Parallel altitudinal clines reveal trends in adaptive evolution of genome size in \u3ci\u3eZea mays\u3c/i\u3e

While the vast majority of genome size variation in plants is due to differences in repetitive sequence, we know little about how selection acts on repeat content in natural populations. Here we investigate parallel changes in intraspecific genome size and repeat content of domesticated maize (Zea mays) landraces and their wild relative teosinte across altitudinal gradients in Mesoamerica and South America. We combine genotyping, low coverage whole-genome sequence data, and flow cytometry to test for evidence of selection on genome size and individual repeat abundance. We find that population structure alone cannot explain the observed variation, implying that clinal patterns of genome size are maintained by natural selection. Our modeling additionally provides evidence of selection on individual heterochromatic knob repeats, likely due to their large individual contribution to genome size. To better understand the phenotypes driving selection on genome size, we conducted a growth chamber experiment using a population of highland teosinte exhibiting extensive variation in genome size. We find weak support for a positive correlation between genome size and cell size, but stronger support for a negative correlation between genome size and the rate of cell production. Reanalyzing published data of cell counts in maize shoot apical meristems, we then identify a negative correlation between cell production rate and flowering time. Together, our data suggest a model in which variation in genome size is driven by natural selection on flowering time across altitudinal clines, connecting intraspecific variation in repetitive sequence to important differences in adaptive phenotypes

The Last Trees Standing: Climate modulates tree survival factors during a prolonged bark beetle outbreak in Europe

Plant traits are an expression of strategic tradeoffs in plant performance that determine variation in allocation of finite resources to alternate physiological functions. Climate factors interact with plant traits to mediate tree survival. This study investigated survival dynamics in Norway spruce (Picea abies) in relation to tree-level morphological traits during a prolonged multi-year outbreak of the bark beetle, Ips typographus, in Central Europe. We acquired datasets describing the trait attributes of individual spruce using remote sensing and field surveys. We used nonlinear regression in a hypothesis-driven framework to quantify survival probability as a function of tree size, crown morphology, intraspecific competition and a growing season water balance. Extant spruce trees that persisted through the outbreak were spatially clustered, suggesting that survival was a nonrandom process. Larger diameter trees were more susceptible to bark beetles, reflecting either life history tradeoffs or a dynamic interaction between defense capacity and insect aggregation behavior. Competition had a strong negative effect on survival, presumably through resource limitation. Trees with more extensive crowns were buffered against bark beetles, ostensibly by a more robust photosynthetic capability and greater carbon reserves. The outbreak spanned a warming trend and conditions of anomalous aridity. Sustained water limitation during this period amplified the consequences of other factors, rendering even smaller trees vulnerable to colonization by insects. Our results are in agreement with prior research indicating that climate change has the potential to intensify bark beetle activity. However, forest outcomes will depend on complex cross-scale interactions between global climate trends and tree-level trait factors, as well as feedback effects associated with landscape patterns of stand structural diversity

Mining alleles for tar spot complex resistance from CIMMYT's maize Germplasm Bank

The tar spot complex (TSC) is a devastating disease of maize (Zea mays L.), occurring in 17 countries throughout Central, South, and North America and the Caribbean, and can cause grain yield losses of up to 80%. As yield losses from the disease continue to intensify in Central America, Phyllachora maydis, one of the causal pathogens of TSC, was first detected in the United States in 2015, and in 2020 in Ontario, Canada. Both the distribution and yield losses due to TSC are increasing, and there is a critical need to identify the genetic resources for TSC resistance. The Seeds of Discovery Initiative at CIMMYT has sought to combine next-generation sequencing technologies and phenotypic characterization to identify valuable alleles held in the CIMMYT Germplasm Bank for use in germplasm improvement programs. Individual landrace accessions of the “Breeders' Core Collection” were crossed to CIMMYT hybrids to form 918 unique accessions topcrosses (F1 families) which were evaluated during 2011 and 2012 for TSC disease reaction. A total of 16 associated SNP variants were identified for TSC foliar leaf damage resistance and increased grain yield. These variants were confirmed by evaluating the TSC reaction of previously untested selections of the larger F1 testcross population (4,471 accessions) based on the presence of identified favorable SNPs. We demonstrated the usefulness of mining for donor alleles in Germplasm Bank accessions for newly emerging diseases using genomic variation in landraces

Genome assembly and population genomic analysis provide insights into the evolution of modern sweet corn.

Sweet corn is one of the most important vegetables in the United States and Canada. Here, we present a de novo assembly of a sweet corn inbred line Ia453 with the mutated shrunken2-reference allele (Ia453-sh2). This mutation accumulates more sugar and is present in most commercial hybrids developed for the processing and fresh markets. The ten pseudochromosomes cover 92% of the total assembly and 99% of the estimated genome size, with a scaffold N50 of 222.2 Mb. This reference genome completely assembles the large structural variation that created the mutant sh2-R allele. Furthermore, comparative genomics analysis with six field corn genomes highlights differences in single-nucleotide polymorphisms, structural variations, and transposon composition. Phylogenetic analysis of 5,381 diverse maize and teosinte accessions reveals genetic relationships between sweet corn and other types of maize. Our results show evidence for a common origin in northern Mexico for modern sweet corn in the U.S. Finally, population genomic analysis identifies regions of the genome under selection and candidate genes associated with sweet corn traits, such as early flowering, endosperm composition, plant and tassel architecture, and kernel row number. Our study provides a high-quality reference-genome sequence to facilitate comparative genomics, functional studies, and genomic-assisted breeding for sweet corn

Origins of temperate adaptation in maize

Four thousand years ago, maize began migrating out of Mexico and into the geographically diverse Southwestern US (1, 2). Maize spread quickly through the lowland deserts, but full maize agriculture in the uplands lagged for another 2,000 years (1, 3). Why did maize agriculture fail to fully develop, despite the persistent presence of maize in the uplands? I tested the hypothesis that early maize in the Southwest US was not adapted to the uplands, specifically testing archaeological maize from the uplands at the beginning of agricultural intensification for early flowering. To better understand temperate adaptation in maize, I led a project to generate tools for accurate imputation and projection of maize inbred haplotypes onto related individuals. We then projected whole genome sequence onto five diverse modern inbred maize populations to further elucidate the genetic architecture of maize flowering through mapping, machine learning and cross population predictions. We find that the genetic architecture of flowering is tightly linked to the temperate-tropical differentiation in American germplasm for four of the five populations, and suggest an independent temperate adaptation occurred in China after 1500. We also find that populations with individuals across the North American temperate adaptation predict others well. We further test that good cross population prediction extends to modern Southwestern landraces by developing a mapping population for flowering time, and show that modern inbred populations can predict divergent modern landraces with high prediction accuracy for days to flowering. We extend flowering predictions to archaeological maize samples dating to the early period of agricultural adoption, and find that this population was already adapted. Using SNPs with the greatest population differentiation over the extremes of temperate-tropical adaptation, we find that temperate adaptation happened in situ in the Southwest, and early southwestern peoples selected on primarily standing variation. 1. J. B. Mabry, ed. Las Capas: Early Irrigation and Sedentism in a Southwestern Floodplain (Center for Desert Archaeology, Tucson, 2008), Anthropological Papers. 2. E. K. Huber, in Fence Lake Project (Statistical Research Inc., Tempe, 2005), vol. 1: Introduction and Site Descriptions of Technical Series. 3. R. G. Matson, B. Chisholm, Basketmaker II Subsistence: Carbon Isotopes and Other Dietary Indicators from Cedar Mesa, Utah. Am. Antiq. 56, 444–459 (1991)

Automation of tree‐ring detection and measurements using deep learning

Abstract Core samples from trees are a critical reservoir of ecological information, informing our understanding of past climates, as well as contemporary ecosystem responses to global change. Manual measurements of annual growth rings in trees are slow, labour‐intensive and subject to human bias, hindering the generation of big datasets. We present an alternative, neural network‐based implementation that automates detection and measurement of tree‐ring boundaries from coniferous species. We trained our Mask R‐CNN extensively on over 8000 manually annotated ring boundaries from microscope‐imaged Norway Spruce Picea abies increment cores. We assessed the performance of the trained model after post‐processing on real‐world data generated from our core processing pipeline. The CNN after post‐processing performed well, with recognition of over 98% of ring boundaries (recall) with a precision in detection of 96% when tested on real‐world data. Additionally, we have implemented automatic measurements based on minimum distance between rings. With minimal editing for missed ring detections, these measurements were 98% correlated with human measurements of the same samples. Tests on other three conifer species demonstrate that the CNN generalizes well to other species with similar structure. We demonstrate the efficacy of automating the measurement of growth increment in tree core samples. Our CNN‐based system provides high predictive performance in terms of both tree‐ring detection and growth rate determination. Our application is readily deployable as a Docker container and requires only basic command line skills. Additionally, an easy re‐training option allows users to expand capabilities to other wood types. Application outputs include both editable annotations of predictions as well as ring‐width measurements in a commonly used .pos format, facilitating the efficient generation of large ring‐width measurement datasets from increment core samples, an important source of environmental data