# The affinity of vascular plants and bryophytes to forest microclimate buffering

Eva Gril, Fabien Spicher, Alain Vanderpoorten, Emilie Gallet-Moron, Boris Brasseur, Vincent Le Roux, Marianne Laslier, Guillaume Decocq, Ronan Marrec, Jonathan Lenoir

► To cite this version:

Eva Gril, Fabien Spicher, Alain Vanderpoorten, Emilie Gallet-Moron, Boris Brasseur, et al. The affinity of vascular plants and bryophytes to forest microclimate buffering. Journal of Ecology, 2025, 113 (1), pp.22-38. 10.1111/1365-2745.14425. hal-04767151

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Distributed under a Creative Commons Attribution 4.0 International License
1 Ecological indicator values of understory plants perform

2 poorly to infer forest microclimate temperature

3 Running title: EIVs to infer forest microclimate temperature

4

5 Eva Gril¹* https://orcid.org/0000-0002-7340-8264

6 Fabien Spicher¹ https://orcid.org/0000-0002-9999-955X

7 Alain Vanderpoorten² https://orcid.org/0000-0002-5918-7709

8 Germain Vital¹

9 Boris Brasseur¹ https://orcid.org/0000-0002-5206-5426

10 Emilie Gallet-Moron¹ https://orcid.org/0000-0003-1579-5013

11 Vincent Le Roux¹ https://orcid.org/0000-0001-5443-3707

12 Guillaume Decocq¹ https://orcid.org/0000-0001-9262-5873

13 Jonathan Lenoir¹ https://orcid.org/0000-0003-0638-9582

14 Ronan Marrec¹ https://orcid.org/0000-0003-1607-4939

15

16 ¹UMR CNRS 7058 "Ecologie et Dynamique des Systèmes Anthropisés" (EDYSAN), Université

17 de Picardie Jules Verne, Amiens, France
18 ² Botany Institute, University of Liège, Liège, Belgium

19 *Corresponding author: eva.gril@u-picardie.fr; gril.eva@free.fr

20

21

## Funding

22 J.L. acknowledges funding from the Agence Nationale de la Recherche (ANR), under the

23 framework of the young investigators' funding scheme (JCJC Grant N°ANR-19-CE32-0005-01:

24 IMPRINT project), which funded E.G.'s PhD, as well as the Région Hauts-de-France, the

25 Ministère de l'Enseignement Supérieur et de la Recherche and the European Fund for

26 Regional Economic Development for their financial support to the CPER ECRIN program.

27

28

## Abstract

29 **Question.** Ecological indicator values (EIVs) reflect species' optimal conditions on an

30 environmental gradient, such as temperature. Averaged over a community, they are used to

31 quantify thermophilization stemming from climate change, i.e. the reshuffling of

32 communities towards more warm-adapted species. In forests, understory plant communities

33 do not keep up with global warming and accumulate a climatic debt. Although the causes are

34 still debated, this thermal lag may be partly explained by forest microclimate buffering. For

35 the first time, we test whether community means of EIVs are able to capture microclimate

36 (here, under forest canopies) temperature across, or also within forests.
37 **Location.** 157 forest plots, across three French deciduous forests covering a large

38 macroclimatic gradient.

39 **Methods.** To assess whether EIVs can be used to infer the mean and range of microclimate

40 temperature in forests, we measured understory air temperature for about one year (10

41 months) with sensors located 1 m above ground. We surveyed bryophytes and vascular

42 plants within 400 m² plots, and computed floristic temperature from ordinal-scale EIVs

43 (Ellenberg, Julve) and degree-scale EIVs (ClimPlant, BET) for both temperature and

44 continentality, i.e. temperature annual range. Finally, we fitted linear models to assess

45 whether EIVs could explain the mean and range of microclimate temperature in forests.

46 **Results.** Vascular plant and bryophyte communities successfully reflected differences in

47 mean annual temperatures across forests, but largely failed to do so for microclimate

48 variation within forests. Bryophytes did not perform better than vascular plants to infer

49 microclimate conditions. The annual range of microclimate temperatures was poorly

50 associated with ordinal-scale EIVs for continentality, but was positively correlated with

51 degree-scale EIVs for annual range within lowland forests, especially for vascular plant

52 communities.

53 **Conclusion.** Overall, the capabilities of EIVs to infer microclimate was inconsistent. Refined

54 EIVs for temperature are needed to capture forest microclimates experienced by understory

55 species.

56
57

# Keywords

58

Buffering, bryophytes, community temperature index, ecological indicator values, forest

59

flora, global warming, microclimate, plant community, thermophilization, vascular plants

60

61

# 1. Introduction

62

Plant species cannot grow everywhere: their presence reflects the environment, and evidences some of its abiotic properties. This knowledge has been used for millennia by humankind to interpret pedoclimatic conditions, for instance to spot humid or dry, rich or poor soils for agriculture and forestry purposes. Ecologists have formalized this knowledge and quantified it into "ecological indicator values" (EIVs; Ellenberg 1974), used to reflect the environmental conditions associated with a given species, indicating its optimum within its realized niche (Diekmann 2003). Averaged over a floristic community, with species sometimes weighted by their abundance, they allow ecologists to infer — or "bioindicate" — environmental conditions such as nutrient availability or temperature.

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Heinz Ellenberg was the first to design a list of EIVs for the main plant species of Western Central Europe, more particularly Germany (Ellenberg 1974). His EIVs, in ordinal scale from 1 to 9, have later been adapted to other geographic situations. For example, Julve adapted them to France (Julve 2015). Ellenberg's EIVs and their derivatives are still actively used by ecologists, *frequently* in forest systems (Zolotova et al. 2023). These ordinal-scale EIVs from Ellenberg or Julve are mainly based on expert knowledge and field observations, while new sets of "biogeographic" EIVs are statistically-derived from spatial occurrences and

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distribution maps covering large spatial extents, such as EcoPlant (Gégout et al. 2005) and

ClimPlant (Vangansbeke et al. 2021). Contrarily to the original ordinal-scale EIVs, these new

EIVs are in a continuous scale, usually in Celsius degrees for temperature.

Plant EIVs are widely used as proxies of environmental conditions, and to infer spatial and temporal vegetation dynamics (Diekmann 2003; Pinto et al. 2016; Hutsemékers et al. 2023).

Plant EIVs for temperature can be averaged across species within a given plant community to

obtain a community mean of temperature EIV, or "floristic temperature" (also called the

community temperature index; e.g. Richard et al. 2021). In the context of global warming, this floristic temperature is used to evaluate thermophilization, i.e. the replacement of cold-

tolerant or mesophilic species by more warm-adapted species (Lenoir et al. 2010; De Frenne

et al. 2013; Martin et al. 2019). Ecosystems that experienced more warming have already

responded with an increased thermophilization (Gottfried et al. 2012; Pacheco-Riaño et al.

2023). Although thermophilization has taken place over the past decades in many plant

communities, it often has not kept pace with climate change, resulting in a thermal "lag" or

"climatic debt", especially pronounced in lowland forest understories (Bertrand et al. 2011;

Lenoir et al. 2022; but see Pacheco-Riaño et al. 2023). This thermal lag may be driven by

species persistence and tolerance to thermal and water stress (Bertrand et al. 2016), but is

often thought to be partly determined by the buffering capacity of forest microclimates (De

Frenne et al. 2013; Zellweger et al. 2020; Richard et al. 2021). Here, we define microclimate

as the proximal climatic conditions that organisms are exposed to, while macroclimate refers

to large-scale and free-air conditions, as measured by standardized weather stations (De

Frenne & Verheyen 2016; Bramer et al. 2018). Under the canopy of most forests, buffered

temperature conditions are recorded with a reduced range of temperatures compared to

macroclimate temperatures, i.e. lower maxima and higher minima (De Frenne et al. 2021).
This buffering effect in forests is caused by two main mechanisms during the day: interception of solar radiations and transpiration by trees (De Frenne et al. 2021). At night, canopies slow down the return of infrareds to the atmosphere. Forests may therefore hold the response of understory plant communities to climate change, at least as long as the canopy stays closed (Lenoir et al. 2010; Zellweger et al. 2020; Christiansen et al. 2022).

**Forest cover also has a cooling effect on understory floristic temperatures at a landscape scale** (Borderieux et al. 2023).

Plant EIVs have been confronted with corresponding field measurements for light, soil reaction, moisture and nitrogen (Diekmann 2003; Szymura et al., 2014; Depauw et al. 2021), but to our knowledge, not yet for temperatures inside forests. Therefore, it is unclear whether EIVs for temperature **mean and range** are solely able to reflect macroclimate (i.e., differences across different forests), or also microclimate gradients (i.e., differences within one forest). The question of how microclimates track macroclimate change remains open (De Frenne et al. 2021). If EIVs could be linked to microclimate temperature from sensors placed directly in the habitat of interest, we could use floristic surveys in forests to model microclimate, and microclimate warming. We might even reconstruct microclimates of the past from historical floristic surveys, using transfer functions (Bertrand et al. 2016; Pacheco-Riaño et al. 2023) similarly to paleoecological studies (Paus 2013). The big advantage would be that vegetation inventories are abundant and may date back a century — contrarily to the relatively recent and scarce in-situ temperature measurements in forests.

Amid the understory flora, bryophytes are more cryptic and harder to identify than their vascular relatives, and are therefore much less studied (Kiebacher et al. 2023; Kutnar et al. 2023). However, they may be better indicators of microclimates than vascular plants for
several reasons. First, bryophytes are much smaller than vascular plants. While large species require a relatively large 'physical-space-niche', so that their realized niche may be accurately described by environmental factors at resolutions of tens to hundreds of meters, smaller species occupy smaller physical spaces that are characterized by specific micro-environmental features (Aarssen et al., 2006; Potter et al., 2013). Second, their spores are often less limited by dispersal than vascular seeds (Vanderpoorten et al. 2019), which may translate into a better capacity to bioindicate environmental conditions favourable to their development. Third, bryophytes are poikilohydric, i.e. their water status completely depends on their environment and they directly rely on rainfall for water uptake, rendering them especially sensitive to subtle variations in climatic conditions (Fenton & Frego 2005; Mills and Macdonald, 2005; He et al. 2016). Finally, bryophytes are highly sensitive to warm and extreme microclimate temperatures, and may thus represent valuable monitors of microclimate (Hylander et al. 2002). Temperate bryophyte species, for instance, typically die when exposed to temperatures above 30 °C (Furness & Grime, 1982), making them better indicators of global warming than vascular plants (Becker Scarpitta et al. 2017; Kiebacher et al. 2023).

With this study, we aimed to compare temperature records from microclimate sensors against temperature estimates from bioindication, and thus assess whether plant EIVs can be used to predict microclimate temperature in the understory of temperate deciduous forests. We considered both mean annual temperature and temperature annual range, as the latter might be more relevant to qualify forest microclimate buffering than mean conditions, over a 10-months period (which we, for simplicity, hereafter refer to as annual). We compared the predictive power of two types of EIVs – ordinal-scale versus degree-scale EIVs – for both vascular plants and bryophytes, and assessed whether local heterogeneities within the
survey area (rocks, humid area or compacted ground) may blur bioindication. We hypothesize that: (i) plant EIVs are able to characterize the mean and range of temperature variation across forest sites (located in different macroclimates) as well as within forests (microclimates); (ii) bryophytes are better indicators of microclimate temperature than vascular plants; and (iii) removing species associated with a local heterogeneity will marginally improve the predictive power of plant EIVs on microclimate temperature.

# 2. Material and methods

## 2.1. Study area and sampling design

The study focuses on three state forests in France: Mormal, Blois and Aigoual. These three forests belong to the RENECOFOR national network of long-term monitoring of forest ecosystems (onf.fr/renecofor; Ulrich 1995) and were chosen because of their contrasted macroclimate and dominant tree species, and because a long-term weather station was installed in open conditions nearby, or within a clearing inside the focal forests (Table 1, Fig. 1). Blois is dominated by sessile oak (*Quercus petraea*), Mormal by pedunculate oak (*Quercus robur*) and beech (*Fagus sylvatica*). Both are lowland forests, while Aigoual is a mountainous forest dominated by beech. The three forests have poor, acidic soils. All three are managed by the National Forest Office (ONF) as even-aged forest stands, except for some stands in Aigoual not managed for reasons of accessibility or conservation.

We surveyed a total of 157 plots, including 53 in Aigoual, 52 in Blois and 52 in Mormal forests (Fig. 1, Appendix S1). Each plot consists of a 400 m² surface of vegetation survey,
centered on a microclimate sensor. Plots were sampled alongside a forest cover and structure gradient, from very dense forests to open areas, in clearings or after tree harvest, and from young regeneration stages to mature forests (Appendix S1): thickets (n = 15); saplings (14); pole stands (24); young stands (22); adult stands (39); old stands (16); irregular stands (7); seed trees (3); open (5); isolated trees; (4) and coppices (8).

## 2.2. Temperature measurements

Hourly microclimate temperatures were measured using Onset® HOBO® Pendant data loggers UA-001-64, which contain commonly used sensors in ecological studies (Bramer et al. 2018) with a manufacturer-reported accuracy of ± 0.53 °C from 0 to 50 °C, and resolution of 0.14 °C at 25 °C. The sensor represented the center of each of our 157 plots. Each sensor was attached on the north side of a tree trunk (George et al. 2015), at 1 m above ground level. Sensors were hung inside homemade white PVC shields, measuring 10 cm × 15 cm (Zellweger et al. 2019; Gril et al. 2023), aiming to minimize overheating from direct radiations. In order to compare microclimates across forests, we selected a common time period of continuous measurements across the three forests representing a 10-months period, from the 9th of July 2021 to the 9th of May 2022. Covering an entire year was impossible due to some sensors malfunctioning in the field. For the sake of simplicity, this period will hereafter be referred to as “annual”, although June and parts of May and July are lacking. For each sensor, we calculated the mean annual temperature conditions experienced by plants in the forest understory. We then computed statistics of the daily minimum and maximum temperatures, and derived the 5th and 95th percentile, respectively,
192 over the whole period so that we could compute the microclimate temperature quasi-annual
193 range.

194 As to macroclimate, we obtained hourly temperature records from three local weather
195 stations operated by RENECOFOR, located in open environments near each studied forest
196 (Figure 1). Macroclimate temperatures were measured in standardized radiation shields, at a
197 1.5 m height. We used this macroclimate temperature for two reasons: (1) to check whether
198 our microclimate gradient across the three studied forests corresponds to a macroclimate
199 gradient (see Figure 2), and (2) to compare the mean macroclimate temperature of 2021,
200 which covers most of the time period we focus on, to the mean temperature of previous
201 years (1979-2013), in order to control for the temporal mismatch between our
202 measurements and the timescale used to assess some of our EIVs (see Appendix S2). Based
203 on the distribution of yearly mean macroclimate temperatures, the year 2021 had a
204 temperature very close to the mean macroclimatic records from 1979 to 2013 in each forest.

205

206 2.3. Floristic inventories

207 Floristic surveys were carried out on 400 m² circular plots (equivalent to an 11.3 m radius)
208 around each temperature sensor, a commonly used and adequate surface for bioindication
209 with EIVs in forests (Otýpková 2009; Pinto et al. 2016). Species occurrences were recorded
210 from mid-May to the beginning of July 2021, so that the three studied forests were
211 approximately sampled during the same phenological stage: Blois first, then Mormal, and
212 finally Aigoual. Both bryophytes and herbaceous vascular plants, including woody plants
213 lower than 50 cm, were identified at species level (species identified at genus or family level
214 were not considered in the present analysis). All bryophytes were collected and identifications were checked in the laboratory. We recorded whenever a species occurred only on a local heterogeneity of the surveyed vegetation plot, either on a rock, a humid area (ditch, stream, or puddle) or an area of compacted ground (such as skid trails and silvicultural paths, ancient or in activity).

## 2.4. Floristic temperatures

220 Floristic temperatures were computed separately for vascular plants and bryophytes, using ordinal-scale and degree-scale EIVs representing either mean annual temperature or temperature annual range (see Table 2 for a list of EIVs).

### 2.4.1. Vascular plants

224 As ordinal-scale EIVs for vascular plants, we used Julve's temperature and continentality indices, from the French Baseflore database (Julve 2015). For the mean temperature index (hereafter T_mean_ord_vasc), each species is attributed an "Ellenberg-like" value (Appendix S3), ranging from 1 (i.e. alpine and high-altitude plants typically found at a mean annual temperature of around 0 °C) to 9 (i.e. thermophilous Mediterranean to sub-desertic plants typically found at a mean annual temperature of around 18 °C). Similarly, the continentality index (hereafter T_range_ord_vasc) ranges from 1 (i.e. marine to maritime conditions corresponding to an temperature annual range around 8 °C) to 9 (i.e. hypercontinental conditions corresponding to an temperature annual range around 40 °C).

233 To compare with degree-scale EIVs, we used the ClimPlant database (Vangansbeke et al. 2021). Temperature preferences were inferred by Vangansbeke et al. using atlas distribution
maps related to a macroclimatic grid, in this case WorldClim's (Fick & Hijmans 2017). Mean values over the 1970-2000 period were used. Among available temperature variables in the ClimPlant database, we selected mean annual temperature (hereafter T_meanDegVASC), and minimum and maximum temperatures, for which we used the 5th and 95th percentile to calculate temperature annual range (hereafter T_rangeDegVASC).

### 2.4.2. Bryophytes

For bryophytes, we used the new Bryophytes of Europe Traits (BET) database (van Zuijlen et al. 2023), with ordinal-scale and degree-scale EIVs matching the ones used above for vascular plants. The BET database includes EIVs adapted from Ellenberg, and subsequently included in databases such as BryForTrait (Bernhardt-Römermann et al. 2018), for mean annual temperature (ranging from 1 for cold alpine-nival conditions to 9 for extreme warmth conditions; hereafter T_mean_OrdBryo) and for continentality (ranging from 1 for extreme oceanic to 9 for extreme continental; hereafter T_range_OrdBryo). It also includes statistic-based values for mean annual temperature (BIO1) and the temperature annual range (BIO7) in degree Celsius, built from IUCN occurrence data overlaid on CHELSA macroclimatic raster layers over the 1979-2013 period (Karger et al. 2017). These were respectively calculated as the mean of daily mean air temperatures over one year (hereafter T_meanDegBryo), and the difference between the maximum temperature of the warmest month and the minimum temperature of the coldest month (hereafter T_rangeDegBryo).

### 2.4.3. Computation of floristic temperature

Across our three forests and 157 plots, we recorded 3,769 occurrences of 338 species of vascular plants and 2,181 occurrences of 145 species of bryophytes. The average number of
species (after excluding genus and family-level recordings) per plot was 21.0 for vascular plants and 12.2 for bryophytes. All analyses were performed with R version 4.2.2 (R Core Team 2022).

Our set of EIVs for T_mean_ord_vasc and T_range_ord_vasc covered almost all vascular plant occurrence records (97.3 %) with one exception, the "group of species" *Rubus fruticosus*. As to T_meanDeg_vasc and T_range Deg_vasc, they covered 94.5 % of vascular plant occurrence records. EIVs for T_mean_ord_bryo covered 74.1 % of bryophyte occurrence records and 90.6 % for T_range_ord_bryo, while T_meanDeg_bryo and T_range Deg_bryo, covered 99.2 % of bryophyte occurrence records.

In each plot, we computed floristic temperatures based on species occurrences and their associated EIVs. Although averaging ordinal values has been criticized, it is the usual way of dealing with EIVs and is recommended by many authors to obtain surrogates for measured environmental variables (Diekmann 2003). Floristic temperatures were calculated as simple arithmetic means of the EIVs (Käfer & Witte 2004). We also calculated floristic temperatures weighted by abundance for vascular species, but similarly to Szymura et al. (2014), the resulting correlations were lower than when based on occurrences only, and are therefore not included in the main text (see Appendix S4 for the methods and results using weighted floristic temperatures). The same procedure was repeated, but excluding the occurrences of species that were restricted to local ground heterogeneities (rock, humid area, or compacted ground, see Appendix S5 for results). We used the function 'functcomp' from the FD package to compute floristic temperatures (Laliberté et al., 2022).
278 2.5. Statistical analyses

279 The correlation between temperature values measured by microclimate sensors and floristic
280 temperature was quantified using simple linear models and linear mixed-effects models. We
281 considered either mean annual temperature or temperature annual range, measured by
282 microclimate sensors, as the response variable. Given the nature of the response variables,
283 Gaussian distribution was chosen using the functions 'descdist' and 'fitdist' of the fitdistrplus
284 package (Delignette-Muller & Dutang 2015). We independently ran models for each
285 taxonomic group (bryophytes or vascular plants) and each EIV group (ordinal-scale or
286 degree-scale), either (1) within each forest separately, or (2) across all three forests
287 together. For global models across all three forests, we used both a simple linear model and
288 a mixed-effects model with the forest site as a random intercept effect, and floristic
289 temperature as the fixed effect. The mixed-effects modelling approach allowed us to
290 compare the variability explained by the fixed effect (floristic temperature) alone versus
291 fixed and random effects together. While a mixed-effects model accounts for the nested
292 structure of our data, we were especially interested in the amount of microclimate variability
293 which could be explained by floristic temperature, within and across sites. Therefore, these
294 two types of models give us complementary information. Linear mixed-effects models were
295 fitted using the 'lmer' function from the lme4 package (Bates et al. 2015). Residuals of all
296 models were visually checked for normality and homoscedasticity, using the 'plot_model'
297 function from the sjPlot package (Lüdecke, 2021). In the result section, stars indicate
298 statistically significant results based on p-values: ns means not significant; \* p < 0.05; \*\* p <
299 0.01; and \*\*\* p < 0.001 (for the actual p-values, see Appendix S6).
301 **3. Results**

302 From the 9th of July 2021 to the 9th of May 2022, hourly microclimate temperatures ranged
303 from -8.6 to 40.8 °C in Blois forest, from -8.7 to 34.5 °C in Mormal forest, and from -9.4 to
304 32.0 °C in Aigoual forest. Over this entire period, the mean annual microclimate temperature
305 ranged from 10.2 to 11.3 °C (mean ± SD: 10.7 ± 0.2 °C) in Blois, from 9.1 to 9.8 °C (9.5 ± 0.2
306 °C) in Mormal, and from 5.6 to 8.3 °C (6.6 ± 0.6 °C) in Aigoual (Fig. 2a). The annual range of
307 microclimate temperature ranged from 25.9 to 39.3 °C (29.7 ± 3.1 °C) in Blois, from 22.5 to
308 34.4 °C (25.7 ± 2.7 °C) in Mormal, and from 25.0 to 32.6 °C (27.4 ± 1.5 °C) in Aigoual (Fig. 2b).
309 Interestingly, in Blois and Mormal, the measured range was thus larger within than between
310 forests. The differences in microclimates across forests can be related to a macroclimate
311 gradient, as shown by weather station records over the same period, especially for the mean
312 annual temperature (Figure 2).

313 Mixed-effects models across all three forests explained the vast majority of microclimate
314 temperature by the random intercept term, i.e. the forest. The conditional R² (proportion of
315 variance explained by both fixed and random effects) ranged between 93.9 to 94.9 %, while
316 the marginal R² (proportion of variance explained by the fixed effect only, i.e. floristic
317 temperature) only reached a maximum of 2.6 % (Appendix S6). This means that once we
318 discriminate forests, floristic temperature does not bring much information to explain
319 microclimate temperature.

320 We then focused on the outputs of simple linear models (see Appendix S6 for p-values).

321 Along the entire latitudinal gradient covered by all three forests, the annual mean and range
322 of temperature measured by microclimate sensors were significantly correlated to their
323 respective floristic temperature, as inferred from vascular plant species composition (Fig. 3).

324 On the contrary, the correlation was much lower or even non-significant when considered
325 within each forest separately. Across all three forests, floristic T_mean_ord_vasc and

326 T_meanDeg_vasc increased with mean annual microclimate temperature (R² = 0.77 and
327 0.50, respectively), as well as within Aigoual forest (R² = 0.11 and 0.08) (Fig. 3a-b). Floristic

328 T_range_ord_vasc increased with annual range of microclimate temperature among forests
329 (R² = 0.10), and decreased within Blois forest along with T_range_degree_vasc (R² = 0.18 and

330 0.58 respectively). In Aigoual forest, floristic T_range_ord_vasc decreased with increasing
331 annual range of microclimate temperature (R² = 0.14), while in Mormal forest, floristic

332 T_range_degree_vasc increased (R² = 0.41; Fig. 3c-d).

333 Similar to vascular plants, the mean and range value of microclimate temperature were
334 significantly and positively correlated to bryophyte floristic temperature along the gradient

335 covered by all three forests (except floristic T_range_ord_bryo), while the correlation was
336 mostly lower or non-significant within each forest considered separately (Fig. 4). Floristic

337 T_mean_ord_bryo and T_mean_degree_bryo increased with increasing mean annual
338 microclimate temperature among forests (R² = 0.65 and 0.42 respectively; Fig. 4a-b). In

339 Aigoual forest, the T_range_ord_bryo increased with mean annual microclimate
340 temperature (R² = 0.21). Floristic T_range_degree_bryo had a very limited explanatory power on

341 the annual range of microclimate temperature (R² = 0.06), while floristic T_range_ord_bryo
342 values did not explain any variation at all among forests. However, temperature annual

343 range increased with floristic T_range_ord_bryo and T_range_degree_bryo within Mormal
344 forest (R² = 0.21 and 0.23 respectively; Fig. 4c-d).
345 Equivalent analyses were conducted on floristic temperatures calculated after removing
346 occurrence records of vascular plants or bryophytes associated with local ground
347 heterogeneities that may represent specific microhabitat conditions (rock, humid area, or
348 compacted ground; 17.3 % of all records of vascular plants and 28.3 % of bryophytes). The
349 results remained the same or yielded slightly lower correlations when forests were
350 considered separately (see Appendix S5).

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352

# 4. Discussion

353

## 4.1. Floristic temperature does not capture microclimate

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### temperature within forests

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Unsurprisingly, and in accordance with previous studies (Marcenò & Guarino 2015; Chytrý et al. 2018; Tichý et al. 2023), floristic temperature was positively correlated to the complete gradient of mean annual microclimate temperature across our three forests with contrasted macroclimates, even though we compared only three different sites in France. Ordinal-scale EIVs (i.e. Julve's or Ellenberg's T) performed better than degree-scale EIVs (from the ClimPlant or BET databases), for vascular plants and bryophytes alike, at capturing differences in microclimatic mean annual temperature among forests. Julve's EIVs are adapted to France, and Ellenberg's to a neighboring country, which might explain their better correlations compared to indices accounting for European-wide or global distributions.
Consistent with our findings, although we used microclimate measurements, Tichý et al. (2023) recently standardized Ellenberg-like temperature EIVs at a European scale, and they obtained an R² value of 46 % by fitting a linear regression between temperature inferred from 364,104 vegetation surveys across Europe and their respective mean summer macroclimate temperature extracted from CHELSA (Karger et al. 2017). Additionally, Chytrý et al. (2018) adapted Ellenberg's EIVs for temperature to the Czech flora and obtained a correlation value of 0.66 between floristic temperature and mean July temperature interpolated from weather stations. Interestingly, they purposely removed major microclimate effects by selecting plots in open areas away from trees, and with a slope less than 5 % to minimize the effect of topography.

Importantly, and contrary to our first hypothesis, the correlations between floristic temperature and mean annual temperature conditions measured by microclimate sensors were very low, or even non-significant when considering the variation in mean annual microclimate temperatures observed within each of the three studied forests separately.

Mixed-effects models also showed that floristic temperature did not improve the modelling of microclimate temperature, once we account for the forest. This suggests that temperature EIVs, either using ordinal or degree scale, are not good indices to infer microclimate variations within forests but rather reflect differences across forests. This might have been expected, however, as temperature EIVs were originally based on macroclimate, not microclimate (De Frenne et al. 2013; Zellweger et al. 2020). Yet contrary to our results, two studies found an increase of floristic temperature after clear-cutting management or storm-induced gaps within boreal and temperate forest canopies, perhaps due to a different sampling gradient of structure (Dietz et al. 2020; Christiansen et al. 2022).
388 Removing plants associated with a local heterogeneity within plots (humid area, compacted ground or rock), which we thought may have different microclimates altering the bioindication signal, did not improve the fit of regressions, neither for bryophytes nor for vascular plants. Contrary to our expectations as well, bryophytes did not perform better than vascular plants as microclimate indicators, perhaps being related to even smaller scale variations of temperature and humidity close to the ground and within microhabitats than vascular plants (Kiebacher et al. 2023). There was one exception in Aigoual forest, where Ellenberg's temperature EIVs for bryophytes had a better relationship with mean microclimate temperature than the equivalent Julve's EIVs for vascular plants. This could be due to a higher diversity in bryophytes than vascular plants in most of the plots in Aigoual forest. The relation for the mountainous plots in Aigoual generally diverged from Blois and Mormal, which could be explained by the fact that they differed not only by forest structure, but also in terms of topographic features such as slope and aspect, causing further topoclimate and microclimate complexities, as well as in the adiabatic lapse rate along elevation (Maclean et al. 2015).

403 To our knowledge, few studies have directly compared local temperature measurements from microclimate sensors to floristic temperatures, and none have done so within gradients of forest microclimates. Gottfried et al. (2012) did correlate the floristic temperature (weighted by abundance) of a thermic vegetation indicator based on altitudinal ranks with temperature measured by sensors placed 10-cm deep in the soil, more specifically the mean of daily minimum temperatures of June. The global Spearman rho coefficient was 0.52, but varied widely between the different European mountain regions, from 0.89 to an insignificant correlation. However, the latter study did not focus on forest microclimate
411 variations within a given macroclimate, and elevation would surely be the prime driver of
412 their temperature variations, similar to the results we obtained for the Aigoual forest.

413 4.2. Temperature range may be predicted by degree-scale EIVs within
414 lowland forests

415 Mean annual temperature conditions are not as important as extreme temperatures for
416 many understory species (Ashcroft & Gollan 2012; Macek et al. 2019). A dense forest stand
417 may have the same mean annual temperature as a more open forest stand nearby, or even
418 an open meadow located next to the forest, although considerably more buffered against
419 temperature extremes throughout the year; which is why we also tested the annual range of
420 microclimate temperature.

421 Floristic temperature for annual temperature range had no (for bryophytes) to very low (for
422 vascular plants) explanatory power on the observed variation of the annual measured range
423 of microclimate temperature across the three forest sites. The correlation within each forest
424 was also mostly low or non-significant, possibly because we only focus on one single (almost
425 complete) year and not on a long-term average. In the mountainous region of the Aigoual
426 forest, the low correlation may be due to the shorter gradient of temperature annual range.
427 Fitting a model along a short gradient of environmental variation indeed critically limits the
428 strength of bioindication (Szymura et al., 2014). On the contrary, a notable exception among
429 these non-significant results, the annual range of microclimate temperature was highly
430 correlated to floristic temperature within the two lowland forests of Blois and Mormal using
431 degree-scale EIVs for vascular plants, while ordinal-scale EIVs had much lower and
432 inconsistent predictive ability on the annual range of microclimate temperature.
Julve's or Ellenberg's ordinal-scale EIVs for continentality (K) have an equivocal meaning, since the concept of continentality not only integrates daily and seasonal ranges of temperatures, but also precipitation and humidity. Continentality is less well-defined and less used than other EIVs (Berg et al. 2017). These ordinal-scale EIVs may be biased by an incomplete vision of their authors, although Ellenberg's or Julve's K and T EIVs rely not only on expert knowledge, but also on mapped distributions of species. Degree-scale, or "biogeographic" EIVs are not exempt from errors and may also be biased; for instance, a given species may be excluded by dispersal limitation, edaphic conditions or biotic factors from a climatically suitable area, which will then wrongly be considered as unsuitable.

Therefore, fundamental climatic niches are usually wider than realized ones (Soberón & Arroyo-Peña 2017; Sanczuk et al. 2022), which are the ones used to derive EIVs. However, our results on vascular plants demonstrate a clear, if not direct link between a degree-scale EIV and microclimate measurements of temperature annual range in lowland forests, which might open up new opportunities for quantifying the buffering effect in lowland forests using plant communities.

## 4.3. Why can't EIVs predict forest microclimate temperatures?

Apart from ClimPlant's degree-scale temperature range (floristic T_range_ord_vasc) in lowland forests, we showed that the relationship between temperature EIVs and microclimate temperature is not consistent, limiting the possibility to use current ordinal-scale or degree-scale EIVs to infer or reconstruct forest microclimate temperature, i.e. using a "transfer function" — as termed by paleoecologists and ecologists (Bertrand et al., 2011; Paus 2013; Pacheco-Riaño et al. 2023).
There are three main possible explanations for the low correlation we observed between measured microclimate temperatures and floristic temperatures. First, although variation in microclimate conditions shape community composition in vascular plants (Zellweger et al., 2020; Haesen et al. 2023) and bryophytes (Fenton & Frego 2005; Man et al. 2022), forest plants may not respond primarily to microclimate temperatures (Bertrand et al. 2016; Schall & Heinrichs 2020). Other non-climatic factors are assumed to play an increasing role at small spatial resolutions (Pearson & Dawson, 2003). Shifting from large spatial extents to fine spatial grains thus raises the question of the relevance of the variables that operate along different combinations of spatial grains and extents (Bruelheide et al., 2018). Beyond stochasticity, species occurrences are related to more than one parameter, and confounding effects could have obscured the bioindication signal, as other abiotic factors such as soil acidity, humidity, or landscape connectivity may be predominant in community assembly rules (Diekmann 2003; Borderieux et al. 2023). Plants may also respond with delay to past land use and management conditions (Lenoir et al., 2022). Biotic interactions such as competition or mutualism may further complicate species coexistence and the resulting plant community composition (Sanczuk et al. 2022).

Second, the response of plants to microclimate differs from their response to macroclimate temperature (Haesen et al. 2023). Species could theoretically bioindicate microclimate temperature, but most likely after a complete redesign of temperature EIVs, for instance using the linear relationship between macroclimate and microclimate temperatures (Gril et al. 2023). Temperature mean or range (continentality) have a special status among EIVs, because these climatic EIVs are not representative of local (i.e. microclimate) temperature conditions, and were built instead from macroclimatic or topoclimatic (i.e. the layering of successive vegetation belts along elevational gradients in mountain regions) conditions.
479 Combining available microclimate maps of understory temperature at the continental scale
480 of Europe (Haesen et al. 2021) with vegetation plot data from the European Vegetation
481 Archive (EVA) (Chytrý et al. 2016), it should now be possible to estimate new, refined
482 temperature EIVs for forest plant species.

483 Third, some specialist plant species may be better bioindicators of local environmental
484 conditions than others, having a narrower ecological niche while generalist species may be
485 indifferent or tolerate a large range of environmental conditions (Diekmann et al., 2003).

486 Therefore, specific species of vascular plants or bryophytes with narrow thermal tolerance
487 may be better microclimate indicators than the whole community (Haesen et al., 2023).

488 **4.4. Using plant communities to track macro- and microclimatic**
489 **changes in forests**

490 Climate change reshuffles plant communities towards more warm-adapted species, creating
491 novel species assemblages, and this ongoing thermophilization process is usually quantified
492 by EIVs averaged at the community level (De Frenne et al. 2013; Govaert et al. 2021; Richard
493 et al. 2021). A concern sometimes raised is the circularity of reasoning when ecologists
494 explain vegetation patterns with plant EIVs (Diekmann 2003; Schall & Heinrichs 2020), but
495 establishing a link between bioindication and local temperature measurements does not
496 suffer from this issue, nor does quantifying the thermophilization of plant communities over
497 time.

498 Current macroclimate and floristic temperature are not at an equilibrium within forest
499 understories, and the thermal lag or climatic debt of forest plant communities relative to
500 macroclimatic conditions is steadily increasing over time (Richard et al. 2021). This lag is
either perceived (i) as a major risk (Bertrand et al. 2016), as many species may go locally extinct whenever the debt is “paid off”, especially in case of brutal canopy openings such as timber harvesting or windstorms, or (ii) as a chance for species to get additional time to adapt to warmer conditions (Zellweger et al. 2020). Understory communities are mainly composed of perennial species with a long lifespan. Their capacity to withstand warming may cause the thermal lag in forest communities (Bertrand et al. 2016; Martin et al. 2019; Schall & Heinrichs 2020), especially pronounced for vascular plants, while bryophytes may be more sensitive and responsive (Becker Scarpitta et al. 2017; Kiebacher et al. 2023; but see Govaert et al. 2021). Alternatively, this thermal lag may be explained by microclimate buffering under dense canopies (De Frenne et al. 2013; Zellweger et al. 2020). If so, one would expect temperature EIVs to be a useful tool to evaluate thermophilization in response to microclimate changes, discriminating between trajectories of canopy opening or closing over time (Dietz et al. 2020; Christiansen et al. 2022). We found a few low, but sometimes significant correlations between floristic and measured temperature within forests, across a gradient of microclimate buffering by the canopy. Yet no direct relationship could be established, questioning the relative importance of microclimate buffering in explaining the macroclimatic debt in forests.

We mostly found very similar results for both bryophytes and vascular plants, two contrasting ecological groups (Becker-Scarpitta et al. 2022; Kutnar et al. 2023), strengthening our point. However, confirmation by larger-scale studies, both in space and time, is needed.

At a European level, future studies may use the newly available harmonized EIVs (Vangansbeke et al. 2021; Tichý et al. 2023; Dengler et al. 2023) together with vegetation plot data from EVA (Chytrý et al. 2016) and high-resolution maps of understory temperature (Haesen et al. 2021) to confirm or deny our findings at a larger spatial extent.
525

526

Acknowledgements

527 We sincerely thank the interns who contributed to temperature data collection in the field,

528 namely Hugo Mahier, Ambre Châline, Soline Chaudet, and Hugo Hayé. We also thank the

529 RENECOFOR network and the National Forest Office (ONF), especially all agents who helped

530 with the project. Finally, we thank the editor Sabine Rumpf and two anonymous reviewers,

531 for their suggestions that significantly clarified and improved our paper.

532

533

Author's contributions

534 Jonathan Lenoir, Ronan Marrec, Fabien Spicher, Eva Gril and Guillaume Decocq conceived

535 the research idea. Eva Gril, Germain Vital, Fabien Spicher, Vincent Le Roux, Boris Brasseur,

536 Jonathan Lenoir and Ronan Marrec conducted the field study. Alain Vanderpoorten and

537 Fabien Spicher checked bryophyte species identifications. Ronan Marrec analyzed the data

538 with inputs from Eva Gril, Fabien Spicher, Germain Vital and Jonathan Lenoir. Eva Gril led the

539 writing with significant contributions from all co-authors.

540
541 Data and Code Availability

542 The R script and associated data will be published online on Figshare upon acceptance. Raw

543 temperature time series and plant community composition data were submitted to the

544 international SoilTemp database (www.soiltempproject.com/) and will be available upon

545 request.

546

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828 **Table 1:** Characteristics of the three studied forests, from ONF (National Forest Office) data

829 with a weather station located near each of the three forests (See Fig. 1). Climatic

830 parameters were averaged over the 1996-2004 period. T = Temperature.

<table><thead><tr><th></th><th>Aigoual</th><th>Blois</th><th>Mormal</th></tr></thead><tbody><tr><td>Surface (ha)</td><td>2,469*</td><td>2,749</td><td>9,137</td></tr><tr><td>Dominant trees</td><td>Fagus sylvatica</td><td>Quercus petraea</td><td>Quercus robur / Fagus sylvatica</td></tr><tr><td>Climate influences</td><td>Mountainous and Mediterranean</td><td>Oceanic</td><td>Oceanic and continental</td></tr><tr><td>Elevation (m)</td><td>829 - 1565</td><td>94 - 141</td><td>140 - 175</td></tr><tr><td>T<sub>absolute min</sub> (C°)</td><td>-14.8</td><td>-12.0</td><td>-15.1</td></tr><tr><td>T<sub>mean</sub> min (C°)</td><td>3.7</td><td>7.1</td><td>5.7</td></tr><tr><td>T mean (C°)</td><td>6.9</td><td>11.3</td><td>9.7</td></tr><tr><td>T<sub>mean max</sub> (C°)</td><td>11.1</td><td>16.4</td><td>14.3</td></tr><tr><td>T<sub>absolute max</sub> (C°)</td><td>33.8</td><td>40.1</td><td>36.7</td></tr><tr><td>Annual rainfall (mm)</td><td>1853</td><td>744</td><td>930</td></tr><tr><td>Number of rainy days (&gt; 1 mm) per year</td><td>143</td><td>179</td><td>211</td></tr></tbody></table>

833 *The complete surface of Aigoual forest is 16,123 ha; but we focused on a central area of
834 about two thousand hectares. The surface and range of elevation presented in this table is
835 for this study area, not the complete forest.
837 **Table 2:** List of plant Ecological Indicator Values (EIVs) used as predictors of microclimate temperature in this study, along with the database and source they were extracted from.
838
839 The names of EIVs we use in this manuscript indicate whether they represent temperature mean or range, are in ordinal or degree scale, and apply to vascular plants or bryophytes. T = Temperature.
840
841

<table><thead><tr><th>Group</th><th>EIV</th><th>Description</th><th>Database</th><th>Source</th></tr></thead><tbody><tr><td rowspan="4">Vascular plants</td><td>T_mean_ord_vasc</td><td>Julve's temperature T</td><td rowspan="2">Baseflore</td><td rowspan="2">Julve 2015</td></tr><tr><td>T_range_ord_vasc</td><td>Julve's continentality K</td></tr><tr><td>T_meanDeg_vasc</td><td>WorldClim's T mean</td><td rowspan="2">ClimPlant</td><td rowspan="2">Vangansbeke et al. 2021</td></tr><tr><td>T_range Deg_vasc</td><td>WorldClim's T range</td></tr><tr><td rowspan="4">Bryophytes</td><td>T_mean_ord_bryo</td><td>Ellenberg's temperature T</td><td rowspan="4">BET</td><td rowspan="4">van Zuijlen et al. 2023</td></tr><tr><td>T_range_ord_bryo</td><td>Ellenberg's continentality K</td></tr><tr><td>T_meanDeg_bryo</td><td>CHELSA's T mean</td></tr><tr><td>T_range Deg_bryo</td><td>CHELSA's T range</td></tr></tbody></table>

843
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Sampling design, with the three forests located in France at contrasting latitudes.</figcaption></figure>

Forest cover is outlined in green (Corine Land Cover 2018, https://land.copernicus.eu/pan-european/corine-land-cover), and darker green for the three studied forests: Mormal (b), Blois (c) and Aigoual (d). The blue and yellow dots represent study plots, the yellow dots in each forest being equipped with a weather station as well as microclimate sensors.

845

846

847

848

849

850
851

<figure><img src="image_3.png" /></figure>

852

853

**Figure 2.** Distribution of mean annual temperature (a) and temperature annual range (b) in °C, recorded by microclimate sensors deployed between the 9th of July 2021 and the 9th of May 2022 across three forests in France (Aigoual, *n* = 53; Blois, *n* = 52; Mormal, *n* = 52). The vertical line depicts the equivalent macroclimate values from a local weather station.

854

855

856
<figure><img src="image_4.png" /></figure>

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

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

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

857

858

**Figure 3.** Correlations between measured microclimate temperature and vascular plant floristic temperature, for the annual mean (a-b) and range (c-d). R² values and significance are computed based on linear regressions for each forest separately (yellow: Aigoual; green: Blois; dark blue: Mormal). Global R² value is computed based on linear regression for all forests together (grey). Floristic temperatures are community means: T_mean_ord_vasc is the community mean of Julve's temperature, T_range_ord_vasc of Julve's continentality, T_meanDeg_vasc of WorldClim's T mean, and T_rangeDeg_vasc of WorldClim's T range.

See Table 2 for the source of each floristic temperature, either ordinal or degree-scale.

859

860

861

862

863

864

865
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

866

**Figure 4.** Correlations between measured microclimate temperature and bryophyte floristic temperature, for the annual mean (a-b) and range (c-d). R² values and significance are computed based on linear regressions for each forest separately (yellow: Aigoual; green: Blois; dark blue: Mormal). Global R² value is computed based on linear regression for all forests together (grey). Floristic temperatures are community means: T_mean_ord_bryo is the community mean of Ellenberg's temperature, T_range_ord_bryo of Ellenberg's continentality, T_meanDeg_bryo of CHELSA's T mean, and T_range Deg Vasc of CHELSA's T range. See Table 2 for the source of each ordinal or degree-scale floristic temperature.
875 **List of Appendices**

876 **Appendix S1:** Plot location (coordinates in WGS84), elevation and forest structure

877 **Appendix S2:** The mean temperature of 2021 compared to 1979-2013 macroclimate records

878 **Appendix S3:** Meaning of Julve's temperature and continentality coefficients for vascular plants, from the Baseflore database (Julve 2015)

880 **Appendix S4:** Weighting vascular plant species by their abundance

881 **Appendix S5:** Results after removing species associated to local heterogeneities – vascular plants and bryophytes

883 **Appendix S6:** Details of model outputs - correlations between measured and floristic temperature, with linear models (LM) or linear mixed models (LMM)