Observation of climatic parameters and plant phenology in the International Phenological Garden of Klaipėda University Botanic Garden, Lithuania

Observation of climatic parameters and plant phenology in the International Phenological Garden of Klaipėda University Botanic Garden, Lithuania | 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 Observation of climatic parameters and plant phenology in the International Phenological Garden of Klaipėda University Botanic Garden, Lithuania Asta Klimiene, Ramutis Klimas, Renata Pilkaitytė This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7213585/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2026 Read the published version in International Journal of Biometeorology → Version 1 posted 4 You are reading this latest preprint version Abstract The phenology of plants varies greatly over broad geographic gradients, according to climate zone and vegetation type. Phenological records were collected from 2007 to 2024 at Klaipėda University Botanic Garden (KUBG), which is one of the 89 gardens belonging to the International Phenological Gardens (IPG No. 151). The garden is located in Western Lithuania, close to the Baltic Sea coastline (about 3.5 km) (55°42′40″N 21°7′50″E). For the analysis were chosen 5 species only. The average annual air temperature in Klaipėda is 7.9°C. The coldest period is in January-February, where the average air temperature is -1.0°C. The warmest period occurs in July-August (aver. 18.2°C). The most influence had autumn temperature of the last season: the strong correlation were with leaf unfolding of all trees, as well strong or moderate correlation were with beginning and full flowering of S. viminalis and S. ×chinensis. Only precipitation of last autumn and precipitation of January-February had the statistically significant influence on spring phenological plant parameters. The longest vegetation period had S. viminalis (224 days) while the S. nigra had the shortest one − 187 days. Precipitation during the January-February had strong or moderate positive correlation with the leaf unfolding of all examined trees, as well as had moderate positive effect for beginning and full flowering of S. chinensis . The summer and autumn temperature had the negative relation for the both vegetation periods. The strongest correlation appeared between summer temperature and vegetation period of C. avellana and S. nigra . Precipitation Air temperature Plant phenological data International Phenological garden Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Phenology is a field of nature and simple research, which studies the annual rhythm of biological development of plants, relationship to climatic variations perceived as a global problem to investigate at a local level. The strong relationship between air temperature and plant development in the northern hemisphere make phenological observations to be a sensitive indicator (Schnelle and Volkert 1974 ; Chmielewski and Rötzer 2001 ; Chmielewski et al. 2013 ; Piao et al. 2019 ). Phenology varies greatly over broad geographic gradients, according to climate zone and vegetation type, and substantial interannual variability growing season start and duration related to the interannual climatic variations. Phenology also varies within populations, and the phenology of individual plants play a key role in the determination of how ecosystems are structured and how they function (Cleland et al. 2007 ). By the number of authors is fixed, that the average global temperature has increased by 0.2°C per decade over the last three decades. It means, that climate warming process influence on the sequence of biological processes (Hughes 2000 ; Chapin et al. 2008 ; Lesica and Kittelson 2010 ; IPCC 2021 ; Woods et al. 2022 ). For phenological observation in world and in countries or regions are joint to the networks. One of the longest-running phenological networks are countries in Europe, where phenological monitoring by the German Weather Service (DWS) start since 1922, for late goes to International Phenological Garden (IPG) network which was established in 1959 (Schnelle and Volkert 1974 ; Chmielewski et al. 2013 ; http://ipg.hu-berlin.de ). In first project PEP725 comprises observations of 139 plants and 33 development phases late national phenological networks and the International Phenological Gardens ( http://ipg.hu-berlin.de ; Chmielewski and Rötzer 2001 ). A major goal of phenology is to understand the effects of climate on plant development. The usual phenological network established within a region may elucidate local patterns to some extent, but the information is not precise enough to evaluate research hypotheses. Variations in plant development may arise from hereditary factors, as well as from location ones (Schnelle and Volkert 1974 ). Various plant species have evolved different life strategies based on different trade – offs between survival and capacity adaptations, and consequently different species phenological responses are also expected (Downs and Borthwick 1956 ; Murray et al. 1989 ; Myking and Heide 1995 ; Rinne et al 1997 ; Heide 1993 ; 2008 ; Mahmood et al. 2000 ; Li et al. 2003 ). Therefore, the study of these differences and their implications is particularly important if the impacts of climate change are to be evaluated at the ecosystem level. At the same time, the goal of our work was to find out what climatic factors influence the phenology of different plants. Materials and methods Phenological records were provided by the International Phenological Gardens database IPG, which has been Physical Geography / Lanscape Ecology and Sustainable Ecosystem Development KU Eichstätt-Ingolstadt ( https://ipg.ku.de ) since 2023. The International Phenological Gardens (IPG) conduct large-scale, standardised phenological observations. Therefore, all IPGs are situated in similar surroundings. To eliminate heritable variability, cloned species of all trees and shrubs are planted in the IPGs with a unique ID number that is the same in all gardens. Study site Phenological records were collected from 2007 to 2024 at Klaipeda University Botanic Garden (KUBG), which is one of the 89 gardens belonging to the International Phenological Gardens (IPG No. 151). KUBG is located in Western Lithuania, in the city of Klaipėda, close to the Baltic Sea coastline (about 3.5 km) (55042’40”N, 2107’50”E), altitude − 9 m. The garden is situated at the riverbank on a plain surface with meadows and some trees. The climate zoning is transitional between the mild maritime climate of Western Europe and the continental climate of Eastern Europe, dominated by westerly air masses coming from the Atlantic Ocean (Galvonaite et al. 2013). Therefore, during winters, the air temperature is a few degrees below freezing, while summers are mild to pleasantly warm. The average annual air temperature in Klaipėda is 7.9°C. The coldest period is in January-February, where the average air temperature is -1.0°C (min − 26.0°C). The warmest period occurs in July-August (average 18.2°C, max. 36.6°C, Fig. 1 A). Over the last half century, the average annual air temperature in Klaipeda has increased by 1.2°C (Dailidienė et al. 2023 ). Meanwhile, the number of days with average negative daily air temperatures has decreased by 10 days over 30 years (Fig. 2 ). The data were taken from Lithuanian Hydrometeorological Service (LHS). The long-term average annual precipitation on the coastal area is 762 mm per year (1991–2024). The driest period is in the spring – April (monthly mean/min/max – 31/2.5/73 mm), the wettest period is in October (monthly mean/min/max – 95/8.9/204 mm) (Fig. 1 B). Phenological observations KUBG has been a part of the IPG network since 2005, while plant observation started in 2006. In the first step, 13 cloned plants were obtained from Humbolt University (Germany). Table 1 List of plants investigated in KUBG Phenological Garden No. Identification no. Species of the plant, origin Locality Taxonomic family 1 324 Salix × smithiana Willd. (Germany) = S. smithiana The basket willow grows in northern continental Europe and in North Asia. It is cultivated to produce wickerwork like baskets of the extremely long rods. In KUBG IPG height 8.2 m, width – 6 m. Local Salicaceae Mirb. 2 326 Salix viminalis L. (Germany) = S. viminalis The basket willow grows in northern continental Europe and in North Asia (native to Europe, Western Asia, and the Himalayas). It is cultivated to produce wickerwork like baskets of or the extremely long rods. In KUBG IPG height – 5.2 m, width – 9 m. Local Salicaceae Mirb. 3 331 Sambucus nigra L. (Germany) = S. nigra The common elder is one of the most common shrub species in Central Europe. It has been introduced to parts of most other continents of the world. Both the flowers and the berries have a long tradition of culinary use. In KUBG IPG height 4 m, width – 3 m. Introduced Sambucaceae Batsch ex Borkh. 4 411 Corylus avellana L. = C. avellana The common hazel is widespread in large parts of Europe, Asia Minor, and the Caucasus. It is known for its edible hazel nuts. In KUBD IPG height 6.5 m, width – 4.5 m. Local Betulaceae Gray 5 431 Syringa × chinensis ‛Red Rothomagensis’ = S. chinensis Syringa are native to woodland and scrub from southeastern Europe to eastern Asia, and widely and commonly cultivated in temperate areas elsewhere. Syringa × chinensis was a result of hybridisation of several species in 1770 in Rouen (France). In KUBG IPG height 3 m, width – 2.5 m. Introduced Oleaceae Hoffmanns et Link. In 2018, there were 19 species growing and being observed. However, for the analysis, 5 species were chosen only; others were abandoned because the plants did not bloom due to their youth. The selected indicator species were: Corylus avellana, Salix ×smithiana, Salix viminalis, Sambucus nigra, and Syringa ×chinensis ‛Red Rothomagensis’ (Table 1 ). All these species are common in the Northern Hemisphere and in Lithuania too, except the last two species (S. nigra and S. chinensis), which are not native to Lithuania, and their vegetation period begins in late spring or early summer. The other three species are native, and their vegetation period starts in early spring. The data of all observed plants were taken from the IPG page ( https://ipg.ku.de ) (Table 1 ). An 18-year data series (2007–2024) was used for the analysis. In this study, the data of 5 vegetation phenophases were observed: leaf unfolding (UL), beginning of flowering (BF), full flowering (FF), autumn leaf colouring (CL), and leaf fall (FL), as well as the two plant vegetation periods, one from beginning of leaf unfolding up to leaf colouring (UL-CL), and second from beginning of leaf unfolding up to leaf fall (UL-FL). Data analysis Firstly, statistical correlations were tested between monthly and interseasonal variations of temperature (monthly average) and precipitation (monthly sum) as abiotic factors, and different phenological parameters. The best correlations appeared when seasonal data were applied. Therefore, the latest were used for the redundancy analysis (RDA). Both correlation and RDA were applied to test the relationships between the abiotic factors (seasonal average of air temperature, number of days with average negative daily temperature, and sum of precipitation) used as explanatory variables and trees' phenological parameters (UL, BF, FF, CL, FL, UL-CL, and UL-FL) as response variables, using Brodgar ( 2000 ) and R (3.3.3) packages. Brodgar generated RDA biplots that were interpreted based on the directions and lengths of explanatory factor lines and response variable lines (Zuur et al. 2007 ). For the spring phenology, the climatic data (summer, autumn) of previous years were used; therefore, the RDA was applied separately for spring and autumn plant phenology. Results A comparison of the phenophases of all studied plants revealed several distinctive characteristics. Vegetation (UL) of early species started on average after 93 (± 21), 98 (± 21), and 101 (± 16) days (in days from 1st January) for S. viminalis , S. smithiana , and C. avellana , respectively. For these three species beginning of flowering (BF) and full flowering (FF) phenophases started earlier than leaf unfolding (UL) (Fig. 3 , 4 ). The non-local species S. nigra and S. chinensis started their growing season later, around April 22nd -23rd (day 114 ± 9) on average (Fig. 3 , 4 ). All studied plants, except S. nigra , showed a delay in the leaf unfolding during the last 18 years. The most pronounced delay was observed for the C. avellana and both Salix (Fig. 3 ). However, the beginning of flowering for the S. nigra tended to be earlier compared to the 18 previous years (Fig. 3 ). The other plants didn’t show any significant changes. For all tree species studied, full flowering (FF) began on average 11–15 days after the beginning of flowering (Fig. 4 ). Based on the RDA analysis, the environmental variables contributed to 56% of the variability observed in the spring phenological characteristics, with two axes capturing 74% of the total variation (Fig. 5 ). Only precipitation of January-February (Prec_I-II) had the statistically significant influence on spring phenological plant parameters, it explained 33% ( p = 0.003) of the variation. Precipitation of last autumn explained 11% of the variation and had a moderated positive correlation with full flowering of C. avelana , while negative moderate correlation with the unfolding leaf of S. viminalis . Precipitation during January-February had a strong or moderate positive correlation with the leaf unfolding of all examined trees, as well as a moderate positive effect on the beginning and full flowering of S. chinensis . However, precipitation during the spring (explained 16% of variation) had a negative influence on leaf unfolding of some examined trees: C. avelana, S. smithiana and S. chinensis . The temperature of no one season statistically significantly could explain the variation of spring phenology distribution data. Nevertheless, the temperature of January-February explained 18% of spring phenological parameters variation, while the autumnal temperature explained 15% of the variation. The most influential factor was the autumn temperature of the last season: strong or moderate correlations were with leaf unfolding of all trees, as well as a moderate correlation was with beginning and full flowering of C. avelana . The temperature of January-February had a moderate positive correlation with the leaf unfolding of S. viminalis and with the flowering phenology of S. chinensis . The sum of days with negative average air temperature (Fig. 5 ) of the previous season explained only 7% of the variation and was closely related to the S. nigra flowering phases, but was not statistically significant. The examined plants started to colour leaves mainly in October (Fig. 6 ). Meanwhile, the S. smithiana leaves turned colour the earliest during the entire observation period. They started to colour on the 14th of August 2007. The earliest onset of leaf colouration and fall was observed for S. nigra – on October 1st and 22nd (average), respectively, while other plants were delayed by one to two weeks. Autumn phenophases were stable for all examined plants over the last 18 years, except Salix species. The leaf colouring tends to appear slightly earlier of S. viminalis , while leaf fall begins slightly later of both Salix species over the last 18 years (Fig. 7 ). The vegetation period from the beginning of leaf unfolding till leaf colouration (UL-CL) appeared much more varied compared to the vegetation period from UL until FL (UL-FL). Nevertheless, the longest vegetation period on average had S. viminalis (219 ± 17 days (UL-FL) and 188 ± 32 days (UL-CL), while the S. nigra had the shortest one – 183 ± 13 days (UL-FL) and 160 ± 20 days (UL-CL) (Fig. 6 ). According to the RDA analysis, the environmental factors accounted for 45% of the variance in the autumnal phenological parameters and vegetation duration, while two axes explained 80% of the variation (Fig. 8 ). The different plant species reacted differently to the climatic factors. The most significant influence on autumnal phenological phases was summer air temperature, explaining 43% ( p = 0.001) of the variability. Air temperature of autumn explained 26%, while precipitation during summer and autumn accounted respectively for 23% and 28% of the variability. However, the influences of these parameters were not statistically significant. The summer temperature had a moderate positive effect on the beginning of leaf fall of both Salix species, as well as weak (for S. smithiana, S. nigra , and C. avellana ) or moderate (for S. viminalis ) negative relation for the beginning of leaf colouration, i.e. the higher the temperature in the summer the earlier leaf colouration starts. However, the autumnal higher temperature had a moderate positive effect on the beginning of leaf fall of both Salix species and was weak to S. chinensis , i.e. the leaf fall started later. Higher amounts of precipitation in the summer and autumn had weak positive or no influence on the beginning of leaf colouration; however, these factors stimulated earlier leaf fall. The correlation of precipitation during the summer was weak with S. nigra and both Salix species, while precipitation during autumn had a moderate relation with both Salix species and S. chinensis , and was weak with C. avellana . The summer and autumn temperatures had a negative relation for both vegetation periods (UL-FL and UL-CL). The strongest correlation appeared between summer temperature and UL-CL vegetation period of C. avellana and S. nigra . For other species, the relation was moderate. However, precipitation had the opposite effect. More precipitation during the summer extended the vegetation season for C. avellana (both for UL-FL and UL-CL), S. smithiana (UL-FL), and S. chinensis (UL-FL), while precipitation in autumn prolonged the vegetation season for S. nigra (UL-CL, UL-FL) and C. avellana (UL-CL). Discussion Phenological observations are one of the most important (and sometimes the only) sources of information on the physiological condition of plants and their reactions to external forcing (Sparks and Menzel 2002 ; Menzel et al. 2006 ; Klimiene et al. 2016). Climate is not constant; it changes little by little. From 1961 to 2020, the average annual air temperature along the Baltic Sea coast in Lithuania rose by 1.2°C (Dailidienė et al. 2023 ), and the number of days with average negative daily air temperatures has decreased by 10 days over 30 years (Fig. 2 ). Though the annual amount of precipitation in Klaipeda changed slightly, intense precipitation, when 20 mm or more precipitation falls per day, increased (LHS). Plants tend to adapt to changes by adjusting their phenology. Long-term observations and systematic data collection are essential for monitoring climate change, conducting thorough analysis, and drawing realistic conclusions. Climate analyses indicate a disproportionately strong warming in winter months across Europe, while summer and autumn temperatures have remained comparatively stable. This aligns with the observations of seasonal temperature patterns, where winter shows a slight but consistent warming trend (LHS). Maybe that's why our observations showed that climate change had a greater impact on spring phenological parameters (Fig. 3 ) than on autumn ones (Fig. 7 ). The beginning of phenological spring in Lithuania is related to the start of the vegetation of Corylus avellana , which is indicated differently by different authors, for example, Klimienė with coauthors (2016) indicated C. avellana beginning of blooming in North Lithuania on 4th of April on average, Romanovskaja with coauthors (2012) specified March 27th, while our the latest observation data showed that flowering has begun on March 10th on average. In Eastern European countries, the vegetation of Corylus avellana is more distinct and exhibits a stronger dependence on climatic factors. Comparison of the onset of flowering of local Corylus avellana at KUBG IPG indicates that during 2007–2024, it occurred 14 days earlier than in the reference period 1961–2010. Kalvane et al. (2009) had evaluated that in the Baltic countries, Latvia and Lithuania, during the observation period of 1971–2000, Corylu s vegetation started earlier in locations closer to the Baltic Sea. Data also showed that the plants with the earliest spring leaf unfolding were very sensitive to climate change. Ahas et al. ( 2002 ) studies show that throughout the 1951–1998 spring phenological phases of C. avellana began earlier in Western Europe and the Baltic Sea regions. Thus, this species is probably one of the most climate-affected species in terms of phenology. Data from the PEP725 phenological database indicate a shift toward earlier flowering and delayed leaf unfolding in Corylus avellana across Europe ( http://www.pep725.eu ), aligning with our 18‑year observation that flowers appear earlier while leaves are unfolding later despite warmer temperatures. Other observed trees ( Salix, Syringa ) also showed similar delays in leaf unfolding (Fig. 3 ). Most probably, it was not the increase in temperature that affected it, but the tendency of the air temperature transition from 0°C to a higher temperature to be delayed, i.e., the delay in the beginning of spring (Galvonaitė et al. 2007 ). Meanwhile, it has been observed that higher summer temperatures lead to faster leaf colouration and shorter vegetation UL-CL period, but later leaf fall. Despite the obvious influence of temperature, its impact was statistically significant only on autumnal phenological parameters. Hydrological data suggest divergent seasonal precipitation trends across Europe: winter precipitation has increased in many regions, whereas summer and autumn precipitation have shown a decreasing or stable trend (Kovats et al. 2014 ; IPCC 2021 ). This applies to Klaipeda as well, with rising winter rainfall and declining autumnal and summer precipitation (Galvonaitė et al. 2007 ). Although those changes are small – a few or a few tens of millimeters - higher precipitation in the winter months resulted in later leaf unfolding, and this effect was statistically significant (Fig. 8 ). Precipitation in Western Lithuania is the lowest in the spring months (Fig. 1 B), and a trend was observed – more precipitation during these months promoted faster leaf spreading. Conclusions The most influential factor affecting spring phenological events was the mean autumn temperature of the preceding year. A strong correlation was observed between this variable and leaf unfolding across all studied tree species. Additionally, strong to moderate correlations were found with both the onset and full flowering of Salix viminalis and Syringa × chinensis . The onset of flowering for Corylus avellana and Sambucus nigra occurred earlier when compared to the previous 15-year average. No statistically significant changes were observed in the phenology of other species. Redundancy Analysis (RDA) revealed that environmental variables accounted for 60% of the total variation in spring phenological traits, with the first two axes explaining 74% of this variability. Among these variables, only the precipitation in the previous autumn and during January–February had statistically significant effects, explaining 21% ( p = 0.05) and 31% ( p = 0.009) of the variation, respectively. Higher temperatures during the previous summer, autumn, and winter were associated with earlier flowering of C. avellana . Among all species, S. viminalis exhibited the longest vegetation period (224 days from UL to FL; 196 days from UL to CL), while S. nigra had the shortest (187 days UL–FL; 164 days UL–CL). Precipitation during the previous autumn explained 11% of the variation and showed a moderate positive correlation with full flowering of C. avellana , and a moderate negative correlation with leaf unfolding in S. viminalis . January–February precipitation exhibited strong to moderate positive correlations with leaf unfolding across all species and moderately influenced both the onset and full flowering of S. chinensis . Conversely, spring precipitation (explaining 16% of the variation) negatively impacted leaf unfolding in C. avellana, S. smithiana , and S. chinensis . Summer precipitation showed weak correlations with S. nigra and both Salix species. Autumn precipitation had moderate associations with both Salix species and S. chinensis , and a weak relationship with C. avellana. Lastly, higher summer and autumn temperatures were negatively associated with vegetation period length (UL–FL and UL–CL). The strongest negative correlations were observed between summer temperature and the UL–CL vegetation period for C. avellana and S. nigra . Declarations Competing Interest declaration The authors declare no conflict of interest. Funding This work received no external funding. Author contributions AK contributed to study conception and data acquisition and introduction description and interpretation of phenological data and conclusions. RK contributed to study conception and design, data interpretation and drafting the work. RP contributed interpretation of statistical data, discussion part and figures design. All authors have read and agreed to the published version of the manuscript. Data availability The datasets analysed during the current study are available from the corresponding author on reasonable request. References Ahas R, Aasa A, Menzel A, Fedotova VG, Scheifinger H (2002) Changes in European spring phenology. Int J Climatol 22:1727 – 1738. https://doi.org/10.1002/joc.818 Brodgar (2000) Software Package for Multivariate Analysis and Multivariate Time Series Analysis, Version 2.7.5. Highland Statistics Ltd. Chapin FS, Matson PA, Vitousek PM (2008) Principles of terrestrial ecosystem ecology (2nd ed.). Springer. https://doi.org/10.1007/978-1-4020-5599-5 Chmielewski FM, Heider S, Moryson S, Bruns E (2013) International Phenological Observation Networks: the concept of IPG and GPM. In: Schwartz DM (ed) Phenology: An Integrative Environmental Science. Springer, Dordrecht Heidelberg, New York, 137–153 Chmielewski FM, Rötzer T (2001) Response of tree phenology to climate change across Europe. Agric Forest Meteorol 108:101–112. https://doi.org/10.1016/S0168-1923(01)00233-7 Cleland EE, Chuine I, Menzel A, Mooney HA, Schwartz MD (2007) Shifting plant phenology in response to global change. Trends in Ecol Evol 22:357–365. https://doi.org/10.1016/j.tree.2007.04.003 Dailidienė I, Servaitė I, Dailidė R, Vasiliauskienė E, Rapolienė L, Povilanskas R, Valiukas D (2023) Increasing Trends of Heat Waves and Tropical Nights in Coastal Regions (The Case Study of Lithuania Seaside Cities). Sustainability, 15: 14281. https://doi.org/10.3390/su151914281 Downs RJ, Borthwick HA (1956) Effects of photoperiod on growth of trees. Bot Gaz 117:310–326 Fitter AH, Fitter RSR (2002) Rapid changes in the flowering time in British plants. Science 296:1689–1691. DOI:10.1126/science.1071617 Galvonaitė A, Misiūnienė M, Valiukas D, Buitkuvienė MS (2007) Lietuvos klimatas. VU leidykla, Vilnius. (In Lithuanian) Galvonaitė A, Valiukas D, Kilpys J, Kitrienė Z, Misiūnienė M (2013) Climate Atlas of Lithuania. Vilnius. Lithuanian Hydrometeorological Service under the Ministry of Environment, 1-175 Heide OM (1993) Dormancy release in beech buds (Fagus sylvatica) requires both chilling and long days. Physiol Plant 89:187–191. https://doi.org/10.1111/j.1399-3054.1993.tb01804.x Heide OM (2008) Interaction of photoperiod and temperature in the control of growth and dormancy of Prunus species. Sci Hortic 115: 309–314. https://doi.org/10.1016/j.scienta.2007.10.005 IPCC (2021) Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press Hughes L (2000) Biological consequences of global warming: Is the signal already apparent? TREE, 15(2), 56–61. https://doi.org/10.1016/S0169-5347(99)01764-4 Kalvāne G, Romanovskaja D, Briede A, Bakšienė E (2009) Influence of climate change on phenological phases in Latvia and Lithuania. Clim Res 39:209–219. https://doi.org/10.3354/cr00813 Klimienė A, Vainorienė R, Klimas R (2016) Phenological research of climate changes in the north part of Lithuania by the phenological garden of Šiauliai University. Int J Biometeorol 61:293–301. DOI: 10.1007/s00484-016-1211-2 Kovats RS, Valentini R, Bouwer LM, Georgopoulou E, Jacob D, Martin E, Soussana JF (2014) Europe. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Cambridge University Press, 1267–1326. DOI:10.1017/CBO9781107415386.003 Li C, Junttila O, Ernstsen A, Heino P, Palva ET (2003) Photoperiodic control of growth, cold acclimation and dormancy development in silver birch (Betula pendula) ecotypes. Physiol Plant 117:206–212. https://doi.org/10.1034/j.1399-3054.2003.00002. Lesica P, Kittelson PM (2010) Precipitation and temperature are associated with advanced flowering phenology in a semi-arid grassland. Journal of Arid Environments, 74(8), 1013–1017. https://doi.org/10.1016/j.jaridenv.2010.02.002 Mahmood K, Carew JG, Hadley P, Battey NH (2000) The effect of chilling and post-chilling temperatures on growth and flowering of sweet cherry (Prunus avium L.). J Hortic Sci Biotechnol 75:598–601. https://doi.org/10.1080/14620316.2000.11511292 LHS – Lithuanian Hydrometeorological Service. http://www.meteo.lt/lt/skn Menzel A, Sparks TH, Estrella N, Koch E, Aasa A, Ahas R, Alm-Kübler K, Bissolli P, Braslavská O, Briede A, Chmielewski FM, Crepinsek Z, Curnel Y, Dahl Å, Defila C, Donnelly A, Filella Y, Jatczak K, Måge F, Mestre A, Nordli Ø, Peñuelas J, Pirinen P, Remišová V, Scheifinger H, Striz M, Susnik A, van Vliet AJH, Wielgolaski F-E, Zach S, Zust A (2006) European phenological response to climate change matches the warming pattern. Global Change Biology 12:1969–1976. https://doi.org/10.1111/j.1365-2486.2006.01193.x Myking T, Heide OM (1995) Dormancy release and chilling requirement of buds of latitudinal ecotypes of Betula pendula and B. pubescens. Tree Physiol 15:697–704. http://dx.doi.org/10.1093/treephys/15.11.697 Murray MB, Cannell GR, Smith RI (1989) Date of budburst of fifteen tree species in Britain following climatic warming. J Appl Ecol 26:693–700. http://dx.doi.org/10.2307/2404093 Piao S, Liu Q, Chen A, Janssens IA, Fu Y, Dai J, Liu L, Lian X, Shen M, Zhu X (2019) Plant phenology and global climate change: Current progresses and challenges. Global Change Biology 25 (6): 1922-1940. https://doi.org/10.1111/gcb.14619 Rinne P, Hänninen H, Kaikuranta P, Jalonen JE, Repo T (1997) Freezing exposure releases bud dormancy in Betula pubescens and Betula pendula. Plant Cell Environ 20:1199–1204. https://doi.org/10.1046/j.1365-3040.1997.d01-148.x Romanovskaja D, Bakšienė E, Raukas A, Tripolskaja L (2012) Influence of climate change on the European hazel (Corylus avellana L.) and Norway maple (Acer platanoides L.) phenology in Lithuania during the period 1961–2010. Balt For 18:228–236 Romanovskaja D, Kalvane G, Briede A, Bakšienė E (2009) Klimato šiltėjimo įtaka fenologinių sezonų trukmės pokyčiams Lietuvoje ir Latvijoje. Žemdirbystė-Agriculture 96(4): 218–231(in Lithuanian) Schnelle F, Volkert E (1974) Phenology and Seasonality Modelling. International Phenological Gardens in Europe The Basic Network for International Phenological Observations, 8: 383–387 Sparks TH, Menzel A (2002) Observed changes in seasons: an overview. Int J Climatol 22:1715–1725. https://doi.org/10.1002/joc.821 Spinoni J, Vogt J, Naumann G, Carrao H, Barbosa P (2015) Towards identifying areas at climatological risk of drought in Europe using the monthly standardized precipitation index. Int J Climatol 35(13), 2210–2222. https://doi.org/10.1002/joc.4124 Templ B, Templ M, Filzmoser P, Lehoczky A, Bakšienè E, Fleck S, Gregow H, Hodzic S, Kalvane G, Kubin E, Palm V, Romanovskaja D, Vučetić V, Žust A, Czúcz B (2017) Phenological patterns of flowering across biogeographical regions of Europe. Int J Biometeorol 6(7):1347–1358. https://doi.org/10.1007/s00484-017-1312-6 Zuur AF, Ieno EN, Smith GM (2007) Analysing Ecological Data. Springer: New York, NY, USA Woods HA, Dillon ME, Pincebourde S (2022) The roles of microclimatic diversity and of behavior in mediating the responses of ectotherms to climate change. Journal of Experimental Biology, 225(Suppl_1). https://doi.org/10.1016/j.jtherbio.2014.10.002 http://ipg.hu-berlin.de/ http://www.pep725.eu/ https://www.meteo.lt/klimatas/klimato-kaita/klimato-kaita-lietuvoje/klimato-indeksai/ Cite Share Download PDF Status: Published Journal Publication published 02 Feb, 2026 Read the published version in International Journal of Biometeorology → Version 1 posted Reviewers agreed at journal 19 Aug, 2025 Reviewers invited by journal 18 Aug, 2025 Editor assigned by journal 28 Jul, 2025 First submitted to journal 28 Jul, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7213585","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502061405,"identity":"765fe7df-0691-4147-bbe3-37da5197354f","order_by":0,"name":"Asta Klimiene","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYNCCAxY8DMwgRkUCAwM7QeUgpQckoFrOALUwMxOnBcJmbCNCC3//+YMfGM5IyOi2Mx978HFeWmIDM/8BvFokbiQzSzDckOAxO8yWbjhzWw5QCyGH3WBmkGD4ANLCYybNu62CsBb584eZf0C08H+T/juHCC0GB5LZoA7jYZNmbCDCYYY3ks0sEs6A/WIm2XMszbiNmdkArxa58wcf3/hwzMbe7PzhZxI/apJl+9kbH+C3BgQSkDlshNWPglEwCkbBKCAEALaKPhhq1dFCAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-6226-8358","institution":"Klaipėda University","correspondingAuthor":true,"prefix":"","firstName":"Asta","middleName":"","lastName":"Klimiene","suffix":""},{"id":502061406,"identity":"79a0a82f-6b80-4285-92ac-5577469a2c78","order_by":1,"name":"Ramutis Klimas","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ramutis","middleName":"","lastName":"Klimas","suffix":""},{"id":502061407,"identity":"46825071-b1d1-4451-9f88-2b932e17f783","order_by":2,"name":"Renata Pilkaitytė","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Renata","middleName":"","lastName":"Pilkaitytė","suffix":""}],"badges":[],"createdAt":"2025-07-25 11:01:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7213585/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7213585/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00484-025-03067-3","type":"published","date":"2026-02-02T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89905364,"identity":"81cdf38e-ca70-4583-9c55-e7929027a800","added_by":"auto","created_at":"2025-08-26 10:00:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":95054,"visible":true,"origin":"","legend":"\u003cp\u003eLong term (in the years 1991-2024) data at Klaipėda Meteorological Station: A – monthly average (±SD) of air temperature, B – monthly sum (±SD) of precipitation. Daily data taken from Lithuanian Hydrometeorological Service (LHS)\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7213585/v1/13f78bd37cd27a5ca69fc08c.jpg"},{"id":89903989,"identity":"93acdba2-60e2-441e-ad64-4415c808269b","added_by":"auto","created_at":"2025-08-26 09:44:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62294,"visible":true,"origin":"","legend":"\u003cp\u003eThe number of days with average negative daily air temperatures. Data from LHS\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7213585/v1/0aa7a0d81b1c53274d21ca28.jpg"},{"id":89904383,"identity":"9e9807f9-9ccf-492f-8a7b-deac2c6537b4","added_by":"auto","created_at":"2025-08-26 09:52:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":160209,"visible":true,"origin":"","legend":"\u003cp\u003eBeginning of the observed plant vegetation during the analysed period. Red dots and linear regression line – number of days till beginning of flowering (BF), green triangles and linear regression line – number of days till full flowering (FF), J days – Julian days\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7213585/v1/38242814523ecdaa16f564c1.jpg"},{"id":89903999,"identity":"dedeb32f-81ce-4164-a30b-0abd501379fc","added_by":"auto","created_at":"2025-08-26 09:44:54","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":81679,"visible":true,"origin":"","legend":"\u003cp\u003ePlant phenophases (Box plots min, max, median, and 25% and 75% quartiles). UL – beginning of leaf unfolding, BF – beginning of flowering, FF – full flowering, CL – beginning of leaf colouring, FL – beginning of leaf fall\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7213585/v1/7ae544d62d33dbdb64db8557.jpg"},{"id":89904381,"identity":"ec3e47cb-b543-4444-a2bd-cfc574162ed1","added_by":"auto","created_at":"2025-08-26 09:52:53","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":55002,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence of the monthly sum of precipitation (Prec_) and the monthly average of temperature (Temp_) of different seasons, and the sum of days with negative average air temperature (Neg_days) of the previous season on trees’ spring phenology. (I-II – January and February, (C.ave. – \u003cem\u003eC. avellana\u003c/em\u003e, S.smi. – \u003cem\u003eS. smithiana\u003c/em\u003e, S.vim. – \u003cem\u003eS. viminalis\u003c/em\u003e, S.nig. – \u003cem\u003eS. nigra\u003c/em\u003e, S.chi. – \u003cem\u003eS. chinensis\u003c/em\u003e, UL – beginning of leaf unfolding, BF – beginning of flowering, FF – full flowering)\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7213585/v1/a00d640ef2d6ed37af34196c.jpg"},{"id":89903995,"identity":"0fb20142-f7d6-4d6c-978b-b70f0a3878ed","added_by":"auto","created_at":"2025-08-26 09:44:53","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":46875,"visible":true,"origin":"","legend":"\u003cp\u003eDuration of the plant vegetation from the beginning of the unfolding leaves till leaf colouring (UL-CL) and till leaf fall (UL-FL)\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7213585/v1/94c5a5dffba8726ef2ff2cee.jpg"},{"id":89904387,"identity":"1efab587-db8e-4d53-9e6b-36166443c862","added_by":"auto","created_at":"2025-08-26 09:52:54","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":146897,"visible":true,"origin":"","legend":"\u003cp\u003eAutumn phenophases for examined plants. Red squares and linear regression line – number of days till beginning of leaf colouring (CL), blue rhombus and linear regression line – number of days till leaf fall (FL). J days – Julian days\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7213585/v1/d5def8c7ea721953e5472339.jpg"},{"id":89904016,"identity":"9f8133f9-cda4-428b-9478-a3d41b6d6564","added_by":"auto","created_at":"2025-08-26 09:44:55","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":56931,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence of the season sum of precipitation (Prec_) and the monthly average of temperature (Temp_) of different seasons on to trees’ autumn phenology. (C.ave. – \u003cem\u003eC. avellana\u003c/em\u003e, S.smi. – \u003cem\u003eS. smithiana\u003c/em\u003e, S.vim. – \u003cem\u003eS. viminalis\u003c/em\u003e, S.nig. – \u003cem\u003eS. nigra\u003c/em\u003e, S.chi. – \u003cem\u003eS. chinensis\u003c/em\u003e, CL – beginning of leaf colouring, FL – beginning of leaf fall, UL-CL – vegetation from the beginning of the unfolding leaves till leaf colouring, and UL-FL – vegetation from the beginning of the unfolding leaves till leaf fall)\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7213585/v1/48865c954556487e7ec4f00b.jpg"},{"id":102233990,"identity":"568c7cac-6551-494e-958f-60644510165a","added_by":"auto","created_at":"2026-02-09 16:02:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1242227,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7213585/v1/e9c7c5ac-b758-4fd2-88e4-39f81daf94ed.pdf"}],"financialInterests":"","formattedTitle":"Observation of climatic parameters and plant phenology in the International Phenological Garden of Klaipėda University Botanic Garden, Lithuania","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePhenology is a field of nature and simple research, which studies the annual rhythm of biological development of plants, relationship to climatic variations perceived as a global problem to investigate at a local level. The strong relationship between air temperature and plant development in the northern hemisphere make phenological observations to be a sensitive indicator (Schnelle and Volkert \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Chmielewski and R\u0026ouml;tzer \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Chmielewski et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Piao et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Phenology varies greatly over broad geographic gradients, according to climate zone and vegetation type, and substantial interannual variability growing season start and duration related to the interannual climatic variations. Phenology also varies within populations, and the phenology of individual plants play a key role in the determination of how ecosystems are structured and how they function (Cleland et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). By the number of authors is fixed, that the average global temperature has increased by 0.2\u0026deg;C per decade over the last three decades. It means, that climate warming process influence on the sequence of biological processes (Hughes \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Chapin et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lesica and Kittelson \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; IPCC \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Woods et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor phenological observation in world and in countries or regions are joint to the networks. One of the longest-running phenological networks are countries in Europe, where phenological monitoring by the German Weather Service (DWS) start since 1922, for late goes to International Phenological Garden (IPG) network which was established in 1959 (Schnelle and Volkert \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Chmielewski et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ipg.hu-berlin.de\u003c/span\u003e\u003cspan address=\"http://ipg.hu-berlin.de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In first project PEP725 comprises observations of 139 plants and 33 development phases late national phenological networks and the International Phenological Gardens (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ipg.hu-berlin.de\u003c/span\u003e\u003cspan address=\"http://ipg.hu-berlin.de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; Chmielewski and R\u0026ouml;tzer \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). A major goal of phenology is to understand the effects of climate on plant development. The usual phenological network established within a region may elucidate local patterns to some extent, but the information is not precise enough to evaluate research hypotheses. Variations in plant development may arise from hereditary factors, as well as from location ones (Schnelle and Volkert \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1974\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eVarious plant species have evolved different life strategies based on different trade \u0026ndash; offs between survival and capacity adaptations, and consequently different species phenological responses are also expected (Downs and Borthwick \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1956\u003c/span\u003e; Murray et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Myking and Heide \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Rinne et al \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Heide \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Mahmood et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTherefore, the study of these differences and their implications is particularly important if the impacts of climate change are to be evaluated at the ecosystem level. At the same time, the goal of our work was to find out what climatic factors influence the phenology of different plants.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003ePhenological records were provided by the International Phenological Gardens database IPG, which has been Physical Geography / Lanscape Ecology and Sustainable Ecosystem Development KU Eichst\u0026auml;tt-Ingolstadt (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ipg.ku.de\u003c/span\u003e\u003cspan address=\"https://ipg.ku.de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) since 2023. The International Phenological Gardens (IPG) conduct large-scale, standardised phenological observations. Therefore, all IPGs are situated in similar surroundings. To eliminate heritable variability, cloned species of all trees and shrubs are planted in the IPGs with a unique ID number that is the same in all gardens.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy site\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePhenological records were collected from 2007 to 2024 at Klaipeda University Botanic Garden (KUBG), which is one of the 89 gardens belonging to the International Phenological Gardens (IPG No. 151). KUBG is located in Western Lithuania, in the city of Klaipėda, close to the Baltic Sea coastline (about 3.5 km) (55042\u0026rsquo;40\u0026rdquo;N, 2107\u0026rsquo;50\u0026rdquo;E), altitude \u0026minus;\u0026thinsp;9 m.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe garden is situated at the riverbank on a plain surface with meadows and some trees. The climate zoning is transitional between the mild maritime climate of Western Europe and the continental climate of Eastern Europe, dominated by westerly air masses coming from the Atlantic Ocean (Galvonaite et al. 2013). Therefore, during winters, the air temperature is a few degrees below freezing, while summers are mild to pleasantly warm. The average annual air temperature in Klaipėda is 7.9\u0026deg;C. The coldest period is in January-February, where the average air temperature is -1.0\u0026deg;C (min \u0026minus;\u0026thinsp;26.0\u0026deg;C). The warmest period occurs in July-August (average 18.2\u0026deg;C, max. 36.6\u0026deg;C, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Over the last half century, the average annual air temperature in Klaipeda has increased by 1.2\u0026deg;C (Dailidienė et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Meanwhile, the number of days with average negative daily air temperatures has decreased by 10 days over 30 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The data were taken from Lithuanian Hydrometeorological Service (LHS).\u003c/p\u003e\u003cp\u003eThe long-term average annual precipitation on the coastal area is 762 mm per year (1991\u0026ndash;2024). The driest period is in the spring \u0026ndash; April (monthly mean/min/max \u0026ndash; 31/2.5/73 mm), the wettest period is in October (monthly mean/min/max \u0026ndash; 95/8.9/204 mm) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhenological observations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eKUBG has been a part of the IPG network since 2005, while plant observation started in 2006. In the first step, 13 cloned plants were obtained from Humbolt University (Germany).\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\u003eList of plants investigated in KUBG Phenological Garden\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=\"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=\"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\u003cp\u003eNo.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIdentification no.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpecies of the plant, origin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLocality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTaxonomic family\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSalix\u003c/em\u003e \u0026times;\u003cem\u003esmithiana\u003c/em\u003e Willd. (Germany)\u0026thinsp;=\u0026thinsp;\u003cem\u003eS. smithiana\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe basket willow grows in northern continental Europe and in North Asia. It is cultivated to produce wickerwork like baskets of the extremely long rods. In KUBG IPG height 8.2 m, width \u0026ndash; 6 m.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLocal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSalicaceae\u003c/em\u003e Mirb.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSalix viminalis\u003c/em\u003e L. (Germany)\u0026thinsp;=\u0026thinsp;\u003cem\u003eS. viminalis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe basket willow grows in northern continental Europe and in North Asia (native to Europe, Western Asia, and the Himalayas). It is cultivated to produce wickerwork like baskets of or the extremely long rods. In KUBG IPG height \u0026ndash; 5.2 m, width \u0026ndash; 9 m.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLocal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSalicaceae\u003c/em\u003e Mirb.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSambucus nigra\u003c/em\u003e L. (Germany)\u0026thinsp;=\u0026thinsp;\u003cem\u003eS. nigra\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe common elder is one of the most common shrub species in Central Europe. It has been introduced to parts of most other continents of the world. Both the flowers and the berries have a long tradition of culinary use. In KUBG IPG height 4 m, width \u0026ndash; 3 m.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntroduced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSambucaceae\u003c/em\u003e Batsch ex Borkh.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eCorylus avellana\u003c/em\u003e L. = \u003cem\u003eC. avellana\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe common hazel is widespread in large parts of Europe, Asia Minor, and the Caucasus. It is known for its edible hazel nuts. In KUBD IPG height 6.5 m, width \u0026ndash; 4.5 m.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLocal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eBetulaceae\u003c/em\u003e Gray\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSyringa\u003c/em\u003e \u0026times;\u003cem\u003echinensis\u003c/em\u003e ‛Red Rothomagensis\u0026rsquo; = \u003cem\u003eS. chinensis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eSyringa are native to woodland and scrub from southeastern Europe to eastern Asia, and widely and commonly cultivated in temperate areas elsewhere. \u003cem\u003eSyringa\u003c/em\u003e \u0026times;\u003cem\u003echinensis\u003c/em\u003e was a result of hybridisation of several species in 1770 in Rouen (France). In KUBG IPG height 3 m, width \u0026ndash; 2.5 m.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntroduced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eOleaceae\u003c/em\u003e Hoffmanns et Link.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn 2018, there were 19 species growing and being observed. However, for the analysis, 5 species were chosen only; others were abandoned because the plants did not bloom due to their youth. The selected indicator species were: Corylus avellana, Salix \u0026times;smithiana, Salix viminalis, Sambucus nigra, and Syringa \u0026times;chinensis ‛Red Rothomagensis\u0026rsquo; (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All these species are common in the Northern Hemisphere and in Lithuania too, except the last two species (S. nigra and S. chinensis), which are not native to Lithuania, and their vegetation period begins in late spring or early summer. The other three species are native, and their vegetation period starts in early spring. The data of all observed plants were taken from the IPG page (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ipg.ku.de\u003c/span\u003e\u003cspan address=\"https://ipg.ku.de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). An 18-year data series (2007\u0026ndash;2024) was used for the analysis. In this study, the data of 5 vegetation phenophases were observed: leaf unfolding (UL), beginning of flowering (BF), full flowering (FF), autumn leaf colouring (CL), and leaf fall (FL), as well as the two plant vegetation periods, one from beginning of leaf unfolding up to leaf colouring (UL-CL), and second from beginning of leaf unfolding up to leaf fall (UL-FL).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eFirstly, statistical correlations were tested between monthly and interseasonal variations of temperature (monthly average) and precipitation (monthly sum) as abiotic factors, and different phenological parameters. The best correlations appeared when seasonal data were applied. Therefore, the latest were used for the redundancy analysis (RDA). Both correlation and RDA were applied to test the relationships between the abiotic factors (seasonal average of air temperature, number of days with average negative daily temperature, and sum of precipitation) used as explanatory variables and trees' phenological parameters (UL, BF, FF, CL, FL, UL-CL, and UL-FL) as response variables, using Brodgar (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and R (3.3.3) packages. Brodgar generated RDA biplots that were interpreted based on the directions and lengths of explanatory factor lines and response variable lines (Zuur et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). For the spring phenology, the climatic data (summer, autumn) of previous years were used; therefore, the RDA was applied separately for spring and autumn plant phenology.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA comparison of the phenophases of all studied plants revealed several distinctive characteristics. Vegetation (UL) of early species started on average after 93 (\u0026plusmn;\u0026thinsp;21), 98 (\u0026plusmn;\u0026thinsp;21), and 101 (\u0026plusmn;\u0026thinsp;16) days (in days from 1st January) for \u003cem\u003eS. viminalis\u003c/em\u003e, \u003cem\u003eS. smithiana\u003c/em\u003e, and \u003cem\u003eC. avellana\u003c/em\u003e, respectively. For these three species beginning of flowering (BF) and full flowering (FF) phenophases started earlier than leaf unfolding (UL) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The non-local species \u003cem\u003eS. nigra\u003c/em\u003e and \u003cem\u003eS. chinensis\u003c/em\u003e started their growing season later, around April 22nd -23rd (day 114\u0026thinsp;\u0026plusmn;\u0026thinsp;9) on average (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAll studied plants, except \u003cem\u003eS. nigra\u003c/em\u003e, showed a delay in the leaf unfolding during the last 18 years. The most pronounced delay was observed for the \u003cem\u003eC. avellana\u003c/em\u003e and both \u003cem\u003eSalix\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, the beginning of flowering for the \u003cem\u003eS. nigra\u003c/em\u003e tended to be earlier compared to the 18 previous years (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The other plants didn\u0026rsquo;t show any significant changes. For all tree species studied, full flowering (FF) began on average 11\u0026ndash;15 days after the beginning of flowering (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on the RDA analysis, the environmental variables contributed to 56% of the variability observed in the spring phenological characteristics, with two axes capturing 74% of the total variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Only precipitation of January-February (Prec_I-II) had the statistically significant influence on spring phenological plant parameters, it explained 33% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) of the variation.\u003c/p\u003e\u003cp\u003ePrecipitation of last autumn explained 11% of the variation and had a moderated positive correlation with full flowering of \u003cem\u003eC. avelana\u003c/em\u003e, while negative moderate correlation with the unfolding leaf of \u003cem\u003eS. viminalis\u003c/em\u003e. Precipitation during January-February had a strong or moderate positive correlation with the leaf unfolding of all examined trees, as well as a moderate positive effect on the beginning and full flowering of \u003cem\u003eS. chinensis\u003c/em\u003e. However, precipitation during the spring (explained 16% of variation) had a negative influence on leaf unfolding of some examined trees: \u003cem\u003eC. avelana, S. smithiana\u003c/em\u003e and \u003cem\u003eS. chinensis\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eThe temperature of no one season statistically significantly could explain the variation of spring phenology distribution data. Nevertheless, the temperature of January-February explained 18% of spring phenological parameters variation, while the autumnal temperature explained 15% of the variation. The most influential factor was the autumn temperature of the last season: strong or moderate correlations were with leaf unfolding of all trees, as well as a moderate correlation was with beginning and full flowering of \u003cem\u003eC. avelana\u003c/em\u003e. The temperature of January-February had a moderate positive correlation with the leaf unfolding of \u003cem\u003eS. viminalis\u003c/em\u003e and with the flowering phenology of \u003cem\u003eS. chinensis\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eThe sum of days with negative average air temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) of the previous season explained only 7% of the variation and was closely related to the \u003cem\u003eS. nigra\u003c/em\u003e flowering phases, but was not statistically significant.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe examined plants started to colour leaves mainly in October (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Meanwhile, the \u003cem\u003eS. smithiana\u003c/em\u003e leaves turned colour the earliest during the entire observation period. They started to colour on the 14th of August 2007. The earliest onset of leaf colouration and fall was observed for \u003cem\u003eS. nigra\u003c/em\u003e \u0026ndash; on October 1st and 22nd (average), respectively, while other plants were delayed by one to two weeks.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAutumn phenophases were stable for all examined plants over the last 18 years, except \u003cem\u003eSalix\u003c/em\u003e species. The leaf colouring tends to appear slightly earlier of \u003cem\u003eS. viminalis\u003c/em\u003e, while leaf fall begins slightly later of both \u003cem\u003eSalix\u003c/em\u003e species over the last 18 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe vegetation period from the beginning of leaf unfolding till leaf colouration (UL-CL) appeared much more varied compared to the vegetation period from UL until FL (UL-FL). Nevertheless, the longest vegetation period on average had \u003cem\u003eS. viminalis\u003c/em\u003e (219\u0026thinsp;\u0026plusmn;\u0026thinsp;17 days (UL-FL) and 188\u0026thinsp;\u0026plusmn;\u0026thinsp;32 days (UL-CL), while the \u003cem\u003eS. nigra\u003c/em\u003e had the shortest one \u0026ndash; 183\u0026thinsp;\u0026plusmn;\u0026thinsp;13 days (UL-FL) and 160\u0026thinsp;\u0026plusmn;\u0026thinsp;20 days (UL-CL) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAccording to the RDA analysis, the environmental factors accounted for 45% of the variance in the autumnal phenological parameters and vegetation duration, while two axes explained 80% of the variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The different plant species reacted differently to the climatic factors. The most significant influence on autumnal phenological phases was summer air temperature, explaining 43% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) of the variability. Air temperature of autumn explained 26%, while precipitation during summer and autumn accounted respectively for 23% and 28% of the variability. However, the influences of these parameters were not statistically significant.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe summer temperature had a moderate positive effect on the beginning of leaf fall of both Salix species, as well as weak (for \u003cem\u003eS. smithiana, S. nigra\u003c/em\u003e, and \u003cem\u003eC. avellana\u003c/em\u003e) or moderate (for \u003cem\u003eS. viminalis\u003c/em\u003e) negative relation for the beginning of leaf colouration, i.e. the higher the temperature in the summer the earlier leaf colouration starts. However, the autumnal higher temperature had a moderate positive effect on the beginning of leaf fall of both \u003cem\u003eSalix\u003c/em\u003e species and was weak to \u003cem\u003eS. chinensis\u003c/em\u003e, i.e. the leaf fall started later.\u003c/p\u003e\u003cp\u003eHigher amounts of precipitation in the summer and autumn had weak positive or no influence on the beginning of leaf colouration; however, these factors stimulated earlier leaf fall. The correlation of precipitation during the summer was weak with \u003cem\u003eS. nigra\u003c/em\u003e and both Salix species, while precipitation during autumn had a moderate relation with both \u003cem\u003eSalix\u003c/em\u003e species and \u003cem\u003eS. chinensis\u003c/em\u003e, and was weak with \u003cem\u003eC. avellana\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eThe summer and autumn temperatures had a negative relation for both vegetation periods (UL-FL and UL-CL). The strongest correlation appeared between summer temperature and UL-CL vegetation period of \u003cem\u003eC. avellana\u003c/em\u003e and \u003cem\u003eS. nigra\u003c/em\u003e. For other species, the relation was moderate.\u003c/p\u003e\u003cp\u003eHowever, precipitation had the opposite effect. More precipitation during the summer extended the vegetation season for \u003cem\u003eC. avellana\u003c/em\u003e (both for UL-FL and UL-CL), \u003cem\u003eS. smithiana\u003c/em\u003e (UL-FL), and \u003cem\u003eS. chinensis\u003c/em\u003e (UL-FL), while precipitation in autumn prolonged the vegetation season for \u003cem\u003eS. nigra\u003c/em\u003e (UL-CL, UL-FL) and \u003cem\u003eC. avellana\u003c/em\u003e (UL-CL).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePhenological observations are one of the most important (and sometimes the only) sources of information on the physiological condition of plants and their reactions to external forcing (Sparks and Menzel \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Menzel et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Klimiene et al. 2016). Climate is not constant; it changes little by little. From 1961 to 2020, the average annual air temperature along the Baltic Sea coast in Lithuania rose by 1.2\u0026deg;C (Dailidienė et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and the number of days with average negative daily air temperatures has decreased by 10 days over 30 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Though the annual amount of precipitation in Klaipeda changed slightly, intense precipitation, when 20 mm or more precipitation falls per day, increased (LHS). Plants tend to adapt to changes by adjusting their phenology. Long-term observations and systematic data collection are essential for monitoring climate change, conducting thorough analysis, and drawing realistic conclusions.\u003c/p\u003e\u003cp\u003eClimate analyses indicate a disproportionately strong warming in winter months across Europe, while summer and autumn temperatures have remained comparatively stable. This aligns with the observations of seasonal temperature patterns, where winter shows a slight but consistent warming trend (LHS). Maybe that's why our observations showed that climate change had a greater impact on spring phenological parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) than on autumn ones (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The beginning of phenological spring in Lithuania is related to the start of the vegetation of \u003cem\u003eCorylus avellana\u003c/em\u003e, which is indicated differently by different authors, for example, Klimienė with coauthors (2016) indicated \u003cem\u003eC. avellana\u003c/em\u003e beginning of blooming in North Lithuania on 4th of April on average, Romanovskaja with coauthors (2012) specified March 27th, while our the latest observation data showed that flowering has begun on March 10th on average.\u003c/p\u003e\u003cp\u003eIn Eastern European countries, the vegetation of \u003cem\u003eCorylus avellana\u003c/em\u003e is more distinct and exhibits a stronger dependence on climatic factors. Comparison of the onset of flowering of local \u003cem\u003eCorylus avellana\u003c/em\u003e at KUBG IPG indicates that during 2007\u0026ndash;2024, it occurred 14 days earlier than in the reference period 1961\u0026ndash;2010. Kalvane et al. (2009) had evaluated that in the Baltic countries, Latvia and Lithuania, during the observation period of 1971\u0026ndash;2000, \u003cem\u003eCorylu\u003c/em\u003es vegetation started earlier in locations closer to the Baltic Sea. Data also showed that the plants with the earliest spring leaf unfolding were very sensitive to climate change. Ahas et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) studies show that throughout the 1951\u0026ndash;1998 spring phenological phases of \u003cem\u003eC. avellana\u003c/em\u003e began earlier in Western Europe and the Baltic Sea regions. Thus, this species is probably one of the most climate-affected species in terms of phenology. Data from the PEP725 phenological database indicate a shift toward earlier flowering and delayed leaf unfolding in \u003cem\u003eCorylus avellana\u003c/em\u003e across Europe (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.pep725.eu\u003c/span\u003e\u003cspan address=\"http://www.pep725.eu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), aligning with our 18‑year observation that flowers appear earlier while leaves are unfolding later despite warmer temperatures. Other observed trees (\u003cem\u003eSalix, Syringa\u003c/em\u003e) also showed similar delays in leaf unfolding (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Most probably, it was not the increase in temperature that affected it, but the tendency of the air temperature transition from 0\u0026deg;C to a higher temperature to be delayed, i.e., the delay in the beginning of spring (Galvonaitė et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Meanwhile, it has been observed that higher summer temperatures lead to faster leaf colouration and shorter vegetation UL-CL period, but later leaf fall. Despite the obvious influence of temperature, its impact was statistically significant only on autumnal phenological parameters.\u003c/p\u003e\u003cp\u003eHydrological data suggest divergent seasonal precipitation trends across Europe: winter precipitation has increased in many regions, whereas summer and autumn precipitation have shown a decreasing or stable trend (Kovats et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; IPCC \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This applies to Klaipeda as well, with rising winter rainfall and declining autumnal and summer precipitation (Galvonaitė et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Although those changes are small \u0026ndash; a few or a few tens of millimeters - higher precipitation in the winter months resulted in later leaf unfolding, and this effect was statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Precipitation in Western Lithuania is the lowest in the spring months (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), and a trend was observed \u0026ndash; more precipitation during these months promoted faster leaf spreading.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe most influential factor affecting spring phenological events was the mean autumn temperature of the preceding year. A strong correlation was observed between this variable and leaf unfolding across all studied tree species. Additionally, strong to moderate correlations were found with both the onset and full flowering of \u003cem\u003eSalix viminalis\u003c/em\u003e and \u003cem\u003eSyringa \u0026times; chinensis\u003c/em\u003e. The onset of flowering for \u003cem\u003eCorylus avellana\u003c/em\u003e and \u003cem\u003eSambucus nigra\u003c/em\u003e occurred earlier when compared to the previous 15-year average. No statistically significant changes were observed in the phenology of other species.\u003c/p\u003e\u003cp\u003eRedundancy Analysis (RDA) revealed that environmental variables accounted for 60% of the total variation in spring phenological traits, with the first two axes explaining 74% of this variability. Among these variables, only the precipitation in the previous autumn and during January\u0026ndash;February had statistically significant effects, explaining 21% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05) and 31% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) of the variation, respectively.\u003c/p\u003e\u003cp\u003eHigher temperatures during the previous summer, autumn, and winter were associated with earlier flowering of \u003cem\u003eC. avellana\u003c/em\u003e. Among all species, \u003cem\u003eS. viminalis\u003c/em\u003e exhibited the longest vegetation period (224 days from UL to FL; 196 days from UL to CL), while \u003cem\u003eS. nigra\u003c/em\u003e had the shortest (187 days UL\u0026ndash;FL; 164 days UL\u0026ndash;CL).\u003c/p\u003e\u003cp\u003ePrecipitation during the previous autumn explained 11% of the variation and showed a moderate positive correlation with full flowering of \u003cem\u003eC. avellana\u003c/em\u003e, and a moderate negative correlation with leaf unfolding in \u003cem\u003eS. viminalis\u003c/em\u003e. January\u0026ndash;February precipitation exhibited strong to moderate positive correlations with leaf unfolding across all species and moderately influenced both the onset and full flowering of \u003cem\u003eS. chinensis\u003c/em\u003e. Conversely, spring precipitation (explaining 16% of the variation) negatively impacted leaf unfolding in \u003cem\u003eC. avellana, S. smithiana\u003c/em\u003e, and S. \u003cem\u003echinensis\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eSummer precipitation showed weak correlations with \u003cem\u003eS. nigra\u003c/em\u003e and both \u003cem\u003eSalix\u003c/em\u003e species. Autumn precipitation had moderate associations with both \u003cem\u003eSalix\u003c/em\u003e species and \u003cem\u003eS. chinensis\u003c/em\u003e, and a weak relationship with \u003cem\u003eC. avellana.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eLastly, higher summer and autumn temperatures were negatively associated with vegetation period length (UL\u0026ndash;FL and UL\u0026ndash;CL). The strongest negative correlations were observed between summer temperature and the UL\u0026ndash;CL vegetation period for \u003cem\u003eC. avellana\u003c/em\u003e and \u003cem\u003eS. nigra\u003c/em\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interest declaration\u003c/h2\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e\u003cp\u003eAK contributed to study conception and data acquisition and introduction description and interpretation of phenological data and conclusions. RK contributed to study conception and design, data interpretation and drafting the work. RP contributed interpretation of statistical data, discussion part and figures design. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe datasets analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhas R, Aasa A, Menzel A, Fedotova VG, Scheifinger H (2002) Changes in European spring phenology. Int J Climatol 22:1727 \u0026ndash; 1738. https://doi.org/10.1002/joc.818\u003c/li\u003e\n\u003cli\u003eBrodgar (2000) Software Package for Multivariate Analysis and Multivariate Time Series Analysis, Version 2.7.5. Highland Statistics Ltd.\u003c/li\u003e\n\u003cli\u003eChapin FS, Matson PA, Vitousek PM (2008) Principles of terrestrial ecosystem ecology (2nd ed.). Springer. https://doi.org/10.1007/978-1-4020-5599-5\u003c/li\u003e\n\u003cli\u003eChmielewski FM, Heider S, Moryson S, Bruns E (2013) International Phenological Observation Networks: the concept of IPG and GPM. In: Schwartz DM (ed) Phenology: An Integrative Environmental Science. Springer, Dordrecht Heidelberg, New York, 137\u0026ndash;153\u003c/li\u003e\n\u003cli\u003eChmielewski FM, R\u0026ouml;tzer T (2001) Response of tree phenology to climate change across Europe. Agric Forest Meteorol 108:101\u0026ndash;112. https://doi.org/10.1016/S0168-1923(01)00233-7\u003c/li\u003e\n\u003cli\u003eCleland EE, Chuine I, Menzel A, Mooney HA, Schwartz MD (2007) Shifting plant phenology in response to global change. Trends in Ecol Evol 22:357\u0026ndash;365. https://doi.org/10.1016/j.tree.2007.04.003\u003c/li\u003e\n\u003cli\u003eDailidienė I, Servaitė I, Dailidė R, Vasiliauskienė E, Rapolienė L, Povilanskas R, Valiukas D (2023) Increasing Trends of Heat Waves and Tropical Nights in Coastal Regions (The Case Study of Lithuania Seaside Cities). Sustainability, 15: 14281. https://doi.org/10.3390/su151914281\u003c/li\u003e\n\u003cli\u003eDowns RJ, Borthwick HA (1956) Effects of photoperiod on growth of trees. Bot Gaz 117:310\u0026ndash;326\u003c/li\u003e\n\u003cli\u003eFitter AH, Fitter RSR (2002) Rapid changes in the flowering time in British plants. Science 296:1689\u0026ndash;1691. DOI:10.1126/science.1071617\u003c/li\u003e\n\u003cli\u003eGalvonaitė A, Misiūnienė M, Valiukas D, Buitkuvienė MS (2007) Lietuvos klimatas. VU leidykla, Vilnius. (In Lithuanian)\u003c/li\u003e\n\u003cli\u003eGalvonaitė A, Valiukas D, Kilpys J, Kitrienė Z, Misiūnienė M (2013) Climate Atlas of Lithuania. Vilnius. Lithuanian Hydrometeorological Service under the Ministry of Environment, 1-175 \u003c/li\u003e\n\u003cli\u003eHeide OM (1993) Dormancy release in beech buds (Fagus sylvatica) requires both chilling and long days. Physiol Plant 89:187\u0026ndash;191. https://doi.org/10.1111/j.1399-3054.1993.tb01804.x \u003c/li\u003e\n\u003cli\u003eHeide OM (2008) Interaction of photoperiod and temperature in the control of growth and dormancy of Prunus species. Sci Hortic 115: 309\u0026ndash;314. https://doi.org/10.1016/j.scienta.2007.10.005\u003c/li\u003e\n\u003cli\u003eIPCC (2021) Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press \u003c/li\u003e\n\u003cli\u003eHughes L (2000) Biological consequences of global warming: Is the signal already apparent? TREE,\u003cem\u003e \u003c/em\u003e15(2), 56\u0026ndash;61. https://doi.org/10.1016/S0169-5347(99)01764-4\u003c/li\u003e\n\u003cli\u003eKalvāne G, Romanovskaja D, Briede A, Bak\u0026scaron;ienė E (2009) Influence of climate change on phenological phases in Latvia and Lithuania. Clim Res 39:209\u0026ndash;219. https://doi.org/10.3354/cr00813\u003c/li\u003e\n\u003cli\u003eKlimienė A, Vainorienė R, Klimas R (2016) Phenological research of climate changes in the north part of Lithuania by the phenological garden of \u0026Scaron;iauliai University. Int J Biometeorol 61:293\u0026ndash;301. DOI: 10.1007/s00484-016-1211-2\u003c/li\u003e\n\u003cli\u003eKovats RS, Valentini R, Bouwer LM, Georgopoulou E, Jacob D, Martin E, Soussana JF (2014) Europe. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Cambridge University Press, 1267\u0026ndash;1326. DOI:10.1017/CBO9781107415386.003 \u003c/li\u003e\n\u003cli\u003eLi C, Junttila O, Ernstsen A, Heino P, Palva ET (2003) Photoperiodic control of growth, cold acclimation and dormancy development in silver birch (Betula pendula) ecotypes. Physiol Plant 117:206\u0026ndash;212. https://doi.org/10.1034/j.1399-3054.2003.00002.\u003c/li\u003e\n\u003cli\u003eLesica P, Kittelson PM (2010) Precipitation and temperature are associated with advanced flowering phenology in a semi-arid grassland. Journal of Arid Environments, 74(8), 1013\u0026ndash;1017. https://doi.org/10.1016/j.jaridenv.2010.02.002\u003c/li\u003e\n\u003cli\u003eMahmood K, Carew JG, Hadley P, Battey NH (2000) The effect of chilling and post-chilling temperatures on growth and flowering of sweet cherry (Prunus avium L.). J Hortic Sci Biotechnol 75:598\u0026ndash;601. https://doi.org/10.1080/14620316.2000.11511292\u003c/li\u003e\n\u003cli\u003eLHS \u0026ndash; Lithuanian Hydrometeorological Service. http://www.meteo.lt/lt/skn\u003c/li\u003e\n\u003cli\u003eMenzel A, Sparks TH, Estrella N, Koch E, Aasa A, Ahas R, Alm-K\u0026uuml;bler K, Bissolli P, Braslavsk\u0026aacute; O, Briede A, Chmielewski FM, Crepinsek Z, Curnel Y, Dahl \u0026Aring;, Defila C, Donnelly A, Filella Y, Jatczak K, M\u0026aring;ge F, Mestre A, Nordli \u0026Oslash;, Pe\u0026ntilde;uelas J, Pirinen P, Remi\u0026scaron;ov\u0026aacute; V, Scheifinger H, Striz M, Susnik A, van Vliet AJH, Wielgolaski F-E, Zach S, Zust A (2006) European phenological response to climate change matches the warming pattern. Global Change Biology 12:1969\u0026ndash;1976. https://doi.org/10.1111/j.1365-2486.2006.01193.x\u003c/li\u003e\n\u003cli\u003eMyking T, Heide OM (1995) Dormancy release and chilling requirement of buds of latitudinal ecotypes of Betula pendula and B. pubescens. Tree Physiol 15:697\u0026ndash;704. http://dx.doi.org/10.1093/treephys/15.11.697\u003c/li\u003e\n\u003cli\u003eMurray MB, Cannell GR, Smith RI (1989) Date of budburst of fifteen tree species in Britain following climatic warming. J Appl Ecol 26:693\u0026ndash;700. http://dx.doi.org/10.2307/2404093\u003c/li\u003e\n\u003cli\u003ePiao S, Liu Q, Chen A, Janssens IA, Fu Y, Dai J, Liu L, Lian X, Shen M, Zhu X (2019) Plant phenology and global climate change: Current progresses and challenges. Global Change Biology 25 (6): 1922-1940. https://doi.org/10.1111/gcb.14619\u003c/li\u003e\n\u003cli\u003eRinne P, H\u0026auml;nninen H, Kaikuranta P, Jalonen JE, Repo T (1997) Freezing exposure releases bud dormancy in Betula pubescens and Betula pendula. Plant Cell Environ 20:1199\u0026ndash;1204. https://doi.org/10.1046/j.1365-3040.1997.d01-148.x\u003c/li\u003e\n\u003cli\u003eRomanovskaja D, Bak\u0026scaron;ienė E, Raukas A, Tripolskaja L (2012) Influence of climate change on the European hazel (Corylus avellana L.) and Norway maple (Acer platanoides L.) phenology in Lithuania during the period 1961\u0026ndash;2010. Balt For 18:228\u0026ndash;236\u003c/li\u003e\n\u003cli\u003eRomanovskaja D, Kalvane G, Briede A, Bak\u0026scaron;ienė E (2009) Klimato \u0026scaron;iltėjimo įtaka fenologinių sezonų trukmės pokyčiams Lietuvoje ir Latvijoje. Žemdirbystė-Agriculture 96(4): 218\u0026ndash;231(in Lithuanian)\u003c/li\u003e\n\u003cli\u003eSchnelle F, Volkert E (1974) Phenology and Seasonality Modelling. International Phenological Gardens in Europe The Basic Network for International Phenological Observations, 8: 383\u0026ndash;387\u003c/li\u003e\n\u003cli\u003eSparks TH, Menzel A (2002) Observed changes in seasons: an overview. Int J Climatol 22:1715\u0026ndash;1725. https://doi.org/10.1002/joc.821\u003c/li\u003e\n\u003cli\u003eSpinoni J, Vogt J, Naumann G, Carrao H, Barbosa P (2015) Towards identifying areas at climatological risk of drought in Europe using the monthly standardized precipitation index. Int J Climatol 35(13), 2210\u0026ndash;2222. https://doi.org/10.1002/joc.4124\u003c/li\u003e\n\u003cli\u003eTempl B, Templ M, Filzmoser P, Lehoczky A, Bak\u0026scaron;ien\u0026egrave; E, Fleck S, Gregow H, Hodzic S, Kalvane G, Kubin E, Palm V, Romanovskaja D, Vučetić V, Žust A, Cz\u0026uacute;cz B (2017) Phenological patterns of flowering across biogeographical regions of Europe. Int J Biometeorol 6(7):1347\u0026ndash;1358. https://doi.org/10.1007/s00484-017-1312-6\u003c/li\u003e\n\u003cli\u003eZuur AF, Ieno EN, Smith GM (2007) Analysing Ecological Data. Springer: New York, NY, USA\u003c/li\u003e\n\u003cli\u003eWoods HA, Dillon ME, Pincebourde S (2022) The roles of microclimatic diversity and of behavior in mediating the responses of ectotherms to climate change. Journal of Experimental Biology, 225(Suppl_1). https://doi.org/10.1016/j.jtherbio.2014.10.002\u003c/li\u003e\n\u003cli\u003ehttp://ipg.hu-berlin.de/\u003c/li\u003e\n\u003cli\u003ehttp://www.pep725.eu/\u003c/li\u003e\n\u003cli\u003ehttps://www.meteo.lt/klimatas/klimato-kaita/klimato-kaita-lietuvoje/klimato-indeksai/ \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":"Precipitation, Air temperature, Plant phenological data, International Phenological garden","lastPublishedDoi":"10.21203/rs.3.rs-7213585/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7213585/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e The phenology of plants varies greatly over broad geographic gradients, according to climate zone and vegetation type. Phenological records were collected from 2007 to 2024 at Klaipėda University Botanic Garden (KUBG), which is one of the 89 gardens belonging to the International Phenological Gardens (IPG No. 151). The garden is located in Western Lithuania, close to the Baltic Sea coastline (about 3.5 km) (55\u0026deg;42\u0026prime;40\u0026Prime;N 21\u0026deg;7\u0026prime;50\u0026Prime;E). For the analysis were chosen 5 species only. The average annual air temperature in Klaipėda is 7.9\u0026deg;C. The coldest period is in January-February, where the average air temperature is -1.0\u0026deg;C. The warmest period occurs in July-August (aver. 18.2\u0026deg;C). The most influence had autumn temperature of the last season: the strong correlation were with leaf unfolding of all trees, as well strong or moderate correlation were with beginning and full flowering of \u003cem\u003eS. viminalis\u003c/em\u003e and \u003cem\u003eS. \u0026times;chinensis.\u003c/em\u003e Only precipitation of last autumn and precipitation of January-February had the statistically significant influence on spring phenological plant parameters. The longest vegetation period had \u003cem\u003eS. viminalis\u003c/em\u003e (224 days) while the \u003cem\u003eS. nigra\u003c/em\u003e had the shortest one \u0026minus;\u0026thinsp;187 days. Precipitation during the January-February had strong or moderate positive correlation with the leaf unfolding of all examined trees, as well as had moderate positive effect for beginning and full flowering of \u003cem\u003eS. chinensis\u003c/em\u003e. The summer and autumn temperature had the negative relation for the both vegetation periods. The strongest correlation appeared between summer temperature and vegetation period of \u003cem\u003eC. avellana\u003c/em\u003e and \u003cem\u003eS. nigra\u003c/em\u003e.\u003c/p\u003e","manuscriptTitle":"Observation of climatic parameters and plant phenology in the International Phenological Garden of Klaipėda University Botanic Garden, Lithuania","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-26 09:44:49","doi":"10.21203/rs.3.rs-7213585/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-08-19T11:45:03+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-18T15:15:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-29T00:49:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Biometeorology","date":"2025-07-28T07:49:42+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":"1d6b0e50-8cc6-460f-9598-04be95306694","owner":[],"postedDate":"August 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:00:31+00:00","versionOfRecord":{"articleIdentity":"rs-7213585","link":"https://doi.org/10.1007/s00484-025-03067-3","journal":{"identity":"international-journal-of-biometeorology","isVorOnly":false,"title":"International Journal of Biometeorology"},"publishedOn":"2026-02-02 15:57:27","publishedOnDateReadable":"February 2nd, 2026"},"versionCreatedAt":"2025-08-26 09:44:49","video":"","vorDoi":"10.1007/s00484-025-03067-3","vorDoiUrl":"https://doi.org/10.1007/s00484-025-03067-3","workflowStages":[]},"version":"v1","identity":"rs-7213585","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7213585","identity":"rs-7213585","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}