Variation In Phenology Research Articles

Comparison of change-based and shape-based data fusion methods in fine-resolution land surface phenology monitoring with Landsat and Sentinel-2 data

Fine-resolution land surface phenology (LSP) is urgently required for applications on agriculture management and vegetation-climate interaction, especially over heterogeneous areas, such as agricultural lands and fragmented forests. The critical challenge of fine-resolution LSP monitoring is how to reconstruct the spatiotemporal continuous vegetation index time series. To solve this problem, various data fusion methods have been devised; however, the comprehensive inter-comparison is lacking across different spatial heterogeneity, data quality, and vegetation types. We divide these methods into two main categories: the change-based methods fusing satellite observations with different spatiotemporal resolutions, and the shape-based methods fusing prior knowledge of shape models and satellite observations. We selected four methods to rebuilt two-band enhanced vegetation index (EVI2) series based on the harmonized Landsat and Sentinel-2 (HLS) data, including two change-based methods, namely the Spatial and temporal Adaptive Reflectance Fusion Model (STARFM), the Flexible Spatiotemporal DAta Fusion (FSDAF), and two shape-based methods, namely the Multiple-year Weighting Shape-Matching (MWSM), and the Spatiotemporal Shape-Matching Model (SSMM). Four phenological transition dates were extracted, evaluated with PhenoCam observations and the 500 m Visible Infrared Imaging Radiometer Suite (VIIRS) phenology product. The 30 m transition dates show more spatial details and reveal more apparent intra-class and inter-class phenology variation compared with 500 m product. The four transition dates of SSMM and FSDAF (R2>0.74, MAD<15 days) show better agreement with PhenoCam-derived dates. The performance difference between fusion methods over various application scenarios are then analyzed. Fusion results are more robust when temporal frequency is higher than 15 observations per year. The shape-based methods are less sensitive to temporal sampling irregularity than change-based methods. Both change-based methods and shape-based methods cannot perform well when the region is heterogeneous. Among different vegetation types, SSMM-like methods have the highest overall accuracy. The findings in this paper can provide references for regional and global fine-resolution phenology monitoring.

Exploring Sensitivity of Phenology to Seasonal Climate Differences in Temperate Grasslands of China Based on Normalized Difference Vegetation Index

The affiliation between vegetation phenology and seasonal climate (start and end times of the growing season, or SOS and EOS) provides a basis for acquiring insight into the dynamic response of terrestrial ecosystems to the effects of climate change. Although climate warming is an important factor affecting the advancement or delay of plant phenology, understanding the sensitivity of phenology to seasonal variation in climate factors (e.g., local air temperature, precipitation) is generally lacking under different climate backgrounds. In this study, we investigated the interannual variability of grassland phenology and its spatial variation in temperate regions of China based on satellite-derived products for the normalized difference vegetation index (NDVI) and weather data acquired from 2001 to 2020. We found that due to differences in local climate conditions, the effects of seasonal warming and precipitation on phenology were divergent or even opposite during the 20 years. The sensitivities of the start of growing season (SOS) to both spring temperature and last-winter precipitation was controlled by mean annual precipitation in terms of spatial variation. The SOS in the semi-humid (200–400 mm) region was most sensitive to spring temperature, advancing 5.24 days for each 1 °C rise in the average spring temperature (p &lt; 0.05), while it was most sensitive to last-winter precipitation in arid regions (&lt;200 mm), with SOS advancing up to 2.23 days for every 1 mm increase in the last-winter precipitation (p &lt; 0.05). The end of growing season (EOS) was sensitive to autumn temperature, being delayed 10.13 days for each 1 °C rise in the average autumn temperature in regions with temperatures between −10 °C and −5 °C (p &lt; 0.05). The uncertainty in the determination of the EOS could conceivably be greater than the determination of the SOS due to the dual effects of pre-autumn climate and growth constraints induced by declining fall temperatures. The effect of atmospheric warming on grassland phenology was lessened with increased atmospheric and soil aridity, suggesting that the interaction of regional drought and climate warming is an important source for local-to-regional differences and uncertainties in grass phenological response.

Year-round dynamics of arbuscular mycorrhizal fungi communities in the roots and surrounding soils of Cryptomeria japonica

Arbuscular mycorrhizal fungi (AMF) live simultaneously inside and outside of host plant roots for a functional mycorrhizal symbiosis. Still, the year-round dynamics and relationships between soil properties and AMF communities of trees in forest ecosystems remain unclear. We collected paired root and soil samples of the same Cryptomeria japonica trees at two forest sites (five trees at each site) every 2 months over a year. Total DNA was extracted from roots and soil separately and soil physicochemical properties were measured. With Illumina’s next-generation amplicon sequencing targeting the small subunit of fungal ribosomal DNA, we clarified seasonal dynamics of soil properties and AMF communities. Soil pH and total phosphorus showed significant seasonality while total carbon, nitrogen, and C/N did not. Only pH was a good predictor of the composition and dynamics of the AMF community. The total AMF community (roots + soil) showed significant seasonality because of variation from May to September. Root and soil AMF communities were steady year-round, however, with similar species richness but contained significantly different AMF assemblages in any sampling month. Despite the weak seasonality in the communities, the top two dominant OTUs showed significant but different shifts between roots and soils across seasons with strong antagonistic relationships. In conclusion, few dominant AMF taxa are dynamically shifting between the roots and soils of C. japonica to respond to seasonal and phenological variations in their microhabitats. AMF inhabiting forest ecosystems may have high environmental plasticity to sustain a functional symbiosis regardless of seasonal variations that occur in the soil.

Impacts of climate change on airborne Quercus pollen trends in Andalusia region (southern Spain)

Quercus species are the most frequent autochthonous arboreal taxa in the natural areas of Andalusia region (southern Spain). Due to their abundance and anemophilous character, high airborne pollen concentrations are detected in natural and urban areas of the region. Variations in its reproductive phenology can be considered an important bio-indicator regarding the impacts of climate change on vegetation in the Mediterranean region. This study aims to analyze trends in flowering patterns using airborne pollen concentrations from the capital cities of the 8 provinces of Andalusia region and to study its relationships with meteorological parameters and climate change over decades. Variations in time and intensity of flowering have been studied using different methods for defining the pollen season. This study demonstrates that for comparing results, it is relevant to choose the right definition to calculate the pollen season. The analysis of historical databases of Quercus airborne pollen in Andalusia showed variations in the floral phenology with an increase in the intensity of the pollen season throughout the studied period and an advance in the start trends due to the increase of temperature. Nevertheless, this increase might have been smoothed by the decrease of precipitations in some sampler locations, a limiting factor in the area. It has been also observed a delay in the end dates that caused a lengthening of the season. Climate change could be provoking longer and more intense pollen seasons that will lead to more intense and persistent symptoms in sensitive patients.

Foliar nitrogen uptake in broadleaf evergreen Mediterranean forests: Fertilisation experiment with labelled nitrogen

Atmospheric nitrogen (N) deposition in Mediterranean sclerophyllous forests of Holm oak (Quercus rotundifolia, Q. ilex) in Spain often exceeds empirical critical loads established for ecosystem conservation. There are still uncertainties on the capacity of canopy retention and uptake of the atmospheric N deposited of these forests. Studying and analysing all the forest nitrogen-cycle processes is essential to understand the potential effect of N deposition in these ecosystems. This study conducted a year-long short-term fertilisation experiment with labelled ammonium (15N-NH4) and nitrate (15N-NO3) to estimate foliar N absorption rates and assess the influence of leaf phenology and meteorological seasonal variations. Fertilising solutions were prepared to simulate low and high wet N deposition concentration, based on data reported from previous studies. Additionally, ecophysiological and meteorological measurements were collected to explore potential relationships between absorption rates, plant activity, and weather conditions. The results showed that Holm oak leaves were able to absorb both oxidised and reduced N compounds, with higher rates of NH4+ absorption. N recovery of both NH4+ and NO3− was higher in the low concentration treatments, suggesting reduced effectiveness of absorption as concentration increases. Foliar absorption rates were leaf-age dependent, with the highest values observed in young developing leaves. Foliar uptake showed seasonal changes with a clear reduction during the summer, linked to drought and dry weather conditions, and showing also smaller leaf net assimilation and stomatal conductance. During the rest of the year, foliar N absorption was not clearly associated to plant physiological activity but with environmental conditions. Our findings suggest that Holm oak canopies could absorb an important part of the incoming N deposition, but this process is compound, season and leaf phenology dependent. Further research is therefore needed to better understand and model this part of the N cycle.

Monitoring soil organic carbon under coastal restoration using time series Sentinel‐1

AbstractSalt marsh soils are key reservoirs of organic carbon. Although the potential for using Sentinel‐1 data to estimate organic carbon in marsh soils has been recognized, there is still a need to find suitable proxies and effective algorithms that enhance predictions. In this study, we assessed the effectiveness of Sentinel‐1 data for predicting soil organic carbon (SOC) stocks in salt marshes located on the coast of eastern‐central China. We defined the relations between SOC stocks and remotely sensed data using a knowledge‐based approach (i.e., structural equation modeling (SEM)), and the results were compared with those obtained using a common data‐driven approach (i.e., random forest (RF)). Predictive models were developed using (1) refined images associated with phenological stages (phenological variables), (2) refined images that were classified as statistically important using Monte Carlo feature selection (MCFS‐based variables), and (3) images from the combination of these refined datasets. The predictions were validated using a leave‐one‐out cross‐validation approach. The results showed that SEM was more accurate than RF when predicting SOC stocks using the same type of predictor variables. Models using phenological variables alone yielded the least accurate predictions. Adding phenological variables to the structural equation model with MCFS‐based variables increased the prediction accuracy by 36% in terms of the R2. The results of the case study suggest that images related to phenological stages are good predictors for mapping SOC stocks in marshes. This study highlights the superiority of SEM over RF for developing effective remote sensing‐based models to quantify and map SOC stocks in salt marshes.

Historical tree phenology data reveal the seasonal rhythms of the Congo Basin rainforest.

Tropical forest phenology directly affects regional carbon cycles, but the relation between species-specific and whole-canopy phenology remains largely uncharacterized. We present a unique analysis of historical tropical tree phenology collected in the central Congo Basin, before large-scale impacts of human-induced climate change. Ground-based long-term (1937-1956) phenological observations of 140 tropical tree species are recovered, species-specific phenological patterns analyzed and related to historical meteorological records, and scaled to characterize stand-level canopy dynamics. High phenological variability within and across species and in climate-phenology relationships is observed. The onset of leaf phenophases in deciduous species was triggered by drought and light availability for a subset of species and showed a species-specific decoupling in time along a bi-modal seasonality. The majority of the species remain evergreen, although central African forests experience relatively low rainfall. Annually a maximum of 1.5% of the canopy is in leaf senescence or leaf turnover, with overall phenological variability dominated by a few deciduous species, while substantial variability is attributed to asynchronous events of large and/or abundant trees. Our results underscore the importance of accounting for constituent signals in canopy-wide scaling and the interpretation of remotely sensed phenology signals.

The Impact of Urbanization on Spatial–Temporal Variation in Vegetation Phenology: A Case Study of the Yangtze River Delta, China

The response of vegetation phenology to urbanization has become a growing concern. As impervious surfaces change as urbanization advances, the variation in vegetation phenology at the dynamic urbanization level was analyzed to significantly quantify the impact of urbanization processes on vegetation phenology. Based on the MOD13Q1 vegetation index product from 2001 to 2020, vegetation phenology parameters, including the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (GSL), were extracted, and the spatial–temporal variation in vegetation phenology, as well as its response to urbanization, was comprehensively analyzed. The results reveal that (1) from 2001 to 2020, the average rates of change for the SOS, EOS, and GSL were 0.41, 0.16, and 0.57 days, respectively. (2) The vegetation phenology changes showed significant spatial–temporal differences at the urbanization level. With each 10% increase in the urbanization level, the SOS and EOS were advanced and delayed by 0.38 and 0.34 days, respectively. (3) The urban thermal environment was a major factor in the impact of urbanization on the SOS and EOS. Overall, this study elucidated the dynamic reflection of urbanization in phenology and revealed the complex effects of urbanization on vegetation phenology, thus helping policymakers to develop effective strategies to improve urban ecological management.

Phenological variation in the life histories of amphidromous gobies endemic to the Hawaiian islands

AbstractDespite the benefits that can come from coordinated movement, many diadromous fishes have evolved partial migration strategies, where a portion of a population does not migrate. Co‐occurring life‐history variants might nonetheless gain some advantage by exhibiting congruent phenologies, with key events timed to follow the same cue. Here, we assess this possibility by examining otolith microchemistry and structure to quantify the lunar periodicity of hatching and metamorphosis according to migration strategy in a facultative amphidromous goby endemic to Hawai'i. Our analyses corroborated prior evidence that Awaous stamineus exhibits partial migration and that residential A. stamineus are often proportionately more abundant than the migratory form. Contrary to expectation, we found a significant difference in hatching time between migratory and residential A. stamineus. A higher proportion of A. stamineus migrants hatched at the full moon and metamorphosed at the new moon, whereas residents exhibited the opposite phenology. Comparisons to Sicyopterus stimpsoni intended to provide a broader context revealed that the obligately amphidromous species tends to hatch at the new moon and metamorphose at the full moon, mirroring the phenology of residential A. stamineus. Evidence of convergent lunar phenologies suggests that synchronising life‐history events can confer benefits that extend beyond species boundaries. Notably, phenological discordance could be acting as a prezygotic isolating barrier between sympatric life‐history variants, pointing to the presence of heretofore unrecognised evolutionary diversity within partially migratory species like A. stamineus.

Fine classification of crops based on an inductive transfer learning method with compact polarimetric SAR images

ABSTRACT Compact polarimetric synthetic aperture radar (CP SAR) reduces fully polarimetric SAR system complexity and expands the imaging swath. Generally, fine classification of crop types relies on many labeled training samples. However, due to the temporal interval of crop phenology and ground environment variations over time, training samples from one dataset usually perform poorly for another. Therefore, in this study, transfer learning is introduced to crop classification to ensure classification accuracy by improving reusability of training samples. A stable and robust inductive transfer learning method, i.e. the Transfer Bagging-based Ensemble Learning (TBEL) algorithm, is proposed. The main idea is to select an adequate number of representative samples from unlabeled datasets to characterize each class in the target domain based on limited labeled samples and construct a classifier set to classify the target domain. This study investigates CP SAR data performance in transfer learning for crop classification. The proposed algorithm in the experimental study is compared with six typical methods (Subspace Alignment (SA), CORrelation ALignment (CORAL), Joint Distribution Adaptation (JDA), Balanced Distribution Adaptation (BDA), Transfer Bagging (TrBagg), and Bagging-based Ensemble Transfer Learning (BETL)). The experimental results show that the crop classification accuracy based on the TBEL algorithm is more stable, with an improved overall classification accuracy of 2–6%. Classifying the same rice harvest stage in the cross-year domain has the highest overall accuracy of 92.2%. Wheat fields in different scenes are also classified. Based on the TBEL algorithm, the overall classification accuracy improves by 1–10% compared with typical methods, with an accuracy of at least 87.6%. Furthermore, by testing the CP mode classification performance over various crops in transfer learning, we find that the circular CP mode performs better than the linear mode in most cases. This conclusion agrees with single-scene applications and was first verified in transfer learning.

USING MACHINE LEARNING ALGORITHMS FOR NATURAL HABITATS ASSESSMENT

The potential of AI to process and interpret large volumes of data can provide researchers with a powerful tool to understand and monitor biodiversity on a global scale. In this paper we aimed to identify dominant individual plant species in natural protected habitats. Mapping the dominant species from the targeted natural habitats was followed by testing machine learning algorithm for differentiating similar species using satellite images. In the end we validated the data generated by machine learning algorithms through extensive field observations. Using the Sentinel-2 mission 10m resolution data and comprehensive field mapping we managed to see different phenology variations between diverse types of plant communities. Using the NDVI and NDII vegetation indexes and Random Forest algorithm during the dominant species phenology stages for each consecutive 10-day periods between May 1st and September 10th, revealed distinct responses to climate fluctuations and environmental factors. The natural habitats different signatures are strongly influenced by their ecological and conservation status and are not yet suitable for identification, but could help improve AI’s automatic detection for multiannual analysis if a favorable conservation trend is reached. The main achievement of the proposed methodology is the ability to differentiate between different species of deciduous trees, with machine learning training accuracy generally exceeding 95% and classification accuracy surpassing 90%.

Individuals departing non-breeding areas early achieve earlier breeding and higher breeding success.

Conditions experienced by an individual during migration have the potential to shape migratory tactic and in turn fitness. For large birds, environmental conditions encountered during migration have been linked with survival and subsequent reproductive output, but this is less known for smaller birds, hindering our understanding of mechanisms driving population change. By combining breeding and tracking data from 62 pied flycatchers (Ficedula hypoleuca) representing two breeding populations collected over 2016-2020, we determine how variation in migration phenology and tactic among individuals affects subsequent breeding. Departure date from West African non-breeding areas to European breeding grounds was highly variable among individuals and had a strong influence on migration tactic. Early departing individuals had longer spring migrations which included longer staging duration yet arrived at breeding sites and initiated breeding earlier than later departing individuals. Individuals with longer duration spring migrations and early arrival at breeding sites had larger clutches, and for males higher fledging success. We suggest that for pied flycatchers, individual carry-over effects may act through departure phenology from West Africa, and the associated spring migration duration, to influence reproduction. While our results confirm that departure date from non-breeding areas can be associated with breeding success in migratory passerines, we identify spring staging duration as a key component of this process.

Combined Effects of Meteorological Factors, Terrain, and Greenhouse Gases on Vegetation Phenology in Arid Areas of Central Asia from 1982 to 2021

Spatiotemporal variations in Central Asian vegetation phenology provide insights into arid ecosystem behavior and its response to environmental cues. Nevertheless, comprehensive research on the integrated impact of meteorological factors (temperature, precipitation, soil moisture, saturation vapor pressure deficit), topography (slope, aspect, elevation), and greenhouse gases (carbon dioxide, methane, nitrous oxide) on the phenology of Central Asian vegetation remains insufficient. Utilizing methods such as partial correlation and structural equation modeling, this study delves into the direct and indirect influences of climate, topography, and greenhouse gases on the phenology of vegetation. The results reveal that the start of the season decreased by 0.239 days annually, the length of the season increased by 0.044 days annually, and the end of the season decreased by 0.125 days annually from 1982 to 2021 in the arid regions of Central Asia. Compared with topography and greenhouse gases, meteorological factors are the dominant environmental factors affecting interannual phenological changes. Temperature and vapor pressure deficits (VPD) have become the principal meteorological elements influencing interannual dynamic changes in vegetation phenology. Elevation and slope primarily regulate phenological variation by influencing the VPD and soil moisture, whereas aspect mainly affects the spatiotemporal patterns of vegetation phenology by influencing precipitation and temperature. The findings of this study contribute to a deeper understanding of how various environmental factors collectively influence the phenology of vegetation, thereby fostering a more profound exploration of the intricate response relationships of terrestrial ecosystems to environmental changes.

Fusarium wilt constrains mungbean yield due to reduction in source availability.

Mungbean is an important source of plant protein for consumers and a high-value export crop for growers across Asia, Australia and Africa. However, many commercial cultivars are highly vulnerable to biotic stresses, which rapidly reduce yield within the season. Fusarium oxysporum is a soil-borne pathogen that is a growing concern for mungbean growers globally. This pathogen causes Fusarium wilt by infecting the root system of the plant resulting in devastating yield reductions. To understand the impact of Fusarium on mungbean development and productivity and to identify tolerant genotypes, a panel of 23 diverse accessions was studied. Field trials conducted in 2016 and 2021 in Warwick, Queensland, Australia under rainfed conditions investigated the variation in phenology, canopy and yield component traits under disease and disease-free conditions. Analyses revealed a high degree of genetic variation for all traits. By comparing the performance of these traits across these two environments, we identified key traits that underpin yield under disease and disease-free conditions. Aboveground biomass components at 50 % flowering were identified as significant drivers of yield development under disease-free conditions and when impacted by Fusarium resulted in up to 96 % yield reduction. Additionally, eight genotypes were identified to be tolerant to Fusarium. These genotypes were found to display differing phenological and morphological behaviours, thereby demonstrating the potential to breed tolerant lines with a range of diverse trait variations. The identification of tolerant genotypes that sustain yield under disease pressure may be exploited in crop improvement programs.

Comparison of the timing of spring phenological events between phenological garden trees and wild populations

Abstract Phenological research is engaged in monitoring the influence of climate change on the natural environment. The International Phenological Gardens (IPG) network provides a valuable dataset of standardized tree phenology records dating back to the mid-20th century. To make best use of this actively growing record, it is important to investigate how network data can be applied to predict the timing of phenological events in natural populations. This study compared clonally propagated IPG downy birch (Betula pubescens Ehrh.) and hazel (Corylus avellana L.) specimens of central European provenance to nearby wild populations at the western-most margin of the IPG network, in the south-west of Ireland. In addition to monitoring by trained scientists, observations by citizen scientists were included. The order of the timing of phenological events among sites was consistent across 2 years, confirming reproducibility of the results. IPG trees had the earliest B. pubescens leaf unfolding and C. avellana flowering dates of the sites studied. In addition, leaf unfolding occurred later in the wild populations than expected from the temperature responses of the B. pubescens and C. avellana IPG clones. Natural variation in phenology also exceeded the historical change observed at the IPG site, suggesting a potential genetic basis for climate adaptation. Trunk circumference, reflecting the age-dependent increase in tree size, was found to influence C. avellana phenology, with earlier timing of phenological events in larger trees. This finding highlights tree size as an important consideration in the management of phenological gardens and tree phenology research in general.

Phenological Variation in Chickpea (Cicer arietinum L.) Varieties through Foliar Application of Cytokinin Analogs and Nutrients under Water Deficit Stress

Chickpea (Cicer arietinum L.), a rainfed crop predominantly grown in temperate and subtropical climates, faces significant challenges in production due to terminal drought stress impacting various phenological stages. This study addresses the challenges posed by terminal drought stress on the phenological stage of chickpea varieties viz., JG 36 and JG 14. The experiment was conducted during the Rabi seasons of 2021-2022 and 2022-2023 at the Experimental Research Farm, Seed Technology Research Unit, JNKVV, Jabalpur (M.P), using a split-split plot design with three replications. This research also investigates the impact of foliar applications of cytokinin analogs viz., Thiourea, Thidiazuron, and Benzyladenine and nutrients viz., ZnSO4 and KCl on chickpea under water deficit stress conditions. The results revealed significant differences in days to pod formation, seed formation, physiological maturity, and harvest maturity among the irrigation levels, varieties, and foliar spray of plant growth regulators and nutrients. Under different irrigation levels, D1 (Irrigation at 30 DAS and flower initiation) exhibited delayed phenological stages of the crop, while D2 (Drought stress at flowering up to physiological maturity) showed an early onset of all the phenological stages. Under water deficit conditions, JG 14 exhibited accelerated maturity beyond its typical early maturation in comparison to well-watered conditions, highlighting the impact of environmental stress on varietal responses. With respect to the application of plant growth regulators and nutrients, treatment T12 (TDZ @ 10 ppm + 1% KCl) significantly delayed pod and seed formation, as well as physiological and harvest maturity as compared to untreated control (T1). Foliar application of TDZ @ 10 ppm + 1% KCl (T12) enhanced seed filling duration by 2.59 days compared to the untreated control. Further investigations are needed to identify the impact of Thidiazuron and KCl in enhancing seed yield and seed weight of chickpea under optimal and sub-optimal soil water conditions, to provide recommendations for chickpea growers.

Agronomic Performances and Seed Yield Components of Lentil (Lens culinaris Medikus) Germplasm in a Semi-Arid Environment

Lentil (Lens culinaris Medik.) is widely known among grain legumes for its high nutritional quality, playing an important role in enhancing Mediterranean farming systems as a sustainable crop. Field experiments comparing 121 lentil accessions (microsperma and macrosperma types from different countries) were conducted in a semi-arid environment of south Italy over two growing seasons (2016/2017 and 2017/2018). Their agronomic performance was determined, focusing on phenological, morphological, productive, and qualitative variability. Changes in rainfall and temperatures affected the agronomic traits, especially yield components. In both years, the average grain yield (GY) (2.31 and 2.22 t ha−1, respectively) was above the threshold of 2 t ha−1. Consistent yield exceeding the field average in both growing seasons revealed the superiority of accessions from Egypt, Cyprus, Algeria, Nepal, and Tunisia. Moreover, microsperma yielded more (+0.31 and +0.41 t ha−1 in the first and second year, respectively) than macrosperma accessions. Flowering (DASF—days after sowing to flowering) and thousand seed weight (TSW) appeared to be the most important traits related to grain yield. Flowering earliness seems to act as a mechanism for overcoming abiotic stresses. The analysis of yield components revealed a different productive determinism within the two subspecies. As also highlighted by the Principal Component Analysis, microsperma accessions presented on average a significantly higher number of pods per plant (PP) and seeds per pod (SP), despite the considerable variability among countries of origin. The results showed phenological and morphological variability among genotypes, which should be taken into account in view of future selection programs focused on obtaining lentil ideotypes suitable for the Mediterranean environment.

Spatial variability in herbaceous plant phenology is mostly explained by variability in temperature but also by photoperiod and functional traits.

Whereas temporal variability of plant phenology in response to climate change has already been well studied, the spatial variability of phenology is not well understood. Given that phenological shifts may affect biotic interactions, there is a need to investigate how the variability in environmental factors relates to the spatial variability in herbaceous species' phenology by at the same time considering their functional traits to predict their general and species-specific responses to future climate change. In this project, we analysed phenology records of 148 herbaceous species, which were observed for a single year by the PhenObs network in 15 botanical gardens. For each species, we characterised the spatial variability in six different phenological stages across gardens. We used boosted regression trees to link these variabilities in phenology to the variability in environmental parameters (temperature, latitude and local habitat conditions) as well as species traits (seed mass, vegetative height, specific leaf area and temporal niche) hypothesised to be related to phenology variability. We found that spatial variability in the phenology of herbaceous species was mainly driven by the variability in temperature but also photoperiod was an important driving factor for some phenological stages. In addition, we found that early-flowering and less competitive species characterised by small specific leaf area and vegetative height were more variable in their phenology. Our findings contribute to the field of phenology by showing that besides temperature, photoperiod and functional traits are important to be included when spatial variability of herbaceous species is investigated.

Genetic and phenological diversity of Tunisian natural populations of Dactylis glomerata L.

Genetic and phenological diversity of Tunisian natural populations of Dactylis glomerata L.

Temporal Dynamics of Tree Crown Fractal Dimension in Two Species of Deciduous Oaks

Fractal dimension (D) can be used to characterise temporal changes of crown architecture of individual trees. Our goal in this study was to analyse seasonal changes in tree crown fractal dimension of two species of deciduous oaks (Quercus castanea and Q. obtusata) coexisting in a natural forest in central Mexico using low cost sampling, and relate these changes to morphological attributes and environmental variables.Every two months, from May 2017 to September 2018, for each oak species, we photographed fixed portions of the crowns of individual trees, measured their trunk diameters, and obtained average temperature and accumulated precipitation data recorded for the sampling date. From the obtained images, we calculated D values by the semivariogram method using three different variability estimators (square increment, isotropic, and transect variation).We identified a positive correlation between D and temperature, and a negative correlation between temperature and crown cover.The fractal dimension (D) of crowns of two deciduous oak species changes according to the tree’s phenological stage. D values varied through time in relation to tree crown phenological variation, but not with crown cover dimension. We propose a model of annual D value fluctuation in deciduous trees, characterised by two high complexity peaks and two low complexity valleys, corresponding to the effects on crown cover of annual periods of leaf abscission and development.