Variation In Phenology Research Articles

Seasonality of Violet-winged grasshopper (Tropidacris collaris) (Stoll, 1813) (Orthoptera: Romaleidae) in the dwarf cashew

Tropidacris collaris (Stoll, 1813) is a polyphagous pest, found throughout the South American continent.It has recently been reported to cause damage to cashew trees. The aim of this study was to evaluate the population dynamics of T. collaris in the dwarf cashew, the effects of climate variables on the population dynamics of T. collaris, and the population dynamics of T. collaris in relation to the phenological stages of the dwarf cashew. Violet-winged grasshopper was collected in a clonal garden with 35 dwarf cashew clones (Anacardium occidentale L.) in Pacajus, Ceará, Brasil. The specimens were randomly captured using an entomological net in two collectors spaced 10 m apart. The collections were made between 09:00 and 11:00, in two 45-min shifts separated by an interval of 15 min. In the laboratory, the insects were separated and weighed, and the females dissected to assess the degree of maturation of the ovaries (I, II and III) and classify the ovipositor valves. A greater abundance of adults was found in December 2017 and June 2018. The number of females with mature ovaries (type III) showed a positive correlation with ovipositors with open valves, such ovaries being registered in December 2017, and in January and July 2018. Wind speed, evapotranspiration and relative humidity showed a significant correlation with the nymph population of T. collaris. The greatest peaks in the nymph population were stimulated by the leaf fall/leaf flux phenological stages of the dwarf cashew. In this study we show some of the phenological aspects of T. collaris under field conditions, correlated with climatic and phenological variables of the cashew tree. These results may help in decision-making about the application of methods to control T. collaris in tropical regions, as this insect is polyphagous, and its large size has great potential for damage to several crops

Phenological Changes of Woody Plants in the Southern and Northern Regions of Nanling Mountains and Their Relationship with Climatic Factors

In addressing the challenges posed by the implications of climate change, understanding the phenological variations of woody plants has become a pivotal research topic. This research centers on the phenological shifts of woody plants and their connections with climatic factors in the southern and northern regions of the Nanling Mountains, which serve as the boundary between the north subtropical climate zone and the south subtropical climate zone in South China. The data were gathered through extensive manual observations conducted at four plant phenology observation stations (Ganxian, Foshan, Guilin, and Changsha) spanning different periods from 1963 to 2008. The study scrutinized four widely distributed woody plant species in the research area, specifically C. mollissima, P. fortunei, M. azedarach, and M. grandiflora. The analytical methods utilized were linear trend estimation and Pearson correlation coefficient analyses. The principal findings were as follows: (i) over the past several decades, the phenological stages of woody plants in the southern region consistently preceded those in the northern region with variations ranging from 2 to 38 days; (ii) an advancing trend of 0.1 to 2 days per decade was discerned in the phenological stages of all woody plants in the southern region; (iii) within the same geographic region, distinct species exhibited varying sensitivities to climatic factors, and M. azedarach demonstrated a particularly high sensitivity to climate fluctuations affecting phenological stages; and (iv) different climatic factors exerted distinct influences on individual plant species. Notably, temperature emerged as the primary driver of phenological changes, which was supported by a significant negative correlation between the phenological stages of the studied plants and spring temperatures. This study contributes to our understanding of the effects of climate change on plant phenology and offers valuable insights to guide ecological conservation and management strategies within the region.

Seed size dimorphism and environmental factors govern the phenological variations in invasive plant Hyptis suaveolens

Persistent intra-individual variations, although understudied, may explain the population- and community-level ecological patterns of plant species. However, there is a lack of studies investigating the intra-individual phenological variations of a species under the influence of seed size dimorphism, especially in the case of invasive plant species. Therefore, a study was conducted to assess the phenological changes in plants grown from dimorphic seeds (small and large) produced within the persistent calyces of the invasive plant Hyptis suaveolens. Furthermore, the impact of various environmental factors (temperature, thermal time, photoperiod, and relative humidity) on the phenological events of plants grown from both seed classes was also analyzed. In this study, the duration and performance (%) of three phenological events—leaf emergence, flowering, and seed formation—were documented throughout the plant's life cycle (from July 2019 to February 2020). It was observed that all the phenological events lasted longer in plants of the large seed class compared to those of the small seed class. While leaf emergence began and ended on nearly similar days for plants of both seed classes, an earlier occurrence of flowering and seed formation events was observed for plants of the small seed class. The plants grown from small-sized seeds also exhibited lower thermal time requirements, greater photoperiodic requirements, and higher temperature requirements for flowering. The study concludes that phenology in H. suaveolens is influenced by both seed size dimorphism and varying environmental factors, resulting in phenological adaptability and subsequent spread over space and time, which may contribute to increased invasiveness of the species.

Arthropod associates of Kenyan buffelgrass (Cenchrus ciliaris): a field survey for biological control candidates of a globally important invasive grass

Invasive species are the second largest contributor to biodiversity loss and drivers of ecosystem change. Buffelgrass is a C4, perennial grass native to Africa and Asia that was widely introduced across tropical and subtropical rangelands as livestock forage. Buffelgrass reduces native biodiversity and ecosystem stability in its introduced range when it escapes livestock pastures. Understanding the ecology of insects associated with buffelgrass in its native range may provide an understanding of invasion processes and biological control opportunities where buffelgrass has become an invasive challenge. Here, we present the results of a six-year survey of herbivorous arthropods of a native buffelgrass population from Kenya. Buffelgrass was examined for externally and internally feeding insects of vegetative and reproductive tissues. We also categorised buffelgrass detritivores and parasitoids that may use buffelgrass herbivores as hosts. The samples were photographed and Sanger sequenced to identify them to the lowest possible taxonomic ranking. We collected information on sample abundances, phenologies, tissues consumed, and putative diet breadths. We identified 25 morphospecies representing seven orders and 16 families. The putative host plant specialisation was as high as 67% for Diptera. Phenological variation in herbivore presence correlated with seasonal rainfall and provided a guide for when to conduct follow-up biological control agent searches. The most abundant herbivore was a gall midge (Diptera: Cecidomyiidae) that inhabits buffelgrass culms. Additional research should focus on bringing this species into containment where host choice trials can be conducted to determine if it is truly monophagous and assess its impact on buffelgrass growth.

A Spatio-Temporal Encoding Neural Network for Semantic Segmentation of Satellite Image Time Series

Remote sensing image semantic segmentation plays a crucial role in various fields, such as environmental monitoring, urban planning, and agricultural land classification. However, most current research primarily focuses on utilizing the spatial and spectral information of single-temporal remote sensing images, neglecting the valuable temporal information present in historical image sequences. In fact, historical images often contain valuable phenological variations in land features, which exhibit diverse patterns and can significantly benefit from semantic segmentation tasks. This paper introduces a semantic segmentation framework for satellite image time series (SITS) based on dilated convolution and a Transformer encoder. The framework includes spatial encoding and temporal encoding. Spatial encoding, utilizing dilated convolutions exclusively, mitigates the loss of spatial accuracy and the need for up-sampling, while allowing for the extraction of rich multi-scale features through a combination of different dilation rates and dense connections. Temporal encoding leverages a Transformer encoder to extract temporal features for each pixel in the image. To better capture the annual periodic patterns of phenological phenomena in land features, position encoding is calculated based on the image’s acquisition date within the year. To assess the performance of this framework, comparative and ablation experiments were conducted using the PASTIS dataset. The experiments indicate that this framework achieves highly competitive performance with relatively low optimization parameters, resulting in an improvement of 8 percentage points in the mean Intersection over Union (mIoU).

Effect of Climatic Variations in the Floral Phenology of Berberis microphylla and Its Pollinator Insects

Berberis microphylla (calafate) is an evergreen shrub that grows spontaneously in the Argentinean and Chilean Patagonia with a very harsh climate. This Sub-Antarctic zone is affected by the “El Niño” and “La Niña” pattern, which is measured using the Oceanic Niño Index (ONI). The objective of this study was to analyze the floral phenology and its pollinator insects, in relation to the climatic conditions observed. This work was developed in Ushuaia (Argentina) during 2010, 2014, 2015, and 2016. Significant differences were observed for the development of flower bud and floral phenology between the four years studied. On 26 October, between 84 and 100% of flower buds was recorded in the shrubs tested during the years 2010, 2014, and 2015 and then decreased abruptly in 2010 and more slowly in 2014 and 2015. However, in 2016 on the same date, it was recorded 70% of button flowers increasing to 90% on 5 November and then slowly evolving towards the anthesis phase. On the other hand, the anthesis phase was developed rapidly for 2010 and 2015 and slower for 2014 and 2016. A peak in anthesis occurred on 2 November in 2010, while in the other years, it was observed later in 16 November. The hoverflies of the genus Carposcalis and Allograpta, both insects verified to be responsible for the pollination of calafate, were registered between 2014 and 2016. Carposcalis was significantly more present in 2014, while in 2015 and 2016, Allograpta was predominant. The insect activity was also related with the climatic conditions, i.e., air temperature, air relative humidity, wind speed, and gust of wind along the day during the anthesis phase, and which also modified its life cycle. So, it was observed in 2016, the year with winter temperatures higher than normal, that adult hoverflies ended their hibernation period earlier and began to lay eggs on the juvenile calafate sprouts; hence, the emerging larvae ate the pollen grain of button flowers, causing a decrease in future fruits harvested. The results presented show that the “La Niña” and “El Niño” effects in these latitudes are the opposite of those expected. Spring 2016 developed under a strong “La Niña” effect, as occurred in 2010 with temperatures warmer than in 2014 and 2015, where both years developed under the “El Niño” effect. This study showed how climatic conditions can modify the floral phenology of B. microphylla and its pollinator insect frequency, their activity, and their life cycle.

Genetic divergence in common bean genotypes from the IRAD gene bank: morpho-agronomic characteristics, fungal and bacterial disease resistance, and opportunities for genetic improvement

For successful plant breeding in any crop species, the importance of diversity in the available germplasm population is known and established. Thirty-two common bean (Phaseolus vulgaris) genotypes from the IRAD gene bank in Cameroon were evaluated for divergence in terms of their morpho-agronomic traits, fungal disease resistance, and bacterial disease resistance to assess the opportunity for genetic improvement of the crop. The trait associations were estimated using correlation coefficients and genotypes were classified into groups using cluster and principal component analyses. Seven qualitative and 16 quantitative traits comprising growth, phenological, yield, and disease variables were evaluated in this study. The qualitative markers revealed the degree of polymorphism among the 32 common bean genotypes. The number of phenotypic classes per character (Na) ranged from 2 to 18, with an average of 5.14. The expected gene diversity (He) ranged from 0.37 to 0.93 (mean = 0.56). The number of effective phenotypic classes (Ne) ranged from 1.82 to 14.22, with a mean of 3.85. An extensive range of variation was evident for the majority of traits, highlighting their utility for characterizing common bean germplasm. Many qualitative traits, including seed coat color, seed shape, and seed size, and also some quantitative traits of economic importance including seed yield, were found to be highly variable within the collection, with the MAC55 genotype displaying the highest yield (32.65 g per plant). Four genotypes, namely MAC55, BOA-5-1M6, FEB 192, and Banguem showed resistance to the two main common bean diseases, angular leaf spot and common blight. We detected highly significant correlations among several traits related to yield. A high broad-sense heritability was found for most of the quantitative traits. We carried out two-dimensional principal component analysis and used hierarchical clustering to group the analyzed germplasm according to their phenotypic similitudes. The evidence of agro-morphological diversity in the present collection and the identification of discriminant characters between the available germplasm through the use of PCA analysis have significant implications for establishing breeding schemes in common bean.

Mapping fruit tree dynamics using phenological metrics from optimal Sentinel-2 data and Deep Neural Network

Accurate and up-to-date crop-type maps are essential for efficient management and well-informed decision-making, allowing accurate planning and execution of agricultural operations in the horticultural sector. The assessment of crop-related traits, such as the spatiotemporal variability of phenology, can improve decision-making. The study aimed to extract phenological information from Sentinel-2 data to identify and distinguish between fruit trees and co-existing land use types on subtropical farms in Levubu, South Africa. However, the heterogeneity and complexity of the study area—composed of smallholder mixed cropping systems with overlapping spectra—constituted an obstacle to the application of optical pixel-based classification using machine learning (ML) classifiers. Given the socio-economic importance of fruit tree crops, the research sought to map the phenological dynamics of these crops using deep neural network (DNN) and optical Sentinel-2 data. The models were optimized to determine the best hyperparameters to achieve the best classification results. The classification results showed the maximum overall accuracies of 86.96%, 88.64%, 86.76%, and 87.25% for the April, May, June, and July images, respectively. The results demonstrate the potential of temporal phenological optical-based data in mapping fruit tree crops under different management systems. The availability of remotely sensed data with high spatial and spectral resolutions makes it possible to use deep learning models to support decision-making in agriculture. This creates new possibilities for deep learning to revolutionize and facilitate innovation within smart horticulture.

Calibration of Land-Use-Dependent Evaporation Parameters in Distributed Hydrological Models Using MODIS Evaporation Time Series Data

The land-use-specific calibration of evapotranspiration parameters in hydrologic modeling is challenging due to the lack of appropriate reference data. We present a MODIS-based calibration approach of vegetation-related evaporation parameters for two mesoscale catchments in western Germany with the physically based distributed hydrological model WaSiM-ETH. Time series of land-use-specific actual evapotranspiration (ETa) patterns were generated from MOD16A2 evapotranspiration and CORINE land-cover data from homogeneous image pixels for the major land-cover types in the region. Manual calibration was then carried out for 1D single-cell models, each representing a specific land-use type based on aggregated 11-year mean ETa values using SKout and PBIAS as objective functions (SKout > 0.8, |PBIAS| < 5%). The spatio-temporal evaluation on the catchment scale was conducted by comparing the simulated ETa pattern to six daily ETa grids derived from LANDSAT data. The results show a clear overall improvement in the SPAEF (spatial efficiency metric) for most land-use types, with some deficiencies for two scenes in spring and late summer due to phenological variation and a particularly dry hydrological system state, respectively. The presented method demonstrates a significant improvement in the simulation of ETa regarding both time and spatial scale.

An improved dynamic threshold method for determining the start of the vegetation greening season in remote sensing monitoring: The case of Inner Mongolia

The start of the greening season (SOS) is the beginning of vegetation phenology, and its time of occurrence affects the carbon cycle and organic matter production of the ecosystem. Accurate retrieval of vegetation phenology in remote sensing monitoring is important for sustainable management of vegetation resources. The dynamic threshold method (DTM) is one of the mainstream methods to retrieve vegetation phenology. However, it suffers the following two shortcomings. First, a subjective threshold percentage of the phenology nodes is used based on the user's empirical thresholds, and the determined threshold is not universal. Second, a common threshold is used for identifying the phenology globally with little consideration of the physiological characteristics of different vegetation types, thus blurring the phenology variability among vegetation types. Here, we developed an improved dynamic threshold method (IDTM) for the retrieval of the start of the greening season and tested it using temperate grassland and forest vegetation in Inner Mongolia. The algorithm in the IDTM uses the curvature of the EVI2 time series curves of different vegetation types to determine the threshold for the SOS, thus eliminating the subjectivity of the original DTM. The predicted SOS of the IDTM is in better agreement with the ground observations than the MCD12 Q2 vegetation phenology product or the DTM with different empirical thresholds. In terms of spatial correlations, the SOS of the IDTM was significantly correlated with that of the MCD12 Q2 phenology product (r = 0.72, p < 0.05). Our study indicates the efficacy of the IDTM in predicting the SOS of vegetation and that the average SOS of vegetation in Inner Mongolia is in early May (on Day 126) but has a large variation across vegetation zones.

Sex-dependent phenological responses to climate vary across species' ranges.

Anthropogenic climate change has significantly altered the flowering times (i.e., phenology) of plants worldwide, affecting their reproduction, survival, and interactions. Recent studies utilizing herbarium specimens have uncovered significant intra- and inter-specific variation in flowering phenology and its response to changes in climate but have mostly been limited to animal-pollinated species. Thus, despite their economic and ecological importance, variation in phenological responses to climate remain largely unexplored among and within wind-pollinated dioecious species and across their sexes. Using both herbarium specimens and volunteer observations of cottonwood (Populus) species, we examined how phenological sensitivity to climate varies across species, their ranges, sexes, and phenophases. The timing of flowering varied significantly across and within species, as did their sensitivity to spring temperature. In particular, male flowering generally happened earlier in the season and was more sensitive to warming than female flowering. Further, the onset of flowering was more sensitive to changes in temperature than leaf out. Increased temporal gaps between male and female flowering time and between the first open flower date and leaf out date were predicted for the future under two climate change scenarios. These shifts will impact the efficacy of sexual reproduction and gene flow among species. Our study demonstrates significant inter- and intra-specific variation in phenology and its responses to environmental cues, across species' ranges, phenophases, and sex, in wind-pollinated species. These variations need to be considered to predict accurately the effects of climate change and assess their ecological and evolutionary consequences.

Exploring reproductive biology in Eucalyptus globulus clones under distinct climatic conditions

AbstractDespite the great economic benefits, one of the major threats of introducing species into exotic ranges is their ability to regenerate and spread to novel environments. For example, Eucalyptus globulus plantations have been established in many parts of the world, using seedlings or clonal material, the latter often from advanced generation tree improvement programs. However, major concerns exist about this species’ ability to invade non-native areas. Therefore, it is necessary to study whether breeding programs affect this species’ reproductive biology to assess the risk of invasiveness of these improved trees. In this sense, this work compared the E. globulus phenological behaviour and reproductive structures production of three clonal and one seedling-based stand across two distinct climatic zones (mesic and xeric) in Portugal. Through binocular observation, four different stands (seedlings descending from open-pollinated crosses and three clonal-stands) were monitored every three weeks for different reproductive phenological stages, namely the presence of flowers and capsules. Clonal stands presented a shorter flowering season, less abundant reproductive structures, and higher phenological variability across sites than seedling-based stands. Phenological patterns and fructification abundance also varied between the three clonal stands. These results suggest that clones with decreased reproductive ability can be chosen during the breeding process, lowering the dispersal risk while enhancing productivity. Thus, understanding the phenological behaviour of E. globulus is critical to more sustainable agroforestry by reducing the spread risks.

Stream groundwater inputs generate fine‐scale variation in brook trout phenology and growth across a warming landscape

Abstract Climate change is increasing global atmospheric temperatures, which can reduce abundance and cause range shifts in species that are sensitive to warming. However, fine‐scale thermal heterogeneity can drive highly variable local responses to climate change, especially in freshwater environments that differ in groundwater inputs and geomorphology. We used temperature data collected during 2012–2021 from 10 small, pristine streams in eastern Canada to characterise thermal variation at a small spatial scale (~25 km2). We then used relationships between daily air and stream temperatures to reconstruct stream temperature since 1980, and assessed how thermal variation influenced the phenology and growth of brook trout (Salvelinus fontinalis). Air–stream temperature relationships varied considerably among streams despite their close proximity, with predicted summer temperatures differing up to 9.5°C between warmer rainfall‐dominated streams and cooler groundwater‐dominated streams. Rainfall‐dominated streams warmed more than twice as fast as groundwater‐dominated streams across all seasons since 1980, with nearly four‐fold differences in rates of warming evident during summer months. Fine‐scale thermal heterogeneity also shaped brook trout phenology, as juveniles in rainfall‐dominated streams were estimated to hatch and emerge much later (~70 and 40 days, respectively) and experience faster phenological shifts than in groundwater‐dominated streams. Relationships between juvenile brook trout size and accumulated degree‐days were positive, but slopes differed over two‐fold and did not vary systematically based on stream hydrology, suggesting more idiosyncratic impacts of warming on early growth. Collectively, our study illustrates how species responses to climate change in freshwater environments can be consistent in direction but vary substantially in magnitude owing to the influence of groundwater. Future climate change is likely to increase the thermal stress experienced by brook trout populations in warmer rainfall‐dominated streams, while potentially benefiting those in groundwater‐dominated streams where current temperatures are often suboptimal. Observed differences in the rates and ecological impacts of warming among streams suggest that fine‐scale thermal variation must be considered when forecasting effects of future climate change on stream fish population dynamics.

Phenological and Floral Variations in Ecotypes Moringa oleiferafor Reproductive Adaptations in Subtropical Conditions

Phenological and Floral Variations in Ecotypes Moringa oleiferafor Reproductive Adaptations in Subtropical Conditions

Nonstructural carbohydrate dynamics' relationship to leaf development under varying environments.

Leaf-out in temperate forests is a critical transition point each spring and advancing with global change. The mechanism linking phenological variation to external cues is poorly understood. Nonstructural carbohydrate (NSC) availability may be key. Here, we use branch cuttings from northern red oak (Quercus rubra) and measure NSCs throughout bud development in branch tissue. Given genes and environment influence phenology, we placed branches in an arrayed factorial experiment (three temperatures × two photoperiods, eight genotypes) to examine their impact on variation in leaf-out timing and corresponding NSCs. Despite significant differences in leaf-out timing between treatments, NSC patterns were much more consistent, with all treatments and genotypes displaying similar NSC concentrations across phenophases. Notably, the moderate and hot temperature treatments reached the same NSC concentrations and phenophases at the same growing degree days (GDD), but 20 calendar days apart, while the cold treatment achieved only half the GDD of the other two. Our results suggest that NSCs are coordinated with leaf-out and could act as a molecular clock, signaling to cells the passage of time and triggering leaf development to begin. This link between NSCs and budburst is critical for improving predictions of phenological timing.

PiCAM: A Raspberry Pi‐based open‐source, low‐power camera system for monitoring plant phenology in Arctic environments

Abstract Time‐lapse cameras have been widely used as a tool to monitor the timing of seasonal vegetation growth. These simple, relatively inexpensive systems can provide high‐frequency observations of leaf development and demography which are critical data sets needed to characterize plant phenology from species to landscapes. This is important for understanding how plants are responding to global changes, as well as for validating satellite‐derived phenology products. However, in remote regions including the high‐latitude Arctic, deploying time‐lapse cameras could be challenging. The remoteness and lack of widespread power and telecommunications infrastructure limit options for the installation, maintenance and retrieval of data and equipment, and make it difficult for cameras to survive in extreme weather (e.g. long cold winters). To improve our understanding of Arctic phenology, new technologies are required to address these challenges. Here, we present a novel, low‐power, compact, lightweight time‐lapse camera system, called power‐interval camera automation module (PiCAM). The PiCAM was designed with explicit consideration to simplify deployment (i.e. without a need for external power supplies) of camera systems and to address the challenges of camera survival in harsh Arctic environments. In this paper, we describe the design, setup and technical details of the PiCAM and provide a roadmap for how to build and operate these systems. As proof of concept, we deployed 26 PiCAMs at three low‐Arctic tundra sites on the Seward Peninsula, Alaska in early August 2021 for characterizing Arctic plant phenology. Of the 26 PiCAMs, 70% remained active at the point of our revisit in late July 2022 despite the extreme winter temperatures they experienced (&lt; −30°C, heavy snow cover). We extracted key plant phenology metrics from the PiCAMs and captured strong differences across key Arctic plant species. We showed that the PiCAM has the potential to be widely used for monitoring plant phenology across the broader Arctic region, addressing the need for ground‐based understanding of Arctic phenological diversity to develop knowledge of plant response to climate change and to validate remote sensing products.

A biophysical model of Calanus hyperboreus in the Gulf of St. Lawrence: Interannual variability in phenology and circulation drive the timing and location of right whale foraging habitat in spring and early summer

Since 2015, North Atlantic right whale (NARW) occurrences have considerably increased in the Gulf of St. Lawrence (GSL), likely driven by a decrease in the availability of their historically preferred prey, the lipid-rich Calanus spp. (Copepoda), in their traditional foraging habitats. In the southern GSL (sGSL) where most NARW sightings occurred in recent years, the large bodied and lipid-rich Calanus hyperboreus dominates the Calanus spp. biomass in the spring and early summer, with important interannual variability hypothesized to be driven by variations in phenology and transport. This study examined the seasonal and interannual variability of phenology and relative distribution of C. hyperboreus biomass in the sGSL during the spring and early summer from 2016 to 2019, using a 3-D coupled biophysical model. We simulated the development and advection of the new cohort of C. hyperboreus from first copepodite (CI) stages in early spring to mid-July, a period during which they enter diapause as lipid-rich fourth copepodite (CIV) stages. Our study confirmed that C. hyperboreus distribution in the sGSL is mainly influenced by transport from adjacent deeper areas through interactions between ocean circulation and early copepodite stages during the spring. Model results indicated variable numbers of simulated individuals from the Laurentian Channel were transported annually to the sGSL during their active stages, entered diapause, and were retained near the seafloor in the summer. Biophysical simulation outputs could therefore contribute to seasonal-scale advice on locations of NARW potential foraging habitat formation and improve understanding NARW seasonal dynamics in the sGSL.

Improved prediction of rice yield at field and county levels by synergistic use of SAR, optical and meteorological data

Timely and accurate rice yield prediction over large regions is imperative to making informed decisions on precision crop management and ensuring regional food security. Previous studies on crop yield prediction mainly used of either optical or synthetic aperture radar (SAR) data alone, and few of them investigated the synergistic use of these two data sources. It remains unclear whether and how synergistic use of optical and SAR imagery would improve crop yield prediction over a large region. In addition, various machine learning (ML) algorithms have been used in regional yield prediction, but the existing findings are mostly inconsistent about the optimal ML algorithm that prevails others across years and spatial levels. To solve these issues, this study designed a meta-learning ensemble regression (MLER) framework and developed an efficient method of integrating multi-source data for accurate prediction of rice yield at field and county levels over Jiangsu Province of China.The results demonstrated that SAR data from Sentinel-1 could compensate for the lack of optical data from Sentinel-2 for rice yield prediction, which was further improved by adding the meteorological data. The MLER algorithm exhibited the highest field-level prediction accuracy for Xinghua and Suining (R2 = 0.89, RMSE = 0.54 t/ha), as compared with individual base algorithms (RF: R2 = 0.80, RMSE = 0.72 t/ha; XGBoost: R2 = 0.76, RMSE = 0.80 t/ha; SVR: R2 = 0.74, RMSE = 0.83 t/ha) and the long short-term memory (LSTM) (R2 = 0.76, RMSE = 0.80 t/ha) with a ten-fold cross-validation. Furthermore, integrating the MLER algorithm with the best-performing data combination could reduce the influence of regional phenological variability for building yield prediction models applicable to the entire province. The MLER models for predicting the rice yield across the province exhibited robust performance in the leave-one-year-out accuracy assessment at both levels (field level: R2: 0.50∼0.61, RMSE: 0.85∼1.22 t/ha; county level: R2: 0.42∼0.67, RMSE: 0.23∼0.27 t/ha) and could be transferred to an extreme-heat year (2022) (R2 = 0.49, RMSE = 1.52 t/ha). This research opens up new possibilities for predicting the rice yield of smallholder fields over large regions at multiple levels by integrating publicly available multi-source data and machine learning.

Isolating the effects of land use and functional variation on Yucatán's forest biomass under global change

Tropical forests hold large stocks of carbon in biomass and face pressures from changing climate and anthropogenic disturbance. Forests' capacity to store biomass under future conditions and accumulate biomass during regrowth after clearance are major knowledge gaps. Here we use chronosequence data, satellite observations and a C-cycle model to diagnose woody C dynamics in two dry forest ecotypes (semi-deciduous and semi-evergreen) in Yucatán, Mexico. Woody biomass differences between mature semi-deciduous (90 MgC ha−1) and semi-evergreen (175 MgC ha−1) forest landscapes are mostly explained by differences in climate (c. 60%), particularly temperature, humidity and soil moisture effects on production. Functional variation in foliar phenology, woody allocation, and wood turnover rate explained c. 40% of biomass differences between ecotypes. Modeling experiments explored varied forest clearance and regrowth cycles, under a range of climate and CO2 change scenarios to 2100. Production and steady state biomass in both ecotypes were reduced by forecast warming and drying (mean biomass 2021–2100 reduced 16–19% compared to 2001–2020), but compensated by fertilisation from rising CO2. Functional analysis indicates that trait adjustments amplify biomass losses by 70%. Experiments with disturbance and recovery across historically reported levels indicate reductions to mean forest biomass stocks over 2021–2100 similar in magnitude to climate impacts (10–19% reductions for disturbance with recovery). Forest disturbance without regrowth amplifies biomass loss by three- or four-fold. We conclude that vegetation functional differences across the Yucatán climate gradient have developed to limit climate risks. Climate change will therefore lead to functional adjustments for all forest types. These adjustments are likely to magnify biomass reductions caused directly by climate change over the coming century. However, the range of impacts of land use and land use change are as, or more, substantive than the totality of direct and indirect climate impacts. Thus the carbon storage of Yucatan's forests is highly vulnerable both to climate and land use and land use change. Our results here should be used to test and enhance land surface models use for dry forest carbon cycle assessment regionally and globally. A single plant functional type approach for modeling Yucatán's forests is not justified.

Effects of changing climate extremes and vegetation phenology on wildlife associated with grasslands in the southwestern United States

Assessments of the potential responses of animal species to climate change often rely on correlations between long-term average temperature or precipitation and species’ occurrence or abundance. Such assessments do not account for the potential predictive capacity of either climate extremes and variability or the indirect effects of climate as mediated by plant phenology. By contrast, we projected responses of wildlife in desert grasslands of the southwestern United States to future climate means, extremes, and variability and changes in the timing and magnitude of primary productivity. We used historical climate data and remotely sensed phenology metrics to develop predictive models of climate-phenology relations and to project phenology given anticipated future climate. We used wildlife survey data to develop models of wildlife-climate and wildlife-phenology relations. Then, on the basis of the modeled relations between climate and phenology variables, and expectations of future climate change, we projected the occurrence or density of four species of management interest associated with these grasslands: Gambel’s Quail (Callipepla gambelii), Scaled Quail (Callipepla squamat), Gunnison’s prairie dog (Cynomys gunnisoni), and American pronghorn (Antilocapra americana). Our results illustrated that climate extremes and plant phenology may contribute more to projecting wildlife responses to climate change than climate means. Monthly climate extremes and phenology variables were influential predictors of population measures of all four species. For three species, models that included climate extremes as predictors outperformed models that did not include extremes. The most important predictors, and months in which the predictors were most relevant to wildlife occurrence or density, varied among species. Our results highlighted that spatial and temporal variability in climate, phenology, and population measures may limit the utility of climate averages-based bioclimatic niche models for informing wildlife management actions, and may suggest priorities for sustained data collection and continued analysis.