Biology subjects

Blackman, B. K.

Publications and source records attributed to Blackman, B. K..

7 recordsLinked to original sources

bioRxiv2026

Divergence in wilt tolerance across the range of Mimulus guttatus in the absence of stomatal regulation

Whether phenotypes and environments predict performance under stress is central to ecology and evolution and can reveal species' vulnerability to global change. We sampled Mimulus guttatus range-wide to test whether reproductive, morphological, and physiological traits predict drought responses. Surprisingly, we find that stomatal response, a key mediator of water balance, did not change as drought intensified. Climate, however, predicted survival after turgor loss: plants from regions with large daily relative to seasonal temperature ranges survived wilting longer. Stress responses also depended on life history: where summers are dry, perennials withstood lower water potentials, whereas annuals increased leaf cooling as they wilted. Known trait associations were not fixed: although many baseline traits tracked climate, none predicted drought responses. Drought performance therefore cannot be inferred from traits measured under well-watered conditions. Lacking stomatal regulation, riparian drought escapers like M. guttatus may be vulnerable if shorter growing seasons bring drought before reproduction.

plant biology↗

bioRxiv2026

Regional gene-environment association models improve insights into agents and targets of selection by climate in Mimulus guttatus

Genotype-environment association (GEA) approaches identify genetic variants that likely contribute to adaptation along environmental gradients. These associations can inform predictive models of maladaptation risk and evolutionary rescue in future climates. However, because population structure and climate parameters often co-vary, tradeoffs arise between accounting for population structure to reduce false positive associations and removing true positives. Here, we examine whether conducting GEA analyses at multiple spatial scales provides more informative associations by mitigating confounds between population structure and environment, and we evaluate what factors influence how congruent GEA results are across regions. We performed GEA analyses for climate parameters on genome resequencing data obtained for 110 annual populations of the common monkeyflower, Mimulus guttatus. We find that more genetic variation was specifically attributable to environmental variation in regional GEA models compared to a full-range model, and that agents of selection differ considerably in importance among regions and range-wide. Few genetic targets of selection shared across regions were detected, yet all regions possess ample standing genetic variation for most environment-associated variants, suggesting that regional differences in climate breadth may be a primary cause of this incongruence. In combination with a genome-wide association study for several climate-associated phenotypes, we also find that regulators of flowering repressors in the FLC/MAF gene family often harbor adaptive variation in this species. Together, our results suggest that GEA analyses of widespread species considerably benefit from examining both range-wide and regional trends to more fully capture and understand the agents and targets of climate adaptation.

evolutionary biology↗

bioRxiv2026

PolliCrop: A high-throughput computer vision pipeline for pollinator monitoring in agroecosystems

Flower-visiting insect populations are declining since the 1990s, especially because of the decrease of floral resources in agricultural settings. Mass flowering crops can help increase resource availability, and plant breeding can be directed towards selecting varieties attracting more flower-visiting insects. This requires the implementation of an automated high-throughput phenotyping tool for assessing the attractiveness of plant genotypes to flower-visiting insects. In this study, (i) we present a procedure to take standardized images of sunflower heads with camera traps continuously at day and night in the field; (ii) we trained two versions of a deep learning model, named PolliCrop, to automatically detect and identify three classes of the main insects visiting sunflower on these images (non-Bombus bees, bumble bees, lepidopterans); (iii) we assessed and validated the ability of PolliCrop to correctly predict the true visitation frequencies of the insect classes on three sunflower genotypes; (iv) we presented two statistical approaches to compare the insect visitation frequencies between plant genotypes, one including weather variables, and the other one without. One PolliCrop version yielded satisfying performance to correctly detect the three insect classes. In particular, it correctly predicted the insect visitation frequencies on two sunflower genotypes in a range of {+/-}10%. The other PolliCrop version can be useful in certain contexts of images and objectives. PolliCrop can be extended in the future to other crop species by training PolliCrop on new images captured in these crops. The field experimental design to set up for comparing the attractiveness between genotypes is also discussed.

animal behavior and cognition↗

bioRxiv2021

Genetic basis and dual adaptive role of floral pigmentation in sunflowers

Variation in floral displays, both between and within species, has been long known to be shaped by the mutualistic interactions that plants establish with their pollinators. However, increasing evidence suggests that abiotic selection pressures influence floral diversity as well. Here we analyze the genetic and environmental factors that underlie patterns of floral pigmentation in wild sunflowers. While sunflower inflorescences appear invariably yellow to the human eye, they display extreme diversity for patterns of ultraviolet pigmentation, which are visible to most pollinators. We show that this diversity is largely controlled by cis-regulatory variation at a single MYB transcription factor, HaMYB111, through accumulation of UV-absorbing flavonol glycosides. As expected, different patterns of ultraviolet pigments in flowers have a strong effect on pollinator preferences. However, variation for floral ultraviolet patterns is also associated with environmental variables, especially relative humidity, across populations of wild sunflowers. Larger ultraviolet patterns, which are found in drier environments, limit transpiration, therefore reducing water loss. The dual role of floral UV patterns in pollination attraction and abiotic responses reveals the complex adaptive balance underlying the evolution of floral traits.

evolutionary biology↗

bioRxiv2020

The scales and signatures of climate adaptation by the Arabidopsis transcriptome

Clines in allele frequency and trait variation can be highly informative for understanding how populations have historically adapted to climate variation across landscapes. However, as a consequence of the many complexities inherent to this process, these climate-associated differentiation patterns can be confounded, misleading, or obscured. Molecular phenotypes like gene expression levels are a potentially valuable means for resolving these complexities. Their intermediate position between genomes and organismal traits and their interrelatedness structured by gene regulatory networks can help parse how different climatic factors contribute to unique components of range-wide or region-specific diversity patterns. Here, we demonstrate these explanatory values of gene expression variation through integrative analyses of transcriptomic data from 665 Arabidopsis thaliana accessions. Differentiation of co-expressed genes is often associated with source site climate. Although some patterns hold range-wide, many other gene expression clines are specific to particular ancestry groups, reflecting how broad-scale and local combinations of selective agents differentially resolve functional interrelationships between plant defense, drought tolerance, and life history traits. We also extend these analyses to parse how different factors explain climate-associated variation in flowering time and its plasticity. Expression of key regulators FLC and SOC1 strongly predicts time to flower, consistent with previous work, but our results also highlight novel relationships that indicate as yet unexplored climate-related connections between defense signaling and flowering. Finally, we show that integrative models combining genotype and gene expression information predict variation in flowering time under ecologically realistic conditions more accurately than models based on either source alone. Significance StatementPopulations often adapt to local conditions along climate gradients, and associations between climate parameters and traits or alleles often indicate a history of adaptive differentiation. However, such signals can be obscured or misleading due to the complex genetics underlying trait variation or other historical processes, frustrating our capacity to reveal how populations adapt to diverse climates. As a molecular intermediate between genetic polymorphisms and their impact on organismal traits, gene expression variation is a useful readout for addressing several of these difficulties. Here, we leverage transcriptomic data from hundreds of Arabidopsis thaliana accessions to reveal continental and region-specific patterns of climate-associated differentiation as well as investigate how gene expression adaptation at both scales shapes flowering time variation along climate gradients.

evolutionary biology↗

bioRxiv2018

Formation of periodic pigment spots by the reaction-diffusion mechanism

Many organisms exhibit visually striking spotted or striped pigmentation patterns. Turings reaction-diffusion model postulates that such periodic pigmentation patterns form when a local autocatalytic feedback loop and a long-range inhibitory feedback loop interact. At its simplest, this network only requires one self-activating activator that also activates a repressor, which inhibits the activator and diffuses to neighboring cells. However, the molecular activators and repressors fully fitting this versatile model remain elusive. Here, we characterize an R2R3-MYB activator and an R3-MYB repressor in monkeyflowers that correspond to Turings model and explain how periodic anthocyanin spots form. Notably, disrupting this pattern impacts pollinator visitation. Thus, subtle changes in simple reaction-diffusion networks are likely essential contributors to the evolution of the remarkable diversity of periodic pigmentation patterns in flowers.

plant biology↗

bioRxiv2018

A synthesis of mapping experiments reveals extensive genomic structural diversity in the Mimulus guttatus species complex

Understanding genomic structural variation such as inversions and translocations is a key challenge in evolutionary genetics. In this paper, we tackle this challenge by developing a novel statistical approach to comparative genetic mapping. The procedure couples a Hidden Markov Model with a Genetic Algorithm to detect large-scale structural variation using low-level sequencing data from multiple genetic mapping populations. We demonstrate the method using five distinct crosses within the flowering plant genus Mimulus. The synthesis of data from these experiments is first used to correct numerous errors (misplaced sequences) in the M. guttatus reference genome. Second, we confirm and/or detect eight large inversions polymorphic within the M. guttatus species complex. Finally, we show how this method can be applied in genomic scans to improve the accuracy and resolution of Quantitative Trait Locus (QTL) mapping.\n\nAUTHOR SUMMARYGenome sequences have proved to be a critical experimental resource for genetic research in many species. However, in some species there is considerable variation in genomic organization, making a single reference genome sequence inadequate. This variation can cause issues in interpreting genomic signals, such as those coming from trait mapping. We introduce a new statistical method and computational tools that use linkage information to reorganize a single reference genome to 1) repair genome assembly errors, and 2) identify variation between individuals or populations of the same species. Using this method we can create a new genome order that improves upon the reference genome. We apply this method to five crosses among plants in the Mimulus guttatus species complex. In this system we detect eight large chromosomal inversions and improve the resolution of a trait mapping study. This work highlights the utility of our method, and indicates how others studying diverse species might use them to improve their own research.

genetics↗