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[This corrects the article DOI: 10.1371/journal.pone.0208010.].

Terrestrial gross primary productivity (GPP) varies greatly over time and space. A better understanding of this variability is necessary for more accurate predictions of the future climate–carbon cycle feedback. Recent studies have suggested that variability in GPP is driven by a broad range of biotic and abiotic factors operating mainly through changes in vegetation phenology and physiological processes. However, it is still unclear how plant phenology and physiology can be integrated to explain the spatiotemporal variability of terrestrial GPP. Based on analyses of eddy–covariance and satellite-derived data, we decomposed annual terrestrial GPP into the length of the CO ₂ uptake period (CUP) and the seasonal maximal capacity of CO ₂ uptake (GPP ₘₐₓ). The product of CUP and GPP ₘₐₓ explained >90% of the temporal GPP variability in most areas of North America during 2000–2010 and the spatial GPP variation among globally distributed eddy flux tower sites. It also explained GPP response to the European heatwave in 2003 ( r ² = 0.90) and GPP recovery after a fire disturbance in South Dakota ( r ² = 0.88). Additional analysis of the eddy–covariance flux data shows that the interbiome variation in annual GPP is better explained by that in GPP ₘₐₓ than CUP. These findings indicate that terrestrial GPP is jointly controlled by ecosystem-level plant phenology and photosynthetic capacity, and greater understanding of GPP ₘₐₓ and CUP responses to environmental and biological variations will, thus, improve predictions of GPP over time and space. Significance Terrestrial gross primary productivity (GPP), the total photosynthetic CO ₂ fixation at ecosystem level, fuels all life on land. However, its spatiotemporal variability is poorly understood, because GPP is determined by many processes related to plant phenology and physiological activities. In this study, we find that plant phenological and physiological properties can be integrated in a robust index—the product of the length of CO ₂ uptake period and the seasonal maximal photosynthesis—to explain the GPP variability over space and time in response to climate extremes and during recovery after disturbance.

Plant phenology - the timing of plant life-cycle events, such as flowering or leafing out - plays a fundamental role in the functioning of terrestrial ecosystems, including human agricultural systems. Because plant phenology is often linked with climatic variables, there is widespread interest in developing a deeper understanding of global plant phenology patterns and trends. Although phenology data from around the world are currently available, truly global analyses of plant phenology have so far been difficult because the organizations producing large-scale phenology data are using non-standardized terminologies and metrics during data collection and data processing. To address this problem, we have developed the Plant Phenology Ontology (PPO). The PPO provides the standardized vocabulary and semantic framework that is needed for large-scale integration of heterogeneous plant phenology data. Here, we describe the PPO, and we also report preliminary results of using the PPO and a new data processing pipeline to build a large dataset of phenology information from North America and Europe.

Because climate change alters patterns of vegetative growth, long-term phenological measurements and observations can provide important data for analyzing its impact. Phenological assessments are usually made as records of calendar dates when specific phase changes occur. Such assessments have benefits and are used in Citizen Science monitoring. However, these kinds of data often have low statistical precision when describing gradual changes. Frequent monitoring of the phenological traits of forest trees and berries as they undergo gradual change is needed to acquire good temporal resolution of transitions relative to other factors, such as susceptibility to frosts, insects, and fungi, and the use of berries as a food resource. Intensive weekly monitoring of the growth of apical and branch buds and the elongation of shoots and leaves on four tree species, and the abundance of flowers and berries of bilberry and lingonberry, has been performed in Sweden since 2006. Here, we present quantitative methods for interpolating such data, which detail the gradual changes between assessments in order to describe average rates of development and amount of interannual variation. Our analysis has shown the active growth period of trees to differ with latitude. We also observed a change in the timing of the maximum numbers of ripening berries and their successive decline. Data from tree phenology assessments can be used to recommend best forestry practice and to model tree growth, while berry data can be used to estimate when food resources for animals are most available.

Accurate mapping of winter wheat over a large area is of great significance for guiding policy formulation related to food security, farmland management, and the international food trade. Due to the complex phenological features of winter wheat, the cloud contamination in time-series imagery, and the influence of the soil/snow background on vegetation indices, there remains no effective method for mapping winter wheat at a medium spatial resolution (10–30 m). In this study, we proposed a novel method called phenology-time weighted dynamic time warping (PT-DTW) for identifying winter wheat based on Sentinel 2A/B time-series data. The main advantages of PT-DTW include (1) the use of phenological features in two periods, i.e., the greenness increase before winter and greenness decrease after heading, which are common to all winter wheat and are distinct from the features of other land cover types, and (2) the use of the normalized differential phenology index (NDPI) instead of traditional vegetation indices to provide more robust vegetation information and to suppress the adverse impacts of soil and snow cover, especially during the before-winter growth period. The proposed PT-DTW method was employed for winter wheat mapping based on Sentinel 2A/B data on the Huang-Huai Plain, China. Validation with visually interpreted samples showed that the produced winter wheat map achieved an overall classification accuracy of 89.98% and a kappa coefficient of 0.7978, outperforming previous winter wheat classification methods. Moreover, the planting area derived from PT-DTW agreed well with census data at the municipal level, with a coefficient of determination of 0.8638, indicating that the winter wheat map produced at 20 m resolution was reliable overall. Therefore, the PT-DTW method is recommended for winter wheat mapping over large areas.

Plant phenology is the study of the timing of recurrent biological events and the causes of their timing with regard to biotic and abiotic forces. Plant phenology affects the structure and function of terrestrial ecosystems and determines vegetation feedback to the climate system by altering the carbon, water and energy fluxes between the vegetation and near-surface atmosphere. Therefore, an accurate simulation of plant phenology is essential to improve our understanding of the response of ecosystems to climate change and the carbon, water and energy balance of terrestrial ecosystems. Phenological studies have developed rapidly under global change conditions, while the research of phenology modeling is largely lagged. Inaccurate phenology modeling has become the primary limiting factor for the accurate simulation of terrestrial carbon and water cycles. Understanding the mechanism of phenological response to climate change and building process-based plant phenology models are thus important frontier issues. In this review, we first summarized the drivers of plant phenology and overviewed the development of plant phenology models. Finally, we addressed the challenges in the development of plant phenology models and highlighted that coupling machine learning and Bayesian calibration into process-based models could be a potential approach to improve the accuracy of phenology simulation and prediction under future global change conditions.

Despite documented intra-urban heterogeneity in the urban heat island (UHI) effect, little is known about spatial or temporal variability in plant response to the UHI. Using an automated temperature sensor network in conjunction with Landsat-derived remotely sensed estimates of start end of the growing season, we investigate the impacts of the UHI on plant phenology in the city of Madison WI (USA) for the 2012-2014 growing seasons. Median urban growing season length (GSL) estimated from temperature sensors is ∼5 d longer than surrounding rural areas, and UHI impacts on GSL are relatively consistent from year-to-year. Parks within urban areas experience a subdued expression of GSL lengthening resulting from interactions between the UHI and a park cool island effect. Across all growing seasons, impervious cover in the area surrounding each temperature sensor explains >50% of observed variability in phenology. Comparisons between long-term estimates of annual mean phenological timing, derived from remote sensing, and temperature-based estimates of individual growing seasons show no relationship at the individual sensor level. The magnitude of disagreement between temperature-based and remotely sensed phenology is a function of impervious and grass cover surrounding the sensor, suggesting that realized GSL is controlled by both local land cover and micrometeorological conditions.

38 taxa of the genera Conocybe and Pholiotina (Agaricales, Agaricomycetes) have been recorded in Estonia. A checklist of these taxa with notes on ecology, phenology and distribution is presented.

Recent climate warming has altered plant phenology at northern European latitudes, but conclusions regarding the spatial patterns of phenological change and relationships with climate are still challenging as quantitative estimates are strongly diverging. To generate consistent estimates of broad-scale spatially continuous spring plant phenology at northern European latitudes (> 50° N) from 2000 to 2016, we used a novel vegetation index, the plant phenology index (PPI), derived from MODerate-resolution Imaging Spectroradiometer (MODIS) data. To obtain realistic and strong estimates, the phenology trends and their relationships with temperature and precipitation over the past 17 years were analyzed using a panel data method. We found that in the studied region the start of the growing season (SOS) has on average advanced by 0.30 day year−1. The SOS showed an overall advancement rate of 2.47 day °C−1 to spring warming, and 0.18 day cm−1 to decreasing precipitation in spring. The previous winter and summer temperature had important effects on the SOS but were spatially heterogeneous. Overall, the onset of SOS was delayed 0.66 day °C−1 by winter warming and 0.56 day °C−1 by preceding summer warming. The precipitation in winter and summer influenced the SOS in a relatively weak and complex manner. The findings indicate rapid recent phenological changes driven by combined seasonal climates in northern Europe. Previously unknown spatial patterns of phenological change and relationships with climate drivers are presented that improve our capacity to understand and foresee future climate effects on vegetation.

Premise of the Study Plant phenology is a critical trait, as the timings of phenophases such as budburst, leafout, flowering, and fruiting, are important to plant fitness. Despite much study about when individual phenophases occur and how they may shift with climate change, little is known about how multiple phenophases relate to one another across an entire growing season. We test the extent to which early phenological stages constrain later ones, throughout a growing season, across 25 angiosperm tree species. Methods We observed phenology (budburst, leafout, flowering, fruiting, and senescence) of 118 individual trees across 25 species, from April through December 2015. Key Results We found that early phenological events weakly constrain most later events, with the strongest constraints seen between consecutive stages. In contrast, interphase duration was a much stronger predictor of phenology, especially for reproductive events, suggesting that the development time of flowers and fruits may constrain the phenology of these events. Conclusions Much of the variation in later phenological events can be explained by the timing of earlier events and by interphase durations. This highlights that a shift in one phenophase may often have cascading effects on later phases. Accurate forecasts of climate change impacts should therefore include multiple phenophases within and across years.