Anthropogenic habitat destruction and climate change are altering the geographic distributions of plant communities. Although mapping vegetation changes is now possible at high resolution using remote sensing data and deep convolutional neural networks, these approaches have not been applied to model the distributions of thousands of plant species to understand spatial changes in biodiversity. To address the current lack of scalable and automatic tools to map plant species distributions at a fine-grained scale, we created a dataset of over half a million citizen science observations of 2,221 plant species across California paired with satellite images at 1 meter resolution from solely free and public sources. With this we trained a deep convolutional neural network, deepbiosphere , that predicts presences of plant species within 256 × 256 meter satellite images and outperforms common low-resolution species distribution models . We showcase the novelty and potential applications of this framework by visualizing high-resolution predictions of keystone species such as coastal redwoods, identifying spatio-temporal ecosystem changes from wildfires and restoration management, and detecting urban biodiversity hotspots. Deep neural networks continuously trained on public remote sensing imagery and citizen science observations could enable cheap, automatic, and scalable monitoring of biodiversity and detect rapid anthropogenic impacts on ecosystems.

To describe a living organism it is often said that "the whole is greater than the sum of its parts". In genetics, we may also think that the effect of multiple mutations on an organism is greater than their additive individual effect, a phenomenon called epistasis or multiplicity. Despite the last decades discovery that many disease- and fitness-related traits are polygenic, or controlled by many genetic variants, it is still debated whether the effects of individual genes combine additively or not. Here we develop a flexible likelihood framework for genome-wide associations to fit complex traits such as fitness under both additive and non-additive polygenic architectures. Analyses of simulated datasets under different true additive, multiplicative, or other epistatic models, confirm that our method can identify global non-additive selection. Applying the model to experimental datasets of wild type lines of Arabidopsis thaliana, Drosophila melanogaster, and Saccharomyces cerevisiae, we find that fitness is often best explained with non-additive polygenic models. Instead, a multiplicative polygenic model appears to better explain fitness in some experimental environments. The statistical models presented here have the potential to improve prediction of phenotypes, such as disease susceptibility, over the standard methods for calculating polygenic scores which assume additivity.

The plant kingdom contains a stunning array of complex morphologies easily observed above ground, but largely unexplored below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental to determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown Arabidopsis thaliana plants from germination to maturity (Rellan-Alvarez et al. 2015). In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the natural variation of RSA in Arabidopsis over time. This dataset describes the developmental dynamics of 93 accessions and reveals highly complex and polygenic RSA traits that show significant correlation with climate variables.

The change in allele frequencies within a population over time represents a fundamental process of evolution. By monitoring allele frequencies, we can analyze the effects of natural selection and genetic drift on populations. To efficiently track time-resolved genetic change, large experimental or wild populations can be sequenced as pools of individuals sampled over time using high-throughput genome sequencing (called the Evolve & Resequence approach, E&R). Here, we present a set of experiments using hundreds of natural genotypes of the model plant Arabidopsis thaliana to showcase the power of this approach to study rapid evolution at large scale. First, we validate that sequencing DNA directly extracted from pools of flowers from multiple plants -- organs that are relatively consistent in size and easy to sample -- produces comparable results to other, more expensive state-of-the-art approaches such as sampling and sequencing of individual leaves. Sequencing pools of flowers from 25-50 individuals at [~]40X coverage recovers genome-wide frequencies in diverse populations with accuracy r > 0.95. Secondly, to enable analyses of evolutionary adaptation using E&R approaches of plants in highly replicated environments, we provide open source tools that streamline sequencing data curation and calculate various population genetic statistics two orders of magnitude faster than current software. To directly demonstrate the usefulness of our method, we conducted a two-year outdoor evolution experiment with A. thaliana to show signals of rapid evolution in multiple genomic regions. We demonstrate how these laboratory and computational Pool-seq-based methods can be scaled to study hundreds of populations across many climates.

The quantitative resistance gene ACCELERATED CELL DEATH 6 (ACD6), which encodes a transmembrane protein with intracellular ankyrin repeats, has been implicated in a trade-off between growth and defense among wild strains of Arabidopsis thaliana. Naturally hyperactive alleles of the ACD6-Est-1 type can lead to spontaneous activation of immune responses, although the extent of visible hyperimmunity in strains with this allele varies substantially. We have identified a natural suppressor locus, MODULATOR OF HYPERACTIVE ACD6 1 (MHA1), which codes for a small protein of ~7 kDa that attenuates activity of the ACD6-Est-1 allele. MHA1 and its paralog MHA1-LIKE (MHAL) differentially interact with specific ACD6 variants, and both MHA1 and MHAL peptides can bind to the ACD6 ankyrin repeats. MHAL also enhances accumulation of an ACD6 complex, thereby increasing activity of the ACD6 standard allele. The ACD6 ankyrin repeats are similar to those of transient receptor potential (TRP) ion channels, and several lines of evidence support that increased ACD6 activity is linked to enhanced calcium signaling. Our work highlights how the study of natural variation reveals new aspects of plant immunity.

How trade-offs between traits constrain adaptation to contrasted environments is critical to understand the distribution range of a given species. In Arabidopsis thaliana, genetic analyses recently revealed that a group of genotypes successfully recolonized Europe from its center after the last glaciation, outcompeting older lineages and leaving them only at the distribution margins, where environmental conditions are more stressing. However, whether trade-offs between traits related to dispersal, competition, and stress tolerance explain the success and persistence of different lineages across the species geographic range remains an open question. Here, we compared the genetic and phenotypic differentiation between 72 ecotypes originating from three geographical groups in Europe (North, South and Center). We measured key traits related to fecundity, dispersal ability, competition tolerance, and stress tolerance, and used genomic data to infer the effect of selection on these traits. We showed that a trade-off between plant fecundity and seed mass constrains the diversification of A. thaliana in Europe. In particular, the success of the cosmopolitan genotypes that recolonized Europe can be explained by their higher dispersal ability at the expense of their competitive ability and stress tolerance. Inversely, peripheral ecotypes exhibited the opposite trait syndrome: high competition and stress tolerance but low dispersal ability. Moreover, peripheral genotypes tend to differentiate from central ones at genes involved in dispersal and competitive traits such as seed mass. Combining ecological and genomic approaches, our study demonstrated the role of key ecological trade-offs as evolutionary drivers of the distribution of plant populations along a geographic gradient.

Species-abundance distributions (SADs) describe the spectrum of commonness and rarity in a community. Beyond the universal observation that most species are rare and only a few common, more-precise description of SAD shape is controversial. Furthermore, the mechanisms behind SADs and how they vary along environmental gradients remain unresolved. We lack a general non-neutral theory of SADs. Here we develop a trait-based framework, focusing on a local community coupled to the region by dispersal. The balance of immigration and exclusion determines abundances, which vary over orders-of-magnitude. Under stabilizing selection, the local trait-abundance distribution (TAD) reflects a transformation of the regional TAD. The left-tail of the SAD depends on scaling exponents of the exclusion function and the regional species pool. More-complex local dynamics can lead to multimodal TADs and SADs. Connecting SADs with trait-based ecological theory provides a way to generate more-testable hypotheses on the controls over commonness and rarity in communities.

Size and shape profoundly influence an organisms ecophysiological performance and evolutionary fitness, suggesting a link between morphology and diversity. However, not much is known about how body shape is related to taxonomic richness, especially in microbes. Here we analyse global datasets of unicellular marine phytoplankton, a major group of primary producers with an exceptional diversity of cell sizes and shapes and, additionally, heterotrophic protists. Using two measures of cell shape elongation, we quantify taxonomic diversity as a function of cell size and shape. We find that cells of intermediate volume have the greatest shape variation, from oblate to extremely elongated forms, while small and large cells are mostly compact (e.g., spherical or cubic). Taxonomic diversity is strongly related to cell elongation and cell volume, together explaining up to 92% of total variance. Taxonomic diversity decays exponentially with cell elongation and displays a log-normal dependence on cell volume, peaking for intermediate-volume cells with compact shapes. These previously unreported broad patterns in phytoplankton diversity reveal selective pressures and ecophysiological constraints on the geometry of phytoplankton cells which may improve our understanding of marine ecology and the evolutionary rules of life.

Rapid evolution in response to environmental change will likely be a driving force determining the distribution of species and the structure of communities across the biosphere in coming decades. This is especially true of microorganisms, many of which may be able to evolve in step with rising temperatures. An ecologically indispensable group of microorganisms with great potential for rapid thermal adaptation are the phytoplankton, the diverse photosynthetic microbes forming the foundation of most aquatic food webs. We tested the capacity of a globally important phytoplankton species, the marine diatom Thalassiosira pseudonana, for rapid evolution in response to temperature. Evolution of replicate populations at 16 and 31{degrees}C for 350-450 generations led to significant divergence in several traits associated with T. pseudonanas thermal reaction norm (TRN) for per-capita population growth, as well as in its competitive ability for nitrogen (commonly limiting in marine systems). Of particular interest were evolution of the optimum temperature for growth, the upper critical temperature, and the derivative of the TRN, an indicator of potential tradeoffs resulting from local adaptation to temperature. This study offers a broad examination of the evolution of the thermal reaction norm and how modes of TRN variation may govern a populations long-term physiological, ecological, and biogeographic response to global climate change.

We measure the molecular gas environment near recent (<100 yr old) supernovae (SNe) using similar to 1 '' or <= 150 pc resolution CO (2-1) maps from the PHANGS-Atacama Large Millimeter/submillimeter Array (ALMA) survey of nearby star-forming galaxies. This is arguably the first such study to approach the scales of individual massive molecular clouds (M (mol) greater than or similar to 10(5.3) M (circle dot)). Using the Open Supernova Catalog, we identify 63 SNe within the PHANGS-ALMA footprint. We detect CO (2-1) emission near similar to 60% of the sample at 150 pc resolution, compared to similar to 35% of map pixels with CO (2-1) emission, and up to similar to 95% of the SNe at 1 kpc resolution, compared to similar to 80% of map pixels with CO (2-1) emission. We expect the similar to 60% of SNe within the same 150 pc beam, as a giant molecular cloud will likely interact with these clouds in the future, consistent with the observation of widespread SN-molecular gas interaction in the Milky Way, while the other similar to 40% of SNe without strong CO (2-1) detections will deposit their energy in the diffuse interstellar medium, perhaps helping drive large-scale turbulence or galactic outflows. Broken down by type, we detect CO (2-1) emission at the sites of similar to 85% of our 9 stripped-envelope SNe (SESNe), similar to 40% of our 34 Type II SNe, and similar to 35% of our 13 Type Ia SNe, indicating that SESNe are most closely associated with the brightest CO (2-1) emitting regions in our sample. Our results confirm that SN explosions are not restricted to only the densest gas, and instead exert feedback across a wide range of molecular gas densities.