alvarez, R. Holderegger, A. Tribsch, T. Englisch, T. Wohlgemuth, L. Colli, M. Gaudeul, L. Gielly, N. Jogan, H. P. Linder, R. Negrini, H. Niklfeld, M. Pellecchia, D. Rioux, P. Schönswetter, P. Taberlet, M. van Loo, M. Winkler, IntraBioDiv Consortium, and F. Gugerli, 2011. Break zones in the distributions of alleles and species in alpine plants. *Journal of Biogeography* 38: 772–782.

Thomson, F. J., A. D. Letten, R. Tamme, W. Edwards, and A. T. Moles, 2018. Can dispersal investment explain why tall plant species achieve longer dispersal distances than short plant species? *New Phytologist* 217: 407–415.

Thomson, F. J., A. T. Moles, T. D. Auld, and R. T. Kingsford, 2011. Seed dispersal distance is more strongly correlated with plant height than with seed mass. *Journal of ecology* 99: 1299–1307.

Tordoni, E., V. Casolo, G. Bacaro, F. Martini, A. Rossi, and F. Boscutti, 2020. Climate and landscape heterogeneity drive spatial pattern of endemic plant diversity within local hotspots in South-Eastern Alps. *Perspectives in Plant Ecology, Evolution and Systematics* 43: 125512.

Tribsch, A., 2004. Areas of endemism of vascular plants in the Eastern Alps in relation to Pleistocene glaciation. *Journal of Biogeography* 31: 747–760.

Valla, P. G., P. Sternai, and M. Fox, 2021. How climate, uplift and erosion shaped the Alpine topography. *Elements* 17: 41–46.
Van Nuland, M. E., C. Qin, P. T. Pellitier, K. Zhu, and K. G. Peay, 2024. Climate mismatches with ectomycorrhizal fungi contribute to migration lag in North American tree range shifts. *Proceedings of the National Academy of Sciences*, 121: e2308811121.

Väre, H., R. Lampinen, C. Humphries, and P. Williams, 2003. *Taxonomic diversity of vascular plants in the European alpine areas*. Pp 133–148 in *Alpine Biodiversity in Europe*, ed. by L. Nagy, G. Grabherr, C. Korner, and D. B. A. Thompson. Belin: Springer.

Voisin, C., C. Dentant, D. Rioux, and F. C. Boucher, 2023. Introgression of an isolated Primula lineage suggests the existence of a glacial refugium in the Écrins range (Southwestern French Alps). *Alpine Botany* 133: 21–33.

Wagner, S., T. Litt, M.-F. Sánchez-Goni, and R. J. Petit, 2015. History of *Larix decidua*
Mill.(European larch) since 130 ka. *Quaternary Science Reviews* 124: 224–247.

Windmaißer, T., S. Kattari, G. Heubl, and C. Reisch, 2016. Glacial refugia and postglacial expansion of the alpine–prealpine plant species *Polygala chamaebuxus*. *Ecology and Evolution* 6: 7809–7819.

Wirsig, C., J. Zasadni, M. Christl, N. Akçar, and S. Ivy-Ochs, 2016. Dating the onset of LGM ice surface lowering in the High Alps. *Quaternary Science Reviews* 143: 37–50.

Wootton, L. M., F. C. Boucher, C. Pouchon, C. Roquet, E. Coissac, J. Renaud, I. G. Alsos, P. G. Valla, L. Husson, M. Bernet, C. Perrier, R. Douzet, M. Rome, J.-G. Valay, A. Alberti, F. Denoeud, N. E. Zimmermann, P. Wincker, W. Thuiller, PhyloNorway consortium, PhyloAlps consortium, and Sébastien Lavergne, 2025. Pleistocene
climatic cycles, landscape dynamics and the late rise of sky-island vegetation in the
European Alps. *Nature Plants* (accepted)

Zimmer, A., R. I. Meneses, A. Rabatel, A. Soruco, O. Dangles, and F. Anthelme, 2018. Time lag between glacial retreat and upward migration alters tropical alpine communities. *Perspectives in Plant Ecology, Evolution and Systematics* 30: 89–102.

Zizka, A., D. Silvestro, T. Andermann, J. Azevedo, C. Duarte Ritter, D. Edler, H. Farooq, A. Herdean, M. Ariza, R. Scharn, S. Svanteson, N. Wengstrom, V. Zizka, and A. Antonelli, 2019. CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases. *Methods in Ecology and Evolution*. R package version 2.0-20: -7.

TABLES

<table><caption>Table 1. Regression outputs for the effects of post-glacial variables on floristic metrics for the best fitting SAR models. See Table S5 for linear regression outputs.</caption><thead><tr><th colspan="3">Assemblage dispersal ability</th></tr><tr><th>Variable</th><th>SAR total effect</th><th>p-value</th></tr></thead><tbody><tr><td>Refugia distance</td><td>-0.001</td><td>0.007</td></tr><tr><td>Climate velocity</td><td>0.106</td><td>&lt;0.001</td></tr><tr><td>Deglaciation</td><td>0.006</td><td>&lt;0.001</td></tr></tbody><tfoot><tr><td colspan="3">Standardized species richness</td></tr></tfoot></table>
<table><caption>Table 2. The importance of post-glacial variables in explaining the deviance in species and phylogenetic turnover. Predictor importance is measured as the percent decrease in deviance explained between the full model and the deviance explained by a model fit where that predictor is permuted. All predictors were significant (p < 0.001). Model significance is determined by comparing the deviance explained by the GDM fit of the original model to the distribution of deviance explained values from GDMs fitted to 50 permuted data sets.</caption><thead><tr><th>Variable</th><th>SAR total effect</th><th>p-value</th></tr></thead><tbody><tr><td>Refugia distance</td><td>-7.43</td><td>&lt;0.001</td></tr><tr><td>Climate velocity</td><td>131.16</td><td>&lt;0.001</td></tr><tr><td colspan="3">Standardized phylogenetic diversity</td></tr><tr><td>Variable</td><td>SAR total effect</td><td>p-value</td></tr><tr><td>Refugia distance</td><td>-0.11</td><td>&lt;0.001</td></tr><tr><td>Climate velocity</td><td>9.35</td><td>&lt;0.001</td></tr><tr><td colspan="3">Phylogenetic endemism</td></tr><tr><td>Variable</td><td>SAR total effect</td><td>p-value</td></tr><tr><td>Refugia distance</td><td>-0.77</td><td>0.012</td></tr><tr><td>Climate velocity</td><td>105.53</td><td>&lt;0.001</td></tr></tbody></table>

<table><thead><tr><th>Predictor variable</th><th>Species turnover</th><th>Phylogenetic turnover</th></tr></thead><tbody><tr><td>Geographic distance</td><td>64.55</td><td>57.58</td></tr><tr><td>Climate velocity</td><td>9.10</td><td>10.95</td></tr><tr><td>Deglaciation</td><td>9.05</td><td>12.67</td></tr><tr><td>Refugia distance</td><td>0.53</td><td>1.33</td></tr></tbody></table>
FIGURE LEGENDS

FIG. 1. The spatial configurations of the sampling scheme and the post-glacial environmental variables across the Alps. A. Density of the occurrence data used in this study. Each region contains the given percentage of sampling density, i.e., the lightest grey region contains 90% of the total occurrence points. Inset shows the location of the study area. B. Time since deglaciation (Seguinot et al. 2018). C. Distance from closest refugium, whether nunatak or peripheral. Peripheral refugia areas are shown in light grey, while nunatak refugia are coloured dark grey. D. Climate change velocity, pixels with a value greater than five (0.4% of pixels) removed for ease of visualization.

FIG. 2. Spatial patterns in floristic characteristics, where the grey background represents all possible grid cells across the Alps, and colored foreground shows grid cells used in our analyses, for A. mean dispersal ability of all species in an assemblage, B. standardized species richness, C. standardized phylogenetic diversity, and D. phylogenetic endemism. Panels E. and F. show the primary differences in predicted species and phylogenetic turnover respectively, based on a principal component analysis of the GDM-transformed post-glacial and contemporary predictors, where more similar colors indicate more similar expected composition.

FIG. 3. The proportion of variation explained by post-glacial (distance from refugia, time since deglaciation, climate velocity), contemporary (mean annual temperature,
precipitation, PET) and landscape (bedrock and terrain ruggedness) variables for each of A. mean assemblage dispersal ability (log(m)), B. standardized species richness, C. standardized phylogenetic diversity and D. phylogenetic endemism.

FIG. 4. The percentage of generalized dissimilarity model deviance in A. species turnover and B. phylogenetic turnover explained by post-glacial variables (distance from refugia, time since deglaciation, climate velocity), contemporary variables (mean annual temperature, precipitation, PET) and geographic distance.

SUPPLEMENTAL TABLES

<table><caption>TABLE S1. Linear regression and SAR models of mean dispersal ability ranked using AIC. Refugia = distance from nearest refugium, Deglac = time since deglaciation, Velocity = climate velocity.</caption><thead><tr><th colspan="2">Linear regression</th><th colspan="2">SAR models</th></tr><tr><th>Model variables</th><th>Delta AIC</th><th>Model variables</th><th>Delta AIC</th></tr></thead><tbody><tr><td>Refugia, Velocity, Deglac</td><td>0.00</td><td>Refugia, Velocity, Deglac</td><td>0.00</td></tr><tr><td>Deglac, Velocity</td><td>-6.67</td><td>Deglac, Velocity</td><td>-5.01</td></tr><tr><td>Refugia, Deglac</td><td>-52.86</td><td>Refugia, Deglac</td><td>-51.10</td></tr><tr><td>Deglac</td><td>-56.57</td><td>Deglac</td><td>-53.31</td></tr><tr><td>Refugia, Velocity</td><td>-67.50</td><td>Refugia, Velocity</td><td>-63.19</td></tr><tr><td>Velocity</td><td>-79.73</td><td>Velocity</td><td>-70.41</td></tr><tr><td>Refugia</td><td>-203.7</td><td>Refugia</td><td>-186.18</td></tr></tbody></table>
<table><caption>TABLE S2. Linear regression and SAR models of standardized species richness ranked using AIC. Refugia = distance from nearest refugium, Deglac = time since deglaciation, Velocity = climate velocity.</caption><thead><tr><th colspan="2">Linear regression</th><th colspan="2">SAR models</th></tr><tr><th>Model variables</th><th>Delta AIC</th><th>Model variables</th><th>Delta AIC</th></tr></thead><tbody><tr><td>Refugia, Velocity</td><td>0.00</td><td>Refugia, Velocity</td><td>0.00</td></tr><tr><td>Refugia, Velocity, Deglac</td><td>-0.43</td><td>Refugia, Velocity, Deglac</td><td>-9.99</td></tr><tr><td>Refugia, Deglac</td><td>-2.70</td><td>Refugia, Deglac</td><td>-15.67</td></tr><tr><td>Refugia</td><td>-11.71</td><td>Refugia</td><td>-16.56</td></tr><tr><td>Deglac, Velocity</td><td>-40.57</td><td>Velocity</td><td>-20.26</td></tr><tr><td>Deglac</td><td>-41.21</td><td>Deglac, Velocity</td><td>-22.17</td></tr><tr><td>Velocity</td><td>-43.42</td><td>Deglac</td><td>-26.62</td></tr></tbody></table>

<table><caption>TABLE S3. Linear regression and SAR models of standardized phylogenetic diversity ranked using AIC. Refugia = distance from nearest refugium, Deglac = time since deglaciation, Velocity = climate velocity.</caption><thead><tr><th colspan="2">Linear regression</th><th colspan="2">SAR models</th></tr><tr><th>Model variables</th><th>Delta AIC</th><th>Model variables</th><th>Delta AIC</th></tr></thead><tbody><tr><td>Refugia, Velocity, Deglac</td><td>0.00</td><td>Refugia, Velocity</td><td>0.00</td></tr><tr><td>Deglac, Velocity</td><td>-33.68</td><td>Refugia, Velocity, Deglac</td><td>-17.82</td></tr><tr><td>Refugia, Velocity</td><td>-70.28</td><td>Deglac, Velocity</td><td>-32.42</td></tr><tr><td>Refugia, Deglac</td><td>-83.60</td><td>Velocity</td><td>-41.36</td></tr><tr><td>Deglac</td><td>-106.92</td><td>Refugia, Deglac</td><td>-83.50</td></tr><tr><td>Velocity</td><td>-130.37</td><td>Deglac</td><td>-91.72</td></tr><tr><td>Refugia</td><td>-280.51</td><td>Refugia</td><td>-122.53</td></tr></tbody></table>
<table><caption>TABLE S4. Linear regression and SAR models of phylogenetic endemism ranked using AIC. Refugia = distance from nearest refugium, Deglac = time since deglaciation, Velocity = climate velocity.</caption><thead><tr><th colspan="2">Linear regression</th><th colspan="2">SAR models</th></tr><tr><th>Model variables</th><th>Delta AIC</th><th>Model variables</th><th>Delta AIC</th></tr></thead><tbody><tr><td>Refugia, Velocity, Deglac</td><td>0.00</td><td>Refugia, Velocity</td><td>0.00</td></tr><tr><td>Deglac, Velocity</td><td>-8.56</td><td>Refugia, Velocity, Deglac</td><td>-1.00</td></tr><tr><td>Velocity</td><td>-26.28</td><td>Deglac, Velocity</td><td>-5.56</td></tr><tr><td>Refugia, Velocity</td><td>-41.80</td><td>Velocity</td><td>-5.61</td></tr><tr><td>Refugia, Deglac</td><td>-100.26</td><td>Refugia, Deglac</td><td>-79.06</td></tr><tr><td>Deglac</td><td>-104.04</td><td>Deglac</td><td>-80.22</td></tr><tr><td>Refugia</td><td>-181.96</td><td>Refugia</td><td>-102.98</td></tr></tbody></table>

<table><caption>TABLE S5. Regression outputs for the best fitting linear regression models</caption><thead><tr><th colspan="4">Assemblage dispersal ability</th></tr><tr><th>Variable</th><th>Coefficient</th><th>p-value</th><th>Adj-R<sup>2</sup></th></tr></thead><tbody><tr><td>Intercept</td><td>0.569</td><td>&lt;0.001</td><td>0.34</td></tr><tr><td>Refugia distance</td><td>-0.001</td><td>0.003</td><td></td></tr><tr><td>Climate velocity</td><td>0.103</td><td>&lt;0.001</td><td></td></tr><tr><td>Deglaciation</td><td>0.006</td><td>&lt;0.001</td><td></td></tr></tbody><thead><tr><th colspan="4">Standardized species richness</th></tr><tr><th>Variable</th><th>Coefficient</th><th>p-value</th><th>Adj-R<sup>2</sup></th></tr></thead></table>
<table><tr><td>Intercept</td><td>539.81</td><td>&lt;0.001</td><td>0.09</td></tr><tr><td>Refugia distance</td><td>-6.01</td><td>&lt;0.001</td><td></td></tr><tr><td>Climate velocity</td><td>95.73</td><td>&lt;0.001</td><td></td></tr></table>

Standardized phylogenetic diversity

<table><thead><tr><th>Variable</th><th>Coefficient</th><th>p-value</th><th>Adj-R<sup>2</sup></th></tr></thead><tbody><tr><td>Intercept</td><td>-5.17</td><td>&lt;0.001</td><td>0.45</td></tr><tr><td>Refugia distance</td><td>-0.07</td><td>&lt;0.001</td><td></td></tr><tr><td>Climate velocity</td><td>3.77</td><td>&lt;0.001</td><td></td></tr><tr><td>Deglaciation</td><td>0.19</td><td>&lt;0.001</td><td></td></tr></tbody></table>

Phylogenetic endemism

<table><thead><tr><th>Variable</th><th>Coefficient</th><th>p-value</th><th>Adj-R<sup>2</sup></th></tr></thead><tbody><tr><td>Intercept</td><td>32.39</td><td>&lt;0.001</td><td>0.31</td></tr><tr><td>Refugia distance</td><td>-0.53</td><td>0.001</td><td></td></tr><tr><td>Climate velocity</td><td>61.89</td><td>&lt;0.001</td><td></td></tr><tr><td>Deglaciation</td><td>1.2</td><td>&lt;0.001</td><td></td></tr></tbody></table>

SUPPLEMENTAL FIGURE LEGENDS

FIG. S1. Partial regression plots of the best linear model for mean dispersal distance.

FIG. S2. Partial regression plots of the best linear model for standardized species richness.

FIG. S3. Partial regression plots of the best linear model for standardized phylogenetic diversity.
FIG. S4. Partial regression plots of the best linear model for phylogenetic endemism.

FIG. S5. GDM spline functions for each predictor variable, for a GDM of species turnover across the Alps, fitted with post-glacial predictor variables only. The maximum height of each spline function, shown with a dashed line, indicates the importance of the predictor in explaining turnover.

FIG. S6. GDM spline functions for each predictor variable, for a GDM of species turnover across the Alps, fitted with both post-glacial predictor variables and contemporary climate predictors. The maximum height of each spline function, shown with a dashed line, indicates the importance of the predictor in explaining turnover. Time since deglaciation and refugia distance are not shown as they had a maximum height of zero.

FIG. S7. GDM spline functions for each predictor variable, for a GDM of phylogenetic turnover across the Alps, fitted with post-glacial predictor variables only. The maximum height of each spline function, shown with a dashed line, indicates the importance of the predictor in explaining turnover.

FIG. S8. GDM spline functions for each predictor variable, for a GDM of phylogenetic turnover across the Alps, fitted with both post-glacial predictor variables and contemporary climate predictors. The maximum height of each spline function, shown with a dashed line, indicates the importance of the predictor in explaining turnover. Time since deglaciation is not shown as it had a maximum height of zero.

FIG S9. Plant height vs elevational preference of Alpine plant species. Darker colors indicate better dispersal ability. Each species' preferred elevation is calculated as the mean elevation of all Grid10 grid cells where the species occurs. Plant height extracted from Lososová et al. 2023.
<figure><img src="image_2.png" /><figcaption>**Figure 1**</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Figure 2</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 3
Mean dispersal ability</figcaption></figure>

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

B

<figure><img src="image_6.png" /><figcaption>Standardized species richness</figcaption></figure>

C

<figure><img src="image_7.png" /><figcaption>Standardized phylogenetic diversity</figcaption></figure>

D

<figure><img src="image_8.png" /><figcaption>Phylogenetic endemism</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Figure S1</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Figure S2</figcaption></figure>

Distance from nearest refugium (km)

Climate velocity (30 arcsec/ky)
<figure><img src="image_11.png" /><figcaption>Figure S3</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>Figure S4</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>Figure S5</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>Figure S6</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>Figure S7</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>Figure S8</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>Figure S9</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>Species turnover</figcaption></figure>

<figure><img src="image_19.png" /></figure>