Phenological State Research Articles

DIAFAN-TL: An instance weighting-based transfer learning algorithm with application to phenology forecasting

The agricultural sector has been, and still is, the most important economic sector in many countries. Due to advances in technology, the amount and variety of available data have been increasing over the years. However, compared to other economic sectors, there is not always enough quality data for one particular domain (crops, plantations, plots) to obtain acceptable forecasting results with machine learning algorithms. In this context, transfer learning can help extract knowledge from different but related domains with enough data to transfer it to a target domain with scarce data. This process can overcome forecasting accuracy compared to training models uniquely with data from the target domain. In this work, a novel instance weighting-based transfer learning algorithm is proposed and applied to the phenology forecasting problem. A new metric named DIAFAN is proposed to weight samples from different source domains according to their relationship with the target domain, promoting the diversity of the information and avoiding inconsistent samples. Additionally, a set of validation schemes is specifically designed to ensure fair comparisons in terms of data volume with other benchmark transfer learning algorithms. The proposed algorithm, DIAFAN-TL, is tested with a proposed dataset of 16 plots of olive groves from different places, including information fusion from satellite images, meteorological stations and human field sampling of crop phenology. DIAFAN-TL achieves a remarkable improvement with respect to 15 other well-known transfer learning algorithms and three nontransfer learning scenarios. Finally, several performance analyses according to the different phenological states, prediction horizons and source domains are also performed.

Photosynthesis phenology, as defined by solar-induced chlorophyll fluorescence, is overestimated by vegetation indices in the extratropical Northern Hemisphere

Vegetation phenology is highly sensitive to climate change, although the data and methods used to estimate key phenological states can influence this sensitivity. Because of its direct relation to leaf photosynthetic carbon uptake, remotely sensed solar-induced chlorophyll fluorescence (SIF) can provide new insight assessing changes in vegetation phenology. Here, we investigated the potential of using a SIF time series product named contiguous SIF (CSIF) to estimate spring, summer, and autumn phenology in the extratropical Northern Hemisphere (>30°N) and compared the results with those based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) for the period 2001–2018. Overall, we found similar spatial patterns in phenological states. However, specific dates of key phenological events differed when using CSIF vs. MODIS NDVI data. NDVI data indicated that the growing season started earlier (by 10.1 days on average) and ended later (11.5 days on average) relative to CSIF data. This implies that actual periods for photosynthetic activity are shorter (by 21.6 days on average) than those estimated from vegetation indices more directly related to changes in canopy structure. These large differences between results from NDVI and that from CSIF suggest that vegetation indices such as NDVI seem to overestimate the period for active photosynthesis over the extratropical Northern Hemisphere. Furthermore, while phenology of the early growing season is dominated by temperature for both NDVI and CSIF data, phenology of the late growing season is mainly controlled by temperature for NDVI but by precipitation for CSIF. Our findings were further confirmed by other SIF (GOME-2 SIF) and vegetation index (MODIS EVI) datasets. Phenology modes in Earth system modelling are often parameterized using leaf unfolding and senescence from either station or satellite observations. Our results imply that canopy structure-based parameterization schemes may have overestimated photosynthesis active period, and thus productivity responses. We conclude that SIF data offers a novel and unique approach for assessing phenological change - one that is more directly tied to the carbon cycle and how it is being influenced by climate change.

Electrical Responses of Pinus halepensis Mill. as an Indicator of Wildfire Risk in Mediterranean Forests by Complementing Live Fuel Moisture

Pinus halepensis forests, as Mediterranean-type ecosystems, are subject to high levels of wildfire risk in times of drought, with meteorological conditions of water stress and very high temperatures, mainly in summer. Considering the difficulty of knowing the phenological state of this species, the objective of this research was to evaluate the possibility of implementing the electrical responses (voltage and short-circuit current) as a variable in fire risk management models, compared to live fuel moisture. On the one hand, the obtained results demonstrate non-significant differences between the moisture content of the different fractions of the living branches (base and half of the branch and live fuel), even in times of drought with hydric stress and very high temperatures. Live fuel moisture of Pinus halepensis does not show significant seasonal variations under the influence of extreme fire risk factors. For this reason, it should be complemented with other variables for fire risk management models. On the other hand, the differences registered in the electrical signal show oscillations with significant variations, which are strongly correlated with the periods of extremely favourable meteorological conditions for wildfires. So, the voltages measured show ranges that correspond with great accuracy to the FWI. Voltage variation is dependent on the hydraulic dynamic plant behaviour and a result of the physiological response of pine trees to abiotic stress of drought. It is an easy-to-measure electrical parameter as well as a very reliable indicator with a high correlation with wildfire risk. Thus, electrical responses could add more knowledge about the phenological state of the trees in dependence on stress climatic conditions, allowing integration of these variables in the preventive wildfire modelling and management.

L-GrassF: a functional–structural and phenological model of Lolium perenne integrating plant morphogenesis and reproductive development

Abstract In the context of climate change and agrosystem complexification, process-based models of the reproductive phenology of perennial grasses are essential to optimize the agronomic and ecologic services provided by grasslands. We present a functional–structural model called L-GrassF, which integrates the vegetative and reproductive development of individual Lolium perenne plants. The vegetative development in L-GrassF was adapted from a previous model of perennial ryegrass where leaf elongation and tillering dynamics partially result from self-regulated processes. Significant improvements have been made to this vegetative module in order to deal with the whole growing cycle during which plants are exposed to contrasting temperatures. The reproductive module is a new functionality describing the floral induction of the individual tiller from daily temperature and photoperiod as well as its phenological state. From the interactions between the vegetative and reproductive developments, L-GrassF simulates the dynamics of plant architecture, the floral transition and heading date (HD) at tiller level. A sensitivity analysis was performed on L-GrassF and showed that parameters controlling the kinetics of leaf elongation and leaf appearance rate have a significant impact on HD. After calibration, L-GrassF was able to simulate the HD on seven L. perenne cultivars grown in a broad range of environmental conditions, as provided by an independent data set. We conclude that L-GrassF is a significant step towards better prediction of grassland phenology in contrasted conditions.

Leaf bacterial microbiota response to flooding is controlled by plant phenology in wheat (Triticum aestivum L.)

Leaf microbiota mediates foliar functional traits, influences plant fitness, and contributes to various ecosystem functions, including nutrient and water cycling. Plant phenology and harsh environmental conditions have been described as the main determinants of leaf microbiota assembly. How climate change may modulate the leaf microbiota is unresolved and thus, we have a limited understanding on how environmental stresses associated with climate change driven weather events affect composition and functions of the microbes inhabiting the phyllosphere. Thus, we conducted a pot experiment to determine the effects of flooding stress on the wheat leaf microbiota. Since plant phenology might be an important factor in the response to hydrological stress, flooding was induced at different plant growth stages (tillering, booting and flowering). Using a metabarcoding approach, we monitored the response of leaf bacteria to flooding, while key soil and plant traits were measured to correlate physiological plant and edaphic factor changes with shifts in the bacterial leaf microbiota assembly. In our study, plant growth stage represented the main driver in leaf microbiota composition, as early and late plants showed distinct bacterial communities. Overall, flooding had a differential effect on leaf microbiota dynamics depending at which developmental stage it was induced, as a more pronounced disruption in community assembly was observed in younger plants.

No Reduction in Yield of Young Robusta Coffee When Grown under Shade Trees in Ecuadorian Amazonia.

Little is known on what impact shade trees have on the physiology of Coffea canephora (robusta coffee) under tropical humid conditions. To fill this gap, a field experiment was conducted in the Ecuadorian Amazon to investigate how growth, nutrition (leaf N), phenological state (BBCH-scale) and yield of 5-year-old robusta coffee shrubs are affected by the presence or absence of leguminous trees, the type (organic v conventional) and intensity of management. The experiment was a factorial 5 × 4 design with four cropping systems: intensive conventional (IC), moderate conventional (MC), intensive organic (IO) and low organic (LO), and with five shading systems in a split-plot arrangement: full sun (SUN), both Erythrina spp. and Myroxylon balsamum (TaE), M. balsamum (TIM), E. spp. (ERY) and Inga edulis (GUA). Three monthly assessments were made. Cherry yields of coffee shrubs under moderate shade (c. 25%) were similar to those under high light exposure. Coffee shrubs grown with either E. spp. or I. edulis were taller (+10%) and had higher leaf N concentrations (22%) than those grown without consistent shade. Unless receiving c. 25% of shade, coffee shrubs grown under organic cropping systems showed reduced growth (25%). No correlation was found between height, cherry yield and leaf N. Both shading and cropping systems affected leaf N concentration, also depending on phenological state and yield. Further research is needed to confirm our findings in the long-term as well as to elucidate how leguminous trees may induce physiological responses in robusta coffee under humid tropical conditions.

The efficacy of combining paraffin oil with conventional fungicide treatments against grape powdery mildew in Eger

We aimed to test the combination of paraffin oil (PFO) with regular fungicide treatment to assess its efficacy against grape powdery mildew (GPM) in a small spraying experiment on two Vitis vinifera L. cultivars (Chardonnay and Kékfrankos) with different susceptibility to Erysiphe necator. The visual symptoms of GPM on leaves and clusters were examined at three phenological states. The harvest yield was characterized by two methods, data were analyzed with one-way ANOVA and Tukey post-hoc test. Regular fungicide treatment (CT) and its combinations with PFO showed better results in both varieties to repress GPM in 2015 relative to sole PFO treatments. Mean values of combined treatments were often lower than CT but did not differ significantly from each other. The same was observed in 2016, despite the higher pressure of GPM, and missed the third survey. No significant differences were detected between treatments in yield. In contrast, the mean cluster weight of CT and combined treatments resulted in (insignificantly) higher values in each variety and year. In summary, the sole PFO showed some disease control capability as reported earlier, but this effect was greatly affected by the given vintage. Combining PFO with CT resulted in increased protection against GPM relative to the solely applied fungicides. However, this effect was not significant in all cases. It also depended on the vintage and cultivar characteristics. The beneficial impact of paraffin oil as an additive to CT may be due to the induction of plant stress responses and/or its ability to support the adherence and absorption of the combined agents.

Ecological Significance of Floristic Structure and Biological Spectrum of Alpine Floral Biodiversity of Khunjerab National Park Gilgit-Baltistan Pakistan

The current study was conducted in Khunjerab National Park which is situated in the subalpine zone. The study area was thoroughly surveyed to ensure the maximum collection of flowering plants diversity. The work aimed to investigate the ecological significance of floral structure and the biological spectrum of prevailing flowering plants' biodiversity in the study area. For this purpose, we recognized four ecological zones based on altitude in the park namely the subalpine zone (3000m to 3500m), alpine zone (3600m to 4000m), super alpine zone (4100-4500m), and sub naval zone was started from (4600-4800m) altitude. The collected specimens comprised (155) plant species that belong to 97 genera and 36 families. The life forms of the collected species were 72% Hemicryptophyte (H), 13% Therophytes, 10% Chaemephyte, and 5% Phanerophyte. While the habit categories of the flora were analyzed with the help of Theophrastus classification. The breakup of the habit categories shows that the herbs with 137 species held the highest percentage to contribute the flora of the study area was with 88%, followed by shrubs with 14 species which contributed to the flora of the area was 9.03%. Similarly, subshrubs and trees contained the same number of 2 spices. We observed the phenological status of each species, i.e., flowering and fruiting conditions, and of the species that were infrequent.

A sentinel-2-based triangular vegetation index for chlorophyll content estimation

Accurate estimation of chlorophyll content is important for diagnosing the physiological and phenological status of vegetation. Establishing the relationship between vegetation indices (VIs) and leaf chlorophyll content using remote sensing is crucial for large-scale earth observation. However, existing VIs for estimating chlorophyll content generally suffer from the saturation effect or depend on specific scenarios, resulting in insufficient estimation accuracy. Based on the physical mechanism of light-vegetation interaction, this study innovatively proposes the absorption triangle and reflectance triangle in the spectral space to construct the chlorophyll-sensitive Sentinel-2 Triangular Vegetation Index (STVI). The STVI uses the Sentinel-2 multispectral instrument (MSI) bands to improve the accuracy of chlorophyll content retrieval by enhancing the relationship with the chlorophyll content and mitigating the saturation effect. Simulated data, measured data, and open data sets were used to test the accuracy and stability of the STVI and 11 classical VIs for retrieving the chlorophyll content using different spectral data types, different winter wheat growth stages, and different vegetation coverages. The results showed that the STVI was more sensitive to the chlorophyll content than the classical VIs and provided the best goodness-of-fit in multiple scenarios. The STVI represents a powerful tool for large-extent chlorophyll content retrieval and a novel approach for scientific research in related fields.

High-resolution map of sugarcane cultivation in Brazil using a phenology-based method

Abstract. Sugarcane is the most important source of sugar, and its cultivation area has undergone rapid expansion, replacing other crops, pastures, and forests. Brazil is the world's largest sugarcane producer and contributed to approximately 38.6 % of the world's total production in 2019. Sugarcane in Brazil can be harvested from April to December in the south-central area and from September to April in the northeast area. The flexible phenology and harvest conditions of sugarcane in Brazil make it difficult to identify the harvest area at state to country scales. In this study, we developed a phenology-based method to identify the harvest area of sugarcane in Brazil by incorporating the multiple phenology conditions into a time-weighted dynamic time warping method (TWDTW). Then, we produced annual 30 m spatial resolution sugarcane harvest maps (2016–2019) for 14 states in Brazil (over 98 % of the harvest area) based on the proposed method using Landsat-7, Landsat-8, and Sentinel-2 optical data. The proposed method performed well in identifying sugarcane harvest area with limited training sample data. Validations for the 2018 harvest year displayed high accuracy, with the user's, producer's, and overall accuracies of 94.35 %, 87.04 %, and 91.47 % in Brazil, respectively. In addition, the identified harvest area of sugarcane exhibited good correlations with the agricultural statistical data provided by the Brazilian Institute of Geography and Statistics (IBGE) at the municipality, microregion, and mesoregion levels. The 30 m Brazil sugarcane harvest maps can be obtained at https://doi.org/10.6084/m9.figshare.14213909 (Zheng et al., 2021).

Assessing biodiversity from space: Impact of spatial and spectral resolution on trait-based functional diversity

Observing functional diversity continuously in time and space using satellite imagery forms the basis for studying impact, interactions, and feedback of environmental change mechanisms on ecosystems and biodiversity globally. Functional diversity of plant traits links ecosystem functioning and biodiversity. This work presents an approach to map and quantify functional diversity of physiological forest traits derived from 20 m Sentinel-2 data in a temperate forest ecosystem. We used two complementary data sources, namely high-resolution, as well as spatially resampled airborne imaging spectroscopy data and Sentinel-2 data, to ensure our methods support consistently mapping functional diversity from space. We retrieved three physiological traits related to forest health, stress, and potential productivity, namely chlorophyll, carotenoid, and water content, from airborne imaging spectroscopy and Sentinel-2 data using corresponding spectral indices as proxies. We analyzed changes in two functional diversity metrics, namely functional richness and divergence, at different spatial resolutions. Both functional diversity metrics depend on the size and number of pixels to derive functional diversity as a function of distance, leading to different interpretations. When mapping functional diversity using Sentinel-2 data, small-scale patterns <1.1 ha were no longer visible, implying a minimum calculation area with 60 m radius recommended for retrieval of functional diversity metrics. The spectrally convolved and spatially resampled airborne spectroscopy data and the native Sentinel-2 data were correlated with r = 0.747 for functional richness and r = 0.709 for divergence in a 3.1 ha neighborhood. Functional richness was more affected by the differences in trait maps between the acquisitions resulting from effects in illumination and topography compared with functional divergence. Further differences could be explained by varying illumination/observation effects and phenological status of the vegetation at acquisition. Our approach demonstrates the importance of spatial and spectral resolution when scaling diversity assessments from regional to continental scales.

Developing Hyperspectral Indices for Assessing Seasonal Variations in the Ratio of Chlorophyll to Carotenoid in Deciduous Forests

Leaf pigments are sensitive to various stress conditions and senescent stages. Variation in the ratio of chlorophyll to carotenoid content provides valuable insights into the understanding of the physiological and phenological status of plants in deciduous forests. While the use of spectral indices to assess this ratio has been attempted previously, almost all indices were derived indirectly from those developed for chlorophyll and carotenoid contents. Furthermore, there has been little focus on the seasonal dynamics of the ratio, which is a good proxy for leaf senescence, resulting in only a few studies ever being carried out on tracing the ratio over an entire growing season by using spectral indices. In this study, we developed a novel hyperspectral index for tracing seasonal variations of the ratio in deciduous forests, based on a composite dataset of two field measurement datasets from Japan and one publicly available dataset (Angers). Various spectral transformations were employed during this process in order to identify the most robust hyperspectral index. The results show that the wavelength difference (D) type index, using wavelengths of 540 and 1396 nm (calculated from the transformed spectra that were preprocessed by the combination of extended multiplicative scatter correction (EMSC) and first-order derivative), exhibited the highest accuracy for the estimation of the chlorophyll/carotenoid ratio (R2 = 0.57, RPD = 1.52). Further evaluation revealed that the index maintained a good performance at different seasonal stages and can be considered a useful proxy for the ratio in deciduous species. These findings provide a basis for the usage of hyperspectral information in the assessment of vegetation functions. Although promising, extensive evaluations of the proposed index are still required for other functional types of plants.

Sentinel-2 Data and Unmanned Aerial System Products to Support Crop and Bare Soil Monitoring: Methodology Based on a Statistical Comparison between Remote Sensing Data with Identical Spectral Bands

The growing need for sustainable management approaches of crops and bare soils requires measurements at a multiple scale (space and time) field system level, which have become increasingly accurate. In this context, proximal and satellite remote sensing data cooperation seems good practice for the present and future. The primary purpose of this work is the development of a sound protocol based on a statistical comparison between Copernicus Sentinel-2 MIS satellite data and a multispectral sensor mounted on an Unmanned Aerial Vehicle (UAV), featuring spectral deployment identical to Sentinel-2. The experimental dataset, based on simultaneously acquired proximal and Sentinel-2 data, concerns an agricultural field in Pisa (Tuscany), cultivated with corn. To understand how the two systems, comparable but quite different in terms of spatial resolution and atmosphere impacts, can effectively cooperate to create a value-added product, statistical tests were applied on bands and the derived Vegetation and Soil index. Overall, as expected, due to the mentioned impacts, the outcomes show a heterogeneous behavior with a difference between the coincident bands as well for the derived indices, modulated in the same manner by the phenological status (e.g., during the canopy developments) or by vegetation absence. Instead, similar behavior between two sensors occurred during the maturity phase of crop plants.

Avifauna diversity and phenology in a Ramsar site: Lake Tonga (Northeastern Algeria)

Between 2013 and 2016 we conducted an inventory and characterization of aquatic avifauna at Lake Tonga, a Ramsar site. We identified 61 species belonging to 17 families, dominated by Anatidae with 14 species. The inventoried species have different phenological status. Some are known for their regular wintering and some for their summer concentrations, while others are residents year round. Population analysis showed the presence of endangered species such as Oxyura leucocephala, Marmaronetta angustirostris and Aythya nyroca, that are protected by national and/or international laws. Bird counts showed monthly variations in total abundance and richness, peaking during winter. Peak numbers were recorded in February and January, reflecting gatherings to prepare for prenuptial migration. Analysis of diversity and equitability indices indicated that maximum values ​​were generally recorded during the pre– and post–nuptial passages (H’= 3.51 and E = 0.72). Our results reflect the importance of this wetland as a wintering site and nesting site for numerous waterbirds species.

Association of weather variables with the migration phenology and body conditions of Siberian warblers

Different elements of weather, such as wind speed, wind direction, precipitation and temperature are very important regulators of bird migration. Weather conditions also play role on the body condition such as body mass and the deposited fat. In this study we selected four warbler species to examine the impact of different weather variables on their spring and autumn migration timing and their body condition in one of the most extreme weather areas of the Earth, at Lake Baikal in Siberia. We also studied the changes in body mass and fat reserves during the spring and autumn migration periods of these species. For the analyses, we used ringing data of 2471 birds from five spring and five autumn seasons during 2015–2019. According to our results, it can be stated that the weather did not have a significant association with the migration timing of the studied warblers, perhaps due to the geographical location of the study site. However, the body mass and the fat reserves of the birds increased during unsuitable weather conditions because of the increased energy requirements. Birds generally migrate with low fat reserves, which is due to the fact that this area is not an important stopover site for these species.

Deep convolutional neural network for damaged vegetation segmentation from RGB images based on virtual NIR-channel estimation

Performing accurate and automated semantic segmentation of vegetation is a first algorithmic step towards more complex models that can extract accurate biological information on crop health, weed presence and phenological state, among others. Traditionally, models based on normalized difference vegetation index (NDVI), near infrared channel (NIR) or RGB have been a good indicator of vegetation presence. However, these methods are not suitable for accurately segmenting vegetation showing damage, which precludes their use for downstream phenotyping algorithms. In this paper, we propose a comprehensive method for robust vegetation segmentation in RGB images that can cope with damaged vegetation. The method consists of a first regression convolutional neural network to estimate a virtual NIR channel from an RGB image. Second, we compute two newly proposed vegetation indices from this estimated virtual NIR: the infrared-dark channel subtraction (IDCS) and infrared-dark channel ratio (IDCR) indices. Finally, both the RGB image and the estimated indices are fed into a semantic segmentation deep convolutional neural network to train a model to segment vegetation regardless of damage or condition. The model was tested on 84 plots containing thirteen vegetation species showing different degrees of damage and acquired over 28 days. The results show that the best segmentation is obtained when the input image is augmented with the proposed virtual NIR channel (F1=0.94) and with the proposed IDCR and IDCS vegetation indices (F1=0.95) derived from the estimated NIR channel, while the use of only the image or RGB indices lead to inferior performance (RGB(F1=0.90) NIR(F1=0.82) or NDVI(F1=0.89) channel). The proposed method provides an end-to-end land cover map segmentation method directly from simple RGB images and has been successfully validated in real field conditions.

Determining the Effects of Nanonutrient Application in Cabbage (Brassica oleracea var. capitate L.) Using Spectrometry and Biomass Estimation with UAV

Geospatial technologies are presented as an alternative for the monitoring and control of crops, as demonstrated through the analysis of spectral responses (SR) of each species. In this study, it was intended to determine the effects of the application of nanonutrients (Zn and Mn) in cabbage (Brassica oleracea var. capitate L.) by analyzing the relationship between the vegetation indices (VI) NDVI, GNDVI, NGRDI, RVI, GVI, CCI RARSa and the content of chlorophyll (CC), from two trials established in the field and in the greenhouse, together with the calculation of dry biomass production in the field through the use of digital models and its further validation. The results indicated that for greenhouse experiments no significant differences were found between the VIs in the implemented treatments, rather for their phenological states. Whereas in the field assays it was evidenced that there were significant differences between the VIs for the treatments, as well as for the phenological states. The SR issued in the field allowed the evaluation of the behavior of the crop due to the application of nanonutrients, which did not occur in the greenhouse, in the same way. The SR also enabled the spectral characterization of the crop in its phenological states in the two trials. All this information was stored in a digital format, which allowed the creation of a spectral library which was published on a web server. The validation of the dry biomass allowed, by statistical analysis, the efficiency of the method used for its estimation to be confirmed.

PHENOLOGICAL SENSITIVITIES TO CLIMATE ARE SIMILAR IN TWO CLARKIA CONGENERS: INDIRECT EVIDENCE FOR FACILITATION, CONVERGENCE, NICHE CONSERVATISM, OR GENETIC CONSTRAINTS

To date, most herbarium-based studies of phenological sensitivity to climate and of climate-driven phenological shifts fall into two categories: detailed species-specific studies vs. multi-species investigations designed to explain inter-specific variation in sensitivity to climate and/or the magnitude and direction of their long-term phenological shifts. Few herbarium-based studies, however, have compared the phenological responses of closely related taxa to detect: (1) phenological divergence, which may result from selection for the avoidance of heterospecific pollen transfer or competition for pollinators, or (2) phenological similarity, which may result from phylogenetic niche conservatism, parallel or convergent adaptive evolution, or genetic constraints that prevent divergence. Here, we compare two widespread Clarkia species in California with respect to: the climates that they occupy; mean flowering date, controlling for local climate; the degree and direction of climate change to which they have been exposed over the last ∼115 yr; the sensitivity of flowering date to inter-annual and to long-term mean maximum spring temperature and annual precipitation across their ranges; and their phenological change over time. Specimens of C. cylindrica were sampled from sites that were chronically cooler and drier than those of C. unguiculata, although their climate envelopes broadly overlapped. Clarkia cylindrica flowers ∼ 3.5 d earlier than C. unguiculata when controlling for the effects of local climatic conditions and for quantitative variation in the phenological status of specimens. However, the congeners did not differ in their sensitivities to the climatic variables examined here; cumulative annual precipitation delayed flowering and higher spring temperatures advanced flowering. In spite of significant spring warming over the sampling period, neither species exhibited a long-term phenological shift. Precipitation and spring temperature interacted to influence flowering date: the advancing effect on flowering date of high spring temperatures was greater in dry than in mesic regions, and the delaying effect of high precipitation was greater in warm than in cool regions. The similarities between these species in their phenological sensitivity and behavior are consistent with the interpretation that facilitation by pollinators and/or shared environmental conditions generate similar patterns of selection, or that limited genetic variation in flowering time prevents evolutionary divergence between these species.

PHENOLOGICAL TRENDS IN THE CALIFORNIA POPPY (ESCHSCHOLZIA CALIFORNICA): DIGITIZED HERBARIUM SPECIMENS REVEAL INTRASPECIFIC VARIATION IN THE SENSITIVITY OF FLOWERING DATE TO CLIMATE CHANGE

Herbarium specimens provide a critical source of phenological data that can be used to identify the direct and indirect drivers of variation in flowering date within and among species. Specimen-based phenological research in California has been accelerated by digitization efforts such as the California Phenology Network, which has scored and archived the phenological status of over 1.4 million specimens to date. Using this new data source in the Consortium of California Herbaria's CCH2 data portal, we obtained data from 993 specimens of the iconic California Poppy, Eschscholzia californica Cham., along with climate data representing all collection sites. Our goal was to determine how long-term and interannual climate variation affect flowering dates, and whether the magnitude of phenological sensitivity to climate varies across the species' range. We found that specimens collected from chronically warm or dry sites flowered relatively early, and flowering date was more sensitive to long-term mean temperature than to long-term mean precipitation. Independent of these effects of long-term conditions, flowering date in E. californica was sensitive to interannual variation in seasonal precipitation, but the direction of this effect depended on the season in which the precipitation occurred. Specimens sampled from sites experiencing warmer-than-average springs in the year of collection flowered 2.7–3.3 days earlier for every 1°C increase in spring temperature relative to long-term mean spring temperature. The magnitude of these effects, however, varied across the range of E. californica, with greater sensitivity to temperature in relatively cooler regions and no discernible sensitivity in relatively warm regions. Consistently, California Poppies exhibited significant phenological advancement over the last 120 years, but this advancement was restricted to the cooler portions of its range. Our results provide one of the first accounts of intraspecific variation in both phenological sensitivity to climate and the magnitude of phenological shifts over time, and demonstrate that, for a single species, location- or population-specific estimates of phenological sensitivity or of temporal trends in phenology might not accurately predict phenological responses to climate change in other locations throughout its range. In this study, we highlight the utility and promise of herbarium specimens for addressing novel questions about the phenological responses of plants to climate trends.

Near‐infrared spectroscopy aids ecological restoration by classifying variation of taxonomy and phenology of a native shrub

Plant communities are composed of complex phenotypes that not only differ among taxonomic groups and habitats but also change over time within a species. Restoration projects (e.g. translocations and reseeding) can introduce new functional variation in plants, which further diversifies phenotypes and complicates our ability to identify locally adaptive phenotypes for future restoration. Near‐infrared spectroscopy (NIRS) offers one approach to detect the chemical phenotypes that differentiate plant species, populations, and phenological states of individual plants over time. We use sagebrush (Artemisia spp.) as a case study to test the accuracy by which NIRS can classify variation within taxonomy and phenology of a plant that is extensively managed and restored. Our results demonstrated that NIRS can accurately classify species of sagebrush within a study site (75–96%), populations of sagebrush within a subspecies (99%), annual phenology within a population (&gt;99%), and seasonal phenology within individual plants (&gt;97%). Low classification accuracy by NIRS in some sites may reflect heterogeneity associated with natural hybridization, translocation of nonlocal seed sources from past restoration, or complex gene‐by‐environment interactions. Advances in our ability to detect and interpret spectral signals from plants may improve both the selection of seed sources for targeted conservation and the capacity to monitor long‐term changes in vegetation.