Patterns Of Spatial Variation Research Articles

Everchanging range: How the changing environment has influenced the genetic diversity and differentiation of an iconic North American palm species.

Understanding spatial patterns of neutral and adaptive genetic variation and linking them to future climate change have become crucial in assessing the genetic vulnerability of species and developing conservation strategies. Using a combination of genomic approaches, this study aimed to explain the demographic history, predict the adaptive potential in Washingtonia palm populations on the Baja California Peninsula (BCP) and southern California and determine the geographic areas where climate change will have the most drastic effects. We used over 5000 SNPs from 155 individuals across 18 populations spanning the entire distribution range of Washingtonia palms on the BCP and southern California. We examined past and current genetic diversity distribution patterns and identified outliers using genetic differentiation and genotype-environment association methods. Genetic vulnerability was predicted, and species distribution modeling was done to the geographic regions that will be at risk under future climate scenarios. Demographic modeling supported a bottleneck related to the Wisconsin glaciation, which was stronger and longer in northern Washingtonia populations. Genomic diversity presented a strong relationship to geography and provided evidence for range expansions from several refugia. Gradient Forest Analysis revealed that the genetic variation was primarily shaped by variables related to latitude and temperature during the coldest quarter, indicating adaptation to local thermal environments. We found limited adaptive potential and high levels of genetic vulnerability in lowland southern and central populations. Accordingly, species distribution modeling found that the southern distribution range will be affected by climate change, particularly under the high-emission scenario. Our findings include a history of population bottleneck related to postglacial range expansion, population divergence with limited gene flow, and probable future changes in distribution under changing conditions. Under long-term climate change, Washingtonia's southern and central lowland populations will experience harsher climate conditions and strong genomic offset.

Elevation gradient effects on grassland species diversity and phylogenetic in the two-river source forest region of the Altai Mountains, Xinjiang, China

Altitude, as a key environmental factor, shapes the spatial patterns of species diversity, phylogenetic diversity, and community phylogenetic structure. Studying grassland diversity and phylogenetic structure along altitudinal gradients helps clarify how altitude-driven environmental changes influence community assembly, and reveal vertical patterns in community formation. This study examines grasslands at 1300–2500 m elevation in the Two-River Source Forest Area, Altai Mountains, Xinjiang. Six elevation gradients (200 m intervals) were surveyed with 90 grassland quadrats, documenting community characteristics and environmental data. The study analyzes the patterns of species composition, diversity, and phylogeny across different elevation gradients and explores their relationships with key environmental factors. The results indicate that the grassland species composition is dominated by species from the Poaceae, Rosaceae, and Asteraceae families, with Poa annua (annual bluegrass) being the dominant species within Poaceae. The species diversity along the elevation gradient exhibits a bimodal trend, with an initial increase, followed by a decrease, another increase, and finally a decline as the elevation rises. In contrast, phylogenetic diversity shows a unimodal pattern, characterized by an initial increase followed by a decline with increasing elevation. Although the phylogenetic structure did not exhibit a significant trend of transitioning from divergence to clustering along the altitudinal gradient, the overall phylogenetic pattern of grassland communities tended toward clustering. Further analysis reveals significant correlations between species diversity and environmental factors such as temperature, precipitation, forest cover, and soil moisture. However, no environmental factors were found to have a significant correlation with the phylogenetic indices.

Global Dynamics of Grassland FVC and LST and Spatial Distribution of Their Correlation (2001–2022)

Fractional Vegetation Cover (FVC) and Land Surface Temperature (LST) are critical indicators for assessing grassland ecosystems. Based on global remote sensing data for FVC and LST from 2001 to 2022, this study employs the Mann–Kendall trend test and Spearman correlation analysis to explore the dynamic changes in and spatial distribution patterns of both variables. The results indicate that the FVC is increasing in regions such as Europe, the eastern southern Sahara, western India, eastern South America, western and southern North America, and central China. However, it is decreasing in southern Canada, the central United States, and northern Australia. Significant increases in LST are observed in subarctic regions and the Tibetan Plateau, attributed to polar warming effects associated with global climate change. Conversely, the LST is decreasing in central China, eastern coastal Australia, and southern Africa. The global FVC–LST relationship exhibits the following four distinct spatial distribution patterns: (1) FVC increase and LST increase (Type 1), (2) FVC increase and LST decrease (Type 2), (3) FVC decrease and LST increase (Type 3), and (4) FVC decrease and LST decrease (Type 4). Type 1, covering 33.72%, is primarily found in high-latitude and high-altitude areas, such as subarctic regions and the Tibetan Plateau. Type 2, the largest group (46.98%), is mainly located in eastern North America, eastern South America, and southern Africa. Type 3, which comprises 18.72%, is concentrated in arid and semi-arid regions, while Type 4, representing only 0.59%, lacks clear spatial distribution patterns.

Intermediate human interference exerts contrasting effects on spatial patterns of island plant diversity across dual scales

Intermediate human interference exerts contrasting effects on spatial patterns of island plant diversity across dual scales

Spatial Patterns of Taxonomic, Functional, and Phylogenetic Diversity of Mammals in Southern Mexico

ABSTRACTBiodiversity is multidimensional and should therefore be characterized using multiple approaches to understand the patterns at various temporal and spatial scales. Taxonomic, functional, and phylogenetic measurements of diversity are now recognized as comprehensive methods for understanding the relationships among species diversity, associated ecological processes, and the evolutionary history of species within communities. However, spatial incongruence among these different dimensions of diversity has been found to be common at global and regional levels, posing challenges for conservation. By using species distribution models built with correlative method, we investigated whether downscaling spatial analyses of multidimensional diversity across mammal groups (flying mammals, small‐sized mammals, and medium and large‐sized mammals) maintain mismatch patterns in a megadiverse mammal unit (Oaxaca state, southern Mexico) at three spatial resolutions and how well diversity hotspots are protected. We found similar spatial patterns and high correlations across the analyzed resolutions, suggesting interchangeability. As expected, we observed incongruence in spatial patterns among multidimensional biodiversity measurements (average of 68.7%). Species richness partially matched the global pattern, as topographic heterogeneity facilitated refugia and speciation in mountains, while productivity in lowlands supported a high number of species. As observed globally, functional diversity was higher in temperate areas; however, redundancy was not found in tropical ecosystems but rather in semiarid ones, suggesting that environmental filtering plays an important role in structuring communities. Contrary to expectations, phylogenetic diversity increased in tropical ecosystems, where dispersal events allow species to accumulate through invasion processes. Among mammal groups, we also found disparate patterns, suggesting differences in dispersal potential and energy demand. Protected area systems overlapped very little with the proposed multidimensional conservation areas, with a higher overlap found in social initiatives than in governmental ones. Our findings support the notion that there is spatial incongruence between the dimensions of biodiversity, which has implications for subregional mammal conservation.

Trophic diversity of the bloom-forming jellyfish community in the coastal waters of China assessed by stable isotope analysis

A growing realization indicates that the trophic ecology of jellyfish is more diverse than once thought, yet a holistic view reflecting the trophic structure and trophodynamics in bloom-forming jellyfish community remains rare. Based on stable isotope δ13C and δ15N analysis, we estimated the trophic characteristics of common blooms jellyfish Nemopilema nomurai, Cyanea spp., Aurelia coerulea and Aequorea spp. in the coastal waters of China (CWC). Our data indicated that most of the isotopic niche space in the overall planktonic food web was occupied by the bloom-forming jellyfish community. The large spectrum of isotopic niche highlights the diverse ecological roles and potentially broad trophic relevance of these jellyfish in the food web. The substantial trophic diversity of these jellyfish resulted from the various trophic positions occupied by different taxa, complicated niche differentiation and overlap patterns, inconsistent size-based trophic variation, and spatial and temporal variation patterns. Isotopic niche comparisons indicated the presence of niche differentiation, reflecting the difference and individual-specific characteristic in resource exploitation and feeding preference among different jellyfish. Additionally, the inconsistent size-based trophic variation among groups derived from an increase in trophic level with size for Cyanea spp., A. coerulea and Aequorea spp. medusae to no change for N. nomurai medusae, which suggests the complexity in size-related trophic shift patterns within the jellyfish group. Additional diversity also arose from variation in the spatiotemporal structuring of jellyfish trophic ecology, which might be caused by the occurrence of trophic heterogeneity at the base of the planktonic food web. In conclusion, our study characterized the trophic structures of the bloom-forming jellyfish community in the CWC, and revealed their trophic diversity resulting from interspecific, intraspecific (ontogenetic), and spatiotemporal variation. These results hold strong potential to further improve the understanding of the trophic ecology and functional roles of the jellyfish community. Furthermore, this study provides a systematic and valuable isotopic data set, spanning from the food web baseline to zooplanktonic organisms and jellyfish community, against which compare with trophic investigations in future in planktonic food web of the CWC.

The neurohormone tyramine stimulates the secretion of an insulin-like peptide from the Caenorhabditis elegans intestine to modulate the systemic stress response.

The DAF-2/insulin/insulin-like growth factor signaling (IIS) pathway plays an evolutionarily conserved role in regulating reproductive development, life span, and stress resistance. In Caenorhabditis elegans, DAF-2/IIS signaling is modulated by an extensive array of insulin-like peptides (ILPs) with diverse spatial and temporal expression patterns. However, the release dynamics and specific functions of these ILPs in adapting to different environmental conditions remain poorly understood. Here, we show that the ILP, insulin-3 (INS-3), plays a crucial role in modulating the response to various environmental stressors in C. elegans. ins-3 mutants display increased resistance to heat, oxidative stress, and starvation; however, this advantage is countered by slower reproductive development under favorable conditions. We find that ins-3 expression is downregulated in response to environmental stressors, whereas, the neurohormone tyramine, which is released during the acute flight response, increases ins-3 expression. We show that tyramine induces intestinal calcium (Ca2+) transients through the activation of the TYRA-3 receptor. Our data support a model in which tyramine negatively impacts environmental stress resistance by stimulating the release of INS-3 from the intestine via the activation of a TYRA-3-Gαq-IP3 pathway. The release of INS-3 systemically activates the DAF-2 pathway, resulting in the inhibition of cytoprotective mechanisms mediated by DAF-16/FOXO. These studies offer mechanistic insights into a brain-gut communication pathway that weighs adaptive strategies to respond to acute and long-term stressors.

Optic nerve-mediated modulation of temporally interfering electric fields for potential targeted retinal disease therapy: a computational modeling study

IntroductionTraditional extraocular electrical stimulation typically produces diffuse electric fields across the retina, limiting the precision of targeted therapy. Temporally interfering (TI) electrical stimulation, an emerging approach, can generate convergent electric fields, providing advantages for targeted treatment of various eye conditions.ObjectiveUnderstanding how detailed structures of the retina, especially the optic nerve, affects electric fields can enhance the application of TI approach in retinal neurodegenerative and vascular diseases, an essential aspect that has been frequently neglected in previous researches.MethodsWe developed an anatomically accurate multi-layer human eye model, incorporating the optic nerve segment and setting it apart from current research endeavors. Based on this model, we conducted in silico investigations to predict the influence of the optic nerve on spatial characteristics of the temporally interfering electric field (TIEF) generated by diverse electrode configurations.ResultsOptic nerve directly influenced spatial distributions and modulation rules of TIEFs. It caused convergent areas to shift nasally or temporally in relation to return electrode positions, and further increased the axial anisotropy within the convergent TIEF. Furthermore, alterations in electrode positions and adjustments to current ratios among channels induced diverse spatial patterns of TIEFs within the macular region, the area surrounding the optic nerve, as well as peripheral retina.ConclusionOur findings suggested that presence of the optic nerve necessitated the utilization of different modulating paradigms when employing TI strategy for targeted treatment of various retinal lesions. And also provided theoretical references for developing a novel retinal electrical stimulation therapeutic device based on TI technology.

Spatial patterns of phylogenetic diversity in the Andean tribe Eudemeae (Brassicaceae) and their association with climatic gradients in western South America

Spatial patterns of phylogenetic diversity in the Andean tribe Eudemeae (Brassicaceae) and their association with climatic gradients in western South America

Spatial variability and changing pattern of wellbeing of women in West Bengal, India: a sociodemographic perspective

Spatial variability and changing pattern of wellbeing of women in West Bengal, India: a sociodemographic perspective

Microbial plankton occurrence database in the North American Arctic region: synthesis of recent diversity of potentially toxic and/or harmful algae

Abstract. The Arctic Ocean is currently undergoing significant transformations due to climate change, leading to profound changes in its microbial plankton communities, including photoautotrophic prokaryotes and eukaryotes (i.e. phytoplankton), as well as hetero-, phago-, and mixotrophic protistan species. Among these unicellular organisms, species of potentially toxic and/or harmful algae (hereafter referred to as HA) are of particular concern, as they pose a threat to human and ecosystem health if they potentially spread into Arctic waters. Despite their importance, the spatiotemporal distribution of these communities in the North American Arctic is poorly understood. To address this gap, we compiled and synthesized a large dataset from various sources, partitioned into nine regions based on the Large Marine Ecosystem classification. Our dataset contains 385 348 georeferenced data points and 18 268 unique sampling events (https://doi.org/10.5281/zenodo.10498858, Schiffrine et al., 2024), encompassing 1442 unique taxa, with Heterokontophyta (notably diatoms) and Dinoflagellata being the most dominant phyla. Our results indicate distinct spatial patterns of diversity, with the highest diversity observed in Atlantic-influenced regions of the North American Arctic. An analysis of the maximum latitude of HA species over time shows a gradual increase, with a notable rise towards the 1990s. However, this trend is likely influenced by increased research at higher latitudes, meaning no substantial spread of HA species into the North American part of the Arctic. Our study underscores the importance of extensive and long-term sampling efforts to understand the Arctic's biodiversity, particularly with respect to documenting the presence and distribution of HA species. While the occurrence of HA species in the Arctic is recognized, our findings highlight the need for further detailed investigations to fully grasp their ecological impacts and variability in the region. Overall, our results provide new insights into the spatial patterns and biodiversity of the microbial plankton communities in the North American Arctic and have implications for understanding the ecological functioning and response of this region to ongoing climate change.

Spatial Distribution Patterns of Herbaceous Vegetation Diversity and Environmental Drivers in the Subalpine Ecosystem of Anyemaqen Mountains, Qinghai Province, China

Spatial Distribution Patterns of Herbaceous Vegetation Diversity and Environmental Drivers in the Subalpine Ecosystem of Anyemaqen Mountains, Qinghai Province, China

Individual Variability in the Structural Connectivity Architecture of the Human Brain.

The human brain exhibits high degree of individual variability in both its structure and function, which underlies inter-subject differences in cognition and behavior. It was previously shown that functional connectivity is more variable in the hetero-modal association cortex but less variable in the unimodal cortices. Structural connectivity is the anatomical substrate of functional connectivity, but the spatial and temporal patterns of individual variability in structural connectivity (IVSC) remain largely unknown. In the present study, we discovered a detailed and robust chart of IVSC obtained by applying diffusion MRI and tractography techniques to 1724 adults (770 males and 954 females) from multiple imaging datasets. Our results showed that the structural connectivity exhibited the highest and lowest variability in the limbic regions and the unimodal sensorimotor regions, respectively. With increased age, higher IVSC was observed across most brain regions. Moreover, the specific spatial distribution of IVSC is related to the cortical laminar differentiation and myelination content. Finally, we proposed a modified ridge regression model to predict individual cognition and generated idiographic brain mapping, which was significantly correlated with the spatial pattern of IVSC. Overall, our findings further contribute to the understanding of the mechanisms of individual variability in brain structural connectivity and link to the prediction of individual cognitive function in adult subjects.Significance Statement White matter connectivity between grey matter regions plays an important role in integrating information from distributed regions when individuals performing complex cognitive functions. Unique white matter connectivity is the neuroanatomical identity of each individual. The authors systematically explored the spatio-temporal pattern of individual variability in structural connectivity, and the results show higher variability in structural connectivity relates to higher neuroplasticity. In addition, this study reveals that structural connectivity involved in executive function and attention task is different among individuals and highlights the importance of individualized statistical methods for mapping neural pathway of complex cognition.

Evaluation of remote sensing soil moisture data products with a new approach to analyse footprint mismatch with in-situ measurements

ABSTRACT Global-scale surface soil moisture (SSM) products (e.g. SMAP L3.0, ASCAT V3.0, ESA/CCI V7.1 and GLDAS V2.2) are vital for applications in hydrology, climate variability, and agriculture. This study uses a new SSM evaluation approach by combining temporal evolution, Coefficient of Variation (CV), Cumulative Distribution Function (CDF), evaluation metrics, and Triple Collocation Analysis (TCA) to assess SSM accuracy and spatial–temporal variability, particularly the impact of footprint mismatch when comparing retrieved SSM with in-situ measurements. Results revealed significant spatial variability and seasonal patterns in SSM, as indicated by the CV values and temporal evaluations at different resampling scales. The variability captured by in-situ measurements was comparable to that of SSM products. The impact of footprint mismatch between in-situ measurements and data products, particularly for SMAP and ASCAT SSM, was more significant and led to substantial differences in evaluation metrics between smaller and larger spatial scales. TCA alone cannot reliably assess the accuracy of global-scale SSM products without in-situ SSM measurements. Overall, our findings highlight the critical role of footprint mismatch on the estimated accuracy of SSM products and underscore the need to combine multiple evaluations into an overall scoring indicator, as proposed in this study.

State-and-Evolution Detection Model for Characterizing Farmland Spatial Pattern Variation in Hengyang Using Long Time Series Remote Sensing Product

Analyzing farmland landscape pattern variations induced by human activities can support effective decision making by governments to improve land use efficiency. However, research on long-term and continuous spatial process changes in farmland is scarce, and spatial pattern changes in farmlands remain insufficiently understood. Moreover, studies in which researchers have utilized dynamic process analysis to describe farmlands are relatively limited. This study aimed to apply the state-and-evolution detection model (SEDM), generated from long-term remote sensing data, to characterize farmland spatial pattern variations in Hengyang City, Hunan Province. Annual farmland data from 1990 to 2022, change type samples, and auxiliary data were collected, and six types of spatial pattern variations (perforation, dissection, shrinkage, creation, enlargement, and aggregation) were defined for the study area. Subsequently, the SEDM was applied based on four landscape indices. Finally, spatiotemporal evolution features, namely evolution times, evolution duration, and dominant evolution pattern, were quantified. The farmland in the study area exhibited a generally upward trend with fluctuations. The maximum area was followed by shrinkage (S), perforation (P), and enlargement (E). In over 70% of the study area, fewer than three evolution times occurred over three decades. The dominant evolution patterns were P–S, S–P, and E–P for single evolution events, and P–S–P, S–P–S, and P–S–S for double events. The model achieved an overall accuracy of 85%, thus demonstrating its effectiveness in characterizing landscape pattern variations and providing valuable insights for researchers and policy makers to develop strategies for farmland protection.

Macro‐evolutionary dynamics dominated by dispersal promote the formation of regional biodiversity hotspot‐insights from hawkmoths (Lepidoptera: Sphingidae) in South China

AbstractAimRapid loss in global insect diversity has generated substantial public worry due to their critical ecological roles. However, there is controversy about the effectiveness of the global‐scale hotspots in guiding the conservation of diversity at the regional scale. Even worse, little is known about the knowledge of insect distributional dynamics in many understudied regions, such as East and Southeast Asia. Here, to guide for setting regional‐scale conservation priorities for insect diversity, we explore hawkmoths (Lepidoptera: Sphingidae) for their distributional dynamics and identify regional hotspots requiring protection.LocationSouth China (including Guangdong, Guangxi, Hainan, Hong Kong and Macau) and northern Vietnam (17°~26.5° N, 102°~117.5° E).MethodsSpecies distribution models were generated for 194 hawkmoth species based on 3597 occurrence records to predict their distributions. We calculated the spatial patterns of taxonomic and phylogenetic diversity and identified regional hotspots. Furthermore, the potential assembly mechanisms underlying insect diversity were explored by analysing the rates of speciation, extinction and dispersal between phyloregions.Results(a) The coastal regions of South China and northern Vietnam represent a regional hotspot of hawkmoths in East and Southeast Asia, with significantly higher α‐diversity than that in inland regions. (b) Dispersal played a more important role than local speciation and extinction in the formation of regional hawkmoth hotspots.Main ConclusionsIn this study, the ‘Out‐of‐the‐tropics model’ can explain the formation of the hawkmoth regional hotspots and the enhanced version of the ‘Pure dispersal model’ can explain the formation of the hotspots in Hainan Island. Compared with the local speciation and extinction, dispersal is the main driving factor that promoted the formation of the regional biodiversity hotspot of hawkmoths in South China. The case of Hainan Island suggests that protection within hotspots needs to account for specific regional macro‐evolutionary dynamics rather than indiscriminate coverage of identified hotspots.

Spatial patterns of macrozoobenthos diversity on sewage-impacted intertidal rocky shores in Central Patagonia, Argentina

The effects of untreated wastewater and shore levels on zoobenthic diversity, species accumulation, and abundance-biomass relationships were studied on intertidal rocky shores of central Patagonia, Argentina. Winter samples (2015) from two sewage-impacted and two reference sites showed that pollution disrupted macrozoobenthic diversity and zonation. A total of 83 taxa were identified, with polychaetes (31 species), mollusks (22), and crustaceans (21) being the most diverse. Impacted sites had greater intra-environmental diversity heterogeneity and higher abundance at the lower midlittoral level, indicating a shift towards lower intertidal levels. Species richness was lower and evenness was higher in the polluted mid-littoral level. ABC curves in the stressed areas showed moderate disturbances with variability across shore zones, while non-polluted rocky shores typically exhibit a higher cumulative faunal biomass than abundance. In impacted areas, chronic habitat changes and disturbances to the mussel matrix by Perumytilus purpuratus are likely responsible for differences in diversity and abundance-biomass relationships.

Phylogeography of Cold Water Soft Coral Alcyonium spp. (Anthozoa, Octocorallia: Alcyonacea) Between South America and the West Antarctic Peninsula.

The Antarctic marine environment has a unique geologic and climatic history that has contributed to the evolution of high species diversity. Given the current trend of environmental warming, understanding the history of Antarctic species is crucial for predicting the impact of climate change on ecosystem function. Soft corals are a group of striking presence in the benthic marine assemblages in the Southern Ocean, which is recognized as a biodiversity hotspot. DNA sequences (Cox1, mtMutS, and 28S rDNA) were utilized for molecular phylogenetic reconstructions, species delimitations, and divergence estimations to investigate the spatial patterns of genetic diversity in Alcyonium species in the southern South American-Antarctic region. Significant genetic divergence was observed between regions, with a clear genetic break between South America and the West Antarctic Peninsula and the identification of four putative species. Divergence time estimates indicated that Alcyonium's diversification began about 41.1 million years ago (Ma), coinciding with the opening of the Drake Passage and the formation of the Antarctic Circumpolar Current (ACC, ~42 Ma). This indicates that Alcyonium has persisted insitu for an extensive period, enduring a wide range of environmental conditions.

Yield prediction through UAV-based multispectral imaging and deep learning in rice breeding trials

ContextPredicting crop yields with high precision and timeliness is essential for crop breeding, enabling the optimization of planting strategies and efficients resource allocation while ensuring food security. Current research in this field typically does not address the problem of yield prediction in the diverse context of breeding experiments involving numerous varieties. However, evaluating the performance of prediction models across multiple varieties is vital for further model refining and enhancing model robustness and adaptability. ObjectiveThis study aims to evaluate the performance of feature- and image-based yield prediction models for yields with multiple varieties to compare their capabilities and determine an appropriate timing for early yield prediction. MethodsThis study combines unmanned aerial vehicle (UAV)-based multispectral remote sensing imagery with machine learning and deep learning-based algorithms to develop rice yield prediction models across multiple varieties. The performances of both feature- and image-based models are evaluated. The feature-based models considered in this study include random forest (RF), deep neural network (DNN), and long short-term memory (LSTM) algorithms, and the image-based models are convolutional neural network (CNN) architectures, including both two-dimensional (2D) and three-dimensional (3D) CNN models. To assess the performance of the multi-variety crop yield prediction models thoroughly, this study considers two sampling scenarios: stratified sampling and group sampling. Results and conclusionsThe results show that the image-based deep learning models outperform the feature-based machine learning models, which indicates their superior robustness in multi-variety scenarios and highlights their significant potential of directly extracting spatiotemporal features from images for yield prediction. The results indicate that the multi-temporal 2D CNN model (i.e., the CNN-M2D model) can achieve the best yield prediction performance among all models, achieving RRMSE = 8.13 % and R2 = 0.73. The prediction results also demonstrate good consistency with the observed data, indicating an efficient capturing of spatial pattern variations in yield across different varieties. Based on the results, with the crops progressing along the growth stages, the accuracy of the yield prediction models improves gradually, achieving the best prediction performance during the flowering to grain-filling stage. Finally, according to the results, the optimal lead time for predicting rice yield is approximately one month before harvest. SignificanceOur study can provide a reference for the research community in yield prediction and high-yield variety selection in breeding trials.

Estimating dispersal rates and locating genetic ancestors with genome-wide genealogies.

Spatial patterns in genetic diversity are shaped by individuals dispersing from their parents and larger-scale population movements. It has long been appreciated that these patterns of movement shape the underlying genealogies along the genome leading to geographic patterns of isolation-by-distance in contemporary population genetic data. However, extracting the enormous amount of information contained in genealogies along recombining sequences has, until recently, not been computationally feasible. Here, we capitalize on important recent advances in genome-wide gene-genealogy reconstruction and develop methods to use thousands of trees to estimate per-generation dispersal rates and to locate the genetic ancestors of a sample back through time. We take a likelihood approach in continuous space using a simple approximate model (branching Brownian motion) as our prior distribution of spatial genealogies. After testing our method with simulations we apply it to Arabidopsis thaliana. We estimate a dispersal rate of roughly 60 km2/generation, slightly higher across latitude than across longitude, potentially reflecting a northward post-glacial expansion. Locating ancestors allows us to visualize major geographic movements, alternative geographic histories, and admixture. Our method highlights the huge amount of information about past dispersal events and population movements contained in genome-wide genealogies.