Pan European Phenological database (PEP725): a single point of access for European data

Time to budbreak is not enough: cold hardiness evaluation is necessary in dormancy and spring phenology studies

BACKGROUND

Dormancy of buds is an important phase in the life cycle of perennial plants growing in environments where unsuitable growth conditions occur seasonally. In regions where low temperature defines these unsuitable conditions, the attainment of cold hardiness is also required for survival. The end of the dormant period culminates in budbreak and flower emergence, or spring phenology, one of the most appreciated and studied phenological events - a time also understood to be most sensitive to low-temperature damage. Despite this, we have a limited physiological and molecular understanding of dormancy, which has negatively affected our ability to model budbreak. This is also true for cold hardiness.

SCOPE

Here we highlight the importance of including cold hardiness in dormancy studies that typically only characterize time to budbreak. We show how different temperature treatments may lead to increases in cold hardiness, and by doing so also (potentially inadvertently) increase time to budbreak.

CONCLUSIONS

We present a theory that describes evaluation of cold hardiness as being key to clarifying physiological changes throughout the dormant period, delineating dormancy statuses, and improving both chill and phenology models. Erroneous interpretations of budbreak datasets are possible by not phenotyping cold hardiness. Changes in cold hardiness were very probably present in previous experiments that studied dormancy, especially when those included below-freezing temperature treatments. Separating the effects between chilling accumulation and cold acclimation in future studies will be essential for increasing our understanding of dormancy and spring phenology in plants.

Spring wood phenology responds more strongly to chilling temperatures than bud phenology in European conifers

A comparative assessment of bud and wood phenology could aid a better understanding of tree growth dynamics. However, the reason for asynchronism or synchronism in leaf and cambial phenology remains unclear. To test the assumption that the temporal relationship between the budburst date and the onset date of wood formation is due to their common or different responses to environmental factors, we constructed a wood phenology dataset from previous literature, and compared it with an existing bud phenology dataset in Europe. We selected three common conifers (Larix decidua Mill., Picea abies (L.) H. Karst. and Pinus sylvestris L.) in both datasets and analyzed 909 records of the onset of wood formation at 47 sites and 238,720 records of budburst date at 3051 sites. We quantified chilling accumulation (CA) and forcing requirement (FR) of budburst and onset of wood formation based on common measures of CA and FR. We then constructed negative exponential CA-FR curves for bud and wood phenology separately. The results showed that the median, variance and probability distribution of CA-FR curves varied significantly between bud and wood phenology for three conifers. The different FR under the same chilling condition caused asynchronous bud and wood phenology. Furthermore, the CA-FR curves manifested that wood phenology was more sensitive to chilling than bud phenology. Thus, the FR of the onset of wood formation increases more than that of budburst under the same warming scenarios, explaining the stronger earlier trends in the budburst date than the onset date of woody formation simulated by the process-based model. Our work not only provides a possible explanation for asynchronous bud and wood phenology from the perspective of organ-specific responses to chilling and forcing, but also develops a phenological model for predicting both bud and wood phenology with acceptable uncertainties.

The phenological response of European vegetation to urbanisation is mediated by macrobioclimatic factors

Plant phenology is a crucial component of ecosystem functioning and is affected by multiple elements of global change; we therefore need to quantify the current phenological changes associated to human activities and understand their impacts on ecosystems. Urbanisation and the intensification of anthropogenic activities alter meteorological conditions and cause phenological changes in urban vegetation worldwide. We used remote sensing data to evaluate the phenological response (start of season date SOS, length of season LOS and end of season date EOS) of five main vegetation types (evergreen forests, deciduous forests, mixed forests, sparse woody vegetation and grasslands) to urbanisation in the 69 most populated pan-European metropolitan areas (i.e., those that include cities with a population over 450,000 inhabitants) for the period 2002-2021. In general, SOS advanced and LOS increased with urbanisation intensity across European metropolitan areas. We found that macrobioclimatic factors strongly determined the strength and direction of the phenological response to urbanisation intensity. The greatest advances in SOS with increasing urbanisation were registered in metropolitan areas in the Mediterranean region, where there was also more uncertainty in this relationship. The EOS advanced with urbanisation in metropolitan areas in the Mediterranean macrobioclimate, whereas in areas with higher precipitation during summer the opposite trend was observed suggesting water availability mediates the response between urbanisation and autumn phenophases. Our results suggest that macrobioclimatic constraints operating at the continental scale are crucial to understand the relationship between plant phenology and urbanisation intensity.

CropLife Europe Crop Development Database: An open-source, pan-European, harmonized crop development database for use in regulatory pesticide exposure modeling and risk assessment

There is a regulatory need for crop development dates to assess current default values used within chemical exposure assessments as well as to justify refinements within risk assessments. However, a readily available pan-European crop phenology database covering key FOrum for the Co-ordination of pesticide fate models and their USe (FOCUS) crops and scenarios to meet this need is not currently available. Therefore, we describe the development of a harmonized, pan-European, CropLife Europe Crop Development Database (C2D2), that is fully aligned with this regulatory requirement utilizing efficacy trials data generated for regulatory submissions when registering plant protection products under Regulation (EU)1107/2009. Evaluation of C2D2 against an independent data set showed good agreement for equivalent time periods, crop growth stages, and geographical regions. We illustrate how this database can be used to evaluate existing default crop development dates mandated by regulatory agencies for use within exposure assessments. Despite the large data set compiled and the geographical coverage of C2D2, not all FOCUSsw/gw scenarios have sufficient data to facilitate comparison, with less significant scenarios, like FOCUSgw Porto, being underrepresented. For those scenarios with sufficient data, clear differences between C2D2 and crop development dates assumed in the FOCUS modeling framework (using the AppDate tool) are often indicated over many growth stages, suggesting that amendment of the existing representation of crop development within the risk assessment process may be required. C2D2 is freely available under a Creative Commons license to facilitate innovation in exposure science to allow for more accurate and realistic risk assessment leading to enhanced crop and environmental protection. Integr Environ Assess Manag 2023;00:1-15. © 2023 CropLife Europe (Corteva Agriscience) and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Warming increases the differences among spring phenology models under future climate change

Phenological models are built upon an understanding of the influence of environmental factors on plant phenology, and serve as effective tools for predicting plant phenological changes. However, the differences in phenological model predictive performance under different climate change scenarios have been rarely studied. In this study, we parameterized thirteen spring phenology models, including six one-phase models and seven two-phase models, by combining phenological observations and meteorological data. Using climatic data from two Shared Socioeconomic Pathways (SSP) scenarios, namely SSP126 (high mitigation and low emission) and SSP585 (no mitigation and high emission), we predicted spring phenology in Germany from 2021 to 2100, and compared the impacts of dormancy phases and driving factors on model predictive performance. The results showed that the average correlation coefficient between the predicted start of growing season (SOS) by the 13 models and the observed values exceeded 0.72, with the highest reaching 0.80. All models outperformed the NULL model (Mean of SOS), and the M1 model (driven by photoperiod and forcing temperature) performed the best for all the tree species. In the SSP126 scenario, the average SOS advanced initially and then gradually shifted towards a delay starting around 2070. In the SSP585 scenario, the average SOS advanced gradually at a rate of approximately 0.14 days per year. Moreover, the standard deviation of the simulated SOS by the 13 spring phenology models exhibited a significant increase at a rate of 0.04 days per year. On average, two-phase models exhibited larger standard deviations than one-phase models after approximately 2050. Models driven solely by temperature showed larger standard deviations after 2060 compared to models driven by both temperature and photoperiod. Our findings suggest investigating the release mechanisms of endodormancy phase and incorporating new insights into future phenological models to better simulate the changes in plant phenology.

Accelerating effects of growing-season warming on tree seasonal activities are progressively disappearing

The phenological changes induced by climate warming have profound effects on water, energy, and carbon cycling in forest ecosystems. In addition to pre-season warming, growing-season warming may drive tree phenology by altering photosynthetic carbon uptake. It has been reported that the effect of pre-season warming on tree phenology is decreasing. However, temporal change in the effect of growing-season warming on tree phenology is not yet clear. Combining long-term ground observations and remote-sensing data, here we show that spring and autumn phenology were advanced by growing-season warming, while the accelerating effects of growing-season warming on tree phenology were progressively disappearing, manifesting as phenological events converted from being advanced to being delayed, in the temperate deciduous broadleaved forests across the Northern Hemisphere between 1983 and 2014. We further observed that the effect of growing-season warming on photosynthetic productivity showed a synchronized decline over the same period. The responses of phenology and photosynthetic productivity had a strong linear relationship with each other, and both showed significant negative correlations with the elevated temperature and vapor pressure deficit during the growing season. These findings indicate that warming-induced water stress may drive the observed decline in the responses of tree phenology to growing-season warming by decelerating photosynthetic productivity. Our results not only demonstrate a close link between photosynthetic carbon uptake and tree seasonal activities but also provide a physiological perspective of the nonlinear phenological responses to climate warming.

Non-symmetric responses of leaf onset date to natural warming and cooling in northern ecosystems

The northern hemisphere has experienced regional cooling, especially during the global warming hiatus (1998-2012) due to ocean energy redistribution. However, the lack of studies about the natural cooling effects hampers our understanding of vegetation responses to climate change. Using 15,125 ground phenological time series at 3,620 sites since the 1950s and 31-year satellite greenness observations (1982-2012) covering the warming hiatus period, we show a stronger response of leaf onset date (LOD) to natural cooling than to warming, i.e. the delay of LOD caused by 1°C cooling is larger than the advance of LOD with 1°C warming. This might be because cooling leads to larger chilling accumulation and heating requirements for leaf onset, but this non-symmetric LOD response is partially offset by warming-related drying. Moreover, spring greening magnitude, in terms of satellite-based greenness and productivity, is more sensitive to LOD changes in the warming area than in the cooling. These results highlight the importance of considering non-symmetric responses of spring greening to warming and cooling when predicting vegetation-climate feedbacks.

Effect of climate warming on the timing of autumn leaf senescence reverses after the summer solstice

Climate change is shifting the growing seasons of plants, affecting species performance and biogeochemical cycles. Yet how the timing of autumn leaf senescence in Northern Hemisphere forests will change remains uncertain. Using satellite, ground, carbon flux, and experimental data, we show that early-season and late-season warming have opposite effects on leaf senescence, with a reversal occurring after the year's longest day (the summer solstice). Across 84% of the northern forest area, increased temperature and vegetation activity before the solstice led to an earlier senescence onset of, on average, 1.9 ± 0.1 days per °C, whereas warmer post-solstice temperatures extended senescence duration by 2.6 ± 0.1 days per °C. The current trajectories toward an earlier onset and slowed progression of senescence affect Northern Hemisphere-wide trends in growing-season length and forest productivity.

Radiation-constrained boundaries cause nonuniform responses of the carbon uptake phenology to climatic warming in the Northern Hemisphere

Climatic warming has lengthened the photosynthetically active season in recent decades, thus affecting the functioning and biogeochemistry of ecosystems, the global carbon cycle and climate. Temperature response of carbon uptake phenology varies spatially and temporally, even within species, and daily total intensity of radiation may play a role. We empirically modelled the thresholds of temperature and radiation under which daily carbon uptake is constrained in the temperate and cold regions of the Northern Hemisphere, which include temperate forests, boreal forests, alpine and tundra biomes. The two-dimensionality of the temperature-radiation constraint was reduced to one single variable, θ, which represents the angle in a polar coordinate system for the temperature-radiation observations during the start and end of the growing season. We found that radiation will constrain the trend towards longer growing seasons with future warming but differently during the start and end of season and depending on the biome type and region. We revealed that radiation is a major factor limiting photosynthetic activity that constrains the phenology response to temperature during the end-of-season. In contrast, the start of the carbon uptake is overall highly sensitive to temperature but not constrained by radiation at the hemispheric scale. This study thus revealed that while at the end-of-season the phenology response to warming is constrained at the hemispheric scale, at the start-of-season the advance of spring onset may continue, even if it is at a slower pace.

Acclimation of phenology relieves leaf longevity constraints in deciduous forests

Leaf phenology is key for regulating total growing-season mass and energy fluxes. Long-term temporal trends towards earlier leaf unfolding are observed across Northern Hemisphere forests. Phenological dates also vary between years, whereby end-of-season (EOS) dates correlate positively with start-of-season (SOS) dates and negatively with growing-season total net CO₂ assimilation (A_net). These associations have been interpreted as the effect of a constrained leaf longevity or of premature carbon (C) sink saturation-with far-reaching consequences for long-term phenology projections under climate change and rising CO₂. Here, we use multidecadal ground and remote-sensing observations to show that the relationships between A_net and EOS are opposite at the interannual and the decadal time scales. A decadal trend towards later EOS persists in parallel with a trend towards increasing A_net-in spite of the negative A_net-EOS relationship at the interannual scale. This finding is robust against the use of diverse observations and models. Results indicate that acclimation of phenology has enabled plants to transcend a constrained leaf longevity or premature C sink saturation over the course of several decades, leading to a more effective use of available light and a sustained extension of the vegetation CO₂ uptake season over time.

Disorder or a new order: How climate change affects phenological variability

Advancing spring phenology is a well documented consequence of anthropogenic climate change, but it is not well understood how climate change will affect the variability of phenology year to year. Species' phenological timings reflect the adaptation to a broad suite of abiotic needs (e.g., thermal energy) and biotic interactions (e.g., predation and pollination), and changes in patterns of variability may disrupt those adaptations and interactions. Here, we present a geographically and taxonomically broad analysis of phenological shifts, temperature sensitivity, and changes in interannual variability encompassing nearly 10,000 long-term phenology time series representing more than 1000 species across much of the Northern Hemisphere. We show that the timings of leaf-out, flowering, insect first-occurrence, and bird arrival were the most sensitive to temperature variation and have advanced at the fastest pace for early-season species in colder and less seasonal regions. We did not find evidence for changing variability in warmer years in any phenophase groups, although leaf-out and flower phenology have become moderately but significantly less variable over time. Our findings suggest that climate change has not to this point fundamentally altered the patterns of interannual phenological variability.

Climate change related phenological decoupling in species belonging to the Betulaceae family

Betulaceae species are anemophilous, and allergens from their pollen are a major cause of respiratory allergies in temperate areas where they are widely distributed. It is expected that, due to the strong influence of temperature on Betulaceae phenology, global warming will impact both the distribution and phenology of these species during the coming decades. This study examines potential decoupling of flowering and leafing phenophases in Betulaceae species (i.e. Alnus glutinosa, Betula pendula and Corylus avellana) over long-term (1951-2015) and as shorter (15-year) periods. Phenological phases for flowering and leaf unfolding of Betulaceae species from the Pan-European Phenology (PEP725) database were examined along with maximum and minimum daily temperature data for the periods September-October-November (SON), December-January-February (DJF) and March-April-May (MAM). Significant increases in temperature since 1951 have been recorded in the relevant chilling and forcing periods. Both flowering and leaf unfolding phenophases are advancing, but flowering is advancing faster than leaf unfolding. This is increasing the time between phenophases, although analysis of 15-year periods shows that the pattern of change was not constant. The results presented here represent the most comprehensive analysis of flowering and leaf unfolding phenophases of Betulaceae species using the PEP725 database to date. It is expected that these changes to Betulaceae phenology will continue and that global warming-related phenological decoupling will increase plant stress in Betulaceae populations in central Europe.

Early spring onset increases carbon uptake more than late fall senescence: modeling future phenological change in a US northern deciduous forest

In deciduous forests, spring leaf development and fall leaf senescence regulate the timing and duration of photosynthesis and transpiration. Being able to model these dates is therefore critical to accurately representing ecosystem processes in biogeochemical models. Despite this, there has been relatively little effort to improve internal phenology predictions in widely used biogeochemical models. Here, we optimized the phenology algorithms in a regionally developed biogeochemical model (PnET-CN) using phenology data from eight mid-latitude PhenoCam sites in eastern North America. We then performed a sensitivity analysis to determine how the optimization affected future predictions of carbon, water, and nitrogen cycling at Bartlett Experimental Forest, New Hampshire. Compared to the original PnET-CN phenology models, our new spring and fall models resulted in shorter season lengths and more abrupt transitions that were more representative of observations. The new phenology models affected daily estimates and interannual variability of modeled carbon exchange, but they did not have a large influence on the magnitude or long-term trends of annual totals. Under future climate projections, our new phenology models predict larger shifts in season length in the fall (1.1-3.2 days decade^-1) compared to the spring (0.9-1.5 days decade^-1). However, for every day the season was longer, spring had twice the effect on annual carbon and water exchange totals compared to the fall. These findings highlight the importance of accurately modeling season length for future projections of carbon and water cycling.

Winter warming offsets one half of the spring warming effects on leaf unfolding

Winter temperature-related chilling and spring temperature-related forcing are two major environmental cues shaping the leaf-out date of temperate species. To what degree insufficient chilling caused by winter warming would slow phenological responses to spring warming remains unclear. Using 27,071 time series of leaf-out dates for 16 tree species in Europe, we constructed a phenological model based on the linear or exponential function between the chilling accumulation (CA) and forcing requirements (FR) of leaf-out. We further used the phenological model to quantify the relative contributions of chilling and forcing on past and future spring phenological change. The results showed that the delaying effect of decreased chilling on the leaf-out date was prevalent in natural conditions, as more than 99% of time series exhibited a negative relationship between CA and FR. The reduction in chilling linked to winter warming from 1951 to 2014 could offset about one half of the spring phenological advance caused by the increase in forcing. In future warming scenarios, if the same model is used and a linear, stable correlation between CA and FR is assumed, declining chilling will continuously offset the advance of leaf-out to a similar degree. Our study stresses the importance of assessing the antagonistic effects of winter and spring warming on leaf-out phenology.

Integrating experiments to predict interactive cue effects on spring phenology with warming

Climate change has advanced plant phenology globally 4-6 d °C^-1 on average. Such shifts are some of the most reported and predictable biological impacts of rising temperatures. Yet as climate change has marched on, phenological shifts have appeared muted over recent decades - failing to match simple predictions of an advancing spring with continued warming. The main hypothesis for these changing trends is that interactions between spring phenological cues - long-documented in laboratory environments - are playing a greater role in natural environments due to climate change. Here, we argue that accurately linking shifts observed in long-term data to underlying phenological cues is slowed by biases in observational studies and limited integration of insights from laboratory studies. We synthesize seven decades of laboratory experiments to quantify how phenological cue-space has been studied and how treatments compare with shifts caused by climate change. Most studies focus on one cue, limiting our ability to make accurate predictions, but some well-studied forest species offer opportunities to advance forecasting. We outline how greater integration of controlled-environment studies with long-term data could drive a new generation of laboratory experiments, built on physiological insights, that would transform our fundamental understanding of phenology and improve predictions.

Drivers of phenological changes in southern Europe

The life cycle of plants is largely determined by climate, which renders phenological responses to climate change a highly suitable bioindicator of climate change. Yet, it remains unclear, which are the key drivers of phenological patterns at certain life stages. Furthermore, the varying responses of species belonging to different plant functional types are not fully understood. In this study, the role of temperature and precipitation as environmental drivers of phenological changes in southern Europe is assessed. The trends of the phenophases leaf unfolding, flowering, fruiting, and senescence are quantified, and the corresponding main environmental drivers are identified. A clear trend towards an earlier onset of leaf unfolding, flowering, and fruiting is detected, while there is no clear pattern for senescence. In general, the advancement of leaf unfolding, flowering and fruiting is smaller for deciduous broadleaf trees in comparison to deciduous shrubs and crops. Many broadleaf trees are photoperiod-sensitive; therefore, their comparatively small phenological advancements are likely the effect of photoperiod counterbalancing the impact of increasing temperatures. While temperature is identified as the main driver of phenological changes, precipitation also plays a crucial role in determining the onset of leaf unfolding and flowering. Phenological phases advance under dry conditions, which can be linked to the lack of transpirational cooling leading to rising temperatures, which subsequently accelerate plant growth.

Warming-induced increase in carbon uptake is linked to earlier spring phenology in temperate and boreal forests

Under global warming, advances in spring phenology due to rising temperatures have been widely reported. However, the physiological mechanisms underlying the advancement in spring phenology still remain poorly understood. Here, we investigated the effect of temperature during the previous growing season on spring phenology of current year based on the start of season extracted from multiple long-term and large-scale phenological datasets between 1951 and 2018. Our findings indicate that warmer temperatures during previous growing season are linked to earlier spring phenology of current year in temperate and boreal forests. Correspondingly, we observed an earlier spring phenology with the increase in photosynthesis of the previous growing season. These findings suggest that the observed warming-induced earlier spring phenology is driven by increased photosynthetic carbon assimilation in the previous growing season. Therefore, the vital role of warming-induced changes in carbon assimilation should be considered to accurately project spring phenology and carbon cycling in forest ecosystems under future climate warming.

The mechanisms underlying plant phenological shifts are debated. Here, based on phenological observations and ecosystem flux and climate data, Gu and colleagues provide evidence that warming-enhanced photosynthesis in a growing season contributes to earlier spring phenology in the following year in temperate and boreal forests.

Consistent temperature-dependent patterns of leaf lifespan across spatial and temporal gradients for deciduous trees in Europe

Temperature affects leaf lifespan (LL) across either space or time, driving long-term adaptation and short-term thermal acclimation, respectively. However, a comprehensive understanding of the phenomenon and the underlying phenological mechanisms remain poorly understood. The present study investigated the relationship between LL and temperature in six common deciduous trees across both spatial and temporal gradients, then explained the LL variation patterns based on phenological shifts. Using long-term (1971-2000) phenological records of six deciduous tree species at 54 sites across central Europe, we analyzed spatial and temporal variations of LL and leaf phenology along temperature gradients. We assessed the relative contribution of phenological shifts to LL variations by comparing absolute changes in leaf-out and leaf fall. We reported positive LL-temperature relationships across all observations along both spatial (+3.32 days/°C) and temporal (+4.43 days/°C) gradients. The paired t-test of the six deciduous tree species showed no significant difference in regression slopes of LL- temperature between the two gradients (t = -1.50, df = 5, P = 0.194). Prolonged LL can be explained mainly by earlier leaf-out induced by warmer temperatures both spatially (-3.22 days/°C) and temporally (-4.08 days/°C). The converging temperature-dependent patterns of LL across time and space indicate that short-term thermal acclimation keeps pace with long-term genetic adaptation for deciduous trees in Europe. Earlier leaf-out is the key force shaping the LL-temperature relationship. These results provide insights for predicting future vegetation dynamics under global warming.

Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

Analyses of climate change based on point observations indicate an extension of the plant growing season, which may have an impact on plant production and functioning of natural ecosystems. Analyses involving remote sensing methods, which have added more detail to results obtained in the traditional way, have been carried out only since the 1980s. The paper presents the results of a bibliometric analysis of papers related to the growing season published from 2000–2021 included in the Web of Science database. Through filtering, 285 publications were selected and subjected to statistical processing and analysis of their content. This resulted in the identification of author teams that mostly focused their research on vegetation growth and in the selection of the most common keywords describing the beginning, end, and duration of the growing season. It was found that most studies on the growing season were reported from Asia, Europe, and North America (i.e., 32%, 28%, and 28%, respectively). The analyzed articles show the advantage of satellite data over low-altitude and ground-based data in providing information on plant vegetation. Over three quarters of the analyzed publications focused on natural plant communities. In the case of crops, wheat and rice were the most frequently studied plants (i.e., they were analyzed in over 30% and over 20% of publications, respectively).

Earth Observation for Phenological Metrics (EO4PM): Temporal Discriminant to Characterize Forest Ecosystems

The study of vegetation phenology has great relevance in many fields since the importance of knowing timing and shifts in periodic plant life cycle events to face the consequences of global changes in issues such as crop production, forest management, ecosystem disturbances, and human health. The availability of high spatial resolution and dense revisit time satellite observations, such as Sentinel-2 satellites, allows high resolution phenological metrics to be estimated, able to provide key information from time series and to discriminate vegetation typologies. This paper presents an automated and transferable procedure that combines validated methodologies based on local curve fitting and local derivatives to exploit full satellite Earth observation time series to produce information about plant phenology. Multivariate statistical analysis is performed for the purpose of demonstrating the capacity of the generated smoothed vegetation curve, temporal statistics, and phenological metrics to serve as temporal discriminants to detect forest ecosystems processes responses to environmental gradients. The results show smoothed vegetation curve and temporal statistics able to highlight seasonal gradient and leaf type characteristics to discriminate forest types, with additional information about forest and leaf productivity provided by temporal statistics analysis. Furthermore, temporal, altitudinal, and latitudinal gradients are obtained from phenological metrics analysis, which also allows to associate temporal gradient with specific phenophases that support forest types distinction. This study highlights the importance of integrated data and methodologies to support the processes of vegetation recognition and monitoring activities.

Using Convolutional Neural Networks to Efficiently Extract Immense Phenological Data From Community Science Images

Community science image libraries offer a massive, but largely untapped, source of observational data for phenological research. The iNaturalist platform offers a particularly rich archive, containing more than 49 million verifiable, georeferenced, open access images, encompassing seven continents and over 278,000 species. A critical limitation preventing scientists from taking full advantage of this rich data source is labor. Each image must be manually inspected and categorized by phenophase, which is both time-intensive and costly. Consequently, researchers may only be able to use a subset of the total number of images available in the database. While iNaturalist has the potential to yield enough data for high-resolution and spatially extensive studies, it requires more efficient tools for phenological data extraction. A promising solution is automation of the image annotation process using deep learning. Recent innovations in deep learning have made these open-source tools accessible to a general research audience. However, it is unknown whether deep learning tools can accurately and efficiently annotate phenophases in community science images. Here, we train a convolutional neural network (CNN) to annotate images of Alliaria petiolata into distinct phenophases from iNaturalist and compare the performance of the model with non-expert human annotators. We demonstrate that researchers can successfully employ deep learning techniques to extract phenological information from community science images. A CNN classified two-stage phenology (flowering and non-flowering) with 95.9% accuracy and classified four-stage phenology (vegetative, budding, flowering, and fruiting) with 86.4% accuracy. The overall accuracy of the CNN did not differ from humans (p = 0.383), although performance varied across phenophases. We found that a primary challenge of using deep learning for image annotation was not related to the model itself, but instead in the quality of the community science images. Up to 4% of A. petiolata images in iNaturalist were taken from an improper distance, were physically manipulated, or were digitally altered, which limited both human and machine annotators in accurately classifying phenology. Thus, we provide a list of photography guidelines that could be included in community science platforms to inform community scientists in the best practices for creating images that facilitate phenological analysis.

The International Phenological Garden network (1959 to 2021): its 131 gardens, cloned study species, data archiving, and future

Collaborative networks that involve the compilation of observations from diverse sources can provide important data, but are difficult to maintain over long periods. The International Phenological Garden (IPG) network, begun in 1959 and still functioning 60 years later, has been no exception. Here we document its history, its monitored 23 species (initially all propagated by cloning), and the locations and years of data contribution of its 131 gardens, of which 63 from 19 countries contributed data in 2021. The decision to use clones, rather than multiple, locally adapted individuals, was based on the idea that this would "control" for genetic effects, and it affects the applicability of the data and duration of the network. We also describe the overlap among the IPG network, the Pan-European Phenology network (PEP725), and the phenological data offered by the German Weather Service. Sustainable data storage and accessibility, as well as the continued monitoring of all 23 species/clones, are under discussion at the moment, as is the fate of other phenological networks, despite a politically mandatory plant-based climate-change monitoring.

Delaying effect of humidity on leaf unfolding in Europe

Understanding the drivers of plant phenology is critical to predict the impact of future warming on terrestrial ecosystem carbon cycling and feedbacks to climate. Using indoor growth chambers, air humidity is reported to influence spring phenology in temperate trees. However, previous studies have not investigated the effect of air humidity on the spring phenology using long-term and large-scale ground observations. Therefore, the role of humidity in spring phenology in temperate trees still remains poorly understood. Here, we synthesized 229,588 records of leaf unfolding dates in eight temperate tree species, including four early-successional and four late-successional species, at 1716 observation sites during 1951-2015 in Europe, and comprehensively analyzed the effect of humidity on the spring phenology. We found that rising humidity significantly delayed spring leaf unfolding for all eight temperate tree species. Leaf unfolding was more sensitive to humidity in early-successional species compared to late-successional species. In addition, the delaying effect of humidity on leaf unfolding increased as temperature warmed over the past 65 years. Our results provide evidence that spring leaf unfolding of temperate trees was significantly delayed by rising humidity. The delaying effect of humidity may restrict earlier spring phenology induced by warming, especially for early-successional species, under future climate warming scenarios in temperate forests.

Artificial light pollution inhibits plant phenology advance induced by climate warming

Natural photic regime has been drastically altered by the artificial night sky luminance. Despite evidence of sufficient light brightness inducing plant physiology and affecting phenology, generalization regarding effects of light pollution on plant phenology across species and locations is less clear. Meanwhile, the relative contributions and joint effects of artificial light pollution and climate change or other anthropic stressors still remain unknown. To fill this knowledge gap, we utilized in situ plant phenological observations of seven tree species during 1991-2015 in Europe, night-time light dataset and gridded temperature dataset to investigate the impacts of the artificial light pollution on spatial-temporal shifts of plant phenological phases under climatic warming. We found 70% of the observation sites were exposed to increased light pollution during 1992-2015. Among them, plant phenological phases substantially delayed at 12-39% observation sites of leaf-out, and 6-53% of flowering. We also found plant species appeared to be more sensitive to artificial light pollution, and phenology advancement was hindered more prominently and even delay phenomenon exhibited when the color level showed stronger sky brightness. Linear mixed models indicate that although temperature plays a dominant role in shifts of plant phenological phases at the spatial scale, the inhibitory effect of artificial light pollution is evident considering the interactions. To our knowledge, this study is the first to quantitatively establish the relationship between artificial light pollution and plant phenology across species and locations. Meanwhile, these findings provide a new insight into the ecological responses of plant phenology to the potential but poorly understood environmental stressors under this warmer world and call for light pollution to be accorded the equal status as other global change phenomena.

A Mechanistic Framework for Understanding the Effects of Climate Change on the Link Between Flowering and Fruiting Phenology

Phenological shifts are a widely studied consequence of climate change. Little is known, however, about certain critical phenological events, nor about mechanistic links between shifts in different life-history stages of the same organism. Among angiosperms, flowering times have been observed to advance with climate change, but, whether fruiting times shift as a direct consequence of shifting flowering times, or respond differently or not at all to climate change, is poorly understood. Yet, shifts in fruiting could alter species interactions, including by disrupting seed dispersal mutualisms. In the absence of long-term data on fruiting phenology, but given extensive data on flowering, we argue that an understanding of whether flowering and fruiting are tightly linked or respond independently to environmental change can significantly advance our understanding of how fruiting phenologies will respond to warming climates. Through a case study of biotically and abiotically dispersed plants, we present evidence for a potential functional link between the timing of flowering and fruiting. We then propose general mechanisms for how flowering and fruiting life history stages could be functionally linked or independently driven by external factors, and we use our case study species and phenological responses to distinguish among proposed mechanisms in a real-world framework. Finally, we identify research directions that could elucidate which of these mechanisms drive the timing between subsequent life stages. Understanding how fruiting phenology is altered by climate change is essential for all plant species but is particularly critical to sustaining the large numbers of plant species that rely on animal-mediated dispersal, as well as the animals that rely on fruit for sustenance.

Green with phenology

[Figure: see text].

Predicting spring migration of two European amphibian species with plant phenology using citizen science data

Habitat fragmentation is one of the drivers for amphibian population declines globally. Especially in industrialized countries roads disrupt the seasonal migration of amphibians between hibernation and reproduction sites, often ending in roadkills. Thus, a timely installing of temporary mitigation measures is important for amphibian conservation. We wanted to find out if plant phenology can be a proxy in advance to determine the start of amphibian migration, since both phenomena are triggered by temperature. We analysed data of 3751 amphibian and 7818 plant phenology observations from citizen science projects in Austria between 2000 and 2018. Using robust regression modelling we compared the migration of common toads (Bufo bufo) and common frogs (Rana temporaria) with the phenology of five tree, one shrub, and one herb species. Results showed close associations between the migration of common frogs and phenological phases of European larch, goat willow and apricot. Models based on goat willow predict migration of common frog to occur 21 days after flowering, when flowering was observed on 60th day of year; apricot based models predict migration to occur 1 day after flowering, observed on the 75th day of year. Common toads showed weaker associations with plant phenology than common frogs. Our findings suggest that plant phenology can be used to determine the onset of temporary mitigation measures for certain amphibian species to prevent roadkills.

Contrasting temporal variations in responses of leaf unfolding to daytime and nighttime warming

Earlier spring phenological events have been widely reported in plants under global warming. Recent studies reported a slowdown in the warming-induced advanced spring phenology in temperate regions. However, previous research mainly focused on daily mean temperature, thus neglecting the asymmetric phenological responses to daytime and nighttime temperature. Using long-term records of leaf unfolding in eight deciduous species at 1300 sites across central Europe, we assessed and compared the effects of daytime temperature, nighttime temperature, and photoperiod on leaf unfolding during 1951-1980 and 1981-2013. Although leaf unfolding was advanced by daytime warming during 1951-2013, the advancing responses of leaf unfolding significantly decreased from 1951-1980 to 1981-2013 due to a lower accumulation of chilling units by daytime warming. Nighttime warming delayed leaf unfolding during 1951-1980 but advanced it during 1981-2013 due to a higher accumulation of chilling units by nighttime warming. In contrast, critical daylength and plasticity of leaf unfolding dates remained unchanged between 1951 and 2013. Our study provided evidence that daytime warming instead of nighttime warming accounts for the slowdown in the advancing spring phenology and implied that nighttime warming-induced earlier spring phenology may be buffering the slowdown of the advanced spring phenology by daytime warming. The response of spring phenology to nighttime temperature may override that to daytime temperature under the actual trends in global warming.

The growing and vital role of botanical gardens in climate change research

Botanical gardens make unique contributions to climate change research, conservation, and public engagement. They host unique resources, including diverse collections of plant species growing in natural conditions, historical records, and expert staff, and attract large numbers of visitors and volunteers. Networks of botanical gardens spanning biomes and continents can expand the value of these resources. Over the past decade, research at botanical gardens has advanced our understanding of climate change impacts on plant phenology, physiology, anatomy, and conservation. For example, researchers have utilized botanical garden networks to assess anatomical and functional traits associated with phenological responses to climate change. New methods have enhanced the pace and impact of this research, including phylogenetic and comparative methods, and online databases of herbarium specimens and photographs that allow studies to expand geographically, temporally, and taxonomically in scope. Botanical gardens have grown their community and citizen science programs, informing the public about climate change and monitoring plants more intensively than is possible with garden staff alone. Despite these advances, botanical gardens are still underutilized in climate change research. To address this, we review recent progress and describe promising future directions for research and public engagement at botanical gardens.

Maps, trends, and temperature sensitivities-phenological information from and for decreasing numbers of volunteer observers

Phenology serves as a major indicator of ongoing climate change. Long-term phenological observations are critically important for tracking and communicating these changes. The phenological observation network across Germany is operated by the National Meteorological Service with a major contribution from volunteering activities. However, the number of observers has strongly decreased for the last decades, possibly resulting in increasing uncertainties when extracting reliable phenological information from map interpolation. We studied uncertainties in interpolated maps from decreasing phenological records, by comparing long-term trends based on grid-based interpolated and station-wise observed time series, as well as their correlations with temperature. Interpolated maps in spring were characterized by the largest spatial variabilities across Bavaria, Germany, with respective lowest interpolated uncertainties. Long-term phenological trends for both interpolations and observations exhibited mean advances of -0.2 to -0.3 days year^-1 for spring and summer, while late autumn and winter showed a delay of around 0.1 days year^-1. Throughout the year, temperature sensitivities were consistently stronger for interpolated time series than observations. Such a better representation of regional phenology by interpolation was equally supported by satellite-derived phenological indices. Nevertheless, simulation of observer numbers indicated that a decline to less than 40% leads to a strong decrease in interpolation accuracy. To better understand the risk of declining phenological observations and to motivate volunteer observers, a Shiny app is proposed to visualize spatial and temporal phenological patterns across Bavaria and their links to climate change-induced temperature changes.

Sensitivity of Spring Phenology Simulations to the Selection of Model Structure and Driving Meteorological Data

Accurate estimation of the timing of intensive spring leaf growth initiation at mid and high latitudes is crucial for improving the predictive capacity of biogeochemical and Earth system models. In this study, we focus on the modeling of climatological onset of spring leaf growth in Central Europe and use three spring phenology models driven by three meteorological datasets. The MODIS-adjusted NDVI3g dataset was used as a reference for the period between 1982 and 2010, enabling us to study the long-term mean leaf onset timing and its interannual variability (IAV). The performance of all phenology model–meteorology database combinations was evaluated with one another, and against the reference dataset. We found that none of the constructed model–database combinations could reproduce the observed start of season (SOS) climatology within the study region. The models typically overestimated IAV of the leaf onset, where spatial median SOS dates were best simulated by the models based on heat accumulation. When aggregated for the whole study area, the complex, bioclimatic index-based model driven by the CarpatClim database could capture the observed overall SOS trend. Our results indicate that the simulated timing of leaf onset primarily depends on the choice of model structure, with a secondary contribution from the choice of the driving meteorological dataset.

Photoperiod decelerates the advance of spring phenology of six deciduous tree species under climate warming

Vegetation phenology in spring has substantially advanced under climate warming, consequently shifting the seasonality of ecosystem process and altering biosphere-atmosphere feedbacks. However, whether and to what extent photoperiod (i.e., daylength) affects the phenological advancement is unclear, leading to large uncertainties in projecting future phenological changes. Here we examined the photoperiod effect on spring phenology at a regional scale using in situ observation of six deciduous tree species from the Pan European Phenological Network during 1980-2016. We disentangled the photoperiod effect from the temperature effect (i.e., forcing and chilling) by utilizing the unique topography of the northern Alps of Europe (i.e., varying daylength but uniform temperature distribution across latitudes) and examining phenological changes across latitudes. We found prominent photoperiod-induced shifts in spring leaf-out across latitudes (up to 1.7 days per latitudinal degree). Photoperiod regulates spring phenology by delaying early leaf-out and advancing late leaf-out caused by temperature variations. Based on these findings, we proposed two phenological models that consider the photoperiod effect through different mechanisms and compared them with a chilling model. We found that photoperiod regulation would slow down the advance in spring leaf-out under projected climate warming and thus mitigate the increasing frost risk in spring that deciduous forests will face in the future. Our findings identify photoperiod as a critical but understudied factor influencing spring phenology, suggesting that the responses of terrestrial ecosystem processes to climate warming are likely to be overestimated without adequately considering the photoperiod effect.

Widespread decline in winds delayed autumn foliar senescence over high latitudes

The high northern latitudes (>50°) experienced a pronounced surface stilling (i.e., decline in winds) with climate change. As a drying factor, the influences of changes in winds on the date of autumn foliar senescence (DFS) remain largely unknown and are potentially important as a mechanism explaining the interannual variability of autumn phenology. Using 183,448 phenological observations at 2,405 sites, long-term site-scale water vapor and carbon dioxide flux measurements, and 34 y of satellite greenness data, here we show that the decline in winds is significantly associated with extended DFS and could have a relative importance comparable with temperature and precipitation effects in contributing to the DFS trends. We further demonstrate that decline in winds reduces evapotranspiration, which results in less soil water losses and consequently more favorable growth conditions in late autumn. In addition, declining winds also lead to less leaf abscission damage which could delay leaf senescence and to a decreased cooling effect and therefore less frost damage. Our results are potentially useful for carbon flux modeling because an improved algorithm based on these findings projected overall widespread earlier DFS than currently expected by the end of this century, contributing potentially to a positive feedback to climate.

Assessing Forest Phenology: A Multi-Scale Comparison of Near-Surface (UAV, Spectral Reflectance Sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) Remote Sensing

The monitoring of forest phenology based on observations from near-surface sensors such as Unmanned Aerial Vehicles (UAVs), PhenoCams, and Spectral Reflectance Sensors (SRS) over satellite sensors has recently gained significant attention in the field of remote sensing and vegetation phenology. However, exploring different aspects of forest phenology based on observations from these sensors and drawing comparatives from the time series of vegetation indices (VIs) still remains a challenge. Accordingly, this research explores the potential of near-surface sensors to track the temporal dynamics of phenology, cross-compare their results against satellite observations (MODIS, Sentinel-2), and validate satellite-derived phenology. A time series of Normalized Difference Vegetation Index (NDVI), Green Chromatic Coordinate (GCC), and Normalized Difference of Green & Red (VIgreen) indices were extracted from both near-surface and satellite sensor platforms. The regression analysis between time series of NDVI data from different sensors shows the high Pearson’s correlation coefficients (r > 0.75). Despite the good correlations, there was a remarkable offset and significant differences in slope during green-up and senescence periods. SRS showed the most distinctive NDVI profile and was different to other sensors. PhenoCamGCC tracked green-up of the canopy better than the other indices, with a well-defined start, end, and peak of the season, and was most closely correlated (r > 0.93) with the satellites, while SRS-based VIgreen accounted for the least correlation (r = 0.58) against Sentinel-2. Phenophase transition dates were estimated and validated against visual inspection of the PhenoCam data. The Start of Spring (SOS) and End of Spring (EOS) could be predicted with an accuracy of <3 days with GCC, while these metrics from VIgreen and NDVI resulted in a slightly higher bias of (3–10) days. The observed agreement between UAVNDVI vs. satelliteNDVI and PhenoCamGCC vs. satelliteGCC suggests that it is feasible to use PhenoCams and UAVs for satellite data validation and upscaling. Thus, a combination of these near-surface vegetation metrics is promising for a holistic understanding of vegetation phenology from canopy perspective and could serve as a good foundation for analysing the interoperability of different sensors for vegetation dynamics and change analysis.

A First Pre-season Pollen Transport Climatology to Bavaria, Germany

Climate impacts on the pollen season are well-described however less is known on how frequently atmospheric transport influences the start of the pollen season. Based on long-term phenological flowering and airborne pollen data (1987–2017) for six stations and seven taxa across Bavaria, Germany, we studied changes in the pollen season, compared pollen and flowering season start dates to determine pollen sources, and analyzed the likelihood of pollen transport by HYSPLIT back trajectories. Species advanced their pollen season more in early spring (e.g., Corylus and Alnus by up to 2 days yr⁻¹) than in mid spring (Betula, Fraxinus, Pinus); Poaceae and Artemisia exhibited mixed trends in summer. Annual pollen sums mainly increased for Corylus and decreased for Poaceae and Artemisia. Start of pollen season trends largely deviated from flowering trends, especially for Corylus and Alnus. Transport phenomena, which rely on comparisons between flowering and pollen dates, were determined for 2005–2015 at three stations. Pre-season pollen was a common phenomenon: airborne pollen was predominantly observed earlier than flowering (median 17 days) and in general, in 63% of the cases (except for Artemisia and Poaceae, and the alpine location) the pollen sources were non-local (transported). In 54% (35%) of these cases, back trajectories confirmed (partly confirmed) the pre-season transport, only in 11% of the cases transport modeling failed to explain the records. Even within the main pollen season, 70% of pollen season start dates were linked to transport. At the alpine station, non-local pollen sources (both from outside Bavaria as well as Bavarian lowlands) predominated, in only 13% of these cases transport could not be confirmed by back trajectories. This prominent role of pollen transport has important implications for the length, the timing, and the severity of the allergenic pollen season, indicating only a weak dependency on flowering of local pollen sources.

Reconciling competing hypotheses regarding flower-leaf sequences in temperate forests for fundamental and global change biology

Phenology is a major component of an organism's fitness. While individual phenological events affect fitness, there is growing evidence to suggest that the relationship between events could be equally or more important. This could explain why temperate deciduous woody plants exhibit considerable variation in the order of reproductive and vegetative events, or flower-leaf sequences (FLSs). There is evidence to suggest that FLSs may be adaptive, with several competing hypotheses to explain their function. Here, we advance existing hypotheses with a new framework that accounts for quantitative FLS variation at multiple taxonomic scales using case studies from temperate forests. Our inquiry provides several major insights towards a better understanding of FLS variation. First, we show that support for FLS hypotheses is sensitive to how FLSs are defined, with quantitative definitions being the most useful for robust hypothesis testing. Second, we demonstrate that concurrent support for multiple hypotheses should be the starting point for future FLS analyses. Finally, we highlight how adopting a quantitative, intraspecific approach generates new avenues for evaluating fitness consequences of FLS variation and provides cascading benefits to improving predictions of how climate change will alter FLSs and thereby reshape plant communities and ecosystems.

Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees

Changes in the growing-season lengths of temperate trees greatly affect biotic interactions and global carbon balance. Yet future growing-season trajectories remain highly uncertain because the environmental drivers of autumn leaf senescence are poorly understood. Using experiments and long-term observations, we show that increases in spring and summer productivity due to elevated carbon dioxide, temperature, or light levels drive earlier senescence. Accounting for this effect improved the accuracy of senescence predictions by 27 to 42% and reversed future predictions from a previously expected 2- to 3-week delay over the rest of the century to an advance of 3 to 6 days. These findings demonstrate the critical role of sink limitation in governing the end of seasonal activity and reveal important constraints on future growing-season lengths and carbon uptake of trees.

The European Forest Condition Monitor: Using Remotely Sensed Forest Greenness to Identify Hot Spots of Forest Decline

Forest decline, in course of climate change, has become a frequently observed phenomenon. Much of the observed decline has been associated with an increasing frequency of climate change induced hotter droughts while decline induced by flooding, late-frost, and storms also play an important role. As a consequence, tree mortality rates have increased across the globe. Despite numerous studies that have assessed forest decline and predisposing factors for tree mortality, we still lack an in-depth understanding of (I) underlying eco-physiological mechanisms, (II) the influence of varying environmental conditions related to soil, competition, and micro-climate, and (III) species-specific strategies to cope with prolonged environmental stress. To deepen our knowledge within this context, studying tree performance within larger networks seems a promising research avenue. Ideally such networks are already established during the actual period of environmental stress. One approach for identifying stressed forests suitable for such monitoring networks is to assess measures related to tree vitality in near real-time across large regions by means of satellite-borne remote sensing. Within this context, we introduce the European Forest Condition monitor (EFCM)-a remote-sensing based, freely available, interactive web information tool. The EFCM depicts forest greenness (as approximated using NDVI from MODIS at a spatial resolution of roughly 5.3 hectares) for the pixel-specific growing season across Europe and consequently allows for guiding research within the context of concurrent forest performance. To allow for inter-temporal comparability and account for pixel-specific features, all observations are set in relation to normalized difference vegetation index (NDVI) records over the monitoring period beginning in 2001. The EFCM provides both a quantile-based and a proportion-based product, thereby allowing for both relative and absolute comparison of forest greenness over the observational record. Based on six specific examples related to spring phenology, drought, late-frost, tree die-back on water-logged soils, an ice storm, and windthrow we exemplify how the EFCM may help identifying hotspots of extraordinary forest greenness. We discuss advantages and limitations when monitoring forest condition at large scales on the basis of moderate resolution remote sensing products to guide users toward an appropriate interpretation.

Climate change reshapes the drivers of false spring risk across European trees

Temperate forests are shaped by late spring freezes after budburst - false springs - which may shift with climate change. Research to date has generated conflicting results, potentially because few studies focus on the multiple underlying drivers of false spring risk. Here, we assessed the effects of mean spring temperature, distance from the coast, elevation and the North Atlantic Oscillation (NAO) using PEP725 leafout data for six tree species across 11 648 sites in Europe, to determine which were the strongest predictors of false spring risk and how these predictors shifted with climate change. All predictors influenced false spring risk before recent warming, but their effects have shifted in both magnitude and direction with warming. These shifts have potentially magnified the variation in false spring risk among species with an increase in risk for early-leafout species (i.e. Aesculus hippocastanum, Alnus glutinosa, Betula pendula) compared with a decline or no change in risk among late-leafout species (i.e. Fagus sylvatica, Fraxinus excelsior, Quercus robur). Our results show how climate change has reshaped the drivers of false spring risk, complicating forecasts of future false springs, and potentially reshaping plant community dynamics given uneven shifts in risk across species.

Improved Estimates of Arctic Land Surface Phenology Using Sentinel-2 Time Series

The high spatial resolution and revisit time of Sentinel-2A/B tandem satellites allow a potentially improved retrieval of land surface phenology (LSP). The biome and regional characteristics, however, greatly constrain the design of the LSP algorithms. In the Arctic, such biome-specific characteristics include prolonged periods of snow cover, persistent cloud cover, and shortness of the growing season. Here, we evaluate the feasibility of Sentinel-2 for deriving high-resolution LSP maps of the Arctic. We extracted the timing of the start and end of season (SoS and EoS, respectively) for the years 2019 and 2020 with a simple implementation of the threshold method in Google Earth Engine (GEE). We found a high level of similarity between Sentinel-2 and PhenoCam metrics; the best results were observed with Sentinel-2 enhanced vegetation index (EVI) (root mean squared error (RMSE) and mean error (ME) of 3.0 d and −0.3 d for the SoS, and 6.5 d and −3.8 d for the EoS, respectively), although other vegetation indices presented similar performances. The phenological maps of Sentinel-2 EVI compared well with the same maps extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) in homogeneous landscapes (RMSE and ME of 9.2 d and 2.9 d for the SoS, and 6.4 and −0.9 d for the EoS, respectively). Unreliable LSP estimates were filtered and a quality flag indicator was activated when the Sentinel-2 time series presented a long period (>40 d) of missing data; discontinuities were lower in spring and early summer (9.2%) than in late summer and autumn (39.4%). The Sentinel-2 high-resolution LSP maps and the GEE phenological extraction method will support vegetation monitoring and contribute to improving the representation of Artic vegetation phenology in land surface models.

Research challenges and opportunities for using big data in global change biology

Global change biology has been entering a big data era due to the vast increase in availability of both environmental and biological data. Big data refers to large data volume, complex data sets, and multiple data sources. The recent use of such big data is improving our understanding of interactions between biological systems and global environmental changes. In this review, we first explore how big data has been analyzed to identify the general patterns of biological responses to global changes at scales from gene to ecosystem. After that, we investigate how observational networks and space-based big data have facilitated the discovery of emergent mechanisms and phenomena on the regional and global scales. Then, we evaluate the predictions of terrestrial biosphere under global changes by big modeling data. Finally, we introduce some methods to extract knowledge from big data, such as meta-analysis, machine learning, traceability analysis, and data assimilation. The big data has opened new research opportunities, especially for developing new data-driven theories for improving biological predictions in Earth system models, tracing global change impacts across different organismic levels, and constructing cyberinfrastructure tools to accelerate the pace of model-data integrations. These efforts will uncork the bottleneck of using big data to understand biological responses and adaptations to future global changes.

Overestimation of the effect of climatic warming on spring phenology due to misrepresentation of chilling

Spring warming substantially advances leaf unfolding and flowering time for perennials. Winter warming, however, decreases chilling accumulation (CA), which increases the heat requirement (HR) and acts to delay spring phenology. Whether or not this negative CA-HR relationship is correctly interpreted in ecosystem models remains unknown. Using leaf unfolding and flowering data for 30 perennials in Europe, here we show that more than half (7 of 12) of current chilling models are invalid since they show a positive CA-HR relationship. The possible reason is that they overlook the effect of freezing temperature on dormancy release. Overestimation of the advance in spring phenology by the end of this century by these invalid chilling models could be as large as 7.6 and 20.0 days under RCPs 4.5 and 8.5, respectively. Our results highlight the need for a better representation of chilling for the correct understanding of spring phenological responses to future climate change.

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.

Finnish National Phenological Network 1997-2017: from observations to trend detection

Plant phenological dataset collected at 42 sites across the mainland of Finland and covering the years 1997-2017 is presented and analysed for temporal trends. The dataset of n = 16,257 observations represents eleven plant species and fifteen phenological stages and results in forty different variables, i.e. phenophases. Trend analysis was carried out for n = 808 phenological time-series that contained at least 10 observations over the 21-year study period. A clear signal of advancing spring and early-summer phenology was detected, 3.4 days decade^-1, demonstrated by a high proportion of negative trends for phenophases occurring in April through June. Latitudinal correlation indicated stronger signal of spring and early-summer phenology towards the northern part of the study region. The autumn signal was less consistent and showed larger within-site variations than those observed in other seasons. More than 60% of the dates based on single tree/monitoring square were exactly the same as the averages from multiple trees/monitoring squares within the site. In particular, the reliability of data on autumn phenology was increased by multiple observations per site. The network is no longer active.

Evaluation of VEGETATION and PROBA-V Phenology Using PhenoCam and Eddy Covariance Data

High-quality retrieval of land surface phenology (LSP) is increasingly important for understanding the effects of climate change on ecosystem function and biosphere–atmosphere interactions. We analyzed four state-of-the-art phenology methods: threshold, logistic-function, moving-average and first derivative based approaches, and retrieved LSP in the North Hemisphere for the period 1999–2017 from Copernicus Global Land Service (CGLS) SPOT-VEGETATION and PROBA-V leaf area index (LAI) 1 km V2.0 time series. We validated the LSP estimates with near-surface PhenoCam and eddy covariance FLUXNET data over 80 sites of deciduous forests. Results showed a strong correlation (R2 > 0.7) between the satellite LSP and ground-based observations from both PhenoCam and FLUXNET for the timing of the start (SoS) and R2 > 0.5 for the end of season (EoS). The threshold-based method performed the best with a root mean square error of ~9 d with PhenoCam and ~7 d with FLUXNET for the timing of SoS (30th percentile of the annual amplitude), and ~12 d and ~10 d, respectively, for the timing of EoS (40th percentile).

Interactive climate factors restrict future increases in spring productivity of temperate and boreal trees

Climate warming is currently advancing spring leaf-out of temperate and boreal trees, enhancing net primary productivity (NPP) of forests. However, it remains unclear whether this trend will continue, preventing for accurate projections of ecosystem functioning and climate feedbacks. Several ecophysiological mechanisms have been proposed to regulate the timing of leaf emergence in response to changing environmental cues, but the relative importance of those mechanisms remains unclear. Here, we use 727,401 direct phenological observations of common European forest trees to examine the dominant controls on leaf-out. Using the emerging mechanisms, we forecast future trajectories of spring arrival and evaluate the consequences for forest carbon dynamics. By representing hypothesized relationships with autumn temperature, winter chilling, and the timing of spring onset, we accurately predicted reductions in the advance of leaf-out. There was a strong consensus between our empirical model and existing process-based models, revealing that the advance in leaf-out will not exceed 2 weeks over the rest of the century. We further estimate that, under a 'business-as-usual' climate scenario, earlier spring arrival will enhance NPP of temperate and boreal forests by ~0.2 Gt per year at the end of the century. In contrast, previous estimates based on a simple degree-day model range around 0.8 Gt. As such, the expected NPP is drastically reduced in our updated model relative to previous estimates-by a total of ~25 Gt over the rest of the century. These findings reveal important environmental constraints on the productivity of broad-leaved deciduous trees and highlight that shifting spring phenology is unlikely to slow the rate of warming by offsetting anthropogenic carbon emissions.

Modeling leaf senescence of deciduous tree species in Europe

Autumnal leaf senescence signals the end of photosynthetic activities in temperate deciduous trees and consequently exerts a strong control on various ecological processes. Predicting leaf senescence dates (LSD) with high accuracy is thus a prerequisite for better understanding the climate-ecosystem interactions. However, modeling LSD at large spatial and temporal scales is challenging. In this study, first, we used 19972 site-year records (848 sites and four deciduous tree species) from the PAN European Phenology network to calibrate and evaluate six leaf senescence models during the period 1980-2013. Second, we extended the spatial analysis by repeating the procedure across Europe using satellite-derived end of growing season and a forest map. Overall, we found that models that considered photoperiod and temperature interactions outperformed models using simple temperature or photoperiod thresholds for Betula pendula, Fagus sylvatica and Quercus robur. On the contrary, no model displayed reasonable predictions for Aesculus hippocastanum. This inter-model comparison indicates that, contrary to expectation, photoperiod does not significantly modulate the accumulation of cooling degree days (CDD). On the other hand, considering the carryover effect of leaf unfolding date could promote the models' predictability. The CDD models generally matched the observed LSD at species level and its interannual variation, but were limited in explaining the inter-site variations, indicating that other environmental cues need to be considered in future model development. The discrepancies remaining between model simulations and observations highlight the need of manipulation studies to elucidate the mechanisms behind the leaf senescence process and to make current models more realistic.

"Green pointillism": detecting the within-population variability of budburst in temperate deciduous trees with phenological cameras

Phenological cameras have been used over a decade for identifying plant phenological markers (budburst, leaf senescence) and more generally the greenness dynamics of forest canopies. The analysis is usually carried out over the full camera field of view, with no particular analysis of the variability of phenological markers among trees. Here we show that images produced by phenological cameras can be used to quantify the within-population variability of budburst (WPVbb) in temperate deciduous forests. Using seven site-years of image analyses, we report a strong correlation (r² = 0.97) between the WPVbb determined with a phenological camera and its quantification through ground observation. We show that WPVbb varies strongly (by a factor of 4) from year to year in a given population and that those variations are linked with temperature conditions during the budburst period, with colder springs associated to a higher differentiation of budburst (higher WPVbb) among trees. Deploying our approach at the continental scale, i.e., throughout phenological cameras networks, would improve the understanding of the spatial (across populations) and temporal (across years) variations of WPVbb, which have strong implications on forest functioning, tree fitness and phenological modelling.