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This article discusses the concept of quiescence and dormancy in plants and their importance in plant development, acclimation, and survival. The article highlights recent research on the molecular and metabolic regulation of quiescence, as well as the ecological modeling and molecular perception of temperature in relation to dormancy. The integration of hormone and cell-to-cell signaling, as well as the role of epigenetic mechanisms in establishing and maintaining dormancy, is also explored. The article concludes by emphasizing the potential applications of this research in enhancing plant resilience in the face of climate change. [Extracted from the article]

This article explores the use of temperature sum models in studying plant spring phenology. These models measure plant development based on temperature-dependent units, such as growing degree hours. The article discusses the limitations of determining the threshold temperature, which represents the lower limit for development, and suggests that it should be estimated for each species. It also examines the implications of using temperature response models as proxies for true physiological responses. The research emphasizes the importance of developing ecophysiological growing degree hour models that consider the unique characteristics of different plant species. Another study focuses on the use of growing degree hour models to predict the timing of spring phenological events in subtropical trees. It finds that using a fixed threshold temperature in these models may lack biological realism, but can still be useful for comparing heat requirements between species or bud types. The authors stress the need for experimental determination of the temperature response and the use of species-specific values in growing degree hour models. [Extracted from the article]

Why nonnative invasive plant species commonly co‐occur, despite their competitive superiority and propensity to displace native species, remains a paradox in invasion biology. Negative interactions among competitively dominant invaders are potentially alleviated by two understudied mechanisms: seasonal priority effects, where phenological separation weakens the effect of competition on species with early phenology; and indirect facilitation, where competition between two species is mitigated by a third species. Although phenological separation has been speculated as a mechanism for explaining co‐occurrence patterns of invasive plants, it has never been directly tested. In a greenhouse experiment, we tested the effect of phenological separation on direct and indirect interactions between three co‐occurring invasive plant species found in the riparian forests of North America. These species have distinct natural phenological separation with reproduction in early spring (Ficaria verna), mid‐spring (Alliaria petiolata), and late summer (Microstegium vimineum). When phenology was experimentally synchronized, direct pairwise interactions among invasive species were overwhelmingly negative, asymmetric, and unlikely to promote co‐occurrence. However, increasing phenological separation generated seasonal priority effects, which weakened the effect of competition on species with early phenology. Furthermore, the addition of a third species generated indirect facilitative effects, which balanced competitive outcomes among the two weakest competitors. Based on these findings, we conclude that phenological separation modulates the strength of both seasonal priority effects and indirect facilitation within species interaction networks and may promote the co‐occurrence of three common invasive species within this study system. We articulate how future studies can test the external validity of these findings in more complex environmental conditions and with a larger range of invasive plants. [ABSTRACT FROM AUTHOR]

This article explores the habitat of macroalgae in sand grains, specifically focusing on the species Monostroma angicava. The study reveals that the microscopic codiolum sporophytes of M. angicava inhabit calcareous sand grains during the summer. The researchers emphasize the importance of studying the ecology and habitats of these microscopic stages of macroalgae. Sand grains provide a stable habitat for the sporophyte stage of M. angicava, with a high density of individuals, and also play a role in their dispersal and survival. Further research is needed to understand the ecological and physiological mechanisms of these microscopic stages and their impact on coastal ecosystems. [Extracted from the article]

Human activities have both positive and negative impacts on forests, altering the extent and composition of various forest vegetation types, and increasing uncertainty in ecological management. A detailed understanding of the historical distribution of forest vegetation is crucial for local conservation efforts. In this study, we integrated phenological features with climatic and terrain variables to enhance the mapping accuracy of forest vegetation in Yunnan. We mapped the historical distributions of five forest vegetation type groups and nine specific forest vegetation types for 2001, 2010, and 2020. Our findings revealed that: (1) rubber plantations can be effectively distinguished from other forest vegetation using phenological features, coniferous forests and broad-leaved forests can be differentiated using visible spectral bands, and environmental variables (temperature, precipitation, and elevation) are effective in differentiating forest vegetation types under varying climate conditions; (2) the overall accuracy and kappa coefficient increased by 14.845% and 20.432%, respectively, when climatic variables were combined with phenological features, and by 13.613% and 18.902%, respectively, when elevation was combined with phenological features, compared to using phenological features alone; (3) forest cover in Yunnan increased by 2.069 × 104 km2 (10.369%) between 2001 and 2020. This study highlights the critical role of environmental variables in improving the mapping accuracy of forest vegetation in mountainous regions. [ABSTRACT FROM AUTHOR]

Purpose and Methods: Crop identification using remotely sensed imagery provides useful information to make management decisions about land use and crop health. This research used phonecams to acquire the Normalized Difference Vegetation Index (NDVI) of various crops for three crop seasons. NDVI time series from Sentinel (L121-L192) images was also acquired using Google Earth Engine (GEE) for the same period. The resolution of satellite data is low therefore gap filling and smoothening filters were applied to the time series data. The comparison of data from satellite images and phenocam provides useful insight into crop phenology. The results show that NDVI is generally underestimated when compared to phenocam data. The Savitzky-Golay (SG) and some other gap filling and smoothening methods are applied to NDVI time series based on satellite images. The smoothened NDVI curves are statistically compared with daily NDVI series based on phenocam images as a reference. Results: The SG method has performed better than other methods like moving average. Furthermore, polynomial order has been found to be the most sensitive parameter in applying SG filter in GEE. Sentinel (L121-L192) image was used to identify wheat during the year 2022–2023 in Sargodha district where experimental fields were located. The Random Forest Machine Leaning algorithm was used in GEE as a classifier. Conclusion: The classification accuracy has been found 97% using this algorithm which suggests its usefulness in applying to other areas with similar agro-climatic characteristics. [ABSTRACT FROM AUTHOR]

Changes in phenology are a common response to climate change, but their impact on population dynamics is often ambiguous and at‐risk species are omitted from most analyses.We assessed the relationship between change in abundance and change in phenology for 114 butterfly populations of 31 at‐risk species from five families in 10 US states. We used data from Pollard‐walk and similar monitoring programmes for which count data were collected on multiple occasions per year. We also collected information from managers on site‐level management interventions.To estimate abundance, we first fit separate smoothing splines for each species at each site. Yearly abundance was estimated as the area under the activity curve for that year, and from this, we estimated abundance trends over the period for which we had population data. Phenological shifts were measured as the changes in median activity date, beginning of flight season, duration of flight season and phenological constancy (negative of the magnitude of trends in median activity date). We also evaluated the association of ecological traits (voltinism, diet breadth, position within geographic range, ecoregion, overwintering stage and seasonality) and management (proportion of years with interventions) with trends in abundance and in phenology.Across all populations, the estimated trend in abundance was −0.085, equivalent to an 8.1% decline/year. Positive trends in abundance were associated with smaller shifts in the median flight date (higher constancy). We also found strong associations between trends in abundance and management interventions, with increased management associated with increasing abundance trends. In general, ecological traits were not strongly associated with trends in phenology or trends in abundance.Synthesis and applications: Populations with less phenological constancy are more likely to be rapidly declining and populations with more frequent management interventions are increasing. These results suggest that one key outcome of management interventions may be to mitigate some of the impacts of climate change, which in turn may contribute to higher population growth. These results also imply that managers may need to alter the timing of appropriate management to synchronize with activities of at‐risk species as species shift their phenology. [ABSTRACT FROM AUTHOR]

Droughts can affect ecosystem CO2 fluxes directly or indirectly by changing plant community composition. However, it is unknown whether shifts in plant community composition buffer or amplify the response of ecosystem CO2 fluxes to droughts with different seasonal timing, as plant phenology and physiology of the different plant functional types respond differently to droughts.To identify the interaction of drought timing and plant community composition in regulating ecosystem CO2 fluxes, we conducted a three‐year manipulative experiment in which extreme droughts occurring in the early, mid and late growing seasons were separately imposed on experimental plot communities comprising graminoids, shrubs and their combination in a semi‐arid grassland of Inner Mongolia, China.Overall, mid‐season drought caused the largest negative effects regardless of plant community composition. In addition to decreasing aboveground biomass, mid‐season drought suppressed fluxes by reducing leaf photosynthetic rate, while early‐season and late‐season drought reduced fluxes mainly by shortening growing season length. All three community compositions had consistent responses to early‐season and mid‐season droughts. However, ecosystem CO2 fluxes in the combination community were less negatively affected by late‐season drought than in either shrub or graminoid communities because the growing season length was shortened less.Synthesis. Our results highlight that it is important to account for interactions of seasonal timing and plant community composition when predicting magnitude and pathways of drought effects on ecosystem carbon cycling. [ABSTRACT FROM AUTHOR]

Warming associated with climate change will advance the onset of spring phenology for many forest plants across the Eastern United States. Understory forbs and spring ephemerals that fix a disproportionate amount of carbon during early spring may be negatively affected by earlier canopy closure; however, information on the spatial patterns of phenological change for these communities is still lacking. To assess the potential for changes in spring phenological windows, we synthesized observations from the Appalachian Mountain Club's (AMCs) Mountain Watch (MW) project, the National Phenology Network (NPN), and AMC's iNaturalist projects between 2004 and 2022 (n = 118,250) across the length of the Appalachian Trail (AT) Corridor (34° N–46° N latitude). We used hierarchical Bayesian modeling to examine the sensitivity of spring flowering and leaf‐out for 11 understory species and 14 canopy tree species to mean spring temperature (April–June). We conducted analyses across the AT Corridor, partitioned by regions of 4° latitude (south, mid‐Atlantic, and north). Spring phenologies for both understory plants and canopy trees advanced with warming (~6 and ~3 days/°C, respectively). However, the sensitivity of each group varied by latitude, with the phenology of trees and understory plants advancing to a greater degree in the mid‐Atlantic region (~10 days/°C) than in the southern or northern regions (~5 days/°C). While we find evidence that phenological windows remain stable in the southern and mid‐Atlantic portions of the AT, we observed an expansion of the spring phenological window in the north where there was greater understory forb temperature sensitivity compared with trees (~2.7 days/°C). Our analyses indicate the differential sensitivity of forest plant phenology to potential warming across a large latitudinal gradient in the Eastern United States. Further, evidence for a temperature‐driven expansion of the spring phenological window suggests a potential beneficial effect for understory plants in the northern AT, although phenological mismatch with potential pollinators and increased vulnerability to late winter frosts are possible. Using extensive citizen‐science datasets allows us to synthesize regional‐ and continental‐scale data to explore spatial and temporal trends in spring phenology related to warming. Such data can help to standardize approaches in phenological research and its application to forest climate resiliency. [ABSTRACT FROM AUTHOR]

Information on planting dates is crucial for modeling crop development, analyzing crop yield, and evaluating the effectiveness of policy-driven planting windows. Despite their high importance, field-level planting date datasets are scarce. Satellite remote sensing provides accurate and cost-effective solutions for detecting crop phenology from moderate to high resolutions, but remote sensing-based crop planting date detection is rare. Here, we aimed to generate field-level crop planting date maps by taking advantage of satellite remote sensing-derived phenological metrics and proposed a two-step framework to predict crop planting dates from these metrics using required growing degree dates (RGDD) as a bridge. Specifically, we modeled RGDD from the planting date to the spring inflection date (derived from phenological metrics) and then predicted the crop planting dates based on phenological metrics, RGDD, and environmental variables. The ∼3-day and 30-m Harmonized Landsat and Sentinel-2 (HLS) products were used to derive crop phenological metrics for corn and soybean fields in the U.S. Midwest from 2016 to 2021, and the ground truth of field-level planting dates from USDA Risk Management Agency (RMA) reports were used for the development and validation of our proposed two-step framework. The results indicated that our framework could accurately predict field-level planting dates from HLS-derived phenological metrics, capturing 77 % field-level variations for corn (mean absolute error, MAE=4.6 days) and 71 % for soybean (MAE=5.4 days). We also evaluated the predicted planting dates with USDA National Agricultural Statistics Service (NASS) state-level crop progress reports, achieving strong consistency with median planting dates for corn (R2=0.90, MAE=2.7 days) and soybeans (R2=0.87, MAE=2.5 days). The model's performance degraded slightly when predicting planting dates for fields with irrigation (MAE=5.4 days for corn, MAE=6.1 days for soybean) and cover cropping (MAE=5.4 days for corn, MAE=5.6 days for soybean). The USDA RMA Common Crop Insurance Policy (CCIP) provides county- or sub-county-level crop planting windows, which drive producers' decisions on when to plant. Within the CCIP-driven planting windows, higher prediction accuracies were achieved (MAE for corn: 4.5 days, soybean: 5.2 days). Our proposed two-step framework (phenological metrics-RGDD-planting dates) also outperformed the traditional one-step model (phenological metrics-planting dates). The proposed framework can be beneficial for deriving planting dates from current and future phenological products and contribute to studies related to planting dates such as the analysis of yield gaps, management practices, and government policies. [ABSTRACT FROM AUTHOR]

Monitoring vulnerable species inhabiting mountain environments is crucial to track population trends and prioritize conservation efforts. However, the challenging nature of these remote areas poses difficulties in implementing effective and consistent monitoring programmes. To address these challenges, we examined the potential of passive acoustic monitoring of a cryptic high mountain bird species, the Rock Ptarmigan Lagopus muta. For 5 months in each of two consecutive years, we deployed 38 autonomous recording units in 10 areas of the Swiss Alps where the species is monitored by a national count monitoring programme. Once the recordings were collected, we built a machine‐learning algorithm to automate call recognition. We focused on studying the species' daily and seasonal calling phenology and relating these to meteorological and climatic data. Rock Ptarmigans were vocally active from March to July, with a peak of activity occurring between mid‐March and late April, 1 or 2 months earlier than the second half of May when the counts of the monitoring programme take place. The calling rate peaked at dawn before dropping rapidly until sunrise. Daily vocal activity demonstrated a consistent association with weather conditions and moon phase, whereas the timing of seasonal vocal activity varied with temperature and snow conditions. We found that the peak of vocal activity occurred when the snowpack was still thick and snow cover was close to 100% but with a local peak of high temperatures. Between our two study years, the peak of vocal activity occurred 30 days later in the colder year, suggesting phenological plasticity in relation to environmental conditions. Passive acoustic monitoring has the potential to complement conventional acoustic counts of cryptic birds by highlighting periods of higher detectability of individuals, and to survey small populations that often remain undetected during single visits. Moreover, our study supports the idea that passive acoustic monitoring can provide valuable data over large spatial and temporal scales, allowing decryption of hidden ecological patterns and assisting in conservation efforts. [ABSTRACT FROM AUTHOR]

Bird communities in agroecosystems bring both ecosystem services (e.g., pollination) and disservices (e.g., crop exploitation) to farmers. However, in the proximity of wetland reserves, farmers disproportionately experience harvest yield loss due to large aggregation of bird species that can utilize various agricultural resources. This often results in negative human–wildlife interactions which lower conservation support among farmers. Knowledge about the distribution of avian species that negatively influence yields, and its environmental drivers is thus fundamental to reconcile crop production and bird conservation. This study aims to examine the spatio-temporal patterns in richness and abundance of bird species known to cause agricultural yield loss as well as species-specific distribution patterns for the six bird species that are most challenging for local farmers. In combination with interview surveys of local farmers (n = 367) and seasonal bird surveys (n = 720), we investigated distribution of crop-exploiting avian species in the Indawgyi wetland ecosystem in Myanmar. Our results showed high richness and abundance of crop-exploiting species in the water habitat across all seasons, with most challenging species exhibiting higher presence closer to these water sources. The crop phenology had positive effect on species richness and abundance during the growing season. The agricultural use of crop-exploiting species was season- and species-specific, where the presence probability in the agricultural habitat was higher in habitat generalists than wetland specialists. Therefore, we suggest improved management of natural wetland habitats (e.g., habitat restoration), sustainable coexistence mechanisms in farms close to water (e.g., bird-friendly rice farming and Ecolabel certification) to reduce avian impacts on the farming communities and, at the same time, to promote bird conservation in wetlands of international importance. [ABSTRACT FROM AUTHOR]

Introduction: Canola (Brassica napus L.) is one of the most important crops worldwide. Turnip yellows virus (TuYV), transmitted by aphids, is one of the most damaging viruses affecting canola crops and is challenging to control. With the prediction of more intense and prolonged drought events due to future climate change, an additional factor may extensively impact the epidemiology of plant diseases. This study aimed to understand the impact of drought on canola plants infected with TuYV and to explore the relationship between virus infection and drought. Methods: Two glasshouse experiments were conducted: 1. Competition: Four plants (two infected, two non-infected) were grown in the same pot. 2. No Competition: One plant was grown per pot. In both experiments, infected and non-infected canola plants were exposed to well-watered conditions, water stress (simulated drought), and terminal drought. Various plant traits were recorded, including biomass, leaf area, height, number of leaves, chlorophyll content, water use efficiency, and virus symptom expression. Results: Both virus infection and water stress reduced dry biomass, leaf area, and height. Virus infection alone reduced canola biomass by up to 49% compared to non-infected, well-watered controls. Under water stress or terminal drought, the biomass of TuYV-infected plants was further reduced by up to 71% and 65%, respectively. Virus infection also reduced the number of leaves, although water treatment alone did not. Chlorophyll content was higher in water-stressed and terminal drought plants compared to well-watered ones, while virus infection reduced chlorophyll content. The impact of drought and virus infection was more pronounced when plants were under competition. Discussion: Given the expected increase in prolonged and frequent droughts in many canola-growing regions due to climate change, a significant detrimental effect on canola production due to the combined influence of drought and TuYV is anticipated. This study underscores the need for developing mitigation strategies to protect canola production in a changing climate. [ABSTRACT FROM AUTHOR]

Monitoring agricultural land over vast geographical areas presents challenges due to the absence of accurate, comprehensive and precise data, which has become a complex process that is difficult to do in terms of both timespans and consistency. Hence, this study presents an improved approach for the identification of agricultural land by utilizing the capabilities of Sentinel-1 and Sentinel-2 satellites with a variety of vegetation and non-vegetation indices and machine learning algorithms. The Multispectral Correlation Mapper (MCM) and Random Forest (RF) algorithms are adopted to train different agricultural lands, crop types and sowing and cultivation seasons. The 45-bands mega-file data cube (MFDC) fusion for each season incorporates essential indices and features derived from the Sentinel-1 and Sentinel-2 datasets for both seasons, i.e. Rabi (winter-spring season) and Kharif (summer-autumn season). The proposed method demonstrated resilience when applied to satellite datasets while effectively reducing the impact of non-agricultural elements such as shrubs, grass, bare soil and orchards. The results demonstrate a notable ability to differentiate between the Rabi and Kharif seasons, resulting in a high level of precision in measuring the extent of cultivated land during the Rabi and Kharif seasons with an area of 626,947 acres and 590,858 acres, respectively. The total land area, ascertained from the observation of the comprehensive cropping pattern and agricultural modifications during the entire year (June 2021–May 2022) is 635,655 acres. The validation exercise shows the higher accuracy of this method for cropland, with an overall accuracy of 98.8%, kappa of 0.97, user accuracy of 98.69% and producer accuracy of 99.13%. Additionally, it was spatially compared with the ESRI, ESA and MODIS cropland layers and government statistical data. Furthermore, the research investigates the temporal dynamics of agricultural growth phases using spectral bands and indices. This approach improves the accuracy of precise cropland identification and provides useful insights into crop phenology. [ABSTRACT FROM AUTHOR]

Increasing ant abundance associated with climate warming has been observed in temperate ecosystems. However, how enhanced ant activity affects plant development and phenology remains unclear. In this study, individuals of a perennial forb (Potentilla tanacetifolia) during flowering with and without an ant (Proformica) nest were marked to explore the impacts of ant nests on plant growth and phenology in a temperate steppe on the Mongolian Plateau. Ant nests delayed the start and end flowering dates simultaneously and, consequently, had no effect on flowering duration. However, presence of an ant nest postponed the fruiting date of individuals. Nests further increased numbers of aborted flowers and thus decreased fruit set. These observations suggest that the delayed flowering phenology disrupted synchrony between plant reproduction and pollinators and thus reduced pollination efficiency under ant nests. Given the increasing abundance of ants with rising temperatures under climate warming, plant delayed reproductive phenology and reduced reproductive output will potentially have negative consequences for plant fitness and plant–arthropod interactions, with consequent impacts on resistance and resilience of perennial species in temperate steppes under climate change. [ABSTRACT FROM AUTHOR]

Summary: Changes to flowering phenology are a key response of plants to climate change. However, we know little about how these changes alter temporal patterns of reproductive overlap (i.e. phenological reassembly).We combined long‐term field (1937–2012) and herbarium records (1850–2017) of 68 species in a flowering plant community in central North America and used a novel application of Bayesian quantile regression to estimate changes to flowering season length, altered richness and composition of co‐flowering assemblages, and whether phenological shifts exhibit seasonal trends.Across the past century, phenological shifts increased species' flowering durations by 11.5 d on average, which resulted in 94% of species experiencing greater flowering overlap at the community level. Increases to co‐flowering were particularly pronounced in autumn, driven by a greater tendency of late season species to shift the ending of flowering later and to increase flowering duration.Our results demonstrate that species‐level phenological shifts can result in considerable phenological reassembly and highlight changes to flowering duration as a prominent, yet underappreciated, effect of climate change. The emergence of an autumn co‐flowering mode emphasizes that these effects may be season‐dependent. [ABSTRACT FROM AUTHOR]

Grassland vegetation phenology has undergone great changes under the influence of climate, which affects ecosystem functions and services. However, the mechanism of preseason climatic factors in driving phenological shifts is unclear. In this study, we determined the start (SOS) and peak (POS) of the growing season for Inner Mongolian grasslands using gross primary productivity data (2000-2018). We investigated the spatiotemporal changes of SOS and POS and elucidated the mechanisms behind these changes by analyzing how these phenological events were influenced by the specific preseason climatic requirements (precipitation, air temperature, and solar radiation). Our results revealed that the SOS significantly advanced at a rate of 0.65 days/a, while the POS was stable across the study area. At the pixel scale, areas with initially later SOS and POS exhibited stronger advanced trends. An earlier SOS and POS were associated with lower thermal requirements, including air temperature and solar radiation. Conversely, a delayed SOS and POS necessitated higher climatic requirements. The impact of preseason precipitation on both SOS and POS demonstrated notable spatial variability. Moreover, the effects of different climatic factors on phenology were not in sync due to regional environmental disparities. Our study provides insight into the mechanisms underlying phenological shifts in grassland ecosystems under climate change. [ABSTRACT FROM AUTHOR]

Betula utilis exhibits intriguing characteristics and interactions with its environment and has specific adaptations that enable it to thrive in various water conditions. Drought has a prominent role in influencing the growth and development of vegetation, while temperature serves as a crucial determinant of species distribution in high-altitude environments. The investigation was centered on the eco-physiological dimension of B. utilis in areas near the treeline. Across different seasons, sites, and years, the most negative pre-dawn twig water potentials (ΨPD) and mid-day twig water potentials (ΨMD) were − 0.81 and − 1.24 MPa, respectively. The highest seasonal change (ΔΨ) in twig water potential (Ψtwig) was in the post-monsoon season. Osmotic potential at full turgor (Ψπ100) declined by − 0.66 MPa and osmotic potential at zero turgor (Ψπ0) declined by − 1.07 MPa. The highest leaf conductance (gw) of 380.26 mmol m−2 s−1 was measured in the afternoon. During the initiation of flowering, ΨPD of the twig was − 0.72 MPa and gradually rose to − 0.17 MPa by the end of the flowering period. This study provides key insight into the Ψ dynamics, leaf conductance, and phenology of B. utilis, highlighting its adaptation to changing environmental conditions and the need for effective management strategies to ensure the resilience and conservation of this Critically Endangered species. [ABSTRACT FROM AUTHOR]

Providing more equitable pedagogies to all students, including those who are traditionally underrepresented, is a high priority of science education. In this paper, we outline how we coupled Indigenous Ways of Knowing with investigations about plant phenology, or the timing of plant development, a trait that is expected to shift with a warming climate. We paired the investigations with lessons that allow secondary students the opportunity to explore the cultural significance of common milkweed (Asclepias syriaca), a host plant for the iconic and threatened monarch butterfly (Danaus plexippus). In our teaching, we specifically investigate the effect of milkweed phenology, or the timing of development, on species interactions. From teaching students in a variety of contexts about milkweed phenology and species interactions, we have learned that the topic is engaging and accessible to many students. We find that coupling inquiry with explorations of the cultural significance of milkweed deepens students learning and generates opportunities to compare Indigenous Ways of Knowing and Western Science and to learn how they can work together to assess the impacts of climate change. [ABSTRACT FROM AUTHOR]

The effects of temperature, and photoperiod on autumn phenology are well established for many species. However, the impact of multiple environmental factors and their interactions on regulating autumn phenology remains insufficiently explored. A large-scale controlled experiment in an artificial climate chamber was conducted from April to October 2021 at the Hebei Gucheng Agricultural Meteorology National Observation and Research Station, Hebei Province. This study aimed to investigate the interactive effects of temperature [T1.5, (1.5 °C above the control), T2, (2 °C above the control)], photoperiod [LP, long photoperiod (4 h photoperiod above the control), SP, short photoperiod (4 h photoperiod below the control)], and nitrogen addition [LN, low nitrogen, (nitrogen at 5 g N·m−2·a−1), MN, medium nitrogen, (nitrogen at 10 g N·m−2·a−1), HN, high nitrogen, (nitrogen at 20 g N·m−2·a−1), control for temperature and photoperiod was the mean monthly temperature and average photoperiod (14 h) from 1989–2020 for Stipa krylovii, while the control for nitrogen treatment was without nitrogen addition] on leaf senescence in Stipa krylovii. A three-way analysis of variance (ANOVA) revealed significant effects of temperature, photoperiod, and nitrogen addition on leaf senescence (p < 0.01), with effects varying across different levels of each factor. Increased temperature notably delayed leaf senescence, with delays averaging of 4.0 and 6.3 days for T1.5 and T2, respectively. The LP treatment advanced leaf senescence by an average of 4.0 days, while the SP treatment delayed it by an average of 6.2 days; nitrogen addition advanced leaf senescence, with the effect intensifying as nitrogen levels increased, resulting in average advancements of 1.5, 1.9, and 4.3 days for LN, MN, and HN, respectively. Additionally, we observed that temperature altered the sensitivity of leaf senescence to the photoperiod, diminishing the advancement caused by LP at 2 °C and amplifying the delay caused by SP. These findings underscore the differential impacts of these three factors on the leaf senescence of Stipa krylovii and provide critical insights into plant phenology in response to varying environmental conditions. [ABSTRACT FROM AUTHOR]

While the impacts of climate change on global food security have been studied extensively, the capability of emerging tools that couple land surface processes and crop growth in reproducing inter‐annual yield variability at regional scale remains to be tested rigorously. In this study, we analyzed the effects of weather variations between years (1999–2019) on regional crop productivity for two agriculturally managed regions with contrasting climate and cropping conditions: the German state of North Rhine‐Westphalia (DE‐NRW) and the Australian state of Victoria (AUS‐VIC), using the latest version of the Community Land Model (CLM5) and the WFDE5 (WATCH Forcing Data methodology applied to ECMWF reanalysis version 5) reanalysis. Overall, the simulation results were able to reproduce the total annual crop yields of certain crops, while also capturing the differences in total yield magnitudes between the domains. However, the simulations showed limitations in correctly capturing inter‐annual differences of crop yield compared to official yield records, which resulted in relatively low correlation coefficients between 0.07 and 0.39 in AUS‐VIC and between 0.11 and 0.42 in DE‐NRW. The mean absolute deviation of simulated winter wheat yields was up to 4.6 times lower compared to state‐wide records from 1999 to 2019. Our results suggest the following limitations of CLM5: (a) limitations in simulating yield responses from plant hydraulic stress; (b) errors in simulating soil moisture contents compared to satellite‐derived data; and (c) errors in the representation of cropland in general, for example, crop parameterizations and human influences. Plain Language Summary: This study evaluates how year‐to‐year weather variations impact crop yield predictions for two regions, North Rhine‐Westphalia in Germany and Victoria in Australia changes. We use the community land model (CLM5) land surface model in combination with reanalysis weather data to investigate the model performance with respect to the representation of crop phenology, plant water stress, and soil moisture. Our results showcase the model's ability to predict total annual crop yield magnitudes for both regions, while also capturing the differences between the respective simulation domains. However, year‐to‐year changes in crop yield were lower in simulation results compared to official records, which indicated a lack of model sensitivity toward drought stress and general limitations in the representation of agricultural land. This research systematically assesses CLM5 model performance over arable land and provides useful insights into limitations of CLM5 that can help guide future empirical and technical model improvements. Key Points: Land surface models (LSMs) with integrated crop models can be used to quantify the impact of climate change on agro‐ecosystemsThe potential value of LSMs for agricultural purposes depends on their ability to adequately simulate inter‐annual variability of yieldThe representation of plant hydraulics and the soil moisture regime play key role in accurately simulating agro‐ecosystems [ABSTRACT FROM AUTHOR]

The European spruce bark beetle Ips typographus is a widespread pest in Norway spruce-dominated forests in Eurasia. Predicting its phenology and voltinism is crucial to plan forest management measures and to mitigate mass outbreaks. Current phenology models are based on constant temperatures inferred from laboratory experiments; however, insect life cycles under natural conditions are rather driven by diurnal and seasonal temperature fluctuations. Therefore, phenology models based on fluctuating temperatures would reflect field conditions more realistically and might thus improve model predictions. In a laboratory experiment, we investigated the development of I. typographus, applying mean temperatures between 3 and 35 °C and diurnal temperature oscillations of up to ± 15 °C. Subsequently, we calibrated developmental rate models and applied them to climate data, in order to assess the effect of temperature fluctuations on voltinism under field conditions. Our results showed that diurnal temperature oscillations significantly affected developmental rates. Compared to constant temperatures, development was faster at temperature oscillations falling below the lower developmental threshold, and slower at temperature oscillations exceeding the developmental optimum. Furthermore, short exposures to suboptimal temperatures affected I. typographus less than expected from constant conditions. Natural temperature fluctuations thus accelerate development under cool, shaded conditions, whilst slowing it under hot, sun-exposed conditions, thereby ultimately affecting voltinism. Our findings highlight the importance to account for diurnal temperature fluctuations for more accurate predictions of developmental rates of I. typographus in natural thermal environments, and provide the fundament for improving current phenology models to support effective bark beetle management in a warming climate. [ABSTRACT FROM AUTHOR]

Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between the biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth's surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different timescales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation development; on the other hand, phenological changes can be driven by past environmental conditions, known as memory effects. However, the processes governing meteorological memory effects on phenology are not completely understood, resulting in their limited performance of vegetation phenology represented in land surface models. A deep learning model, specifically a long short-term memory network (LSTM), has the potential to capture and model the meteorological memory effects on vegetation phenology. Here, we apply the LSTM to model the vegetation phenology using meteorological drivers and high-temporal-resolution canopy greenness observations through digital repeat photography by the PhenoCam network. We compare a multiple linear regression model, a no-memory-effect LSTM model and a full-memory-effect LSTM model to predict the whole seasonal greenness trajectory and the corresponding phenological transition dates across 50 sites and 317 site years during 2009–2018, covering deciduous broadleaf forests, evergreen needleleaf forests and grasslands. Results show that the deep learning model outperforms the multiple linear regression model, and the full-memory-effect LSTM model performs better than the no-memory-effect model for all three plant function types (median R2 of 0.878, 0.957 and 0.955 for broadleaf forests, evergreen needleleaf forests and grasslands). We also find that the full-memory-effect LSTM model is capable of predicting the seasonal dynamic variations of canopy greenness and reproducing trends in shifting phenological transition dates. We also performed a sensitivity analysis of the full-memory-effect LSTM model to assess its plausibility, revealing its coherence with established knowledge of vegetation phenology sensitivity to meteorological conditions, particularly changes in temperature. Our study highlights that (1) multi-variate meteorological memory effects play a crucial role in vegetation phenology, and (2) deep learning opens up new avenues for improving the representation of vegetation phenological processes in land surface models via a hybrid modelling approach. [ABSTRACT FROM AUTHOR]

Operational crop monitoring applications, including crop type mapping, condition monitoring, and yield estimation, would benefit from the ability to robustly detect and map crop phenology measures related to the crop calendar and management activities like emergence, stem elongation, and harvest timing. However, this has proven to be challenging due to two main issues: first, the lack of optimised approaches for accurate crop phenology retrievals, and second, the cloud cover during the crop growth period, which hampers the use of optical data. Hence, in the current study, we outline a novel calibration procedure that optimises the settings to produce high-quality N D V I time series as well as the thresholds for retrieving the start of the season (SOS) and end of the season (EOS) of different crops, making them more comparable and related to ground crop phenological measures. As a first step, we introduce a new method, termed UE-WS, to reconstruct high-quality N D V I time series data by integrating a robust upper envelope detection technique with the Whittaker smoothing filter. The experimental results demonstrate that the new method can achieve satisfactory performance in reducing noise in the original N D V I time series and producing high-quality N D V I profiles. As a second step, a threshold optimisation approach was carried out for each phenophase of three crops (winter wheat, corn, and sugarbeet) using an optimisation framework, primarily leveraging the state-of-the-art hyperparameter optimization method (Optuna) by first narrowing down the search space for the threshold parameter and then applying a grid search to pinpoint the optimal value within this refined range. This process focused on minimising the error between the satellite-derived and observed days of the year (DOY) based on data from the German Meteorological Service (DWD) covering two years (2019–2020) and three federal states in Germany. The results of the calculation of the median of the temporal difference between the DOY observations of DWD phenology held out from a separate year (2021) and those derived from satellite data reveal that it typically ranged within ±10 days for almost all phenological phases. The validation results of the detection of dates of phenological phases against separate field-based phenological observations resulted in an R M S E of less than 10 days and an R-squared value of approximately 0.9 or greater. The findings demonstrate how optimising the thresholds required for deriving crop-specific phenophases using high-quality N D V I time series data could produce timely and spatially explicit phenological information at the field and crop levels. [ABSTRACT FROM AUTHOR]

Seasonal variation in animal activity influences fitness and the intensity of ecological interactions (e.g., competition, predation), yet aspects of global change in the Anthropocene may catalyze shifts in seasonal activity. Invasive plants are components of global change and can modify animal daily activity, but their influence on animal seasonal activity is less understood. We examined how invasive woody shrubs (Autumn olive [Elaeagnus umbellata] and Amur honeysuckle [Lonicera maackii]) affect seasonal activity of three common small‐mammal species by coupling experimental shrub removal with autumnal camera trapping for two consecutive years at six paired forest sites (total 12 plots). Eastern chipmunks (Tamias striatus) foraged more, and foraging was observed at least 20 days longer, in shrub‐invaded forests. White‐footed mice (Peromyscus leucopus) foraged more in invaded than cleared plots in one study year, but P. leucopus autumn activity timing did not differ between shrub‐removal treatments. Fox squirrel (Sciurus niger) activity displayed year‐specific responses to shrub removal suggesting intraannual cues (e.g., temperature) structure S. niger autumnal activity. Our work highlights how plant invasions can have species‐specific effects on seasonal animal activity, may modify the timing of physiological processes (e.g., torpor), and could generate variation in animal‐mediated interactions such as seed dispersal or granivory. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are widely used to infer species–environment relationships, predict spatial distributions and characterise species' environmental niches. While the importance of space and spatial scales is widely acknowledged in SDM applications, temporal components of the niche are rarely addressed.We discuss how phenology and demographic stages affect model inference in plant SDMs. Ignoring conspicuousness and timing of phenological stages may bias niche estimates through increased observer bias, while ignoring stand age may bias niche estimates through temporal mismatches with environmental variables, especially during times of rapid global warming.We present different methods to consider phenology and demographic stages in plant SDMs, including the selection of causal, spatiotemporally explicit predictors and the calibration of stage‐specific SDMs. Based on a case study with citizen science data, we illustrate how spatiotemporal SDMs provide deeper insights on the coincidence of range and phenological shifts under climate change.The proliferation of digitally available biodiversity and citizen science data increasingly allows considering time explicitly in SDMs. This offers a more mechanistic understanding of plant distributions, and more robust predictions under global change, especially if the reporting of phenological stages and age is facilitated and promoted by relevant data portals. [ABSTRACT FROM AUTHOR]

Near real-time (NRT) crop type mapping plays a crucial role in modeling crop development, managing food supply chains, and supporting sustainable agriculture. The low-latency updates on crop type distribution also help assess the impacts of weather extremes and climate change on agricultural production in a timely fashion, aiding in identification of early risks in food insecurity as well as rapid assessments of the damage. Yet NRT crop type mapping is challenging due to the obstacle in acquiring timely crop type reference labels during the current season for crop mapping model building. Meanwhile, the crop mapping models constructed with historical crop type labels and corresponding satellite imagery may not be applicable to the current season in NRT due to spatiotemporal variability of crop phenology. The difficulty in characterizing crop phenology in NRT remains a significant hurdle in NRT crop type mapping. To tackle these issues, a novel emergence-based thermal phenological framework (EMET) is proposed in this study for field-level NRT crop type mapping. The EMET framework comprises three key components: hybrid deep learning spatiotemporal image fusion, NRT thermal-based crop phenology normalization, and NRT crop type characterization. The hybrid fusion model integrates super-resolution convolutional neural network (SRCNN) and long short-term memory (LSTM) to generate daily satellite observations with a high spatial resolution in NRT. The NRT thermal-based crop phenology normalization innovatively synthesizes within-season crop emergence (WISE) model and thermal time accumulation throughout the growing season, to timely normalize crop phenological progress derived from temporally dense fusion imagery. The NRT normalized fusion time series are then fed into an advanced deep learning classifier, the self-attention based LSTM (SAtLSTM) model, to identify crop types. Results in Illinois and Minnesota of the U.S. Corn Belt suggest that the EMET framework significantly enhances the model scalability with crop phenology normalized in NRT for timely crop mapping. A consistently higher overall accuracy is yielded by the EMET framework throughout the growing season compared to the calendar-based and WISE-based benchmark scenarios. When transferred to different study sites and testing years, EMET maintains an advantage of over 5% in overall accuracy during early- to mid-season. Moreover, EMET reaches an overall accuracy of 85% a month earlier than the benchmarks, and it can accurately characterize crop types with an overall accuracy of 90% as early as in late July. F1 scores for both corn and soybeans also achieve 90% around late July. The EMET framework paves the way for large-scale satellite-based NRT crop type mapping at the field level, which can largely help reduce food market volatility to enhance food security, as well as benefit a variety of agricultural applications to optimize crop management towards more sustainable agricultural production. [ABSTRACT FROM AUTHOR]

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In recent decades, vegetation phenology, as one of the most sensitive and easily observed features under climate change, has changed significantly under the influence of the global warming as a result of the green house effect. Vegetation phenological change is not only highly related to temperature change, but also to precipitation, a key factor affecting vegetation phenological change. However, the response of vegetation phenology to climate change is different in different regions, and the current research still does not fully understand the climate drivers that control phenological change. The study focuses on the Loess Plateau, utilizing the GIMMS NDVI3g dataset to extract vegetation phenology parameters from 1982 to 2015 and analyzing their spatial–temporal variations and responses to climate change. Furthermore, by incorporating emission scenarios of RCP4.5 (medium and low emission) and RCP8.5 (high emission), the study predicts and analyzes the changes in vegetation phenology on the Loess Plateau from 2030 to 2100. The long-term dynamic response of vegetation phenology to climate change and extreme climate is explored, so as to provide a scientific basis for the sustainable development of the fragile Loess Plateau. The key findings are as follows: (1) From 1982 to 2015, the start of the growing season (SOS) on the Loess Plateau shows a non-significant delay (0.06 d/year, p > 0.05), while the end of the growing season (EOS) is significantly delayed at a rate of 0.1 d/year (p < 0.05). (2) In the southeastern part of the Loess Plateau, temperature increases led to a significant advancement of SOS. Conversely, in the Maowusu Desert in the northwest, increased autumn precipitation caused a significant delay in EOS. (3) From 2030 to 2100, under the RCP4.5 and RCP8.5 scenarios, temperatures are projected to rise significantly at rates of 0.018 °C/year and 0.06 °C/year, respectively. Meanwhile, precipitation will either decrease insignificantly at −0.009 mm/year under RCP4.5 or increase significantly at 0.799 mm/year under RCP8.5. In this context, SOS is projected to advance by 19 days and 28 days, respectively, under RCP4.5 and RCP8.5, with advancement rates of 0.049 days/year and 0.228 days/year. EOS is projected to be delayed by 14 days and 27 days (p < 0.05), respectively, with delay rates of 0.084 d/year and 0.2 d/year. [ABSTRACT FROM AUTHOR]

Climate change, a pressing global concern, poses significant challenges to agricultural systems worldwide. Among the myriad impacts of climate change, the cultivation of kiwifruit trees (Actinidia spp.) faces multifaceted challenges. In this review, we delve into the intricate effects of climate change on kiwifruit production, which span phenological shifts, distributional changes, physiological responses, and ecological interactions. Understanding these complexities is crucial for devising effective adaptation and mitigation strategies to safeguard kiwifruit production amidst climate variability. This review scrutinizes the influence of rising global temperatures, altered precipitation patterns, and a heightened frequency of extreme weather events on the regions where kiwifruits are cultivated. Additionally, it delves into the ramifications of changing climatic conditions on kiwifruit tree physiology, phenology, and susceptibility to pests and diseases. The economic and social repercussions of climate change on kiwifruit production, including yield losses, livelihood impacts, and market dynamics, are thoroughly examined. In response to these challenges, this review proposes tailored adaptation and mitigation strategies for kiwifruit cultivation. This includes breeding climate-resilient kiwifruit cultivars of the Actinidia species that could withstand drought and high temperatures. Additional measures would involve implementing sustainable farming practices like irrigation, mulching, rain shelters, and shade management, as well as conserving soil and water resources. Through an examination of the literature, this review showcases the existing innovative approaches for climate change adaptation in kiwifruit farming. It concludes with recommendations for future research directions aimed at promoting the sustainability and resilience of fruit production, particularly in the context of kiwifruit cultivation, amid a changing climate. [ABSTRACT FROM AUTHOR]

The research aims to assess the significance of early (winter) jasmine as a fundamental component within Belgrade's urban green infrastructure (UGI) in Serbia. Through a specific case study, it investigates the intricate interplay between climate, biodiversity, and vegetation across spatial and temporal scales influenced by climate change. The study analizes the layout of UGI, its various spatial typologies, and the importance of relation between these characteristics, morphology and phenology for the social and cultural context. Results demonstrate that Weeping Winter Jasmine, particularly during its cold-season flowering phase, enhances visual appeal and attractiveness without conflicting with ethical, spiritual, or cultural values. Morphological analysis of 2,250 flowers revealed a distinction between trimmed and free-growing plants, yet differing characteristics did not impact flowering abundance or visual perception. Flowering phenology was monitored across the municipalities of Savski venac, Vračar, and Čukarica to assess shifts in flowering patterns. Over the autumn, winter, and spring of 2022/2023, 30,925 phenological observations were recorded, while 125,600 from the period 2007-2022 were utilized to determine key flowering phases and events important for landscape design. Cluster analysis delineated location 3 from locations 1 and 2, which share similarities and are influenced by the urban heat island (UHI) effect, confirming phenological changes are a prominent indicator of climate change. Despite belonging to the oldest group, location 3 individuals exhibited the longest flowering phenophase, commencing 54.5 days earlier and concluding 15 days later compared to the previous sixteen-year period. The study confirmed the importance of Jasminum nudiflorum Lindl. as a UGI element in Belgrade, sustaining vibrant and cascading displays throughout the colder months and maintianing social and cultural contribution of the location amidst climate change. [ABSTRACT FROM AUTHOR]

Maize is the world's largest food crop and plays a critical role in global food security. Accurate phenology information is essential for improving yield estimation and enabling timely field management. Yet, much of the research has concentrated on general crop growth periods rather than on pinpointing key phenological stages. This gap in understanding presents a challenge in determining how different vegetation indices (VIs) might accurately extract phenological information across these stages. To address this, we employed the shape model fitting (SMF) method to assess whether a multi-index approach could enhance the precision of identifying key phenological stages. By analyzing time-series data from various VIs, we identified five phenological stages (emergence, seven-leaf, jointing, flowering, and maturity stages) in maize cultivated in Jilin Province. The findings revealed that each VI had distinct advantages depending on the phenological stage, with the land surface water index (LSWI) being particularly effective for jointing and flowering stages due to its correlation with vegetation water content, achieving a root mean square error (RMSE) of three to four days. In contrast, the normalized difference vegetation index (NDVI) was more effective for identifying the emergence and seven-leaf stages, with an RMSE of four days. Overall, combining multiple VIs significantly improved the accuracy of phenological stage identification. This approach offers a novel perspective for utilizing diverse VIs in crop phenology, thereby enhancing the precision of agricultural monitoring and management practices. [ABSTRACT FROM AUTHOR]

Ectotherms are largely impacted by extreme climate events, essentially heatwaves. In Tunisia, the pine processionary moth (PPM), Thaumetopoea pityocampa, is a defoliator typically laying eggs in summer. Its geographical range is expanding northwards in Europe while retracting from southern Tunisia where summer temperatures can reach extreme heats. We explored the effects of climate change over the last 30 years on PPM at its southern range edge. We investigated variations of fecundity and causes of egg mortality over time using historical and contemporary collections of egg masses. Due to incomplete historical series, a clustering method was used to group climatically homologous sampling sites and perform allochronic analyses on clusters instead of individual localities. Our results suggest negative effects of summer heat on egg survival, down to 0% hatching rate in one site during a major heatwave. Such high hatching failures resulted partly from high egg sterility and aborted embryos, with little effects of parasitism rate, suggesting heat susceptibility during embryonic development, but our results do not allow distinguishing impeded mating success from failed egg maturation or early embryo death. Decreasing female fecundity was observed in regions where allochronic comparisons were possible, associated with a decrease in parasitism rate, while climatic variability increased. Previous studies have confirmed experimentally the thermal sensitivity of PPM early instars to temperatures observed in the present work, including one population from southern Tunisia. However, further work is required to evaluate the relative importance of warming summers among populations, since the risk of heat stress depends on the phenology of sensitive instars, and populations from the warmest areas may not necessarily be the most vulnerable to climate change if they already evolved phenological heat avoidance. In addition to heat‐induced mortality, the ultimate fitness of heat survivors should also be explored to determine potential carry‐over effects on subsequent life stages. [ABSTRACT FROM AUTHOR]

Phenology and photosynthetic capacity both regulate carbon uptake by vegetation. Previous research investigating the impact of phenology on vegetation productivity has focused predominantly on the start and end of growing seasons (SOS and EOS), leaving the influence of peak phenology metrics—particularly in typical climatic transition zones—relatively unexplored. Using a 24-year (2000–2023) enhanced vegetation index (EVI) dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS), we extracted and examined the spatiotemporal variation for peak of season (POS) and peak growth (defined as EVImax) of forest vegetation in the Funiu Mountain region, China. In addition to quantifying the factors influencing the peak phenology metrics, the relationship between vegetation productivity and peak phenological metrics (POS and EVImax) was investigated. Our findings reveal that POS and EVImax showed advancement and increase, respectively, negatively and positively correlated with vegetation productivity. This suggested that variations in EVImax and peak phenology both increase vegetation productivity. Our analysis also showed that EVImax was heavily impacted by precipitation, whereas SOS had the greatest effect on POS variation. Our findings highlighted the significance of considering climate variables as well as biological rhythms when examining the global carbon cycle and phenological shifts in response to climate change. [ABSTRACT FROM AUTHOR]

Global change has substantially shifted vegetation phenology, with important implications in the carbon and water cycles of terrestrial ecosystems. Various vegetation phenology datasets have been developed using remote sensing data; however, the significant uncertainties in these datasets limit our understanding of ecosystem dynamics in terms of phenology. It is therefore crucial to generate a reliable large-scale vegetation phenology dataset, by fusing various existing vegetation phenology datasets, to provide comprehensive and accurate estimation of vegetation phenology with fine spatiotemporal resolution. In this study, we merged four widely used vegetation phenology datasets to generate a new dataset using the Reliability Ensemble Averaging fusion method. The spatial resolution of the new dataset is 0.05° and its temporal scale spans 1982–2022. The evaluation using the ground-based PhenoCam dataset from 280 sites indicated that the accuracy of the newly merged dataset was improved substantially. The start of growing season and the end of growing season in the newly merged dataset had the largest correlation (0.84 and 0.71, respectively) and accuracy in terms of the root mean square error (12 and 17 d, respectively). Using the new dataset, we found that the start of growing season exhibits a significant (p < 0.01) advanced trend with a rate of approximately 0.24 d yr−1, and that the end of growing season exhibits a significant (p < 0.01) delayed trend with a rate of 0.16 d yr−1 over the period 1982–2022. This dataset offers a unique and novel source of vegetation phenology data for global ecology studies. [ABSTRACT FROM AUTHOR]

Accurate prediction of flowering times is essential for efficient orchard management for kiwifruit, facilitating timely pest and disease control and pollination interventions. In this study, we developed a predictive model for flowering time using weather data and observations of budbreak dynamics for the 'Hayward' and 'Zesy002' kiwifruit. We used historic data of untreated plants collected from 32 previous studies conducted between 2007 and 2022 and analyzed budbreak and flowering timing alongside cumulative heat sum (growing degree days, GDDs), chilling unit (CU) accumulation, and other environmental variables using weather data from the weather stations nearest to the study orchards. We trained/parameterized the model with data from 2007 to 2019, and then evaluated the model's efficacy using testing data from 2020 to 2022. Regression models identified a hierarchical structure with the accumulation of GDDs at the start of budbreak, one of the key predictors of flowering time. The findings suggest that integrating climatic data with phenological events such as budbreak can enhance the predictability of flowering in kiwifruit vines, offering a valuable tool for kiwifruit orchard management. [ABSTRACT FROM AUTHOR]

Summary Plant phenology, the timing of recurrent biological events, shows key and complex response to climate warming, with consequences for ecosystem functions and services. A key challenge for predicting plant phenology under future climates is to determine whether the phenological changes will persist with more intensive and long‐term warming. Here, we conducted a meta‐analysis of 103 experimental warming studies around the globe to investigate the responses of four phenophases – leaf‐out, first flowering, last flowering, and leaf coloring. We showed that warming advanced leaf‐out and flowering but delayed leaf coloring across herbaceous and woody plants. As the magnitude of warming increased, the response of most plant phenophases gradually leveled off for herbaceous plants, while phenology responded in proportion to warming in woody plants. We also found that the experimental effects of warming on plant phenology diminished over time across all phenophases. Specifically, the rate of changes in first flowering for herbaceous species, as well as leaf‐out and leaf coloring for woody species, decreased as the experimental duration extended. Together, these results suggest that the real‐world impact of global warming on plant phenology will diminish over time as temperatures continue to increase. [ABSTRACT FROM AUTHOR]

Given that ecosystems are composed of sounds created by geophysical events (e.g., wind, rain), animal behaviors (e.g., dawn songbird chorus), and human activities (e.g., tourism) that depend on seasonal climate conditions, the phenological patterns of a soundscape could be coupled with long-term weather station data as a complimentary ecological indicator of climate change. We tested whether the seasonality of the soundscape coincided with common weather variables used to monitor climate. We recorded ambient sounds hourly for five minutes (01 January-30 June) over three years (2019-2021) near a weather-station in a subarctic ecosystem in south-central Alaska. We quantified sonic information using the Acoustic Complexity Index (ACItf), coupled with weather data, and used machine learning (TreeNet) to identify sonic-climate relationships. We grouped ACItf according to time periods of prominent seasonal events (e.g., days with temperatures >0°C, no snow cover, green up, dawn biophony, and road-based tourism) and identified distinct sonic phenophases (sonophases) for groups with non-overlapping 95% confidence intervals. In general, sonic activity increased dramatically as winter transitioned to spring and summer. We identified two winter sonophases, a spring sonophase, and a summer sonophase, each coinciding with hours of daylight, temperature, precipitation, snow cover, and the prevalence of animal and human activities. We discuss how sonophases and weather data combined serve as a multidimensional, systems-based approach to understanding the ecological effects of climate change in subarctic environments. [ABSTRACT FROM AUTHOR]

Detecting changes in the phenological responses of herbaceous species as a function of predicted climate change is important for forecasting future scenarios for the functioning of dry tropical forests, especially when predicting an increase in the frequency and intensity of extreme droughts. Because of the sensitivity of plants to water availability, our study hypothesizes that if years become drier or wetter, herbaceous plants will synchronously change the onset, duration, and intensity of their vegetative phenophases.We used a historical series of 60 years of precipitation observations for the Caatinga vegetation to define daily average of precipitation for rainy (Twet), median (Tcontrol), and dry (Tdry) years. We simulated past average daily rainfall (Twet, Tcontrol, and Tdry) while growing two herbaceous perennials and two herbaceous annuals. We monitored plant growth and measured the activity (absence or presence) and intensity of vegetative phenophases. We used circular statistical analysis to assess differences between treatments.Our results revealed that leaf production was seasonal but relatively uniform for perennial species and highly seasonal (wet season) for annual species. Simulated dry years induced lower leaf emergence concentrated over a few months in annual species, but this effect was more strongly significant in one of the two perennial species. Both annual and perennial species can experience delayed and less intense leaf abscission during the rainy season in years with below‐average precipitation. In contrast, large voluminous rains in years with above‐average precipitation can accelerate and intensify the process of leaf renewal.If future precipitation reductions occur, the changes in phenological response indicate that the cover of annual and perennial herbaceous species in this study will likely decrease, altering the landscape and functioning of dry tropical forests. However, the potential trade‐offs observed may help populations of these species to persist during years of severe drought in the Caatinga. [ABSTRACT FROM AUTHOR]

Climate change and global warming in the Sahelian region cause dramatic drought and advancing of the desert. This phenomenon could affect the plant survival and community composition, but even for surviving plants, it could affect their phenology and the insect community associated with them. In a space‐for‐time approach, we studied the case of Annona senegalensis Pers. (Annonaceae), a common shrub in tropical areas, to determine the impact of climate change on its phenology and the insects associated with its flowers and fruits. We determined the phenology phases of Annona senegalensis during a 1‐year period and assessed the abundance and diversity of insects in the Sudanian and the Sudano‐Sahelian climatic zones of Burkina Faso. Temperature, rainfall and relative humidity were recorded during 12 months in two sites per zone. Leafing of Annona senegalensis lasted 10 months in the Sudanian zone, flowering and fruiting were 3 months long. In the Sudano‐Sahelian zone, leafing lasted 8 months while flowering and fruiting were 3 and 4 months long, respectively. A total of 10,040 insects belonging to 48 species were collected in the two climatic zones. Forty‐six species were found in the Sudanian zone while 25 species were recorded in the Sudano‐Sahelian one. The variations in the plant phenology and the insect community were mainly due to the variation in rainfall across both climatic zones. Our results emphasize that advancing of the desert due to climate change could not only affect the survival of plants but for resistant species it also affect their interactions with insects and the whole insect community associated. [ABSTRACT FROM AUTHOR]

Assessing Leaf Area Index (LAI) is one of the key indicators to study and understand cropland's productivity. To this end, Sentinel-2 (S2) ideally serves to monitor LAI given its high spatial and temporal resolution. Thanks to the cloud computing prowess of the revolutionary Google Earth Engine (GEE), assessing the temporal changes in LAI over a large area can be achieved quickly, making crop health monitoring in near real-time plausible. To achieve this, this work has integrated the Gaussian Process Regression (GPR) algorithm and the PROSAIL model into GEE to map LAI values for the crops being cultivated accurately. The Automated Radiative Transfer Mode Operator (ARTMO) software generated PROSAIL simulation spectra. The GPR model was trained using the PROSAIL simulation spectra and optimized through the Euclidean distance-Based Diversity (EBD) Active Learning (AL) method. The final GPR model was validated against in-situ data collected on 23rd February, 2023 from the research farms of the Indian Agricultural Research Institute (IARI), New Delhi, India. Through ARTMO, the validated GPR model produced an estimated LAI map over croplands managed by IARI, with an accuracy of 0.688 R2 value, and these LAI values and a precision of 0.96 R2 were observed between the LAI values observed in the ARTMO LAI map and the GEE-generated map. This model was then run into GEE to study the temporal changes in LAI values for Mustard, Chickpea, and Wheat, three prominent crops for the Rabi season. These temporal LAI patterns can assist in understanding the cropland's phenological changes. Thanks to GEE, crop phenology monitoring can be efficiently realized using temporal LAI estimates. [ABSTRACT FROM AUTHOR]

Spaceborne spectroscopic imaging offers the potential to improve our understanding of biodiversity and ecosystem services, particularly for challenging and rich environments like mangroves. Understanding the signals present in large volumes of high-dimensional spectroscopic observations of vegetation communities requires the characterization of seasonal phenology and response to environmental conditions. This analysis leverages both spectroscopic and phenological information to characterize vegetation communities in the Sundarban riverine mangrove forest of the Ganges–Brahmaputra delta. Parallel analyses of surface reflectance spectra from NASA's EMIT imaging spectrometer and MODIS vegetation abundance time series (2000–2022) reveal the spectroscopic and phenological diversity of the Sundarban mangrove communities. A comparison of spectral and temporal feature spaces rendered with low-order principal components and 3D embeddings from Uniform Manifold Approximation and Projection (UMAP) reveals similar structures with multiple spectral and temporal endmembers and multiple internal amplitude continua for both EMIT reflectance and MODIS Enhanced Vegetation Index (EVI) phenology. The spectral and temporal feature spaces of the Sundarban represent independent observations sharing a common structure that is driven by the physical processes controlling tree canopy spectral properties and their temporal evolution. Spectral and phenological endmembers reside at the peripheries of the mangrove forest with multiple outward gradients in amplitude of reflectance and phenology within the forest. Longitudinal gradients of both phenology and reflectance amplitude coincide with LiDAR-derived gradients in tree canopy height and sub-canopy ground elevation, suggesting the influence of surface hydrology and sediment deposition. RGB composite maps of both linear (PC) and nonlinear (UMAP) 3D feature spaces reveal a strong contrast between the phenological and spectroscopic diversity of the eastern Sundarban and the less diverse western Sundarban. [ABSTRACT FROM AUTHOR]

Spatial information about plant health and productivity are essential when assessing the progress towards Sustainable Development Goals such as life on land and zero hunger. Plant health and productivity are strongly linked to a plant's phenological progress. Remote sensing, and since the launch of Sentinel-1 (S1), specifically, radar-based frameworks have been studied for the purpose of monitoring phenological development. This study produces insights into how crop phenology shapes S1 signatures of PolSAR features and InSAR coherence of wheat, canola, sugar beet. and potato across multiple years and orbits. Hereby, differently smoothed time series and a base line of growing degree days are stacked to estimate the patterns of occurrence of extreme values and break points. These patterns are then linked to in situ observations of phenological developments. The comparison of patterns across multiple orbits and years reveals that a single optimized fit hampers the tracking capacities of an entire season monitoring framework, as does the sole reliance on extreme values. VV and VH backscatter intensities outperform all other features, but certain combinations of phenological stage and crop type are better covered by a complementary set of PolSAR features and coherence. With regard to PolSAR features, alpha and entropy can be replaced by the cross-polarization ratio for tracking certain stages. Moreover, a range of moderate incidence angles is better suited for monitoring crop phenology. Also, wheat and canola are favored by a late afternoon overpass. In sum, this study provides insights into phenological developments at the landscape level that can be of further use when investigating spatial and temporal variations within the landscape. [ABSTRACT FROM AUTHOR]

Accurate identification of crop phenology timing is crucial for agriculture. While remote sensing tracks vegetation changes, linking these to ground-measured crop growth stages remains challenging. Existing methods offer broad overviews but fail to capture detailed phenological changes, which can be partially related to the temporal resolution of the remote sensing datasets used. The availability of higher-frequency observations, obtained by combining sensors and gap-filling, offers the possibility to capture more subtle changes in crop development, some of which can be relevant for management decisions. One such dataset is Planet Fusion, daily analysis-ready data obtained by integrating PlanetScope imagery with public satellite sensor sources such as Sentinel-2 and Landsat. This study introduces a novel method utilizing Dynamic Time Warping applied to Planet Fusion imagery for maize phenology detection, to evaluate its effectiveness across 70 micro-stages. Unlike singular template approaches, this method preserves critical data patterns, enhancing prediction accuracy and mitigating labeling issues. During the experiments, eight commonly employed spectral indices were investigated as inputs. The method achieves high prediction accuracy, with 90% of predictions falling within a 10-day error margin, evaluated based on over 3200 observations from 208 fields. To understand the potential advantage of Planet Fusion, a comparative analysis was performed using Harmonized Landsat Sentinel-2 data. Planet Fusion outperforms Harmonized Landsat Sentinel-2, with significant improvements observed in key phenological stages such as V4, R1, and late R5. Finally, this study showcases the method's transferability across continents and years, although additional field data are required for further validation. [ABSTRACT FROM AUTHOR]

A cropping system practice is the sequential cultivation of crops in different crop seasons of a year. Cropping system practices determine the land productivity and sustainability of agriculture in regions and, therefore, information on cropping systems of different regions in the form of maps and statistics form critical inputs in crop planning for optimal use of resources. Although satellite-based crop mapping is widely practiced, deriving cropping systems maps using satellites is less reported. Here, we developed moderate-resolution maps of the major cropping systems of South Asia for the year 2014–2015 using multi-temporal satellite data together with a spectral matching technique (SMT) developed with an extensive set of field observation data supplemented with expert-identified crops in high-resolution satellite images. We identified and mapped 27 major cropping systems of South Asia at 250 m spatial resolution. The rice-wheat cropping system is the dominant system, followed by millet-wheat and soybean-wheat. The map showing the cropping system practices of regions opens up many use cases related to the agriculture performance of the regions. Comparison of such maps of different time periods offers insights on sensitive regions and analysis of such maps in conjunction with resources maps such as climate, soil, etc., enables optimization of resources vis-à-vis enhancing land productivity. Thus, the current study offers new opportunities to revisit the cropping system practices and redesign the same to meet the challenges of food security and climate resilient agriculture. [ABSTRACT FROM AUTHOR]

California contains a broad geography over which climate conditions can be suitable for cultivating multiple varieties of winegrapes. However, climate change is projected to make winegrape cultivation more challenging across many of California's winegrowing regions. In order to understand the potential effects of climate change on winegrapes, this study models variety-specific phenology for six winegrape varieties and quantifies the change in phenology and viticulturally-important agroclimate metrics over 12 of California's American Viticultural Areas (AVAs) by the mid-21st century. Results show more rapid development for winegrapes with earlier budburst, flowering, veraison, and maturation across all varieties and AVAs. Cabernet Sauvignon shows the greatest change in phenology timing, while Chardonnay shows the least change. Likewise, the West Sonoma Coast AVA shows the greatest average change in phenology timing across varieties and development stages and Lodi AVA shows the least. Projected changes in agroclimatic metrics include an additional month of potentially damaging heat days (above 35 °C) in some AVAs, and decreases in frost days. These results have implications for numerous factors related to viticultural production, including water resources management and crop yield and quality, and underscore the need for California winegrape growers to improve their resilience to climate change by adopting strategies such as increasing soil health and water use efficiency and selecting cultivars suited for future climate conditions. By conducting climate effects analyses at the variety-specific and AVA scale, important information is provided to the winegrowing industry at a resolution that can support decision-making towards resilience. [ABSTRACT FROM AUTHOR]

Extreme hydrological and thermal regimes characterize the Mediterranean zone and can influence the phenology of greenhouse gas (GHG) emissions in reservoirs. Our study examined the seasonal changes in GHG emissions of a shallow, eutrophic, hardwater reservoir in Spain. We observed distinctive seasonal patterns for each gas. CH4 emissions substantially increased during stratification, influenced predominantly by the increase in water temperature, net ecosystem production, and the decline in reservoir mean depth. N2O emissions mirrored CH4's seasonal trend, significantly correlating to water temperature, wind speed, and gross primary production. Conversely, CO2 emissions decreased during stratification and displayed a quadratic, rather than a linear relationship with water temperature—an unexpected deviation from CH4 and N2O emission patterns—likely associated with photosynthetic uptake of bicarbonate and formation of intracellular calcite that might be exported to sediments. This investigation highlights the imperative of integrating these idiosyncratic patterns into GHG emissions models, enhancing their predictive power. [ABSTRACT FROM AUTHOR]

Elucidating physical transport phenologies in large lakes can aid understanding of larval recruitment dynamics. Here, we integrate a series of climate, hydrodynamic, biogeochemical, and Lagrangian particle dispersion models to: (1) simulate hatch and transport of fish larvae throughout an illustrative large lake, (2) evaluate patterns of historic and potential future climate‐induced larval transport, and (3) consider consequences for overlap with suitable temperatures and prey. Simulations demonstrate that relative offshore transport increases seasonally, with shifts toward offshore transport occurring earlier during relatively warm historic and future simulations. Intra‐ and inter‐annual trends in transport were robust to assumed pelagic larval duration and precise location and timing of hatching. Larvae retained nearshore generally encountered more favorable temperatures and zooplankton densities compared to larvae transported offshore. Larval exploitation of nearshore resources under climate change may depend on a concomitant shift to earlier spawning and hatch times in advance of earlier offshore transport. [ABSTRACT FROM AUTHOR]

The PEG (Plankton Ecology Group) model predicts differences in phenology between eutrophic and oligotrophic lakes regarding the occurrence, timing and magnitude of annual chlorophyll maxima and minima. While these predictions have been tested between lakes, hardly any tests exist using long‐term data. We test these predictions using chlorophyll time‐series (1980–2019) from Lake Constance in which trophic status shifted from eutrophic to oligotrophic conditions. We show that oligotrophication subsequently resulted in reduction of the summer and spring blooms, and finally the loss of the clear‐water phase. In contrast to the PEG model the spring bloom was not delayed, but advanced with oligotrophication. Warming modified the seasonal patterns via advancing clear‐water timing. Oligotrophication did not only influence phenologies, but also the importance of independent variables driving phenologies. Thus, the decline of nutrients was the dominant factor in shaping the seasonal patterns of chlorophyll in Lake Constance during the last four decades. [ABSTRACT FROM AUTHOR]

Benthic primary producers (BPP) in inland waters, including aquatic macrophytes and periphyton, are foundational habitats that are highly sensitive to multiple human drivers of environmental change. However, long‐term seasonal monitoring of BPP is limited, leaving us with little information on the cause, directionality, and consequences of the potential shifts in timing of BPP life cycle events. Here, we review the literature on the phenological changes of BPP and show that BPP respond primarily to temperature, but also to other interactive drivers related to climate change and eutrophication. In addition, we present four rare case studies where BPP display strong and earlier shifts in event timing associated with increasing temperature and discuss potential impacts of these changes on ecosystem functioning. Given the responsive nature of BPP to multiple human drivers, we provide suggestions on how to improve basic monitoring to better understand the future impact of phenological changes of this critical habitat. [ABSTRACT FROM AUTHOR]