Assessment of the potential shifts in the phenological development of representative spring plant species in Slovenia until the end of the 21st century using a model-based approach

Assessment of the potential shifts in the phenological development of representative spring plant species in Slovenia until the end of the 21st century using a model-based approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment of the potential shifts in the phenological development of representative spring plant species in Slovenia until the end of the 21st century using a model-based approach Gal Oblišar, Urša Vilhar, Gregor Gregorič, Andreja Sušnik, Marko Puškarić This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7702297/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Mar, 2026 Read the published version in International Journal of Biometeorology → Version 1 posted 4 You are reading this latest preprint version Abstract To assess the changes in the spring phenology in the future with particular emphasison the elevation dependence of phenophase onset, a climate-driven phenological model was developed based on the spring indices methodology. Our study investigates both current and projected changes in the timing of flowering onset for common hazel (Corylus avellana), dandelion (Taraxacum officinale), and common lilac (Syringa vulgaris). We compiled comprehensive climate data and phenological records from 46 phenological stations of the National Phenological Network of the Slovenian Environment Agency for the period 1971–2020. In addition, we used climate projection data for the 21st century under two climate scenarios to evaluate potential future shifts in the onset of the selected phenophases. Specifically, we examined whether the agreement between model predictions and observed records varies with elevation during the reference period (1981–2010) and whether this relationship changes across three future climate periods: 2011–2040, 2041–2070, and 2071–2100. Model results indicate that spring phenophases are expected to occur earlier in Slovenia by the end of the 21st century, consistent with the projected increase in air temperatures. Moreover, the advancement in spring phenology will be more pronounced at higher elevations. Plant phenology Phenological model Elevation dependence Climate change Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Phenology is the study of the periodically recurring patterns and behaviour of biological events, such as flowering and leaf unfolding in plants (Lieth, 1974). In the context of climate change, phenology plays a crucial role in understanding ecosystem dynamics and biodiversity under changing environmental conditions. As temperatures rise and climate patterns shift due to global climate change, these phenological events are occurring earlier in many regions, which has significant implications for ecological interactions and agricultural practises (Parmesan and Yohe, 2003; Menzel et al., 2006; Fu et al., 2015; Cui and Shi 2021). Advanced flowering times, polinator migrations and breeding schedules shift established ecological relationships and can lead to mismatches that threaten the survival of species (Cleland et al., 2007; Forrest & Thomson, 2011; Kudu and Ida, 2023). Changes in plant phenology can also feedback to the climate system by influencing the exchange of water and energy between terrestrial ecosystems and the atmosphere (Richardson et al., 2013). Minimum and maximum air temperatures and the photoperiod play a crucial role in the phenological phases of plant flowering and leaf unfolding in spring. In recent decades, the occurrence of spring phenophases in the various species has become increasingly synchronised. The temporal differences between the onset of the growing season for different species at regional level (Wang et al., 2016) and along elevation gradients (Vitasse et al., 2018) are decreasing. In recent years, phenology has evolved from an empirical topic of observing and recording the timing of some important annual natural events for selected species to a comprehensive scientific field that includes extended observations, experiments and modelling (Schwartz et al., 2006, Vilhar et al., 2018; Noumanovi et al, 2021). For phenological modelling of the interactions between ecosystems and the climate system, improved knowledge of phenological changes, their main drivers and the impact on the ecosystem is essential (Liu et al., 2019). Ground-based observations can accurately capture the timing of phenological events for specific sites and species. Networks of long‐term ground‐based phenological observations, such as those provided by national meteorological services (Vliet et al., 2003, Menzel et al., 2006, Aono and Kazui, 2008) in accordance with World Meteorological Organization guidelines (Koch et al., 2009) or those conducted by forest research institutes following the harmonized guidelines of the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) (Vilhar et al., 2013, Raspe et al., 2020) are particularly useful to investigate phenological variations over a large geographical area and their potential changes in response to climate change (Cleland et al., 2007). Given the increasing concern about climate change and its potential impacts, the establishment of international phenological networks has facilitated collaborationin large‐scale and standardized phenological data collection and sharing (Templ et al., 2018). More recently, the development of smartphones, automated cameras and other communication technologies has taken ground-based phenology monitoring by citizen science to a new level, significantly expanding the coverage of phenological events over a large area and for many more species (Dickinson et al., 2012; Hufkens et al., 2019). Slovenia has a very dense network of phenological stations with good coverage of the diverse Slovenian terrain. Phenological observations in Slovenia were organized in 1951 as part of the national phenological network within the framework of the agrometeorological service of the Hydrometeorological Institute of the Republic Slovenia in the former Yugoslavia. After 2001, phenological observations became a regular activity of the Department of Agrometeorology of the Environment Agency of the Republic of Slovenia. Initially, the observations were carried out at 30 phenological stations, later the number increased to over 200 stations. Currently, there are still 46 active phenological stations evenly distributed throughout the country at elevations ranging from 55 to 1050 m a.s.l. and with different site and climate characteristics. In a global comparison, the Slovenian network of phenological stations is one of the best continuously maintained (Žust, 2015). Changes in plant phenophases in spring can have profound effects on the ecosystem dynamics, and due to the rapid climate changes in recent decades, predictions of these changes are becoming increasingly important. Since it is not possible to rely solely on long-term phenological observations to assess temporal phenological changes, models have been developed that simulate the onset of selected phenophases. These models, driven by daily maximum and minimum air temperatures, provide a biologically relevant approach to track phenological changes over larger spatial and temporal scales, using the availability of meteorological data. In contrast to raw meteorological data such as monthly or seasonal average air temperatures, models offer higher precision as they capture phenoclimatic processes on a daily to weekly scale that trigger critical events such as plant leafing and flowering that determine ecosystem dynamics. When a phenological event is triggered by a short period of extreme temperatures, this can be inadequately represented in general metrics such as monthly or seasonal average temperature (Schwartz et al., 2006; Gerst et al., 2020) The aim of the present study was to assess the changes in the spring phenology in the future, focusing on the elevation dependence of phenophase occurrence. For this purpose, we developed a climate-driven phenological model based on the methodology of the American Spring Index using air temperature and spring phenology of the common hazel ( Corylus avellana ), dandelion ( Taraxacum officinale ) and common lilac ( Syringa vulgaris ). We used daily maximum and minimum air temperatures and phenological data on selected plant species and phenophases collected by at 46 phenological stations of the Slovenian Environment Agency for the period 1971–2020. Subsequently, we modelled the occurrence of selected phenophases until the end of the 21st century using the climate projections data of the EURO-CORDEX dataset for the RCP4.5 and RCP8.5 climate scenarios. The paper is organised as follows. In the “Materials and methods” section, we describe the data sets, the selected phenophases and the models. In the “Results” section, we show the results of the application of the methods. In the “Discussion” section, the results obtained are discussed and compared. The conclusions are drawn in the “Conclusions” section. Materials and methods Phenological data For the development and verification of the climate-driven phenological model, we selected 46 phenological stations of the national phenological network of the Slovenian Environment Agency with homogeneous and continuous data series evenly distributed across Slovenia. Slovenia is characterised by relatively large gradients of climatic factors due to its location between the Alps, the Mediterranean and continental Europe (Čufar et al., 2012), and consequently a wide variety of habitats can be found in the country, from lowlands to high mountains (Kermavnar and Kutnar, 2020). The plant species and phenological phases included in the model were selected based on several criteria: (i) ease of identification and observation, (ii) average timing of occurrence to cover the entire spring period, (iii) wide distribution of the species across the country. Phenological stations were selected based on the homogeneity of their record for the selected phenophases and the presence of at least one continuous data series of 10 years or more during the period 1971–2020. The selected plant species and phenological phases were the onset the male catkins flowering of the common hazel ( Corylus avellana ), the onset of flowering of the dandelion ( Taraxacum officinale ) and the onset of flowering of the common lilac ( Syringa vulgaris ). Phenological observations at each phenological station was done according to the national guidelines and supervised by national phenological coordinator (Žust, 2015), following the World Meteorological Organisation (WMO) guidelines for phenological observations (Koch et al., 2007). Observations were carried out daily. The onset of common hazel flowering was defined as stage when the two-part yellow anthers become visible on the elongated catkins, and the yellow pollen began to shed. This phase corresponds to Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie code 60 (BBCH60). The onset of dandelion flowering was recorded when some fully developed and open flowers could be seen in the observed meadow (BBCH60). For common lilac, flowering was recorded when the first flowers opened at the lower edge of the first inflorescences and with two stamens visible per flower (BBCH60). Climate data The meteorological data used were daily minimum and maximum air temperatures for the period 1971–2020, obtained from raster datasets provided by Slovenian Environment Agency (SEA). The air temperature data were recalculated to the elevation and geographical position of the selected phenological station using the methodology described in Huld and Pascua (2015). Climate projection data for the 21st century were obtained from the EURO-CORDEX dataset, the the European branch of the CORDEX initiative: EURO-CORDEX provides an ensemble of climate simulations generated bymultiple dynamical and empirical-statistical downscaling models forced by several global climate models of the Coupled Model Intercomparison Project Phase 5 (CMIP5) (Gobiet and Jacob, 2011). We considered two climate change scenarios based on the IPCC methodology: RCP4.5 – a moderately optimistic scenario that assumes a total radiative forcing of 4.5 W m − 2 by 2100 and a CO 2 equivalent of 630 ppm by 2100, and RCP8.5 – a pessimistic scenario that assumes a total radiative forcing of 8.5 W m − 2 by 2100 and a CO 2 equivalent of 1313 ppm by 2100 (IPCC, 2021). Climate models contain systematic biases arising from factors such as limited horizontal and vertical resolution, simplified equations for some physical processes, numerical approximations, incomplete understanding of all climate dynamics. In this study, the climate projection data used were bias-corrected within OPS21 project (Bertalanič et al., 2018), which applied a tailored quantile mapping approach to daily model outputs for the period 1981–2100. The reference period for bias correction was period 1981–2005. Bias correction was applied separately for each model grid cell and time step using a moving 61-day window and 100 quantile classes. This approach preserved inter-variable dependencies and long-term trends while effectively reducing systematic errors in climate model projections. The climate projection dataset represents six combinations of regional and global climate models, providing with daily meteorological variables for a period 1981–2100 downscaled to the location of each phenological station. For model verification, we used the dataset for the period 1981–2005 and compared simulated values with observed phenological data against historical reanalyses. The period 2005–2100 represents bias-corrected climate projection dataset. Results of these projections are presented in three thirty-year climate periods: 2011–2040, 2041–2070 and 2071–2100 as a deviation in the timing of individual phenophases compared to the reference period 1981–2010. Phenological model and statistical analyses We have developed a climate-driven phenological model to assess future changes in the onset of selected plant phenophases using climate projection data. The model was implemented in the MATLAB programming environment and based on the methodology of the American Spring Index methodology, which incorporates the effects of both air temperature and photoperiod. The American Spring Phenological Index model consists of two models that represent the average response of three reference species and estimate the "onset of spring” as either the the first leaves emergence or the the first flowering at a given location. These models were originally developed using observations of the first leaf and flowering phases of the Syringa × chinensis " Red Rothomagensis" and two honeysuckle clones ( Lonicera tatarica " Arnold Red " and Lonicera korolkowii Stapf) (Schwartz et al., 2006; Schwartz et al., 2013; Ault et al., 2015; Rosemartin et al., 2015).To investigate the elevation dependence of phenophase occurrence, stations were stratified into three elevation zones: (i) 0–300 m (low elevation), (ii) 301–600 m (mid- elevation) and (iii) 601–1050 m above sea level (high elevation) following de Groot and Vrezec (2019). Model outputs represent the onset of the selected phenological phase expressed in Julian days (day of the year - DOY). Model performance was evaluated for the period 1981–2005. Relationships between observed and simulated phenophases were quantified using the Spearman correlation test. Model fit was further assessed with the coefficient of determination (R²), which indicates the proportion of variance in observed values explained by the model (Rodgers and Nicewander, 1988). In addition, we calculated the root mean square error (RMSE), defined as the square root of the mean squared deviation between predicted estimated and observed values (Both et al., 2009). All statistical analyses, were conducted in the programme R, version 4.2.3 (R Development Core Team, 2024). Results The climate-driven phenological model for the period 1971–2020 During the period 1971–2020, the onset of common hazel male catkins flowering occurred on average at DOY 51 at all selected phenological stations, with observations ranging from DOY 29 at the Portorož station (2 m a.s.l.) to DOY 74 at the Planina pod Golico station (1050 m a.s.l.). The long-term trend indicated an advancement of 3.77 days per decade (R 2 = 0.13) across all phenological stations. Dandelion flowering onset was observed on average at DOY 100, ranging from DOY 71 at Portorož to DOY 124 at Zgornje Jezersko (879 m a.s.l.). The mean trend was an advancement of 2.19 days per decade (R² = 0.15) across all phenological stations. The onset of flowering of common lilac was observed on average on the DOY 120, ranging from DOY 103 at the Portorož station to DOY 144 at the Planina pod Golico station. The The corresponding long-term trend showed an advancement of 3.45 days per decade (R² = 0.43). The developed climate-driven phenological model demonstrated a good agreement between simulated and observed spring phenophases across all selected phenological stations. Model performance was slightly reduced at coastal stations and at higher-elevation sites in the Alps. The highest and statistically significant agreement was obtained for the onset of dandelion flowering (δ avr = 0.86; p < 0.001), RMSE = 8.80, R 2 = 0.68), followed by the onset of common lilac flowering (δ avr = 0.80; p < 0.001), RMSE = 7.19, R 2 = 0.73) and the onset of flowering of common hazel male catkins (δ avr = 0.76; p < 0.001), RMSE = 11.05, R 2 = 0.59) (Fig. 2 , Table 1 ). Table 1 Average day of the year (DOY) of observed phenophases: onset of flowering of male catkins of the common hazel ( Corylus avellana ), onset of flowering of dandelion ( Taraxacum officinale ) and common lilac ( Syringa vulgaris ) across all selected phenological stations in Slovenia during the the period 1971–2020. Reported are Sperman correlation coefficient (δ avr ), significance levels ( p) , coefficient of determination (R 2 ), and root mean square errors (RMSE) comparing observed and modelled data, n denotes the total number of observations. Mean obs (DOY) δ avr R 2 RMSE n Onset of flowering of male catkins of the common hazel ( Corylus avellana ) 51,1 0,76*** 0,59 11,06 2269 Onset of flowering of dandelion ( Taraxacum officinale ) 100 0,86*** 0,68 8,80 2325 Onset of flowering of common lilac ( Syringa vulgaris ) 120,5 0,80*** 0,73 7,19 2300 * p < 0,05, ** p < 0,01, *** p < 0,001 Agreement between observed and modelled spring phenology using climate projection data for the reference period 1981–2010. The climate-driven model showed a high and statistically significant agreement between observed and simulated onset of common lilac flowering in 6-year moving averages using the EURO-CORDEX climate dataset for the reference period 1981–2005, under both climate scenarios RCP4.5 and RCP8.5 (δ povp = 0.84; p < 0.001; RMSE RCP4.5 = 6.94, RMSE RCP8.5 = 6.99). Agreement was slightly lower for the onset of dandelion flowering (δ povp = 0.80 ( p < 0.001; RMSE RCP4.5 = 6.43) [RCP4.5] and δ povp = 0.79 ( p < 0.001; RMSE RCP8.5 =6.41) [RCP8.5]. The lowest agreement was observed for common hazel male catkins flowering with δ povp = 0.55 (p < 0.001; RMSE RCP4.5 = 20.43) [RCP4.5] and δ povp = 0.46 (p < 0.01; RMSE RCP8.5 =22.53) [RCP 8.5] (Table 2 ). Table 2 Sperman corellation coefficient (δ povp ), significance levels ( p) and root mean square errors (RMSE) between observed and simulated spring phenophases on two climate change climate scenarios RCP4.5 and RCP8.5 using EURO-CORDEX climate dataset for the reference period 1981–2005 RCP 4.5 RCP 8.5 δ povp RMSE δ povp RMSE Onset of flowering of male catkins of the common hazel ( Corylus avellana ) 0,55*** 20,43 0,46** 22,53 Onset of flowering of dandelion ( Taraxacum officinale ) 0,80*** 6,43 0,79*** 6,41 Onset of flowering of common lilac ( Syringa vulgaris ) 0,84*** 6,94 0,84*** 6,99 * p < 0,05, ** p < 0,01, *** p < 0,001 Modelled changes in spring phenology until the end of the 21st century Climate-driven model projections indicate that all three studied spring phenophases in Slovenia are expected to occur earlier by the end of the 21st century, consistent with the projected rise in expected air temperatures (IPCC, 2021). For common lilac, under RCP4.5 the onset of flowering is projected to advance by 4.0 days in 2011–2040 relative to 1981–2010, increasing to 7.5 days in 2041–2070 and 10.3 days in 2071–2100. Under RCP8.5, advances are slightly larger: 4.5 days (2011–2040), 11.8 days (2041–2070), and 20,8 days (2071–2100). The maximum modelled deviation in the last period 2071–2100 for the onset of common lilac flowering from the average for the period 1981–2010 was modelled at 22.1 days for the RCP4.5 climate scenario and 34.9 days for the RCP8.5 climate scenario. The climate-driven model predicts that the dandelion under RCP4.5 will onset flowering 6.1 days earlier in 2011–2040 relative to 1981–2010, increasing to 11.2 days 2041–2070 and 15.3 days in 2071–2100. Under RCP8.5, advances are even larger: 7.0 days (2011–2040), 18.5 days (2041–2070), and 31.8 days (2071–2100). The maximum deviation in the last period 2071–2100 for the onset of dandelion flowering from the 1981–2010 average was modelled at 37.2 days for the RCP4.5 climate scenario and 58.5 days for the RCP8.5 climate scenario. For common hazel, under RCP4.5 the onset of flowering of the male catkins is projected to advance by 7.2 days in 2011–2040 relative to 1981–2010, increasing to 15.2 days in 2041–2070 and 18.7 days in 2071–2100. Under RCP8.5 advances are larger 8.9 days (2011–2040), 20.1 days (2041–2070), and 33.1 days (2071–2100). The maximum deviation in the last period 2071–2100 for the onset of flowering of the male catkins of the common hazel from the average of the period 1981–2010 was modelled at 50.1 days for the climate scenario RCP4.5 and 73.2 days for the climate scenario RCP8.5 (Fig. 3 .). Elevation dependence of spring phenology until the end of the 21st century To asses the elevation dependence of spring phenology, model results for the selected spring phenophases were analysed at phenological stations by the elevation groups: 0–300 m (low elevation), 301–600 m (mid-elevation) and 601–1050 m above sea level (high-elevation). Results show that the magnitude of changes in selected phenophases varies with elevation. For onset of flowering of common lilac, the projected average deviation from the period 1981–2010 in 2011–2040 under RCP4.5 was similar across all elevation groups. In 2041–2070, the high-elevation group (601–1050 m) exhibited a slightly smaller advancement (7 days) compared to lower elevations. By 2071–2100, this trend reversed, with the highest average phenophase advancement (8.1 days) observed at high elevations. Under RCP8.5, a similar pattern was predicted, with even larger advancements at high elevations in 2071–2100. The onset of dandelion flowering in the period 2011–2040 under RCP4.5 has the highest average deviation (6.2 days) in the medium elevation group (301–600 m a.s.l.). In the period 2041–2070, the average deviation is equally distributed in all elevation groups, while in the period 2071–2100 the highest average deviation (16.3 days) was projected in the high-elevation group (601–1050 m a.s.l.). Under RCP8.5 the lowest average deviation is projected in the low elevation group (0–300 m a.s.l.) for all three periods. In the second period (2041–2070), the model predicted a lower deviation in the medium elevation group (301–600 m a.s.l.), while in the period 2011–2040 and in the period 2071–2100 a lower deviation was predicted in the high-elevation group (601–1050 m a.s.l.). Under climate scenarios RCP4.5 and RCP8.5, the flowering of the male catkins of the common hazel showed the highest average deviation in the high-elevation group (601–1050 m a.s.l.), while the lowest average deviation was predicted in the low elevation group (0–300 m a.s.l.). Under RCP8.5, the model shows the highest average deviation of 43.3 days in the high-elevation group (601–1050 m a.s.l.) for the period 2071–2100, compared to the average for the period 1981–2010. While the difference in average deviation between the elevation groups was similar under RCP4.5, the model predicted an increase in differences under RCP8.5 by the end of the 21st century (Fig. 4 , Appendix 1). Discussion Plant phenology has been shown to be a very sensitive indicator of the effects of climate change (Menzel et al., 2006). This study investigates the potential shifts in spring phenology of the three most wide distributed plants in Slovenia under two different climate scenarios using a newly developed climate-driven phenological model. The model is based on the Spring Indices (SI) model, which is a set of complex phenological models that have been successfully applied to assess variations and trends in spring onset in temperate regions of the Northern Hemisphere (Schwartz et al., 2013). Previous studies shown that one-phase models perform comparably to biphasic models for most species (Vitasse et al., 2011) when tested phenological models using phenological observations of six European tree species collected over 2–3 years. The advance of spring phenology as a result of air temperature increase was extensively documented and mostly showed a trend towards advancement, although variation in predicted phenology may reflect differences in study scale and models used (Fu et al., 2015, Asse et al. 2020, Wang et al., 2022, Zimmer et al., 2022). Chilling accumulation is incorporated in our phenological model, but results indicate that has little or no influence on the onset of selected phenophases through the end of the 21st century. This is consistent with previous studies showing that the advancing effect of increased forcing was mainly stronger than the effect of chilling which varies considerably among species (Hu et al., 2022). Insufficient chilling may limit phenological advancement in response to warming at lower elevations and/or lower latitudes (Fu et al., 2015), suggesting that future studies should consider additional phenophases and chilling requirements. The present study shows that the onset of male catkins flowering of the common hazel is highly dependent on climate conditions, in particulaty air temperature, resulting in considerable interannual variability. This phenophase exhibited the highest standard deviation among all investigated phenophases, which is consistent with previous findings (Piskornik et al. 2001; Taghavi et al.,2018). Consistent with (Črepinšek et al., 2012) who reported and advancement of 7.0–8.8 days per 1°C increase in air temperature. Our study has indicate that under RCP8.5 scenario, the projected 3.0–5.1°C increase in spring warming by the end of 21 st century in Slovenia could advance male floweirng for 8.17 days/ 1°C. Bergant et al., 2001) reported that the flowering of dandelion ( Taraxacum officinale ) in Slovenia in the period 2020–2049 will advance for 10–11 days compared to the period 1960–1989 or 1.6–1.8 days per decade. Our simulations showed comparable results, under RCP4.5 scenario, dandelion flowering advances by 6.1 days or 2.0 days/decade in the period 2011–2040; 11.2 days or 1.87 days/decade in the period 2041–2070 and 15.7 days or 1.7 days/decade in the period 2071–2100. Under RCP8.5, the corespoding advances are 2.36, 3.08 and 3.53 days/decade. These findings align with Hu et al. (2022), who reported that the mean trend of spring phenology of the 14 species for the period 2021–2099 will be − 1.30 days/decade (RCP4.5) and − 2.79 days/decade (RCP8.5). This is consistent with our results on different plants with comparable spring phenophase timing, with modelled average trend of 1.9 days/decade ( RCP4.5) and in the − 2.68 days/decade (RCP8.5). The strongest response was observed for the onset of male catkins flowering of of common hazel − 2.62 days/decade (RCP4.5); -3.14 days/decade (RCP8.5) and the weakes shift was recorded for onset of common lilac flowering of (-1.22 days/decade, RCP4.5; -1.92 days/decade, RCP8.5). Our study showed that spring phenology will continue to advance at both moderate and high emission scenarios; with species specifics differences in sensitivity. It will be greater at higher elevations in the future, which is consistent with previous studies (Güsewell et al., 2017; Vitasse et al., 2018, Mei et al., 2021). The greatest difference in our study between the elevation stations 0–300 m a.s.l. and 601–1050 m a.s.l till the end of 21st century was shown for the onset of flowering of male catkins of common hazel RCP4.5–4.7 days; RCP8.5–8.0 days and the smallest difference between for onset of flowering of common lilac − 0.4 days RCP4.5; − 2.0 days RCP8.5. Overall, our study confirms that both climate and elevation exert significant infleuence on current and future spring phenology in the investigated species. The climate-driven phenology model was constructed using data from phenological stations in different regions of Slovenia, however, given the pronounced climatic heterogeneity within relative small geographic area, regional differences my have introduced bias. Future research should include additional datasets and improvements (regional characteristics, etc.) into the model in order to to improve accuracy and reliability of phenological projections. Conclusions This study demonstrates that rising air temperature significantly advance the onset of spring phenology in selected plant species in Slovenia throughout 21st century. In all considered species, and climate scenarios the advancement of flowering is more pronounced at higher elevations, highlighting the interaction between temperature increase and topography. Overall, these results provide strong evidence that future warming will substantially alter spring phenology in Slovenia, with species-specific and elevation-dependent sensitivities. This underlines the importance of phenological monitoring and climate-informed modeling as essential tools for assessing ecological impacts and informing adaptation strategies under ongoing climate change. Declarations Funding: This research was funded by the Programme Research Group "Forest Biology, Ecology and Technology” (No. P4–0107) and the Development Funding Pillar of the Slovenian Forestry Institute, founded by the Slovenian Research and Innovation Agency and a project »Updating data on phenological development of plants in a changing climate« (No. 2551-21-700022), founded by the Slovenian Environment Agency of the Ministry for the Environment, Climate and Energy. Author contributions : The manuscript was written through the contributions of all the authors. Gal Oblišar performed the literature research, analysed the data and draft the manuscript. Urša Vilhar contributed to draft the manuscript. Marko Puškarić collected and prepared phenological data. Gregor Gregorič and Andreja Sušnik critically revised the work. All the authors approved the final version of the manuscript. Data Availability Statement : The data presented in this study are openly available in FigShare at https://doi.org/10.6084/m9.figshare.30233563, reference number 30233563. Conflicts of Interest : The authors declare that no conflict of interest exists. References Asse D, Randin CF, Bonhomme M, Delestrade A, Chuine I (2020) Process-based models outcompete correlative models in projecting spring phenology of trees in a future warmer climate. 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Modeling Earth Systems and Environment, 8, 5389–5405. https://doi-org. /10.1007/s40808-022-01389-4 Zust A (2015) Fenologija v Sloveniji: Priročnik za fenološka opazovanja [Phenology in Slovenia: Manual for phenological observations]. Ministry of Environment, Slovenian Environment Agency, Ljubljana, Slovenia, pp. 104. [in Slovenian] Wang C, Tang Y, Chen J (2016) Plant phenological synchrony increases under rapid within-spring warming. Scientific Reports, 6(1), 25460. https://doi.org/10.1038/srep25460 Wang H, Lin S, Dai J, Ge Q (2022) Modeling the effect of adaptation to future climate change on spring phenological trend of European beech (Fagus sylvatica L.). Science of the Total Environment, 846, 157540 Supplementary Files Appendix1.docx Cite Share Download PDF Status: Published Journal Publication published 23 Mar, 2026 Read the published version in International Journal of Biometeorology → Version 1 posted Reviewers agreed at journal 03 Oct, 2025 Reviewers invited by journal 02 Oct, 2025 Editor assigned by journal 30 Sep, 2025 First submitted to journal 29 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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16:57:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":131145,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots of observed versus modelled day of year (DOY) of phenological phases: onset of flowering of a) male catkins of the common hazel, b) onset of flowering of dandelion, and the c) onset of flowering of common lilac across all phenological stations in Slovenia for the period 1971–2020. Blue lines indicate regression fits.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7702297/v1/caf0c8f5482ed007ef37ccbc.png"},{"id":93617067,"identity":"a341b518-508a-49c6-88cc-640e15275902","added_by":"auto","created_at":"2025-10-15 17:05:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":81171,"visible":true,"origin":"","legend":"\u003cp\u003eAverage deviation of modelled phenophases onset under climate scenarios RCP4.5 (left panels) and RCP8.5 (right panels) for three future periods 2011–2040, 2041–2070 and 2071–2100. Deviations are calculated relative to average phenophase occurance during period 1981–2010 across all selected phenological stations in Slovenia. Panels show the onset of flowering for common lilac (\u003cem\u003eSyringa vulgaris\u003c/em\u003e) (first row), dandelion (\u003cem\u003eTaraxacum officinale\u003c/em\u003e) (second row) and male catkin ofof the common hazel (\u003cem\u003eCorylus avellana\u003c/em\u003e) (third row).\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7702297/v1/49a6113624c841a76e3e6ff4.png"},{"id":93617068,"identity":"c3bcad94-0d43-4520-a5fe-5e31e674d852","added_by":"auto","created_at":"2025-10-15 17:05:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":107843,"visible":true,"origin":"","legend":"\u003cp\u003eAverage deviation of modelled phenophases onset under climate scenarios RCP4.5 (left panels) and RCP8.5 (right panels) for three future periods 2011–2040, 2041–2070 and 2071–2100 based on elevation zonation: 0–300 m, 301–600 m and 601–1050 m above sea level. Deviations are calculated relative to average phenophase occurance during period 1981–2010 across all selected phenological stations in Slovenia. Panels show the onset of flowering for common lilac (\u003cem\u003eSyringa vulgaris\u003c/em\u003e) (first row), dandelion (\u003cem\u003eTaraxacum officinale\u003c/em\u003e) (second row) and male catkin of the common hazel (\u003cem\u003eCorylus avellana\u003c/em\u003e) (third row).\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7702297/v1/f5c4fa4e6923fcdc330bdf26.png"},{"id":105755986,"identity":"5d952fef-c92a-400c-b9ad-f4cc08484ef6","added_by":"auto","created_at":"2026-03-30 16:33:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":971300,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7702297/v1/d597eab6-d4f8-45aa-a32f-071e98164c25.pdf"},{"id":93616302,"identity":"bfb7bea8-4018-4041-8fe2-098b6ad5a220","added_by":"auto","created_at":"2025-10-15 16:57:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23461,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7702297/v1/2ad416815d3d906e37b68671.docx"}],"financialInterests":"","formattedTitle":"Assessment of the potential shifts in the phenological development of representative spring plant species in Slovenia until the end of the 21st century using a model-based approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePhenology is the study of the periodically recurring patterns and behaviour of biological events, such as flowering and leaf unfolding in plants (Lieth, 1974). In the context of climate change, phenology plays a crucial role in understanding ecosystem dynamics and biodiversity under changing environmental conditions. As temperatures rise and climate patterns shift due to global climate change, these phenological events are occurring earlier in many regions, which has significant implications for ecological interactions and agricultural practises (Parmesan and Yohe, 2003; Menzel et al., 2006; Fu et al., 2015; Cui and Shi 2021). Advanced flowering times, polinator migrations and breeding schedules shift established ecological relationships and can lead to mismatches that threaten the survival of species (Cleland et al., 2007; Forrest \u0026amp; Thomson, 2011; Kudu and Ida, 2023). Changes in plant phenology can also feedback to the climate system by influencing the exchange of water and energy between terrestrial ecosystems and the atmosphere (Richardson et al., 2013). Minimum and maximum air temperatures and the photoperiod play a crucial role in the phenological phases of plant flowering and leaf unfolding in spring. In recent decades, the occurrence of spring phenophases in the various species has become increasingly synchronised. The temporal differences between the onset of the growing season for different species at regional level (Wang et al., 2016) and along elevation gradients (Vitasse et al., 2018) are decreasing.\u003c/p\u003e\u003cp\u003eIn recent years, phenology has evolved from an empirical topic of observing and recording the timing of some important annual natural events for selected species to a comprehensive scientific field that includes extended observations, experiments and modelling (Schwartz et al., 2006, Vilhar et al., 2018; Noumanovi et al, 2021). For phenological modelling of the interactions between ecosystems and the climate system, improved knowledge of phenological changes, their main drivers and the impact on the ecosystem is essential (Liu et al., 2019). Ground-based observations can accurately capture the timing of phenological events for specific sites and species. Networks of long‐term ground‐based phenological observations, such as those provided by national meteorological services (Vliet et al., 2003, Menzel et al., 2006, Aono and Kazui, 2008) in accordance with World Meteorological Organization guidelines (Koch et al., 2009) or those conducted by forest research institutes following the harmonized guidelines of the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) (Vilhar et al., 2013, Raspe et al., 2020) are particularly useful to investigate phenological variations over a large geographical area and their potential changes in response to climate change (Cleland et al., 2007). Given the increasing concern about climate change and its potential impacts, the establishment of international phenological networks has facilitated collaborationin large‐scale and standardized phenological data collection and sharing (Templ et al., 2018). More recently, the development of smartphones, automated cameras and other communication technologies has taken ground-based phenology monitoring by citizen science to a new level, significantly expanding the coverage of phenological events over a large area and for many more species (Dickinson et al., 2012; Hufkens et al., 2019). Slovenia has a very dense network of phenological stations with good coverage of the diverse Slovenian terrain. Phenological observations in Slovenia were organized in 1951 as part of the national phenological network within the framework of the agrometeorological service of the Hydrometeorological Institute of the Republic Slovenia in the former Yugoslavia. After 2001, phenological observations became a regular activity of the Department of Agrometeorology of the Environment Agency of the Republic of Slovenia. Initially, the observations were carried out at 30 phenological stations, later the number increased to over 200 stations. Currently, there are still 46 active phenological stations evenly distributed throughout the country at elevations ranging from 55 to 1050 m a.s.l. and with different site and climate characteristics. In a global comparison, the Slovenian network of phenological stations is one of the best continuously maintained (Žust, 2015).\u003c/p\u003e\u003cp\u003eChanges in plant phenophases in spring can have profound effects on the ecosystem dynamics, and due to the rapid climate changes in recent decades, predictions of these changes are becoming increasingly important. Since it is not possible to rely solely on long-term phenological observations to assess temporal phenological changes, models have been developed that simulate the onset of selected phenophases. These models, driven by daily maximum and minimum air temperatures, provide a biologically relevant approach to track phenological changes over larger spatial and temporal scales, using the availability of meteorological data. In contrast to raw meteorological data such as monthly or seasonal average air temperatures, models offer higher precision as they capture phenoclimatic processes on a daily to weekly scale that trigger critical events such as plant leafing and flowering that determine ecosystem dynamics. When a phenological event is triggered by a short period of extreme temperatures, this can be inadequately represented in general metrics such as monthly or seasonal average temperature (Schwartz et al., 2006; Gerst et al., 2020)\u003c/p\u003e\u003cp\u003eThe aim of the present study was to assess the changes in the spring phenology in the future, focusing on the elevation dependence of phenophase occurrence. For this purpose, we developed a climate-driven phenological model based on the methodology of the American Spring Index using air temperature and spring phenology of the common hazel (\u003cem\u003eCorylus avellana\u003c/em\u003e), dandelion (\u003cem\u003eTaraxacum officinale\u003c/em\u003e) and common lilac (\u003cem\u003eSyringa vulgaris\u003c/em\u003e). We used daily maximum and minimum air temperatures and phenological data on selected plant species and phenophases collected by at 46 phenological stations of the Slovenian Environment Agency for the period 1971\u0026ndash;2020. Subsequently, we modelled the occurrence of selected phenophases until the end of the 21st century using the climate projections data of the EURO-CORDEX dataset for the RCP4.5 and RCP8.5 climate scenarios.\u003c/p\u003e\u003cp\u003eThe paper is organised as follows. In the \u0026ldquo;Materials and methods\u0026rdquo; section, we describe the data sets, the selected phenophases and the models. In the \u0026ldquo;Results\u0026rdquo; section, we show the results of the application of the methods. In the \u0026ldquo;Discussion\u0026rdquo; section, the results obtained are discussed and compared. The conclusions are drawn in the \u0026ldquo;Conclusions\u0026rdquo; section.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003ePhenological data\u003c/p\u003e\u003cp\u003eFor the development and verification of the climate-driven phenological model, we selected 46 phenological stations of the national phenological network of the Slovenian Environment Agency with homogeneous and continuous data series evenly distributed across Slovenia. Slovenia is characterised by relatively large gradients of climatic factors due to its location between the Alps, the Mediterranean and continental Europe (Čufar et al., 2012), and consequently a wide variety of habitats can be found in the country, from lowlands to high mountains (Kermavnar and Kutnar, 2020).\u003c/p\u003e\u003cp\u003eThe plant species and phenological phases included in the model were selected based on several criteria: (i) ease of identification and observation, (ii) average timing of occurrence to cover the entire spring period, (iii) wide distribution of the species across the country. Phenological stations were selected based on the homogeneity of their record for the selected phenophases and the presence of at least one continuous data series of 10 years or more during the period 1971\u0026ndash;2020.\u003c/p\u003e\u003cp\u003eThe selected plant species and phenological phases were the onset the male catkins flowering of the common hazel (\u003cem\u003eCorylus avellana\u003c/em\u003e), the onset of flowering of the dandelion (\u003cem\u003eTaraxacum officinale\u003c/em\u003e) and the onset of flowering of the common lilac (\u003cem\u003eSyringa vulgaris\u003c/em\u003e).\u003c/p\u003e\u003cp\u003ePhenological observations at each phenological station was done according to the national guidelines and supervised by national phenological coordinator (Žust, 2015), following the World Meteorological Organisation (WMO) guidelines for phenological observations (Koch et al., 2007). Observations were carried out daily. The onset of common hazel flowering was defined as stage when the two-part yellow anthers become visible on the elongated catkins, and the yellow pollen began to shed. This phase corresponds to Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie code 60 (BBCH60). The onset of dandelion flowering was recorded when some fully developed and open flowers could be seen in the observed meadow (BBCH60). For common lilac, flowering was recorded when the first flowers opened at the lower edge of the first inflorescences and with two stamens visible per flower (BBCH60).\u003c/p\u003e\u003cp\u003eClimate data\u003c/p\u003e\u003cp\u003eThe meteorological data used were daily minimum and maximum air temperatures for the period 1971\u0026ndash;2020, obtained from raster datasets provided by Slovenian Environment Agency (SEA). The air temperature data were recalculated to the elevation and geographical position of the selected phenological station using the methodology described in Huld and Pascua (2015).\u003c/p\u003e\u003cp\u003eClimate projection data for the 21st century were obtained from the EURO-CORDEX dataset, the the European branch of the CORDEX initiative: EURO-CORDEX provides an ensemble of climate simulations generated bymultiple dynamical and empirical-statistical downscaling models forced by several global climate models of the Coupled Model Intercomparison Project Phase 5 (CMIP5) (Gobiet and Jacob, 2011). We considered two climate change scenarios based on the IPCC methodology: RCP4.5 \u0026ndash; a moderately optimistic scenario that assumes a total radiative forcing of 4.5 W m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e by 2100 and a CO\u003csub\u003e2\u003c/sub\u003e equivalent of 630 ppm by 2100, and RCP8.5 \u0026ndash; a pessimistic scenario that assumes a total radiative forcing of 8.5 W m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e by 2100 and a CO\u003csub\u003e2\u003c/sub\u003e equivalent of 1313 ppm by 2100 (IPCC, 2021).\u003c/p\u003e\u003cp\u003eClimate models contain systematic biases arising from factors such as limited horizontal and vertical resolution, simplified equations for some physical processes, numerical approximations, incomplete understanding of all climate dynamics. In this study, the climate projection data used were bias-corrected within OPS21 project (Bertalanič et al., 2018), which applied a tailored quantile mapping approach to daily model outputs for the period 1981\u0026ndash;2100. The reference period for bias correction was period 1981\u0026ndash;2005. Bias correction was applied separately for each model grid cell and time step using a moving 61-day window and 100 quantile classes. This approach preserved inter-variable dependencies and long-term trends while effectively reducing systematic errors in climate model projections. The climate projection dataset represents six combinations of regional and global climate models, providing with daily meteorological variables for a period 1981\u0026ndash;2100 downscaled to the location of each phenological station. For model verification, we used the dataset for the period 1981\u0026ndash;2005 and compared simulated values with observed phenological data against historical reanalyses. The period 2005\u0026ndash;2100 represents bias-corrected climate projection dataset. Results of these projections are presented in three thirty-year climate periods: 2011\u0026ndash;2040, 2041\u0026ndash;2070 and 2071\u0026ndash;2100 as a deviation in the timing of individual phenophases compared to the reference period 1981\u0026ndash;2010.\u003c/p\u003e\u003cp\u003ePhenological model and statistical analyses\u003c/p\u003e\u003cp\u003eWe have developed a climate-driven phenological model to assess future changes in the onset of selected plant phenophases using climate projection data. The model was implemented in the MATLAB programming environment and based on the methodology of the American Spring Index methodology, which incorporates the effects of both air temperature and photoperiod. The American Spring Phenological Index model consists of two models that represent the average response of three reference species and estimate the \"onset of spring\u0026rdquo; as either the the first leaves emergence or the the first flowering at a given location. These models were originally developed using observations of the first leaf and flowering phases of the \u003cem\u003eSyringa \u0026times; chinensis\u003c/em\u003e \"\u003cem\u003eRed Rothomagensis\"\u003c/em\u003e and two honeysuckle clones (\u003cem\u003eLonicera tatarica\u003c/em\u003e \"\u003cem\u003eArnold Red\u003c/em\u003e\" and \u003cem\u003eLonicera korolkowii Stapf)\u003c/em\u003e (Schwartz et al., 2006; Schwartz et al., 2013; Ault et al., 2015; Rosemartin et al., 2015).To investigate the elevation dependence of phenophase occurrence, stations were stratified into three elevation zones: (i) 0\u0026ndash;300 m (low elevation), (ii) 301\u0026ndash;600 m (mid- elevation) and (iii) 601\u0026ndash;1050 m above sea level (high elevation) following de Groot and Vrezec (2019). Model outputs represent the onset of the selected phenological phase expressed in Julian days (day of the year - DOY). Model performance was evaluated for the period 1981\u0026ndash;2005. Relationships between observed and simulated phenophases were quantified using the Spearman correlation test. Model fit was further assessed with the coefficient of determination (R\u0026sup2;), which indicates the proportion of variance in observed values explained by the model (Rodgers and Nicewander, 1988). In addition, we calculated the root mean square error (RMSE), defined as the square root of the mean squared deviation between predicted estimated and observed values (Both et al., 2009). All statistical analyses, were conducted in the programme R, version 4.2.3 (R Development Core Team, 2024).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe climate-driven phenological model for the period 1971\u0026ndash;2020\u003c/p\u003e\u003cp\u003eDuring the period 1971\u0026ndash;2020, the onset of common hazel male catkins flowering occurred on average at DOY 51 at all selected phenological stations, with observations ranging from DOY 29 at the Portorož station (2 m a.s.l.) to DOY 74 at the Planina pod Golico station (1050 m a.s.l.). The long-term trend indicated an advancement of 3.77 days per decade (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.13) across all phenological stations.\u003c/p\u003e\u003cp\u003eDandelion flowering onset was observed on average at DOY 100, ranging from DOY 71 at Portorož to DOY 124 at Zgornje Jezersko (879 m a.s.l.). The mean trend was an advancement of 2.19 days per decade (R\u0026sup2; = 0.15) across all phenological stations.\u003c/p\u003e\u003cp\u003eThe onset of flowering of common lilac was observed on average on the DOY 120, ranging from DOY 103 at the Portorož station to DOY 144 at the Planina pod Golico station. The The corresponding long-term trend showed an advancement of 3.45 days per decade (R\u0026sup2; = 0.43).\u003c/p\u003e\u003cp\u003eThe developed climate-driven phenological model demonstrated a good agreement between simulated and observed spring phenophases across all selected phenological stations. Model performance was slightly reduced at coastal stations and at higher-elevation sites in the Alps. The highest and statistically significant agreement was obtained for the onset of dandelion flowering (δ\u003csub\u003eavr\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.86; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), RMSE\u0026thinsp;=\u0026thinsp;8.80, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.68), followed by the onset of common lilac flowering (δ\u003csub\u003eavr\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.80; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), RMSE\u0026thinsp;=\u0026thinsp;7.19, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.73) and the onset of flowering of common hazel male catkins (δ\u003csub\u003eavr\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.76; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), RMSE\u0026thinsp;=\u0026thinsp;11.05, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.59) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAverage day of the year (DOY) of observed phenophases: onset of flowering of male catkins of the common hazel (\u003cem\u003eCorylus avellana\u003c/em\u003e), onset of flowering of dandelion (\u003cem\u003eTaraxacum officinale\u003c/em\u003e) and common lilac (\u003cem\u003eSyringa vulgaris\u003c/em\u003e) across all selected phenological stations in Slovenia during the the period 1971\u0026ndash;2020. Reported are Sperman correlation coefficient (δ\u003csub\u003eavr\u003c/sub\u003e), significance levels (\u003cem\u003ep)\u003c/em\u003e, coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e), and root mean square errors (RMSE) comparing observed and modelled data, n denotes the total number of observations.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003csub\u003eobs (DOY)\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eδ\u003csub\u003eavr\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRMSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnset of flowering of male catkins of the common hazel (\u003cem\u003eCorylus avellana\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51,1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,76***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11,06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2269\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnset of flowering of dandelion (\u003cem\u003eTaraxacum officinale\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,86***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8,80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2325\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnset of flowering of common lilac (\u003cem\u003eSyringa vulgaris\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120,5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,80***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7,19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0,05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0,01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0,001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAgreement between observed and modelled spring phenology using climate projection data for the reference period 1981\u0026ndash;2010.\u003c/p\u003e\u003cp\u003eThe climate-driven model showed a high and statistically significant agreement between observed and simulated onset of common lilac flowering in 6-year moving averages using the EURO-CORDEX climate dataset for the reference period 1981\u0026ndash;2005, under both climate scenarios RCP4.5 and RCP8.5 (δ\u003csub\u003epovp\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.84; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; RMSE\u003csub\u003eRCP4.5\u003c/sub\u003e = 6.94, RMSE\u003csub\u003eRCP8.5\u003c/sub\u003e = 6.99). Agreement was slightly lower for the onset of dandelion flowering (δ\u003csub\u003epovp\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.80 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; RMSE\u003csub\u003eRCP4.5\u003c/sub\u003e = 6.43) [RCP4.5] and δ\u003csub\u003epovp\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.79 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; RMSE\u003csub\u003eRCP8.5\u003c/sub\u003e =6.41) [RCP8.5]. The lowest agreement was observed for common hazel male catkins flowering with δ\u003csub\u003epovp\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.55 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; RMSE\u003csub\u003eRCP4.5\u003c/sub\u003e = 20.43) [RCP4.5] and δ\u003csub\u003epovp\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.46 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; RMSE\u003csub\u003eRCP8.5\u003c/sub\u003e =22.53) [RCP 8.5] (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSperman corellation coefficient (δ\u003csub\u003epovp\u003c/sub\u003e), significance levels (\u003cem\u003ep)\u003c/em\u003e and root mean square errors (RMSE) between observed and simulated spring phenophases on two climate change climate scenarios RCP4.5 and RCP8.5 using EURO-CORDEX climate dataset for the reference period 1981\u0026ndash;2005\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eRCP 4.5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eRCP 8.5\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eδ\u003csub\u003epovp\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRMSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eδ\u003csub\u003epovp\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRMSE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnset of flowering of male catkins of the common hazel (\u003cem\u003eCorylus avellana\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,55***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20,43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,46**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22,53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnset of flowering of dandelion (\u003cem\u003eTaraxacum officinale\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,80***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6,43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,79***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6,41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnset of flowering of common lilac (\u003cem\u003eSyringa vulgaris\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,84***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6,94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,84***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6,99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0,05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0,01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0,001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModelled changes in spring phenology until the end of the 21st century\u003c/p\u003e\u003cp\u003eClimate-driven model projections indicate that all three studied spring phenophases in Slovenia are expected to occur earlier by the end of the 21st century, consistent with the projected rise in expected air temperatures (IPCC, 2021). For common lilac, under RCP4.5 the onset of flowering is projected to advance by 4.0 days in 2011\u0026ndash;2040 relative to 1981\u0026ndash;2010, increasing to 7.5 days in 2041\u0026ndash;2070 and 10.3 days in 2071\u0026ndash;2100. Under RCP8.5, advances are slightly larger: 4.5 days (2011\u0026ndash;2040), 11.8 days (2041\u0026ndash;2070), and 20,8 days (2071\u0026ndash;2100). The maximum modelled deviation in the last period 2071\u0026ndash;2100 for the onset of common lilac flowering from the average for the period 1981\u0026ndash;2010 was modelled at 22.1 days for the RCP4.5 climate scenario and 34.9 days for the RCP8.5 climate scenario. The climate-driven model predicts that the dandelion under RCP4.5 will onset flowering 6.1 days earlier in 2011\u0026ndash;2040 relative to 1981\u0026ndash;2010, increasing to 11.2 days 2041\u0026ndash;2070 and 15.3 days in 2071\u0026ndash;2100. Under RCP8.5, advances are even larger: 7.0 days (2011\u0026ndash;2040), 18.5 days (2041\u0026ndash;2070), and 31.8 days (2071\u0026ndash;2100). The maximum deviation in the last period 2071\u0026ndash;2100 for the onset of dandelion flowering from the 1981\u0026ndash;2010 average was modelled at 37.2 days for the RCP4.5 climate scenario and 58.5 days for the RCP8.5 climate scenario. For common hazel, under RCP4.5 the onset of flowering of the male catkins is projected to advance by 7.2 days in 2011\u0026ndash;2040 relative to 1981\u0026ndash;2010, increasing to 15.2 days in 2041\u0026ndash;2070 and 18.7 days in 2071\u0026ndash;2100. Under RCP8.5 advances are larger 8.9 days (2011\u0026ndash;2040), 20.1 days (2041\u0026ndash;2070), and 33.1 days (2071\u0026ndash;2100). The maximum deviation in the last period 2071\u0026ndash;2100 for the onset of flowering of the male catkins of the common hazel from the average of the period 1981\u0026ndash;2010 was modelled at 50.1 days for the climate scenario RCP4.5 and 73.2 days for the climate scenario RCP8.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eElevation dependence of spring phenology until the end of the 21st century\u003c/p\u003e\u003cp\u003eTo asses the elevation dependence of spring phenology, model results for the selected spring phenophases were analysed at phenological stations by the elevation groups: 0\u0026ndash;300 m (low elevation), 301\u0026ndash;600 m (mid-elevation) and 601\u0026ndash;1050 m above sea level (high-elevation). Results show that the magnitude of changes in selected phenophases varies with elevation. For onset of flowering of common lilac, the projected average deviation from the period 1981\u0026ndash;2010 in 2011\u0026ndash;2040 under RCP4.5 was similar across all elevation groups. In 2041\u0026ndash;2070, the high-elevation group (601\u0026ndash;1050 m) exhibited a slightly smaller advancement (7 days) compared to lower elevations. By 2071\u0026ndash;2100, this trend reversed, with the highest average phenophase advancement (8.1 days) observed at high elevations. Under RCP8.5, a similar pattern was predicted, with even larger advancements at high elevations in 2071\u0026ndash;2100.\u003c/p\u003e\u003cp\u003eThe onset of dandelion flowering in the period 2011\u0026ndash;2040 under RCP4.5 has the highest average deviation (6.2 days) in the medium elevation group (301\u0026ndash;600 m a.s.l.). In the period 2041\u0026ndash;2070, the average deviation is equally distributed in all elevation groups, while in the period 2071\u0026ndash;2100 the highest average deviation (16.3 days) was projected in the high-elevation group (601\u0026ndash;1050 m a.s.l.). Under RCP8.5 the lowest average deviation is projected in the low elevation group (0\u0026ndash;300 m a.s.l.) for all three periods. In the second period (2041\u0026ndash;2070), the model predicted a lower deviation in the medium elevation group (301\u0026ndash;600 m a.s.l.), while in the period 2011\u0026ndash;2040 and in the period 2071\u0026ndash;2100 a lower deviation was predicted in the high-elevation group (601\u0026ndash;1050 m a.s.l.).\u003c/p\u003e\u003cp\u003eUnder climate scenarios RCP4.5 and RCP8.5, the flowering of the male catkins of the common hazel showed the highest average deviation in the high-elevation group (601\u0026ndash;1050 m a.s.l.), while the lowest average deviation was predicted in the low elevation group (0\u0026ndash;300 m a.s.l.). Under RCP8.5, the model shows the highest average deviation of 43.3 days in the high-elevation group (601\u0026ndash;1050 m a.s.l.) for the period 2071\u0026ndash;2100, compared to the average for the period 1981\u0026ndash;2010. While the difference in average deviation between the elevation groups was similar under RCP4.5, the model predicted an increase in differences under RCP8.5 by the end of the 21st century (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Appendix 1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePlant phenology has been shown to be a very sensitive indicator of the effects of climate change (Menzel et al., 2006). This study investigates the potential shifts in spring phenology of the three most wide distributed plants in Slovenia under two different climate scenarios using a newly developed climate-driven phenological model. The model is based on the Spring Indices (SI) model, which is a set of complex phenological models that have been successfully applied to assess variations and trends in spring onset in temperate regions of the Northern Hemisphere (Schwartz et al., 2013). Previous studies shown that one-phase models perform comparably to biphasic models for most species (Vitasse et al., 2011) when tested phenological models using phenological observations of six European tree species collected over 2\u0026ndash;3 years. The advance of spring phenology as a result of air temperature increase was extensively documented and mostly showed a trend towards advancement, although variation in predicted phenology may reflect differences in study scale and models used (Fu et al., 2015, Asse et al. 2020, Wang et al., 2022, Zimmer et al., 2022). Chilling accumulation is incorporated in our phenological model, but results indicate that has little or no influence on the onset of selected phenophases through the end of the 21st century. This is consistent with previous studies showing that the advancing effect of increased forcing was mainly stronger than the effect of chilling which varies considerably among species (Hu et al., 2022). Insufficient chilling may limit phenological advancement in response to warming at lower elevations and/or lower latitudes (Fu et al., 2015), suggesting that future studies should consider additional phenophases and chilling requirements. The present study shows that the onset of male catkins flowering of the common hazel is highly dependent on climate conditions, in particulaty air temperature, resulting in considerable interannual variability. This phenophase exhibited the highest standard deviation among all investigated phenophases, which is consistent with previous findings (Piskornik et al. 2001; Taghavi et al.,2018). Consistent with (Črepinšek et al., 2012) who reported and advancement of 7.0\u0026ndash;8.8 days per 1\u0026deg;C increase in air temperature. Our study has indicate that under RCP8.5 scenario, the projected 3.0\u0026ndash;5.1\u0026deg;C increase in spring warming by the end of 21 st century in Slovenia could advance male floweirng for 8.17 days/ 1\u0026deg;C. Bergant et al., 2001) reported that the flowering of dandelion (\u003cem\u003eTaraxacum officinale\u003c/em\u003e) in Slovenia in the period 2020\u0026ndash;2049 will advance for 10\u0026ndash;11 days compared to the period 1960\u0026ndash;1989 or 1.6\u0026ndash;1.8 days per decade. Our simulations showed comparable results, under RCP4.5 scenario, dandelion flowering advances by 6.1 days or 2.0 days/decade in the period 2011\u0026ndash;2040; 11.2 days or 1.87 days/decade in the period 2041\u0026ndash;2070 and 15.7 days or 1.7 days/decade in the period 2071\u0026ndash;2100. Under RCP8.5, the corespoding advances are 2.36, 3.08 and 3.53 days/decade. These findings align with Hu et al. (2022), who reported that the mean trend of spring phenology of the 14 species for the period 2021\u0026ndash;2099 will be \u0026minus;\u0026thinsp;1.30 days/decade (RCP4.5) and \u0026minus;\u0026thinsp;2.79 days/decade (RCP8.5). This is consistent with our results on different plants with comparable spring phenophase timing, with modelled average trend of 1.9 days/decade ( RCP4.5) and in the \u0026minus;\u0026thinsp;2.68 days/decade (RCP8.5). The strongest response was observed for the onset of male catkins flowering of of common hazel \u0026minus;\u0026thinsp;2.62 days/decade (RCP4.5); -3.14 days/decade (RCP8.5) and the weakes shift was recorded for onset of common lilac flowering of (-1.22 days/decade, RCP4.5; -1.92 days/decade, RCP8.5).\u003c/p\u003e\u003cp\u003eOur study showed that spring phenology will continue to advance at both moderate and high emission scenarios; with species specifics differences in sensitivity. It will be greater at higher elevations in the future, which is consistent with previous studies (G\u0026uuml;sewell et al., 2017; Vitasse et al., 2018, Mei et al., 2021). The greatest difference in our study between the elevation stations 0\u0026ndash;300 m a.s.l. and 601\u0026ndash;1050 m a.s.l till the end of 21st century was shown for the onset of flowering of male catkins of common hazel RCP4.5\u0026ndash;4.7 days; RCP8.5\u0026ndash;8.0 days and the smallest difference between for onset of flowering of common lilac \u0026minus;\u0026thinsp;0.4 days RCP4.5; \u0026minus;\u0026thinsp;2.0 days RCP8.5. Overall, our study confirms that both climate and elevation exert significant infleuence on current and future spring phenology in the investigated species.\u003c/p\u003e\u003cp\u003eThe climate-driven phenology model was constructed using data from phenological stations in different regions of Slovenia, however, given the pronounced climatic heterogeneity within relative small geographic area, regional differences my have introduced bias. Future research should include additional datasets and improvements (regional characteristics, etc.) into the model in order to to improve accuracy and reliability of phenological projections.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that rising air temperature significantly advance the onset of spring phenology in selected plant species in Slovenia throughout 21st century. In all considered species, and climate scenarios the advancement of flowering is more pronounced at higher elevations, highlighting the interaction between temperature increase and topography. Overall, these results provide strong evidence that future warming will substantially alter spring phenology in Slovenia, with species-specific and elevation-dependent sensitivities. This underlines the importance of phenological monitoring and climate-informed modeling as essential tools for assessing ecological impacts and informing adaptation strategies under ongoing climate change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was funded by the Programme Research Group \"Forest Biology, Ecology and Technology” (No. P4–0107) and the Development Funding Pillar of the Slovenian Forestry Institute, founded by the Slovenian Research and Innovation Agency and a project »Updating data on phenological development of plants in a changing climate« (No. 2551-21-700022), founded by the Slovenian Environment Agency of the Ministry for the Environment, Climate and Energy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Author contributions\u003c/strong\u003e: The manuscript was written through the contributions of all the authors. Gal Oblišar performed the literature research, analysed the data and draft the manuscript. Urša Vilhar contributed to draft the manuscript. Marko Puškarić collected and prepared phenological data. Gregor Gregorič and Andreja Sušnik critically revised the work. All the authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e: The data presented in this study are openly available in FigShare at https://doi.org/10.6084/m9.figshare.30233563, reference number 30233563.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e: The authors declare that no conflict of interest exists.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAsse D, Randin CF, Bonhomme M, Delestrade A, Chuine I (2020) Process-based models outcompete correlative models in projecting spring phenology of trees in a future warmer climate. 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Science of the Total Environment, 846, 157540\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-biometeorology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbm","sideBox":"Learn more about [International Journal of Biometeorology](http://link.springer.com/journal/484)","snPcode":"484","submissionUrl":"https://www.editorialmanager.com/ijbm/default2.aspx","title":"International Journal of Biometeorology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Plant phenology, Phenological model, Elevation dependence, Climate change","lastPublishedDoi":"10.21203/rs.3.rs-7702297/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7702297/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo assess the changes in the spring phenology in the future with particular emphasison the elevation dependence of phenophase onset, a climate-driven phenological model was developed based on the spring indices methodology. Our study investigates both current and projected changes in the timing of flowering onset for common hazel (Corylus avellana), dandelion (Taraxacum officinale), and common lilac (Syringa vulgaris). We compiled comprehensive climate data and phenological records from 46 phenological stations of the National Phenological Network of the Slovenian Environment Agency for the period 1971\u0026ndash;2020. In addition, we used climate projection data for the 21st century under two climate scenarios to evaluate potential future shifts in the onset of the selected phenophases. Specifically, we examined whether the agreement between model predictions and observed records varies with elevation during the reference period (1981\u0026ndash;2010) and whether this relationship changes across three future climate periods: 2011\u0026ndash;2040, 2041\u0026ndash;2070, and 2071\u0026ndash;2100. Model results indicate that spring phenophases are expected to occur earlier in Slovenia by the end of the 21st century, consistent with the projected increase in air temperatures. Moreover, the advancement in spring phenology will be more pronounced at higher elevations.\u003c/p\u003e","manuscriptTitle":"Assessment of the potential shifts in the phenological development of representative spring plant species in Slovenia until the end of the 21st century using a model-based approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 16:57:37","doi":"10.21203/rs.3.rs-7702297/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-10-03T07:47:01+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-02T20:20:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-30T22:37:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Biometeorology","date":"2025-09-29T08:49:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-biometeorology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbm","sideBox":"Learn more about [International Journal of Biometeorology](http://link.springer.com/journal/484)","snPcode":"484","submissionUrl":"https://www.editorialmanager.com/ijbm/default2.aspx","title":"International Journal of Biometeorology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"91bbe761-9a10-441f-a372-f338026a3900","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:27:36+00:00","versionOfRecord":{"articleIdentity":"rs-7702297","link":"https://doi.org/10.1007/s00484-026-03143-2","journal":{"identity":"international-journal-of-biometeorology","isVorOnly":false,"title":"International Journal of Biometeorology"},"publishedOn":"2026-03-23 16:10:54","publishedOnDateReadable":"March 23rd, 2026"},"versionCreatedAt":"2025-10-15 16:57:37","video":"","vorDoi":"10.1007/s00484-026-03143-2","vorDoiUrl":"https://doi.org/10.1007/s00484-026-03143-2","workflowStages":[]},"version":"v1","identity":"rs-7702297","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7702297","identity":"rs-7702297","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}