Lags in phenological acclimation of mountain grasslands after recent warming

Keywords: phenology, reciprocal transplant, warming experiment, transient dynamics, mountain grasslands, NDVI, global change ecology, climate change

Flower onset according to the

Introduction

Climate warming is altering the biodiversity and functioning of mountain grasslands. Plant communities will not immediately restructure to adjust to new air and soil temperatures and associated changes in growing season length and snow cover [START_REF] Carlson | Observed long-term greening of alpine vegetation-a case study in the French Alps[END_REF][START_REF] Choler | Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration[END_REF][START_REF] Gobiet | 21st century climate change in the European Alps-A review[END_REF] but will show a number of transient responses [START_REF] Hastings | Transient phenomena in ecology[END_REF] that are susceptible to lag behind climate warming (Alexander et al., 2018) . Plastic responses of plant phenotypes are prone to be triggered before species' abundance distributions and, finally, community composition change [START_REF] Nicotra | Plant phenotypic plasticity in a changing climate[END_REF] . By monitoring these transient dynamics, we can identify the short-term acclimation lags, i.e. the difference between the current state to the one completely accustomed to a new climate, that can temporarily impair ecosystem functions and services and can serve as early-warning signals of long-term degredations.

Quantifying and characterizing acclimation lags in addition to the responses to warming is pivotal to understand how strongly adjusting plant species and communities are still underperforming in comparison to well-adapted ones, how prone they are to be replaced and outcompeted by better adapted ones, and how strongly ecosystem processes are impaired [START_REF] Ryo | Basic principles of temporal dynamics[END_REF][START_REF] Ström | Hydrologic effects on riparian vegetation in a boreal river: an experiment testing climate change predictions[END_REF] Among plastic responses to warming, plant phenology is one of the first to be triggered [START_REF] Bellard | Impacts of 25 Journal of Ecology: Confidential Review copy Journal of Ecology: Confidential Review copy climate change on the future of biodiversity[END_REF][START_REF] Parmesan | Plants and climate change: complexities and surprises[END_REF][START_REF] Shen | Plant phenological responses to climate change on the Tibetan Plateau: research status and challenges[END_REF] . Phenology refers to the timing and duration of events in species' plant growth and reproduction over the year including budburst, flowering, seed production and browning. Phenology strongly affects demographic performance, community assembly and functions [START_REF] Ackerly | The evolution of plant ecophysiological traits: recent advances and future directions[END_REF][START_REF] Richardson | Climate change, phenology, and phenological control of vegetation feedbacks to the climate system[END_REF] . As climate warming does not only result in higher mean temperatures but also in changed snow cover 1 Page 1 of 60 Journal of Ecology: Confidential Review copy Journal of Ecology: Confidential Review copy regimes and earlier starts and longer durations of growing seasons, it will affect different aspects of plant phenology in different periods of the season. In the short-term, phenology can allow acclimation of plant communities, notably for their main functions such as productivity [START_REF] Carlson | Observed long-term greening of alpine vegetation-a case study in the French Alps[END_REF][START_REF] Piao | Plant phenology and global climate change: current progresses and challenges[END_REF] . However, many questions on the transient responses of mountain plant phenology to warming remain open: Do early or late season phenological stages acclimate the fastest? How much time does it require to close acclimation lags? Are species plastic in their responses or does acclimation require a restructuring of community composition? It will depend on the answers to these questions which species and ecosystem functions can prevail under climate warming. Therefore, we need to effectively measure phenological responses to realistic warming scenarios and use informative phenological indicators and ways to characterize acclimation lags.

Plant phenology and its response to warming climate is a globally long-studied phenomenon with different experimental methods and at different organism levels [START_REF] Piao | Plant phenology and global climate change: current progresses and challenges[END_REF][START_REF] Winkler | Responses of alpine plant communities to climate warming[END_REF][START_REF] Wolkovich | Warming experiments underpredict plant phenological responses to climate change[END_REF] . For mountain grasslands, experimental warming showed an advancement in greening, flowering and fruiting but a delayed browning at community-level [START_REF] Meng | Divergent responses of community reproductive and vegetative phenology to warming and cooling: asymmetry versus symmetry[END_REF] . However, even so community-level phenological responses often resemble species-specific responses to warming [START_REF] Diez | Forecasting phenology: from species variability to community patterns[END_REF] , species with different characteristics still show idiosyncratic responses [START_REF] Arft | Responses of tundra plants to experimental warming: meta-analysis of the International Tundra Experiment[END_REF][START_REF] Dunne | Subalpine meadow flowering phenology responses to climate change: integrating experimental and gradient methods[END_REF][START_REF] Li | Responses of sequential and hierarchical phenological events to warming and cooling in alpine meadows[END_REF][START_REF] Smith | Phenological changes in alpine plants in response to increased snowpack, temperature, and nitrogen[END_REF]; S. P. Wang et al., 2014) . Yet, it has been suggested that responses can potentially be generalized at the growth form-level [START_REF] Arft | Responses of tundra plants to experimental warming: meta-analysis of the International Tundra Experiment[END_REF][START_REF] Dunne | Subalpine meadow flowering phenology responses to climate change: integrating experimental and gradient methods[END_REF][START_REF] Oberbauer | Phenological response of tundra plants to background climate variation tested using the International Tundra Experiment[END_REF] . For example, graminoids and forbs are prone to give different responses as they differ in functional and reproductive strategies [START_REF] Reich | Variation in growth rate and ecophysiology among 34 grassland and savanna species under contrasting N supply: a test of functional group differences[END_REF][START_REF] Shen | Plant phenological responses to climate change on the Tibetan Plateau: research status and challenges[END_REF][START_REF] Siebenkäs | Phenotypic plasticity to light and nutrient availability alters functional trait ranking across eight perennial grassland species[END_REF] .

Both at community level and at species level, a plethora of indicators have been suggested to track changes in phenology for different periods in the season and to approximate different demographic processes (Table 1). Consequently these indicators can respond differently to warming. Therefore, a comprehensive study of warming effects and acclimation lags requires not only to combine species and community level measures but also to integrate different indicators to have the most complete overview on phenological changes. Here, we refer to indicators that capture the start, end or pace of phenological stages as proxies for the timing of plant growth and reproduction (Time-related indicators, Table 1) and we refer to indicators that capture the length of phenophases (e.g. length of greening or flowering) and the number of individuals passing to a certain phenophase as proxies of demographic performance (Performance-related indicators, Table 1).

For time-related indicators, warming generally leads to an earlier and quicker onset of phenophases in early-season while late-season responses are much more variable [START_REF] Parmesan | Plants and climate change: complexities and surprises[END_REF] . We are not aware of acclimation lag studies but expect faster acclimation to warming in the early season as it has been shown that plants are plastic in adjusting the leaf-out to changes in snow cover dynamics [START_REF] Choler | Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration[END_REF][START_REF] Körner | Alpine Plant Life[END_REF] . In contrast, high mountain grassland plants are adapted to short growing seasons and we expect that adjusting to longer season lengths may be more difficult than to start the season earlier. For performance-related indicators, warming leads to more plant growth but also more idiosyncratic species-specific responses, e.g. to either decreases or increases in reproductive performance [START_REF] Aldridge | Emergence of a mid-season period of low floral resources in a montane meadow ecosystem associated with climate change[END_REF][START_REF] Carlson | Observed long-term greening of alpine vegetation-a case study in the French Alps[END_REF][START_REF] Liu | Global warming reduces plant reproductive output for temperate multi-inflorescence species on the Tibetan plateau[END_REF][START_REF] Li | Responses of sequential and hierarchical phenological events to warming and cooling in alpine meadows[END_REF] . Additionally, while climate warming might generally improve conditions for plant growth, it often is associated with lower snow cover protection and more frequent exposure to freezing events due to early snowmelt which may limit some species [START_REF] Choler | Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration[END_REF] . In contrast to time-related indicators, we expect performance-related indicators to be more limited by physiological constraints, to be less plastic and thus, be much slower in adjusting [START_REF] Baptist | No increase in alpine snowbed productivity in response to experimental lengthening of the growing season[END_REF] .

Hitherto, warming studies have mainly focussed on a single type of indicator [START_REF] Aldridge | Emergence of a mid-season period of low floral resources in a montane meadow ecosystem associated with climate change[END_REF][START_REF] Li | Responses of sequential and hierarchical phenological events to warming and cooling in alpine meadows[END_REF][START_REF] Meng | Divergent responses of community reproductive and vegetative phenology to warming and cooling: asymmetry versus symmetry[END_REF]S. P. Wang et al., 2014;[START_REF] Wang | The influence of local spring temperature variance on temperature sensitivity of spring phenology[END_REF] (but see counter examples: (H. [START_REF] Wang | Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change[END_REF]S. Wang et al., 2014) . and quantified phenological changes as a response to warming (i.e. the warming effect) [START_REF] Piao | Plant phenology and global climate change: current progresses and challenges[END_REF][START_REF] Shen | Plant phenological responses to climate change on the Tibetan Plateau: research status and challenges[END_REF][START_REF] Wolkovich | Warming experiments underpredict plant phenological responses to climate change[END_REF] but rarely measured acclimation lags [START_REF] Ryo | Basic principles of temporal dynamics[END_REF] . Measuring acclimation lags is a challenge as it requires to identify the adjusted community state after complete acclimation [START_REF] Visser | Shifts in phenology due to global climate change: the need for a yardstick[END_REF] .

Here, we suggest a combination of a transplant experiment with a conceptual framework to measure transient warming and cooling effects and acclimation lags in mountain grasslands (Fig. 1). We added cooling effects to test for reciprocality and to find out whether the acclimation to new thermal conditions in mountain grasslands is generally a slow phenomenon (Crous, 2019). Our reciprocal transplant experiment along an elevation gradient simulates three degrees of warming and longer growing seasons hence, a realistic warming scenario for the Alps for this century (see projections for RCP4.5 and 8.5 scenarios: [START_REF] Jacob | EURO-CORDEX: new high-resolution climate change projections for European impact research[END_REF][START_REF] Vorkauf | Past and future snowmelt trends in the Swiss Alps: the role of temperature and snowpack[END_REF] .

We transplanted alpine communities 500m downwards to subalpine conditions to simulate climate warming (Fig. 1A, AlpineWarmed) and, reciprocally, we transplanted subalpine communities 500m upwards (Fig. 1A, SubalpineCooled). We controlled for transplantation effects at both sites with control plots (Fig. 1A, AlpineControl and SubalpineControl, resp.). We To measure acclimation lags we assume that plants in control plots are in equilibrium with climatic conditions. To justify this assumption despite ongoing climate change, we argue that i) the historical climate warming is a slow and long-term phenomenon compared to the experimental manipulation and thus negligible in comparison (Appendix Table S3), and ii) the ongoing climate change has not induced yet a trend of longer growing seasons even though this is an expected phenomenon [START_REF] Jacob | EURO-CORDEX: new high-resolution climate change projections for European impact research[END_REF][START_REF] Wang | Changing lengths of the Four Seasons by global warming[END_REF] . Moreover, results would only be more extreme if we would have transplanted communities from long-term climate equilibrium. Acclimation lags can then be measured by comparing where the state of AlpineWarmed plots (or SubalpineCooled plots) is relative to the shortest trajectory of acclimation which we define here as the shortest distance between states of AlpineControl and Subalpine Control plots (Fig. 1B,D). We suggest two measurements (Fig. 1D): Acclimation lags can be big either because little acclimation has been achieved yet and the still to cover distance is much larger than the distance already covered (deficient acclimation) and/or because transient adjustments deviate in direction from the shortest trajectory to complete acclimation (deviating acclimation). Theoretically, we expect that in the very long-term lags will close and compared plots will be indistinguishable.

Combining a species to community scale approach, a multi index approach for phenology and our conceptual framework, we asked whether: i) mountain grassland phenology completely acclimated in timing and performance over three years of warming; ii) the lag in acclimation was due to deficient and/or divergent warming effects and iii) the transient responses to warming and cooling were symmetric.

Page 6 of 60 Journal of Ecology: Confidential Review copy Journal of Ecology: Confidential Review copy transport, each block was cut in 4 x 1m 2 subblocks that were re-assembled in their original composition when re-planted. To simulate the effect of cooling on subalpine communities, 10 intact 4 m 2 blocks of vegetation and soils were transplanted from the subalpine to the alpine site (SubalpineCooled plots). To control for the transplantation stress, we also horizontally transplanted 10 intact 1m 2 blocks at both the subalpine and the alpine sites (SubalpineControl plots and AlpineControl plots, respectively). Around the turfs we placed water permeable root barriers to avoid root ingrowth from the natural vegetation around the plots. At both sites, cattle and sheep were kept out by electric fences.

Defining the growing season

We considered the start and end of phenological events always relative to the start of the growing season and the end of growing season, respectively (Table S1). This choice assures that i) plots in different sites and years are comparable and ii) we compare the direct phenological response rather than the differences in calendar dates [START_REF] Johansson | Climate change and the optimal flowering time of annual plants in seasonal environments[END_REF] .

We defined the start (end) of the growing season as the first date when the NDVI based on MODIS (Moderate Resolution Imaging Spectroradiometer) remote-sensed data was above (below) 0.1 and the locally measured daily soil temperature was above (below) 1 ℃ [START_REF] Choler | Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration[END_REF][START_REF] Choler | Winter soil temperature dependence of alpine plant distribution: Implications for anticipating vegetation changes under a warming climate[END_REF] (Fig. 2). For the remote-sensing data (RS-based NDVI), we downloaded the MOD09Q1 surface reflectance products corresponding to tile h18.v4 (40-50• N, 0-15.6• E) from the Land Processes Distributed Active Archive Center (LP DAAC). We used surface reflectance in the red and near-infrared to calculate the 250m-resolution NDVI 8-days times series corresponding to the two sites. The RS-based NDVI time series data were BISE (Best Index Slope Extraction) corrected, gap-filled with cubic spline interpolation and smoothed using the Savitzky-Golay filter [START_REF] Choler | Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration[END_REF] . For the local soil temperature measurements, we equipped Journal of Ecology: Confidential Review copy Journal of Ecology: Confidential Review copy the two sites with standalone soil temperature data loggers (Hobo pendant UA, Onset Computer Corporation, Bourne, MA) buried at 5 cm below ground. We smoothed daily soil temperature time series with cubic spline interpolation.

On site measurements and phenological indicators

Community-level

We tracked the phenology at community-level using NDVI as a measurement of greening and a proxy for photosynthetic activity and plant growth [START_REF] Myneni | On the relationship between FAPAR and NDVI[END_REF]) (Fig. 2.C). We measured NDVI (Normalized Difference Vegetation Index) in each treatment subblock (10 x 4 x 1 m 2 subblocks for AlpineWarmed and for SubalpineCooled) and in each control block (10 x 1 m 2 blocks for AlpineControl and SubalpineControl) with three measurement repetitions once (twice at maximum growth) per week during the growing seasons of the years 2017, 2018 and 2019. We took the measurements with a Decagon ProCheck spectral reflectance sensor for NDVI with 630 nm (red) and 800 nm (near-infrared) spectral irradiance. The measurements were taken at clear sky from 11am to 2pm during the same day for both sites. We derived indicators from the NDVI curves over the growing season independently for each year, treatment subblocks, control blocks and replicates. We gap-filled the time series (i.e. the other days of the week) using cubic smoothing spline interpolation and smoothed using Savitzky-Golay filter [START_REF] Choler | Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration[END_REF] .

Based on these time series, we calculated several indicators. These indicators were based on the definition of seasons within the whole growing season: i) early-season as the period starting from the onset of the growing season to the last day when the NDVI values were below the 90% of the maximum NDVI, ii) mid season as the period when the NDVI values stayed above 90% of the maximum NDVI, iii) late-season as the period between the first day where NDVI values dropped below 90% of the maximum NDVI until the offset of the growing season. The threshold choice (e.g. 80% of the maximum NDVI) did not affect the results (unpublished). While the start and end of growing seasons were identified with MODIS remote-sensed data, we got to capture the unique phenological dynamics in plant growth of each plot repetition by i) starting NDVI measurements right after the snow melt when the field site was accessible, ii) identifying the maximum NDVI based on field measurements for each plot.

We calculated time-related indicators as (Table 1): i) the days from the start of growing season until 90% of the maximum plant growth (greening days), ii) greening speed as the rate of increase in the NDVI values (i.e. from 0.1 of onset value to 90% of maximum NDVI value) during early-season (Eq.2), iii) ratio of mid-season days to growing season days, iv) days after 90% of the maximum plant growth until the end of growing season (browning days), v) browning speed as the rate of decrease in the NDVI values (from 90% of maximum NDVI value to 0.1 of offset value) during late-season (Eq.3). We calculated performance-related indicators as proxies for biomass production and plant growth as the areas under NDVI curves for each season (Table 1): i) early plant growth, ii) 9 mid-season plant growth, iii) late plant growth and iv) annual (i.e. whole growing season) plant growth.

Species-level

We tracked the phenology of focal dominant species (Appendix Table S2) in one sub-block of each treatment block (20 x 1 m 2 sub-blocks in total) and in each control block (20 x 1 m 2 blocks in total) every week during the growing seasons of 2017, 2018 and 2019. The selected dominant species for the blocks with subalpine origin (SubalpineControl and SubalpineCooled)

were Patzkea paniculata , Carex sempervirens , Festuca nigrescens , Centaurea uniflora , Helianthemum nummularium, and Meum athamanticum . The selected species for the blocks with alpine origin (AlpineWarmed and AlpineControl) were Carex sempervirens , Festuca nigrescens , Poa alpina, Potentilla aurea, and Trifolium alpinum , Geum montanum . We focus here on the flowering, fruiting and dissemination phases as they reflect both early-and late-season plant growth and reproduction phenologies. For each treatment subblock and control block and each species, we determined the phenophases of 10 randomly selected individuals (or the maximum number of individuals available when less than 10, see Appendix

TableS2 for percentage of observations when less than 10 individuals were observed). During the peak of the growing season, we increased the frequency of NDVI and phenology measurements to two times a week.

To reduce observation bias, we determined the start of the phenophase for a species as the average of i) the last date when no individuals of the given species in the given phenophase were observed and ii) the first date when the individuals of the given species in the given phenophase were observed (vice versa for the end of phenophase) [START_REF] Li | Responses of sequential and hierarchical phenological events to warming and cooling in alpine meadows[END_REF] . In case this 10

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We calculated time-related indicators as the days from the start of the growing season until the mean date of the phenophase (Table 1) [START_REF] Moussus | Featuring 10 phenological estimators using simulated data[END_REF] .

We calculated performance-related indicators as (Table 1): i) the maximum ratio of individuals in the phenophase to the number of sampled individuals (maximum 10) and ii) length of the phenophase as the number of days between the onset and the end of the phenophase. [START_REF] Li | Responses of sequential and hierarchical phenological events to warming and cooling in alpine meadows[END_REF] . We excluded the length of the dissemination period from our study as we could not collect the data over the whole dissemination period.

Statistical analyses

For our sets of phenological indicators (community-vs. species-level and time vs. performance related), we analyzed four different treatment effects (Fig. 1.C): i) a warming effect by comparing AlpineWarmed to AlpineControl plots, ii) a cooling effect by comparing SubalpineCooled to SubalpineControl plots, iii) an acclimation lag after warming by comparing SubalpineControl to AlpineWarmed plots, and iv) an acclimation lag after cooling by comparing AlpineControl to SubalpineCooled plots. We conducted all the statistical analyses on R (R Core Team, 2017) .

First, we tested the different treatment effects for each phenology indicator independently using linear mixed effects models ( nlme R package). At the community-level, we took the median of the measurement repetitions to avoid including measurement errors due to the handheld NDVI sensor. We considered treatment (custom contrast setting of above-explained comparisons, Fig. 11 case of a single significant PC (i.e. species level), angles were set either to 0° (i.e. on the acclimation trajectory) or to 180° (opposite direction of the acclimation trajectory).

For interpretation, we speak of "acclimation lag" or "deficient warming effect" if we found a significant difference between SubalpineControl and AlpineWarmed or AlpineControl and SubalpineCooled and of "complete acclimation" otherwise. We speak of a divergent warming or cooling effect if the observed trajectory deviates from the shortest trajectory of complete acclimation. Importantly, acclimation lags can be much higher than 100% when the distance to the optimally adapted community is not narrowing down over time but is instead increasing during the transient dynamics. This can either happen when the adjustment response goes in a "wrong" direction or when the adjustment response goes in the "right" direction but then overshoots largely. We consider that the acclimation lags in warming and cooling are symmetrical when they have almost the same (i.e. maximum 10% difference) multidimensional acclimation lag.

Results

Was phenology completely acclimated after three years of warming?

In a first step, we summarized results for the six sets of indicators (Table 1, combinations of community, forbs, gramminoids with time-related and performance-related) and depending on whether at least one indicator per set showed a significant acclimation lag, we give further details in upcoming sections.

After three years of warming, the phenology of communities and focal species did not completely acclimate, with the exception of graminoids (Fig. 3). For time-related indicators (Fig. 3.A), we observed complete acclimation to warming in most cases, especially during 13 early-season and flowering. However, late-season phenology (i.e. days after max plant growth)

for communities and fruiting phenophase for forbs showed a significant acclimation lag.

For performance-related indicators (Fig. 3.B), we observed significant acclimation lags under warming except for the graminoids. Both annual and mid-season plant growth for communities and flowering phenophase (i.e. both flowering length and max % flowering) for forbs failed to reach complete acclimation.

Was the warming effect on phenology deficient and/or divergent?

Both deficient and divergent warming effects were responsible for the observed acclimation lags (Fig. 4). However, lag size and deviation angle were dependent on the focal organism level and phenological indicators.

For time-related indicators, the community-level acclimation lag was relatively small, 10%, and deviated only slightly from the shortest trajectory, 5° (Fig. 4). With warming and longer season length (Figs 2 & Table S1), alpine community plant growth in the early-season slowed down and communities took longer to reach their maximum plant growth (compared to AlpineConrol). In late-season, communities decreased browning speed and increased browning days (i.e. days after max plant growth) but failed to acclimate to the complete use of the late-season which constitutes the major part of the acclimation lag. Interestingly, plant communities could only For performance-related indicators, the community-level acclimation lag was relatively large, 26%, but deviated only moderately from the shortest trajectory, 15° (Fig. 4). Thus, even if communities could increase their performance during early-and late-season, their underperformance in the most important mid-season resulted in an important acclimation lag (Fig. 3). In addition, this acclimation lag tended to increase over years (Appendix Fig. S7.A).

Forbs showed an even bigger acclimation lag (685%) because they responded in the "right" direction but exceeded far beyond the acclimation state. Notably, such large lags occur because the original distance between the phenology of alpine and subalpine plants was small and the response is measured relative to this original distance (Fig. 4). This was especially the case for reproduction in the indispensable flowering stage (length of flowering period and number of individuals reaching this phenophase ) (Fig. 3).

Were acclimation lags in warming and cooling symmetric?

Acclimation lags in warming and cooling were not always symmetric. However, the strongest acclimation lags caused by warming (i.e. performance-related indicators for communities and forbs and time-related indicators for forbs) were symmetric for cooling (Fig. 4, Appendix Figs S4

& S5).

For time-related indicators, cooling caused a larger acclimation lag and stronger deviation from the shortest trajectory than warming at the community-level. Forbs were on the shortest acclimation trajectory after cooling in contrast to their deviating response to warming response but the sizes of acclimation lags were similar (Fig. 4).

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For performance-related indicators, at the community-level, acclimation lags and deviations from shortest trajectories were comparable for warming and cooling (Fig. 4 & Appendix Fig. S5.A). After cooling, annual and mid-season plant-growth could acclimate completely but earlyand late-season plant growth failed to acclimate (Appendix Fig. S3.B). Forbs suffered even more from cooling than from warming and this was true for lag size and deviation from shortest trajectories. This trend holds for graminoids even though their acclimation lags were not significant.

Discussion

The phenology of mountain grasslands is known to strongly respond to climate change but so far little was known about remaining acclimation lags. Here, we built on our conceptual framework and a reciprocal transplant experiment to unravel whether phenological responses to warming and cooling follow the shortest trajectories to complete acclimation and how big remaining lags to complete acclimation are. We found that important aspects of phenology did not acclimate after three years of warming. This suggests that the projected climate warming is exceeding the limits of phenological plasticity of the here studied grasslands. This was especially true for indicators that are key for demographic performance and thus success. Forbs and overall community phenology were most affected, while graminoids coped overall well with climate changes. Importantly, even though we observed strong warming effects, they were not strong enough to lead to complete acclimation and we also found that transient dynamics largely deviated from the shortest trajectory to acclimation, especially for forbs.

Timing of phenological events

Earlier works found that climate warming led to earlier phenology (experimental warming: [START_REF] Meng | Nonlinear responses of temperature sensitivities of community phenophases to warming and cooling events are mirroring plant functional diversity[END_REF][START_REF] Meng | Divergent responses of community reproductive and vegetative phenology to warming and cooling: asymmetry versus symmetry[END_REF]; review on existing studies: Parmesan & Hanley, 2015; review on alpine ecosystem: [START_REF] Winkler | Responses of alpine plant communities to climate warming[END_REF] and faster plant growth (H. [START_REF] Wang | Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change[END_REF] . In apparent contradiction, our climate warming experiment promoted later phenology (i.e. later maximal plant growth, later starts of fruiting and dissemination periods of graminoids) and slower early-season increase of growth (i.e. slower greening speed). These warming effects allowed complete acclimation of the early-and mid-season timing of phenology to subalpine conditions for graminoids and communities. One major reason for this difference with other studies is that we used the growing season start and not calendar days as a reference [START_REF] Johansson | Climate change and the optimal flowering time of annual plants in seasonal environments[END_REF] . Our main arguments for this choice are that the snow-free growing season is a major driver of alpine ecosystems [START_REF] Choler | Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration[END_REF] , that plants physiologically respond to the first frost free days and that thus the comparison among study sites is ecologically less meaningful when it is based on calendar days. Another reason for our results is that with our in situ transplant experiment, we did not only manipulate temperature but also the expected accompanying changes in snow cover and growing season length [START_REF] Choler | Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration[END_REF]. In fact, once we calculated the warming effect on greening days based on calendar days, we found an advancement too (i.e. 24.85, with p < .001 according to two sample t-test) but this reflects mainly the advancement of snow melt.

Our results are consistent with apparent strategies of alpine communities: alpine conditions force plants to squeeze life cycles in shorter growing seasons [START_REF] Körner | Alpine Plant Life[END_REF] and induce faster plant growth right after the snow melt that is later in the year and thus, plants experience higher Journal of Ecology: Confidential Review copy Journal of Ecology: Confidential Review copy air temperatures directly after the snowmelt [START_REF] Jonas | How alpine plant growth is linked to snow cover and climate variability[END_REF]) . Yet, with warming and longer growing seasons (just as in subalpine conditions), snowmelt is earlier, the protective effect of snow cover is lost and thus, lower air temperatures are accompanied by potential spring freezing events that together slow down plant growth in the early-season and can kill flower buds and leaves of frost-sensitive species [START_REF] Choler | Winter soil temperature dependence of alpine plant distribution: Implications for anticipating vegetation changes under a warming climate[END_REF][START_REF] Inouye | The ecological and evolutionary significance of frost in the context of climate change[END_REF][START_REF] Inouye | Effects of climate change on phenology, frost damage, and floral abundance of montane wildflowers[END_REF] .

For the end of the growing season, we found in concordance with H. [START_REF] Wang | Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change[END_REF] that plant communities could not sustain the high mid-season plant growth rates. Potential explanations are warming-associated increases of drought stress (H. [START_REF] Wang | Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change[END_REF] (i.e.

even though the air-borne estimated water balance of precipitation and evapotranspiration is comparable between sites, unpublished results ) or inherent allocation trade-offs [START_REF] Johansson | Climate change and the optimal flowering time of annual plants in seasonal environments[END_REF] .

In sum, alpine plant communities adjusted well to warmer conditions and higher uncertainties at the beginning of the growing season but could not fully achieve their adaptation to faster cycles at the end of the growing season.

Phenological indicators linked to performance

In our experiment, the warming of grasslands increased yearly plant community growth (i.e.

photosynthetic activity approximated by NDVI curves) which is consistent with several earlier studies [START_REF] Cao | Grazing intensifies degradation of a Tibetan Plateau alpine meadow through plant-pest interaction[END_REF][START_REF] Carlson | Observed long-term greening of alpine vegetation-a case study in the French Alps[END_REF]S. Wang et al., 2012;[START_REF] Yang | Divergent shifts in peak photosynthesis timing of temperate and alpine grasslands in China[END_REF] warming and ii) defined early-and late-seasons on the basis of months rather than considering yearly differences in seasonal dynamics (i.e. interannual differences in start and end of growing season). Here, we suggest that i) when 3°C is exceeded, a negative warming effect can be imposed on mid-season community performance and ii) it is important to consider interannual differences in identifying the seasons and warming effects on them.

Interestingly, in our experiment these strong warming effects were mostly due to short-term responses of the communities, such as phenotypic plasticity, and not due to compositional changes of plant communities. As most species in our observed grasslands were perennials, limited species turnover took place in the first three years after transplantation (Appendix Fig. S8).

Going a step further than merely describing the warming effect, we also showed that the significant increase of growth was not sufficient to close the acclimation lag completely. In other words, better-adapted grassland species originating from the subalpine area take better advantage of the more favorable climate and grow better over the year. In line with these results, it has already been shown that a positive demographic response to warming is not always able to prevent extinction [START_REF] Sheth | Demographic compensation does not rescue populations at a trailing range edge[END_REF] . The reason for the remaining acclimation lag is probably functional traits of subalpine communities (e.g. height, Specific leaf area ) being better adapted to the favorable climate than those of alpine communities.

The community-wide warming effects on growth do not necessarily translate into increased reproductive performance at the species-level [START_REF] Doak | Demographic compensation and tipping points in climate-induced range shifts[END_REF] . Earlier studies found idiosyncratic warming effects on reproductive performance at the species-level. Some found an 19 already found indications of a significant acclimation lag on pollinator diversity after three years of warming (Appendix Fig. S10).

The short-term community-level and growth form-specific results point to expected long-term responses of mountain grasslands [START_REF] Pironon | Geographic variation in genetic and demographic performance: new insights from an old biogeographical paradigm[END_REF] , including range shifts and restructuring of communities. With warming, we know that some plant communities will be able to track their climatic niche and some will not, either due to migration failures or absence of niche space on mountain tops [START_REF] Alexander | Novel competitors shape species' responses to climate change[END_REF][START_REF] Alexander | When climate reshuffles competitors: A call for experimental macroecology[END_REF][START_REF] Matteodo | Elevation gradient of successful plant traits for colonizing alpine summits under climate change[END_REF] . In a scenario where subalpine plant communities can track their climate but alpine communities remain within their current niche, alpine species may not be able to increase their demographic performance either in plant growth or in reproduction fast enough and will thus be outcompeted by the subalpine species [START_REF] Alexander | Novel competitors shape species' responses to climate change[END_REF][START_REF] Alexander | When climate reshuffles competitors: A call for experimental macroecology[END_REF] . The increase in community plant growth and the competitive exclusion of slower-growing alpine species will also change plant-soil feedbacks. According to the "fast-slow" plant economics spectrum, faster growth is associated to more exploitative plant traits (and thus, functional strategies), especially under favorable environmental conditions (Martinez-Almoyna et al., 2020;[START_REF] Reich | The world-wide "fast-slow" plant economics spectrum: a traits manifesto[END_REF] . Such an aboveground functional strategy shift towards the faster part of the economic spectrum will affect the microorganisms involved in plant litter composition (e.g. higher bacteria:fungi ratio), will change their ecosystem functions (e.g. faster nutrient cycling) and will then feedback to the plant communities, potentially further accelerating changes [START_REF] Bardgett | Aboveground-belowground linkages: biotic Interactions, ecosystem processes, and global change[END_REF] .

Divergent warming effects

It has been recently highlighted that perturbations to an ecosystem's steady state can trigger transient responses of ecological relevance [START_REF] Mari | A generalized definition of reactivity for ecological systems and the problem of transient species dynamics[END_REF] . The question is not only how long it 21

Page 21 of Journal of Ecology: Confidential Review copy Journal of Ecology: Confidential Review copy will take until the new steady state is reached but also how strongly responses are initially amplified before decaying towards a final state. Here we demonstrate that warming effects, especially on the phenology of alpine forbs, can be not only divergent [START_REF] Ryo | Basic principles of temporal dynamics[END_REF] but can lead phenology far away from any acclimation. I n line with a previous long-term study [START_REF] Wu | Biogeochemical and ecological feedbacks in grassland responses to warming[END_REF] , we found a similar but weaker response at the community-level. Overall, determining the divergent responses can inform us on the long-term trajectory of the warming effect, and can serve as an early warning signal for negative impacts on ecosystem functioning [START_REF] Scheffer | Early-warning signals for critical transitions[END_REF] . For example, fine scale NDVI measurements on communities are relatively easy to implement and according to our results, monitoring only for community productivity can serve for early detecting shifts in communities and ecosystem functioning.

Symmetry between acclimation lag after warming and cooling

We found most of the acclimation lags after warming and cooling to be symmetric, indicating some plasticity in phenological responses to climate change that is however not sufficient for short-term acclimation to realistic warming scenarios. We conclude that slow phenological acclimation to new thermal conditions is a general phenomenon for mountain plant communities. Notably, for some phenological indicators (i.e. timing of community growth and flowering performance of forbs), the acclimation lag after cooling was significantly wider than for warming. It has been suggested earlier that for mountain grasslands, physiological limits at the cold thermal range edge are much more pronounced than at the warm edge [START_REF] Pellissier | Thermal niches are more conserved at cold than warm limits in arctic-alpine plant species[END_REF] . In addition, it has been shown that a major driver of the warm edge is competition [START_REF] Alexander | Novel competitors shape species' responses to climate change[END_REF] , which has not yet started to act at its full force in our experiment.

However, decrease in flowering performance both with warming and cooling indicate that flowering phenology is highly sensitive at both ends of the thermal limits [START_REF] Li | Responses of sequential and hierarchical phenological events to warming and cooling in alpine meadows[END_REF]; S. P. Wang et al., 2014) . The question of symmetry might also have practical implications. With the advancing research on climate warming, the reversibility of the warming effect at many levels is still an open debate [START_REF] Scheffer | Catastrophic shifts in ecosystems[END_REF] . The symmetric response between acclimation lag after warming and cooling can also inform us on the reversibility of the impacts of warming. Here, it seems like most of the warming impacts especially on aboveground productivity and flowering performance can be reversible in the short-term. However, we also acknowledge that these observed transient responses might be buffered by the ones at different levels or can even lead to other cascading effects or critical transitions.

Conclusion and perspectives

With our reciprocal transplant experiment on mountain grasslands, we revealed that community and species-level phenological responses to climate warming were not enough for their complete acclimation to their new conditions in the short-term, except for graminoids. These phenological acclimation lags probably impair other ecosystem functions, such as pollination and plant-soil feedbacks. Short-term acclimation lags, and especially strong deviations from acclimation trajectories, may also hint at long-term impairment of ecosystem functions and restructuring of communities. This emphasizes the importance of characterizing the size and direction of acclimation lags in upcoming global change research.

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 44 of 60 Journal of Ecology: Confidential Review copy Journal of Ecology: Confidential Review copy suggest measuring warming (or cooling) effects by comparing AlpineControl to AlpineWarmed plots (or SubalpineControl to SubalpineCooled plots, Fig. 1C).

  acclimate their greening speed to longer season length in the third year but failed to do so in the first two years after transplantation (Appendix Fig.S6.A). At the species-level, forbs did not only fail to close the acclimation lag due to the absence of any warming effect (Figs3A, 4), but they even increased the lag by responding in the "wrong" direction (i.e. 180° away from the shortest 14 Page 14 of 60 Journal of Ecology: Confidential Review copy Journal of Ecology: Confidential Review copy trajectory to complete acclimation. Specifically, fruiting for the AlpineWarmed species started earlier than for the SubalpineControls in all three years after the transplantation.

  Finally, the symmetry between acclimation lags under warming and cooling underlines that important phenological processes adjust too slowly. This might indicate that the functioning of mountain grasslands under the threat of climate change can be reversible in the short-term. Page 16 of 60 Journal of Ecology: Confidential Review copy Journal of Ecology: Confidential Review copy

  but in contradiction to H.[START_REF] Wang | Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change[END_REF]. Our observed yearly increase was due to increased early and late plant growth, despite reduced mid-season plant growth. While our early-season results coincide with those of H.[START_REF] Wang | Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change[END_REF], they differ for the mid and late-season (i.e. no change and decrease). Reasons might be the fact that they i) could only record for 2°C of Page 18 of 60 Journal of Ecology: Confidential Review copy Journal of Ecology: Confidential Review copy

Table S3 :

 S3 Changes in annual air and soil temperature (°C) and in community-level phenological indicators (NDVI). Historical changes are between 1960-2016 for temperature and between 2000-2016 for phenological indicators. Experimental changes are between 2016 observed data and average of three-years experimental outputs. The last column shows how much change was already taken due to ongoing climate warming in comparison to the experimental warming. The sign difference indicates that the changes are in different directions. *Daily mean air temperature data were extracted from SAFRAN-CROCUS-MEPRA meteorological model developed by Météo-France for the French Alps. NDVI data were extracted from MODIS remote sense time series data for historical changes. Changes were calculated from predicted values of a linear model between indicator values and years. Remote sense data were prepared and phenological indicators were calculated in the same way as it was elaborated on the main manuscript. Annual air temperature for EC is missing as the experimental measurements have gaps in the data to damaged probes on the field site.

Figure S1 :

 S1 Figure S1: Standardized effect sizes as the magnitude of warming effect for timing and performance related indicators and for each growth form and each species. Significant results are shown in black. Error bars represent 95% confidence intervals. Warming effect: AlpineWarmed-AlpineControl.

Figure S2 :

 S2 Figure S2: Standardized effect sizes as the magnitude of origin and climate effects at each focal organism level (community level, species level forbs and graminoids) for (A) timing of events and (B) performance related phenological indicators. Significant results are shown in black. Error bars represent 95% confidence intervals. Origin effect: Subalpine origin -Alpine origin; Climate effect: Subalpine site -Alpine site.

Figure S3 :

 S3 Figure S3: Standardized effect sizes as the magnitude of cooling effect and acclimation lag after cooling at each focal organism level (community level, species level forbs and graminoids) for (A) timing of events and (B) performance related phenological indicators. Significant results are shown in black. Error bars represent 95% confidence intervals. Warming effect: AlpineWarmed-AlpineControl. Acclimation lag after warming: SubalpineControl-AlpineWarmed.

Figure S4 :

 S4 FigureS4: Differences in timing related phenological indicators (plotted in a two-dimensional PCA space) between two treatments and controls (AlpineWarmed, SubalpineCooled, AlpineControl and SubalpineControl) at the community (A) and the species level (forbs (B) and gramminoids (C)). Straight arrows highlight warming (AlpineWarmed-AlpineControl) or cooling effect (SubalpineCooled-SubalpineControl) and dotted lines the acclimation lag after warming (SubalpineControl-AlpineWarmed) or cooling (AlpineControl-SubalpineCooled). Here, the responses over 3 years were shown (diamond: AlpineControl, square: SubalpineControl, inverse triangle: AlpineWarmed, triangle: SubalpineCooled). For further analyses, the first two PCs were selected from (A) and only the first PC from (B) and (C).

Figure S5 :

 S5 Figure S5: Differences in performance related phenological indicators (plotted in a two-dimensional PCA space) between two treatments and controls (AlpineWarmed, SubalpineCooled, AlpineControl and SubalpineControl) at the community (A) and the species level (forbs (B) and gramminoids (C)). Straight arrows highlight warming (AlpineWarmed-AlpineControl) or cooling effect (SubalpineCooled-SubalpineControl) and dotted lines the acclimation lag after warming (SubalpineControl-AlpineWarmed) or cooling (AlpineControl-SubalpineCooled). Here, the responses over 3 years were shown (diamond: AlpineControl, square: SubalpineControl, inverse triangle: AlpineWarmed, triangle: SubalpineCooled). For further analyses, the first two PCs were selected from (A) and only the first PC from (B) and (C).

Figure S6 :

 S6 Figure S6: Differences in timing related phenological indicators (plotted in a two-dimensional PCA space) between the three treatments (AlpineWarmed, AlpineControl and SubalpineControl) at the community (A) and the species level (forbs (B) and gramminoids (C)). Straight arrows highlight warming effect (AlpineWarmed-AlpineControl) and dotted lines the acclimation lag after warming (SubalpineControl-AlpineWarmed). Thicker lines indicate the overall response for three years. Thinner lines indicate annual response. (diamond: AlpineControl, square: SubalpineControl, inverse triangle: AlpineWarmed). For further analyses, the first two PCs were selected from (A) and only the first PC from (B) and (C).

Figure S7 :

 S7 Figure S7: Differences in performance related phenological indicators (plotted in a two-dimensional PCA space) between the three treatments (AlpineWarmed, AlpineControl and SubalpineControl) at the community (A) and the species level (forbs (B) and gramminoids (C)). Straight arrows highlight warming effect (AlpineWarmed-AlpineControl) and dotted lines the acclimation lag after warming (SubalpineControl-AlpineWarmed). Thicker lines indicate the overall response for three years. Thinner lines indicate annual response. (diamond: AlpineControl, square: SubalpineControl, inverse triangle: AlpineWarmed). For further analyses, the first two PCs were selected from (A) and only the first PC from (B) and (C).

Table 1 :

 1 Grouping of phenological indicators and their representation at different levels. Shading indicates the focal organism level. (g: indicator related to plant growth; r: indicator related to reproductive period) (Please refer to the main text for further explanation of their calculation and interpretation)

	BB, WT and TM conceived the ideas and designed methodology; PC substantially helped with the
	interpretation of the results and design of the conceptual framework; AS and TM organized the fieldwork;

MPC, JR, AS and TM established the protocol; JR managed the database; BB, RDV, JR and MPC

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Acknowledgements

This research was funded by the DIPEE/FREE-Alpes federation, the French Agence Nationale de la Recherche (ANR) through the TransAlp project (ANR-20-CE02-0021) and the Lautaret Garden-UMS 3370 (Univ. Grenoble Alpes, CNRS, SAJF, 38000 Grenoble, France), a member of the Zone Atelier Alpes and eLTER network and of AnaEE-France (ANR-11-INBS-0001AnaEE-Services, Investissements d'Avenir frame). This research was conducted within the Long-Term Socio-Ecological Research (LTSER) platform Lautaret-Oisans, a site of the European Research Infrastructure eLTER. This study would not have been possible without the help of Melodie Bourreau , Matthieu Fleuet and many other interns and colleagues during the field and laboratory work.

Journal of Ecology: Confidential Review copy Journal of Ecology: Confidential Review copy collected the data; BB analysed the data; BB, WT and TM led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication. Journal of Ecology: Confidential Review copy Journal of Ecology: Confidential Review copy

Journal of Ecology: Confidential Review copy Journal of Ecology: Confidential Review copy

Methods

Experimental Design

The experiment was implemented close to the Col du Lautaret ( 45°02′04″N 06°24′18″E ) in the French Alps in September 2016 (Fig. 1.A). The transplantation sites are the subalpine site "Lautaret" ( 45°04′00″N 06°41′90″E ) at 1920 m altitude and the alpine site "Galibier" ( 45°05′44″N 06°40′06″E ) at 2450 m altitude. Sites are close to each other (~2km airline distance), have a similar orientation (South-East at alpine and South-South-West at subalpine site), bedrock (base-riched flysch) and soil (dystric cambisols). During the observation-period (autumn 2016autumn 2019), average annual soil temperature at the subalpine site was 3.2°C warmer than at the alpine site (and days with similar temperatures at the two sites were very rare) , average July soil temperature was 3.1 °C warmer and snow free season was 58.7 days longer (Table S1, Fig. 2.A). At the subalpine site, plant communities were mainly dominated by the graminoids Patzkea paniculata , Carex sempervirens , Festuca nigrescens and the forbs Centaurea uniflora , Helianthemum nummularium , and Meum athamanticum (Appendix Fig. S13). At the alpine site, plant communities were mainly dominated by the graminoids Carex sempervirens , Festuca nigrescens , Poa alpina and the forbs Potentilla aurea , Trifolium alpinum , and Geum montanum (Appendix Fig. S13). Overall, the dominant graminoid species in AlpineControl and SubalpineControl plots makeup to 22.5% and 26.5% of relative abundance, respectively. All plant communities were dominated by perennial species.

In 2016, to simulate the effect of warming on alpine grasslands, we transplanted 10 replicates 4m 2 blocks of vegetation with at least 20 cm of their intact belowground parts and the surrounding soils from the alpine to the subalpine site (AlpineWarmed plots in the following). For Journal of Ecology: Confidential Review copy Journal of Ecology: Confidential Review copy Journal of Ecology: Confidential Review copy 1.C) and year as fixed effects and subplot identity nested in plot identity as random effect. At the species-level, we accounted for treatment, year and functional group (forbs or graminoids) as fixed effects and block and species identity as random effects. As we conducted multiple tests, we adjusted the p-values with multivariate t-distribution adjustment within the emmeans package in R [START_REF] Lenth | Least-Squares Means: TheRPackagelsmeans[END_REF] . Here, we discuss the common responses of forbs and graminoids, and report the species-specific responses in the Appendix (Fig. S1).

Secondly, in order to summarize results for timing vs. performance related phenological indicators, we performed six different Principal Component Analyses (PCA) for time-related vs. performance-related indicators for communities, forbs and graminoids. We visualized the first two components of each PCA in the Appendix . For further analyses we determined the number of significant principal components (PC) with Horn's parallel analysis in the R package paran [START_REF] Dinno | paran: Horn's Test of Principal Components/Factors (R package version 26 Page 26 of 60[END_REF][START_REF] Franklin | Parallel Analysis: a method for determining significant principal components[END_REF] (Appendix Fig. S12). As a result, we retained the first two PCs For community-level PCAs two PCs were necessary to well capture the variation, for species-level PCAs one PC was sufficient. Then calculated i) the multidimensional acclimation lag (here "multidimensional" refers to the multiple indicators and the one or two PC axes describing their variation) as the percentage of the remaining acclimation relative to the complete acclimation (Fig. [START_REF] Iler | Reproductive losses due to climate change-induced earlier flowering are not the primary threat to plant population viability in a perennial herb[END_REF][START_REF] Kudernatsch | Short-term effects of temperature enhancement on growth and reproduction of alpine grassland species[END_REF][START_REF] Li | Responses of sequential and hierarchical phenological events to warming and cooling in alpine meadows[END_REF] , no change at all [START_REF] Dorji | Impacts of climate change on flowering phenology and production in alpine plants: The importance of end of flowering[END_REF] or a decrease [START_REF] Aldridge | Emergence of a mid-season period of low floral resources in a montane meadow ecosystem associated with climate change[END_REF][START_REF] Gugger | Lower plasticity exhibited by highversus mid-elevation species in their phenological responses to manipulated temperature and drought[END_REF][START_REF] Liu | Global warming reduces plant reproductive output for temperate multi-inflorescence species on the Tibetan plateau[END_REF] depending on species and ecosystem and others identified species-specific responses (S. Wang et al., 2014;[START_REF] Winkler | Responses of alpine plant communities to climate warming[END_REF] . In our study, we could relate the species-specific responses to growth forms with forbs failing to acclimate to warmer conditions (i.e. shorter flowering period, fewer individuals flowering) but alpine graminoids performing as well as subalpine graminoids under warmer, subalpine climate. This is in line with independent Ellenberg indicator values (i.e. categorical values indicating the species' abiotic niche; [START_REF] Bartelheimer | Functional characterizations of Ellenberg indicator values -a review on ecophysiological determinants[END_REF] suggesting that the dominant forbs in our study system are much less tolerant to variation in temperature (and moisture) than the dominant graminoids (Appendix Fig. S9). Specifically, we argue that problems of forbs to acclimate could be linked to: i) disrupted vernalization (i.e. flower bud formation depends on low temperatures, especially during winter; [START_REF] Liu | Global warming reduces plant reproductive output for temperate multi-inflorescence species on the Tibetan plateau[END_REF] , ii) more allocation of resources to somatic growth than to reproduction [START_REF] Johansson | Climate change and the optimal flowering time of annual plants in seasonal environments[END_REF][START_REF] Liu | Global warming reduces plant reproductive output for temperate multi-inflorescence species on the Tibetan plateau[END_REF] , iii) shading created by graminoids that have the potential to grow taller than forbs.

The observed acclimation lag of forbs in flowering performance is prone to cascade to the other trophic levels, specifically to associated pollinators [START_REF] Gezon | Phenological change in a spring ephemeral: implications for pollination and plant reproduction[END_REF] and thus to further disrupt functioning of warmed alpine communities. In addition, responses specific to growth form may also cause a community composition shift in favor of graminoids. This is in line with a recent global review on the ecological flexibility of gramminoids in their climate and habitat range, their success in establishment and dispersal and ecological competitiveness due to their functional and physiological traits [START_REF] Linder | Global grass (Poaceae) success underpinned by traits facilitating colonization, persistence and habitat transformation[END_REF] . Yet, such shift in communities also risks the well-being of the whole multitrophic network and ecosystem functioning through further reduction of pollination services [START_REF] Burkle | Plant-pollinator interactions over 120 years: loss of species, co-occurrence, and function[END_REF][START_REF] Gezon | Phenological change in a spring ephemeral: implications for pollination and plant reproduction[END_REF] For the warming effect, AlpineWarmed is contrasted to AlpineControl. For the cooling effect, SubalpineCooled is contrasted to SubalpineControl. For the acclimation lag after warming, SubalpineControl is contrasted to AlpineWarmed. For the acclimation lag after cooling, AlpineControl is contrasted to SubalpineCooled. (D) Application of the framework to real data based on a Principal Component Analysis (PCA) in order to identify a warming effect, an acclimation lag, and a deviation from the full acclimation trajectory.

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Journal of Ecology: Confidential Review copy Journal of Ecology: Confidential Review copy Grassland phenology can have strong impacts on pollination services. As we found that warmed alpine forbs' communities flowered less often, for shorter time periods and tendentially earlier than subalpine forbs' communities (Figure 2), we asked whether this had any cascading effect on flower visiting behavior of insects.

To answer this question we observed insects visiting flowers in the AlpineWarmed, AlpineControl and SubalpineControl plots between June and September in 2019. Observations were limited to conditions favourable for insect visits, i.e. between 10:00 and 13:00 and only if it was warm enough, not too cloudy (either > 13°C if less than half of the sky was covered with clouds or > 15°C otherwise) and not too windy (maximum 29.5 km/h). Each week (and twice at the peak of growing season) we spent 10 minutes at each of 8 randomly chosen replicates of each treatment to observe flower visits (summing up to a total of 1400 observation minutes). We counted all insects that visited a flower within a 1 m² focal area during this standardized time period. We only identified main insect groups following the guidelines of the public science UK PoMS, Pollinator Monitoring Scheme (https://www.ceh.ac.uk/our-science/projects/pollinator-monitoring) because the observations were done by non-expert interns.

Pollinator visits per minute were generally quite low (AlpineControl: 0.34, AlpineWarmed: 0.04, SubalpineControl:0.2, SubalpineCooled: 0.32). We found strong site-specific effects on pollinator communities, with more flies and small insects (<3mm) in AlpineControl plots and more Coleoptera and Hymenoptera species in SubalpineControl plots. Overall, AlpineControl plots tended to attract slightly more visits than SubalpineControl plots. Due to these site specific differences, we focus on acclimation lags comparing AlpineWarmed and SubalpineControl plots that are at the same site and thus available to the same pollinator community. Our main results show that the flowers in AlpineWarmed plots were visited by a significantly less diverse group of insects and also had a tendency to be overall less visited than the SubalpineControl plots (Figure S10).

As AlpineWarmed and Subalpine Control plots are at the same location (randomly placed at the site) and have been observed at the same moment, differences in flower visiting insects are clearly due to plot-level vegetation differences. We conclude that reduced flower numbers, shorter flowering periods and premature flowering are among the main drivers of impaired flower visiting of insects creating a cascading acclimation lag from plant phenology to insect pollination.