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Abstract Ground‐based phenological observation data are the most accurate phenological monitoring data currently available. Making effective use of available information on social media to retrieve phenological data is of considerable value in alleviating the lack of phenological data in regions with missing observation sites. In this study, a logistic curve fitting method was developed to extract phenological data on specific species from social media data. After verifying the relationship between the site observation data and the temperature, timing data for two typical phenological phenomena in China, namely cherry blossom flowering in spring and ginkgo leaf coloration in autumn were reconstructed and published. The data availability is from 2010 to 2019 in 176 cities and 2009 to 2018 in 155 cities. This dataset is an effective supplement for existing phenological data, and this method also provides a reference for obtaining phenological data for specific species.

Understanding how spring phenology of temperate trees responds to global change is essential for assessing the vegetation dynamics in the future. Besides chilling and forcing temperatures, the influences of other environmental and biotic factors on leaf unfolding date (LUD) are still unclear. Based on long-term records (1960–2015) of LUD for 6 typical temperate tree species at 4242 stations in Europe, we first used a classic process-based model (the Unified model) to describe the relationship between chilling and forcing requirements of LUD. Furthermore, we used 3 explainable machine learning (ML) algorithms (RF, EBM, and GAMI-Net) to quantify the influences of 45 biotic and environmental factors on the LUD. The root-mean-square error (RMSE) of the ML-based models averaged from all species (7.03 to 7.33 days) was lower than the Unified model (8.73 days). The ML algorithms detected 2 biotic (elevation and previous leaf senescence date) and 3 temperature-related variables (chilling accumulation, freezing days in March and annual temperature range) with high importance for most species. The trees adapted to a higher elevation or with later leaf senescence date in the previous year exhibited later LUD. The increase in chilling accumulation, freezing days in March and the annual temperature range could reduce the forcing requirement of LUD. Water-related and light-related variables were only important for one or two species. The increase in relative humidity in February could advance LUD, while soil moisture in March had the opposite effect. The increase in March day length and downward surface shortwave radiation exerted an advancing influence on the LUD of light-sensitive species. Our results suggest that incorporating multiple biotic and environmental factors into ML algorithms not only could effectively improve the accuracy of phenological prediction but also enhance our understanding of how spring phenology responds to each factor.

The tropical dry forests (TDF) have an enormously rich flora and fauna that offer various ecological services to the surrounding human societies. Biodiversity assessment is mandatory for implementing any sustainable forest management policy, which is why it is one of the important criteria and indicators currently used. Threats to TDF biodiversity are the primary challenges arising from environmental concerns caused by anthropogenic activity leading to global warming issues. The study aimed to investigate the vegetation assessment and several environmental and anthropogenic variables influencing forest biodiversity from 5 threatened forest sites of District Sialkot (Ghalotian, Kishan Garh, Daburgi Chanda Singh, Pir Kot, and Ghulab Garh), Pakistan. We collected 170 distinct plant species, including 135 dicots, 27 monocots, seven pteridophytes, and one bryophyte, categorized into 138 genera and 62 families, divided into 114 herbs, 32 trees, and 24 shrubs. The phytosociological analysis described the quantitative characteristics, including % frequency, % density, % cover, and importance Value Index (IVI) of all forest areas. Gulab Garh forest has the richest biodiversity forest area, and herbs are the dominant species that have been documented. Environmental factors such as temperature, precipitation, organic matter, soil pH, Ca+2, Mg+2, Na+, Cl−, and electric conductivity (EC) strongly affect forest vegetation investigated by principal coordinate analysis. Shannon and Simpson’s diversity indexes reveal that all sites contain loamy and sandy soil and display a significant relationship between alpha diversity and richness. Increasing trends in temperature and decreasing trends in rainfall suggested that climate significantly affects the Sialkot region’s plant biodiversity. SWOT analysis highlighted that population growth leads to increasing anthropogenic activities such as constructing housing societies and roads, inadequate farming, and excessive grazing, impacting the forest vegetation and altering TDF ecosystem properties/services and functioning. Our findings reinforce the vegetational assessment and importance of local forest biodiversity and significant environmental drivers that influence the plant species diversity in TDF areas. Future conservation strategies are suggested to reduce unlawful resource consumption, restore plant biodiversity in designated protected areas, and conserve rare species locally.

The increased intensity and frequency of extreme climate events (ECEs) have significantly impacted vegetation phenology, further profoundly affecting the structure and functioning of terrestrial ecosystems. However, the mechanisms by which ECEs affect the end of the growing season (EOS), a crucial phenological phase, remain unclear. In this study, we first evaluated the temporal variations in the EOS anomalies in Northern China (NC) based on the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from 2001 to 2018. We then used event coincidence analysis (ECA) to assess the susceptibility of EOS to four ECEs (i.e., extreme heat, extreme cold, extreme wet and extreme dry events). Finally, we examined the dependence of the response of EOS to ECEs on background climate conditions. Our results indicated a slight decrease in the proportion of areas experiencing extreme heat and dry events (1.10% and 0.66% per year, respectively) and a slight increase in the proportion of areas experiencing extreme wet events (0.77% per year) during the preseason period. Additionally, EOS exhibited a delaying trend at a rate of 0.25 days/a during the study period. The susceptibility of EOS to ECEs was closely related to local hydrothermal conditions, with higher susceptibility to extreme dry and extreme hot events in drier and warmer areas and higher susceptibility to extreme cold and extreme wet events in wetter regions. Grasslands, in contrast to forests, were more sensitive to extreme dry, hot and cold events due to their weaker resistance to water deficits and cold stress. This study sheds light on how phenology responds to ECEs across various ecosystems and hydrothermal conditions. Our results could also provide a valuable guide for ecosystem management in arid regions.

Phenological traits, such as leaf and flowering dates, are proven to be phylogenetically conserved. The relationship between phylogenetic conservation, plant phenology, and climatic factors remains unknown. Here, we assessed phenological features among flowering plants as evidence for phylogenetic conservatism, the tendency for closely related species to share similar ecological and biological attributes. We use spring phenological traits data from 1968-2018 of 65 trees and 49 shrubs in Xi’an (temperate climate) and Guiyang (subtropical climate) to understand plant phenological traits’ relationship with phylogeny. Molecular datasets are employed in evolutionary models to test the phylogenetic conservatism in spring phenological characteristics in response to climate-sensitive phenological features. Significant phylogenetic conservation was found in the Xi’an plant’s phenological traits, while there was a non-significant conservation in the Guiyang plant species. Phylogenetic generalized least squares (PGLS) models correlate with phenological features significantly in Xi’an while non-significantly in Guiyang. Based on the findings of molecular dating, it was suggested that the Guiyang species split off from their relatives around 46.0 mya during the middle Eocene of the Tertiary Cenozoic Era, while Xi’an species showed a long evolutionary history and diverged from their relatives around 95 mya during the late Cretaceous Mesozoic Era. First leaf dates (FLD) indicative of spring phenology, show that Xi’an adjourned the case later than Guiyang. Unlike FLD, first flower dates (FFD) yield different results as Guiyang flowers appear later than Xi’an’s. Our research revealed that various factors, including phylogeny, growth form, and functional features, influenced the diversity of flowering phenology within species in conjunction with local climate circumstances. These results are conducive to understanding evolutionary conservation mechanisms in plant phenology concerning evolutionary processes in different geographical and climate zones.

Over the past half-century, there has been a significant rise in the frequency and intensity of extreme climate events in dryland ecosystems. While vegetation phenology acts as an important indicator of how ecosystems respond to climate change, the impact of extreme climate events on autumn phenology in dryland ecosystems remains uncertain. In this study, we firstly assessed the temporal variations in the end of growing season (EOS) in Central Asia using the Normalized Difference Vegetation Index and enhanced vegetation index over 2001–2018. Then, we assessed the impacts of eight climate indices, which capture the average and extreme conditions of the local climate, on EOS across diverse vegetation types using linear mixed models. Finally, we conducted partial correlation analysis to explore how the primary influencing factors affect EOS along an elevational gradient. Our results suggested increased occurrences of drought and heat events in the pre-growing season (April–September) in Central Asia. Under the background, a general trend towards earlier EOS (ca. 1.01 days/decade) was observed for grassland. This trend was mostly attributed to the changes in extreme climate indices (CIextreme), especially heavy rainfall and heat stress. The total explanatory power of CIextreme was almost twice that of mean climate indices (CImean). However, the impact of CIextreme was less pronounced in regions with elevations exceeding 1500 m. For forests and shrublands, the EOS exhibited delaying trend of 1.29 days/decade and advancing trend of 1.21 days/decade, respectively. Contrary to grasslands, the variations in EOS of forests and shrublands were largely influenced by the shift of CImean, especially mean minimum temperature and accumulated precipitation. The contribution of CIextreme accounted for only 1/6 of the contribution of CImean, likely attributed to the adaptation of forests to cold conditions and shrublands to arid conditions. The results provide valuable insights into the phenological response to extreme climate events and serve as a reference for ecosystem management in arid regions.

Both biotic and abiotic factors restrict changes in autumn phenology, yet their effects remain ambiguous, which hinders the accurate prediction of phenology under future climate change. In this study, based on the phenological records of 135 tree species at ten sites in China during 1979–2018, we first investigated the effects of climatic factors (temperature, precipitation, insolation and wind speed) and spring phenology on interannual changes in leaf coloring date (LCD) with the partial correlation analysis, and assessed the relative importance of phylogeny and native climate to LCD differences among species by using multivariate regression and phylogenetic eigenvector regression approach. The results showed that the effects of climate factors on interannual changes in LCD were more significant than spring phenology. In general, temperature played a more important role in cold regions (e.g. the northeast region), while the control of insolation on LCD was stronger in the warmer and wetter regions (e.g. the north, east and southwest regions). In addition, the effects of precipitation and wind speed were more evident in arid regions (e.g. the northwest region). We also found considerable effects of both native climate and phylogeny on the LCD differences among species, despite the contribution of native climate being almost 2~5 times greater than that of the phylogeny. Our findings confirmed and quantified the combined effects of climate, spring phenology and phylogeny on the autumn phenology of plants, which could help better understand the driving factors and influencing mechanism of plant phenology and provide a reference for the calibration and optimization of phenological models.

Woody plant species in temperate regions must withstand a cold winter and freezing events through cold acclimation and dormancy in autumn and winter. However, how seasonal changes in dormancy depth and cold hardiness affect the frost risk of temperate species is unclear because few studies have assessed dormancy depth and cold hardiness simultaneously. In this study, an experiment was conducted to estimate the dormancy depth and cold hardiness of five common woody temperate plant species during the winter of 2018/2019 in Beijing, China. Twigs of each species were collected at different dates during winter and the timing of budburst was monitored under the same forcing conditions. The dormancy depth was quantified as growing degree day (GDD) requirements of spring events. Simultaneously, the cold hardiness of buds at each sampling date was determined based on the electrical conductivity of the holding solution. Two indices (chilling accumulation and cold hardiness index) were used to simulate the past dynamics of dormancy depth, spring phenology, and cold hardiness from 1952 to 2021. The maximum dormancy depth of the study species was observed between early October and early December, and thereafter decreased exponentially. The cold hardiness peaked in mid-winter (end of December) through cold acclimation and thereafter decreased in spring (deacclimation). During the past 70 years, the budburst date (first flowering date or first leaf date) of five species was estimated to have advanced significantly, and dormancy depth in early spring was predicted to have increased owing to the warming-associated decrease in chilling accumulation. However, cold hardiness has decreased because of weakened acclimation and accelerated deacclimation under a warming climate. The frost risk before and after budburst remained unchanged because of the reduction in occurrence and severity of low-temperature events and earlier late spring frosts. The present methods could be generalized to estimate and predict the seasonal changes in dormancy depth and cold hardiness of temperate species in the context of climate change.

Vegetation phenology, i [...]

The effects of climate change on plant phenology have been widely recognized around the world. However, the effect of plant internal factors (such as phylogeny) on the variations in phenology among plant species remains unclear. In this study, we investigated the phylogenetic conservatism in spring phenological traits using phylogenetic signal and evolutionary models, including Brownian motion (BM) model, Ornstein–Uhlenbeck (OU) model and white noise (WN) model, based on the phenological data of 48 temperate plant species in Northeast China. We also explored the relative contributions of phylogeny and adaptation to native climate (i.e., the climate in native range of species) to the variations in the phenological traits among species using phylogenetic eigenvector regression and variance partitioning analysis. The results showed thatspring phenological traits conformed to the OU model, indicating thatspring traits were phylogenetically conserved. The effect of phylogeny on flowering traits was stronger than that on leaf-out traits. Additionally, the adaptation to native climate contributed more to the variations in spring phenological traits among species than phylogeny, and adaptation to native climate explained more variations in leaf-out traits than in flowering traits. Our results suggested that the spring phenological traits were constrained by both phylogeny and adaptation to native climate. However, the adaptation to native climate had a stronger effect on the variations in phenological traits than phylogeny. Therefore, the degree of similarity in spring phenological traits across closely related species depends on the degree of similarity in the environmental conditions where these close relatives are distributed.

Pollen allergy has already been an increasingly prominent ecosystem disservice in tourism attractions. However, few studies have assessed the tourist risk of pollen allergy through integrating multidisciplinary knowledge of ecology, medicine, phenology, and risk management. Basing on the conceptual framework of risk assessment proposed by UNISDR, we first established an index system of pollen-allergy risk for tourists in attractions and outlined assessment methods 18 available indexes were put forward to cover three aspects: hazard of plant allergen, tourist vulnerability, and resilience of assessment units. Subsequently, taking the Summer Palace as the case study area, we conducted a tourist risk assessment of pollen allergy. Values of nine available indexes were obtained via ecological investigation, phenological observation, and data mining of visitors' logs on Sina Weibo. Risk levels of spring pollen allergy for tourists in different assessment units were revealed by combining the green zone allergenicity index model and three-dimensional risk assessment matrix. The results showed that: (1) There were seven primary pollen-allergenic plants in the Summer Palace, including Platycladus orientalis, Sabina chinensis, Salix babylonica, Pinus tabulaeformis, Populus tomentosa Carr, Morus alba L. and Fraxinus chinesis, among which Platycladus orientalis and Salix babylonica were the highest allergenic. (2) Among 18 spots, tourists faced the highest risk level of pollen allergy in spring at three spots, namely the Hall of Serenity, Hall of Benevolence and Longevity, and Gallery of Literary and Prosperity. (3) The two routes of the Long Corridor and Longevity Hill scored high on the risk level. (4) Among four areas, risk levels of the Front-hill and Rear-hill areas were high. Given the increasing spatial-temporal uncertainty of pollen allergy and tourist behaviors under global warming and urbanization, the related monitoring should be strengthened in the future. Furthermore, the dynamic and improved assessment of pollen-allergy risk should be institutionalized and be integrated into the evaluation of tourism experience quality. Tourism administration should make full use of relevant assessment results and conduct more effective risk communication.

Wetlands are important CO2 sinks and methane sources, and their seasonality and phenological cycle play an essential role in understanding the carbon budget. However, given the spatial heterogeneity of wetland landscapes and the coarser spatial resolution of satellites, the phenological retrievals of wetlands are challenging. Here we examined the phenology of wetlands from 30 m harmonized Landsat/Sentinel-2 (LandSent30) and 500 m MODIS satellite observations using the ground phenology network PhenoCam as a benchmark. This study used all 11 available wetland PhenoCam sites (about 30 site years), covering diverse wetland types from different climate zones. We found that the LandSent30-based phenology results were in overall higher consistency with the PhenoCam results compared to MODIS, which could be related to the better explanation capacity of LandSent30 data in the heterogeneous landscapes of wetlands. This also means that the LandSent30 has an advantage over the 500 m MODIS regarding wetland vegetation phenological retrievals. It should be noted that the LandSent30 did not show a greatly improved performance, which could be related to the specificity and complexity of the wetlands landscape. We also illustrated the potential effects of the location and observation direction of PhenoCam cameras, the selection of Region of Interest (ROI), as well as the landscape composition of the site. Overall, this study highlights the complexity of wetland phenology from both ground and remote sensing observations at different scales, which paves the road for understanding the role of wetlands in global climate change and provides a basis for understanding the real phenological changes of wetland surfaces.

Spring warming could induce earlier leaf-out or flowering of temperate plant species, and decreased chilling in winter has a delaying effect on spring phenology. However, the relative contribution of the decreased chilling and increased forcing on spring phenological change is unclear. Here, we analyzed the experimental data for 14 temperate woody species in Beijing, China and quantified the forcing requirements (FR) of spring phenology and chilling sensitivity (the ratio of the FR at the low chilling condition to the FR at the high chilling condition) for each species. Furthermore, using species-specific functions between the amount of chilling and FR, we established a phenological model to simulate the annual onset dates of spring events during the past 69 years (1952–2020) and in the future (2021–2099) under RCP 4.5 and RCP 8.5 climate scenarios. We also developed a novel method to quantitatively split the predicted phenological change into the effects caused by changes in forcing and those caused by changes in chilling. The results show that the FR of spring events decreased with the increase in the amount of chilling, and this relationship could be described as an exponential decay function. The basic FR (the FR at the high chilling condition) and chilling sensitivity varied greatly among species. In the 1952–2020 period, the advancing effect of increased forcing was stronger than the effect of chilling, leading to earlier spring events with a mean trend of −1.96 days/decade. In future climate scenarios, the spring phenology of temperate species would continue to advance but will be limited by the decreased chilling. The species with lower chilling sensitivities showed stronger trends than those with high chilling sensitivities. Our results suggested that the delaying effect of declining chilling could only slow down the spring phenological advance to a certain extent in the future.

Climate warming is advancing spring leaf unfolding, but it is also reducing the cold periods that many trees require to break winter dormancy. Here, the authors show that 7 of 12 current chilling models fail to account for the correct relationship between chilling accumulation and heat requirement, leading to substantial overestimates of the advance of spring phenology under climate change.

The effects of winter chilling, spring forcing temperature, and photoperiod on spring phenology are well known for many European and North American species, but the environmental cues that regulate the spring phenology of East Asian species have not yet been thoroughly investigated. Here, we conducted a growth chamber experiment to test the effects of chilling (controlled by different lengths of exposure to natural chilling conditions), forcing temperature (12, 15, or 18°C) and photoperiod (14 or 10 h) on first flowering date (FFD) of six woody species (three shrubs and three trees) native to East Asia. The three-way analysis of variance (ANOVA) separately for each species showed that the effects of chilling and forcing temperature were significant for almost all species (P < 0.05). Averaged over all chilling and photoperiod treatments, the number of days until FFD decreased by 2.3–36.1 days when the forcing temperature increased by 3°C. More chilling days reduced the time to FFD by 0.7–26 days, when averaged over forcing and photoperiod treatments. A longer photoperiod could advance the FFD by 1.0–5.6 days, on average, but its effect was only significant for two species (including one tree and one shrub). The effects of forcing temperature and photoperiod interacted with chilling for half of the studied species, being stronger in the low chilling than high chilling treatment. These results could be explained by the theory and model of growing degree-days (GDD). Increased exposure to chilling coupled to a longer photoperiod reduced the GDD requirement for FFD, especially when plants grew under low chilling conditions. However, shrubs (except Viburnum dilatatum) had lower chilling and heat requirements than trees, suggesting that, by leafing out sooner, they engage in a more opportunistic life strategy to maximize their growing season, especially before canopy closure from trees' foliage. Our results confirmed the varying effects of these three cues on the flowering phenology of woody species native to East Asia. In future climate change scenarios, spring warming is likely to advance the spring phenology of those woody species, although the reduced chilling and shorter photoperiod may partly offset this spring warming effect.

Climate change could affect botanical tourism by altering the plant phenology (e.g., flowering and leaf coloring date) and the physical comfort of tourists. To date, few studies have simultaneously considered the influence of plant phenology and physical comfort on the travel suitability of botanical tourism. Taking Beijing as an example, this study used phenological data of 73 species from 1963 to 2017 to construct a phenological ornamental index (POI) according to the flowering and leaf coloring date of ornamental plant. The climate comfort index (CCI) of tourism was calculated by using meteorological data of the corresponding periods. Finally, the travel suitability index (TSI) was constructed by integrating the two indices (POI and CCI). The POI showed that the best period for spring flower viewing was from April 4 to May 10, while the best period for autumn leaves viewing was from October 11 to November 6 on average. According to the variation of the CCI within the year, the most comfortable period for spring tourism was matched with the best period for spring flower viewing (April 4 to June 1), but the most comfortable period for autumn tourism (September 4 to October 19) was earlier than the best period for autumn leaves viewing. The TSI indicated that the best periods for spring and autumn botanical tourism were April 7 to May 10 and October 10 to November 7, respectively. Based on the climate data under different scenarios (representative concentration pathways 4.5 and 8.5), we simulated the climate and phenological suitability for botanical tourism in the next thirty years. The results showed that the best period for spring botanical tourism during 2040–2050 was earlier and the period for autumn botanical tourism was later than that in the past 55 years. Meanwhile, the duration would shorten by 2–7 days for both seasons. This study provided a reference for assessing the impact of global climate change on the best season of botanical tourism.

Autumn phenology plays a critical role in terrestrial ecosystem circulations. However, the changes in autumn phenology and their correlation with temperature remain uncertain because mean temperature alone was not able to determine the changes in autumn phenology at various sites. Here, the leaf coloring season (LCS) was defined as the period when the leaves of more than half of the species had recognized changes in color. We systematically studied the changes in peak, start, end, and duration of LCS and their correlations with five temperature parameters (mean temperature, accumulated cold temperature, day temperature, night temperature, and temperature difference between day and night) in four periods. Similarly to previous findings, the start date of LCS advanced and the end of LCS delayed over the past 50 years, which consequently led to a lengthened duration of LCS in Xi’an, Harbin, Minqin, and Shenyang. In general, the rise in mean temperature, day temperature, and night temperature would delay the peak, start, and end of LCS and lengthen the duration of LCS in most cases. We also proved that the changes in LCS metrics not only could completely be explained by mean temperature but also were influenced by day temperature, night temperature, temperature difference, and even other climatic factors such as precipitation, at different sites.

Based on the phenological data from China Phenological Observation Network, we compiled the phenological calendars of 3 phenological observation stations (Shanghai, Nanjing, and Hefei) in East China for 1987–1996 and 2003–2012 according to the sequences of mean phenophases. We calculated the correlated coefficient and the root mean square error (RMSE) between phenophases and the beginning of meteorological seasons to determine the beginning date of phenological season. By comparing new phenological calendars with the old ones, we discussed the variation of phenophases and their responses to temperature. The conclusions are as follows. (1) The beginning dates of spring and summer advanced, while those of autumn and winter delayed. Thus, summers got longer and winters got shorter. (2) The beginning time of the four phenological seasons was advancing during 1987–1996, while it was delaying during 2003–2012. (3) Most spring and summer phenophases occur earlier and most autumn and winter phenophases occur later in 2003–2012 than in 1987–1996. (4) The beginning time of phenological seasons was significantly correlated with temperature. The phenological sensitivities to temperature ranged from −6.49 to −6.55 days/°C in spring, −3.65 to −5.02 days/°C in summer, 8.13 to 10.27 days/°C in autumn, and 4.76 to 10.00 days/°C in winter.

Continuous satellite datasets are widely used in tracking vegetation responses to climate variability. Start of season (SOS), for example, can be derived using a number of methods from the time series of satellite reflectance data; however, various methods often produce different SOS measures which limit the application of satellite data in phenological studies. Therefore, we employed five methods to estimate SOS from the Advanced Very High Resolution Radiometer (AVHRR)/normalized difference vegetation index (NDVI) dataset. Subsequently, we compared the SOS with the ground-based first leaf date (FLD) of 12 deciduous broadleaved plant species at 12 sites of the Chinese Phenological Observation Network (CPON). The results show that the latitudinal patterns of five satellite-derived SOS measures are similar to each other but different from the pattern of ground phenology. For individual methods, the variability of SOS time series is significantly different from ground phenology except for HANTS, Polyfit, and Midpoint methods. The SOS calculated using the Midpoint method showed significant correlations with ground phenophases most frequently (in 47.1% of cases). Using the SOS derived from the Midpoint method, significantly earlier trends in SOS were detected in 50.7% of the natural vegetation area from 1982 to 2006.

In order to understand past plant phenological responses to climate change in China (1963–2009), we conducted trends analysis of spring phenophases based on observation data at 33 sites from the Chinese Phenological Observation Network (CPON). The phenological data on first leaf date (FLD) and first flowering date (FFD) for five broad-leaved woody plants from 1963 to 2009 were analyzed. Since most phenological time series are discontinuous because of observation interruptions at certain period, we first interpolated phenological time series by using the optimal model between the spring warming (SW) model and the UniChill model to form continuous time series. Subsequently, by using regression analysis, we found that the spring phenophases of woody plants in China advanced at a mean rate of 0.18 days/year over the past 50 years. Changes of spring phenophases exhibited strong regional difference. The linear trends in spring phenophases were −0.18, −0.28, −0.21, −0.04, and −0.14 days/year for the Northeast China Plain, the North China Plain, the Middle-Lower Yangtze Plain, the Yunnan-Guizhou Plateau, and South China, respectively. The spatial differences in phenological trends can be attributed to regional climate change patterns in China.

Satellite-derived vegetation phenophases are frequently used to study the response of ecosystems to climate change. However, limited studies have identified the common phenological variability across different climate and vegetation zones. Using NOAA/Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) dataset, we estimated start of growing season (SOS) and end of growing season (EOS) for Chinese vegetation during the period 1982–2012 based on the Midpoint method. Subsequently, the empirical orthogonal function (EOF) analysis was applied to extract the main patterns of phenophases and their annual variability. The impact of climate parameters such as temperature and precipitation on phenophases was investigated using canonical correlation analysis (CCA). The first EOF mode of phenophases exhibited widespread earlier or later SOS and EOS signals for almost the whole country. The attendant time coefficients revealed an earlier SOS between 1996 and 2008, but a later SOS in 1982–1995 and 2009–2012. Regarding EOS, it was clearly happening later in recent years, mainly after 1993. The preseason temperature contributed to such spatiotemporal phenological change significantly. The first pair of CCA patterns for phenology and preseason temperature was found to be similar and its time coefficients were highly correlated to each other (correlation coefficient >0.7). These results indicate that there is a substantial amount of common variance in SOS and EOS across different vegetation types that is related to large-scale modes of climate variability.

The tropical dry forests (TDF) have an enormously rich flora and fauna that offer various ecological services to the surrounding human societies. Biodiversity assessment is mandatory for implementing any sustainable forest management policy, which is why it is one of the important criteria and indicators currently used. Threats to TDF biodiversity are the primary challenges arising from environmental concerns caused by anthropogenic activity leading to global warming issues. The study aimed to investigate the vegetation assessment and several environmental and anthropogenic variables influencing forest biodiversity from 5 threatened forest sites of District Sialkot (Ghalotian, Kishan Garh, Daburgi Chanda Singh, Pir Kot, and Ghulab Garh), Pakistan. We collected 170 distinct plant species, including 135 dicots, 27 monocots, seven pteridophytes, and one bryophyte, categorized into 138 genera and 62 families, divided into 114 herbs, 32 trees, and 24 shrubs. The phytosociological analysis described the quantitative characteristics, including % frequency, % density, % cover, and importance Value Index (IVI) of all forest areas. Gulab Garh forest has the richest biodiversity forest area, and herbs are the dominant species that have been documented. Environmental factors such as temperature, precipitation, organic matter, soil pH, Ca+2, Mg+2, Na+, Cl-, and electric conductivity (EC) strongly affect forest vegetation investigated by principal coordinate analysis. Shannon and Simpson's diversity indexes reveal that all sites contain loamy and sandy soil and display a significant relationship between alpha diversity and richness. Increasing trends in temperature and decreasing trends in rainfall suggested that climate significantly affects the Sialkot region's plant biodiversity. SWOT analysis highlighted that population growth leads to increasing anthropogenic activities such as constructing housing societies and roads, inadequate farming, and excessive grazing, impacting the forest vegetation and altering TDF ecosystem properties/services and functioning. Our findings reinforce the vegetational assessment and importance of local forest biodiversity and significant environmental drivers that influence the plant species diversity in TDF areas. Future conservation strategies are suggested to reduce unlawful resource consumption, restore plant biodiversity in designated protected areas, and conserve rare species locally. [ABSTRACT FROM AUTHOR]

Phenological traits, such as leaf and flowering dates, are proven to be phylogenetically conserved. The relationship between phylogenetic conservation, plant phenology, and climatic factors remains unknown. Here, we assessed phenological features among flowering plants as evidence for phylogenetic conservatism, the tendency for closely related species to share similar ecological and biological attributes. We use spring phenological traits data from 1968-2018 of 65 trees and 49 shrubs in Xi'an (temperate climate) and Guiyang (subtropical climate) to understand plant phenological traits' relationship with phylogeny. Molecular datasets are employed in evolutionary models to test the phylogenetic conservatism in spring phenological characteristics in response to climate-sensitive phenological features. Significant phylogenetic conservation was found in the Xi'an plant's phenological traits, while there was a non-significant conservation in the Guiyang plant species. Phylogenetic generalized least squares (PGLS) models correlate with phenological features significantly in Xi'an while non-significantly in Guiyang. Based on the findings of molecular dating, it was suggested that the Guiyang species split off from their relatives around 46.0 mya during the middle Eocene of the Tertiary Cenozoic Era, while Xi'an species showed a long evolutionary history and diverged from their relatives around 95 mya during the late Cretaceous Mesozoic Era. First leaf dates (FLD) indicative of spring phenology, show that Xi'an adjourned the case later than Guiyang. Unlike FLD, first flower dates (FFD) yield different results as Guiyang flowers appear later than Xi'an's. Our research revealed that various factors, including phylogeny, growth form, and functional features, influenced the diversity of flowering phenology within species in conjunction with local climate circumstances. These results are conducive to understanding evolutionary conservation mechanisms in plant phenology concerning evolutionary processes in different geographical and climate zones. [ABSTRACT FROM AUTHOR]