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Abstract Global climate change and land use change underlie a need to develop new crop breeding strategies, and crop wild relatives (CWR) have become an important potential source of new genetic material to improve breeding efforts. Many recent approaches assume adaptive trait variation increases towards the relative environmental extremes of a species range, potentially missing valuable trait variation in more moderate or typical climates. Here, we leveraged distinct genotypes of wild chickpea (Cicer reticulatum) that differ in their relative climates from moderate to more extreme and perform targeted assessments of drought and heat tolerance. We found significance variation in ecophysiological function and stress tolerance between genotypes but contrary to expectations and current paradigms, it was individuals from more moderate climates that exhibited greater capacity for stress tolerance than individuals from warmer and drier climates. These results indicate that wild germplasm collection efforts to identify adaptive variation should include the full range of environmental conditions and habitats instead of only environmental extremes, and that doing so may significantly enhance the success of breeding programs broadly.

Endosymbiosis drives evolutionary innovation and underpins the function of diverse ecosystems. The mechanistic origins of symbioses, however, remain unclear, in part because early evolutionary events are obscured by subsequent evolution and genetic drift. This Essay highlights how experimental studies of facultative, host-switched, and synthetic symbioses are revealing the important role of fitness trade-offs between within-host and free-living niches during the early-stage evolution of new symbiotic associations. The mutational targets underpinning such trade-offs are commonly regulatory genes, such that single mutations have major phenotypic effects on multiple traits, thus enabling and reinforcing the transition to a symbiotic lifestyle.

Abstract Tradeoffs between the energetic benefits and costs of traits can shape species and trait distributions along environmental gradients. Here we test predictions based on such tradeoffs using survival, growth, and 50 photosynthetic, hydraulic, and allocational traits of ten Eucalyptus species grown in four common gardens along an 8-fold gradient in precipitation/pan evaporation (P/E p ) in Victoria, Australia. Phylogenetically structured tests show that most trait-environment relationships accord qualitatively with theory. Most traits appear adaptive across species within gardens (indicating fixed genetic differences) and within species across gardens (indicating plasticity). However, species from moister climates have lower stomatal conductance than others grown under the same conditions. Responses in stomatal conductance and five related traits appear to reflect greater mesophyll photosynthetic sensitivity of mesic species to lower leaf water potential. Our data support adaptive cross-over, with realized height growth of most species exceeding that of others in climates they dominate. Our findings show that pervasive physiological, hydraulic, and allocational adaptations shape the distributions of dominant Eucalyptus species along a subcontinental climatic moisture gradient, driven by rapid divergence in species P/E p and associated adaptations.

This article provides novel data on the microstructure and crystallographic texture of modern giant clam shells (Tridacna squamosa and Hippopus hippopus) from the Coral Triangle region of northeast Borneo. Giant clams have two aragonitic shell layers—the inner and outer shell layer. This dataset focuses on the inner shell layer as this is well preserved and not affected by diagenetic alteration. To prepare samples for analysis, shells were cut longitudinally at the axis of maximum growth and mounted onto thin sections. Data collection involved scanning electron microscopy (SEM) to determine microstructure and SEM based electron backscatter diffraction (EBSD) for quantitative measurement of crystallographic orientation and texture. Post-acquisition reanalysis of saved EBSD patterns to optimize data quality included changing the number of reflectors and band detection mode. We provide EBSD data as band contrast images and colour-coded orientation maps (inverse pole figure maps). Crystallographic co-orientation strength obtained with multiple of uniform density (MUD) values are derived from density distributed pole figures of indexed EBSD points. Raw EBSD data files are also given to ensure repeatability of the steps provided in this article and to allow extraction of further crystallographic properties for future researchers. Overall, this dataset provides 1. a better understanding of shell growth and biomineralization in giant clams and 2. important steps for optimizing data collection with EBSD analyses in biogenic carbonates.

Prosopanche is a poorly known genus of parasitic plants from South and Central America. Growing almost entirely underground with a complete lack of leaves, plants of Prosopanche bear more resemblance to fungi than plants and have many remarkable aspects to their life history and ecology that require further investigation. Here, we provide an up‐to‐date revision of the current state of understanding of Prosopanche, including taxonomy, biogeography, plant‐pollinator ecology and host‐parasite interactions. We discuss the untapped research potential of this group of plants and recommend the Prosopanche as an ideal candidate for exploring the evolutionary origins of parasitism among flowering plants.

Societal Impact Statement Production and heavy application of chemical‐based fertilizers to maintain crop yields is unsustainable due to pollution from run‐off, high CO2 emissions, and diminishing yield returns. Access to fertilizers will be limited in the future due to rising energy costs and dwindling rock phosphate resources. A growing number of companies produce and sell arbuscular mycorrhizal fungal (AMF) inoculants, intended to help reduce fertilizer usage by facilitating crop nutrient uptake through arbuscular mycorrhizas. However, their success has been variable. Here, we present information about the efficacy of a commercially available AMF inoculant in increasing AMF root colonization and fungal contribution to plant nutrient uptake, which are critical considerations within the growing AMF inoculant industry. Summary Arable agriculture needs sustainable solutions to reduce reliance on large inputs of nutrient fertilizers while continuing to improve crop yields. By harnessing arbuscular mycorrhizal symbiosis, there is potential to improve crop nutrient assimilation and growth without additional inputs, although the efficacy of commercially available mycorrhizal inocula in agricultural systems remains controversial. Using stable and radioisotope tracing, carbon‐for‐nutrient exchange between arbuscular mycorrhizal fungi and three modern cultivars of wheat was quantified in a non‐sterile, agricultural soil, with or without the addition of a commercial mycorrhizal inoculant. While there was no effect of inoculum addition on above‐ground plant biomass, there was increased root colonization by arbuscular mycorrhizal fungi and changes in community structure. Inoculation increased phosphorus uptake across all wheat cultivars by up to 30%, although this increase was not directly attributable to mycorrhizal fungi. Carbon‐for‐nutrient exchange between symbionts varied substantially between the wheat cultivars. Plant tissue phosphorus increased in inoculated plants potentially because of changes induced by inoculation in microbial community composition and/or nutrient cycling within the rhizosphere. Our data contribute to the growing consensus that mycorrhizal inoculants could play a role in sustainable food production systems of the future.

Adiabatic temperature rise is an important method for determining isocyanate conversion in polyurethane foam reactions as well as many other exothermic chemical reactions. Adiabatic temperature rise can be used in conjunction with change in height and mass measurements to gain understanding into the blowing and gelling reactions that occur during polyurethane foaming as well as give important information on cell morphology. FoamPi is an open-source Raspberry Pi device for monitoring polyurethane foaming reactions. The device effectively monitors temperature rise, change in foam height as well as changes in the mass during the reaction. Three Python scripts are also presented. The first logs raw data during the reaction. The second corrects temperature data such that it can be used in adiabatic temperature rise reactions for calculating isocyanate conversion; additionally this script reduces noise in all the data and removes erroneous readings. The final script extracts important information from the corrected data such as maximum temperature change and maximum height change as well as the time to reach these points. Commercial examples of such equipment exist however the price (>£10000) of these equipment make these systems inaccessible for many research laboratories. The FoamPi build presented is inexpensive (£350) and test examples are shown here to indicate the reproducibility of results as well as precision of the FoamPi.

Wheat yields have plateaued in the UK over the last 25 years, during which time most arable land has been annually cropped continuously with short rotations dominated by cereals. Arable intensification has depleted soil organic matter and biology, including mycorrhizas, which are affected by tillage, herbicides, and crop genotype. Here, we test whether winter wheat yields, mycorrhization, and shoot health can be improved simply by adopting less intensive tillage and adding commercial mycorrhizal inoculum to long-term arable fields, or if 3-year grass-clover leys followed direct drilling is more effective for biological regeneration of soil with reduced N fertiliser. We report a trial of mycorrhization, ear pathology, and yield performance of the parents and four double haploid lines from the Avalon x Cadenza winter wheat population in a long-term arable field that is divided into replicated treatment plots. These plots comprised wheat lines grown using ploughing or disc cultivation for 3 years, half of which received annual additions of commercial arbuscular mycorrhizal (AM) inoculum, compared to 3-year mown grass-clover ley plots treated with glyphosate and direct-drilled. All plots annually received 35 kg of N ha−1 fertiliser without fungicides. The wheat lines did not differ in mycorrhization, which averaged only 34% and 40% of root length colonised (RLC) in the ploughed and disc-cultivated plots, respectively, and decreased with inoculation. In the ley, RLC increased to 52%. Two wheat lines were very susceptible to a sooty ear mould, which was lowest in the ley, and highest with disc cultivation. AM inoculation reduced ear infections by >50% in the susceptible lines. In the ley, yields ranged from 7.2 to 8.3 t ha−1, achieving 92 to 106% of UK average wheat yield in 2018 (7.8 t ha−1) but using only 25% of average N fertiliser. Yields with ploughing and disc cultivation averaged only 3.9 and 3.4 t ha−1, respectively, with AM inoculum reducing yields from 4.3 to 3.5 t ha−1 in ploughed plots, with no effect of disc cultivation. The findings reveal multiple benefits of reintegrating legume-rich leys into arable rotations as part of a strategy to regenerate soil quality and wheat crop health, reduce dependence on nitrogen fertilisers, enhance mycorrhization, and achieve good yields.

Wild-type KRAS amplification is known to induce KRAS activation in cancer leading to poor prognostic outcomes. Here the authors identify a KRAS-responsive lncRNA, KIMAT1 that maintains KRAS signalling in lung cancer, suggesting that its targeting may prevent KRAS-driven tumourigenesis.

Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free-to-use data processing and analysis tools exist for various untargeted MS approaches, including liquid chromatography (LC), but choosing the ‘correct’ pipeline isn’t straight-forward. This tutorial, in conjunction with a user-friendly online guide presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. We provide practical advice concerning experimental design, organisation of data and downstream analysis, and offer details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/changing methodologies and increased clarity and detail as user participation becomes more common. Hence, the authors welcome contributions and improvements to the workflow via the online repository. We believe that this workflow will streamline and condense complex mass-spectrometry approaches into easier, more manageable, analyses thereby generating opportunities for researchers previously discouraged by inaccessible and overly complicated software.

Invasive insects pose an increasing risk to global agriculture, environmental stability, and public health. Giant pine scale (GPS), Marchalina hellenica Gennadius (Hemiptera: Marchalinidae), is a phloem feeding scale insect endemic to the Eastern Mediterranean Basin, where it primarily feeds on Pinus halepensis and other Pinaceae. In 2014, GPS was detected in the southeast of Melbourne, Victoria, Australia, infesting the novel host Pinus radiata. An eradication program was unsuccessful, and with this insect now established within the state, containment and management efforts are underway to stop its spread; however, there remains a need to understand the insect’s phenology and behaviour in Australia to better inform control efforts. We documented the annual life cycle and seasonal fluctuations in activity of GPS in Australia over a 32 month period at two contrasting field sites. Onset and duration of life stages were comparable to seasons in Mediterranean conspecifics, although the results imply the timing of GPS life stage progression is broadening or accelerating. GPS density was higher in Australia compared to Mediterranean reports, possibly due to the absence of key natural predators, such as the silver fly, Neoleucopis kartliana Tanasijtshuk (Diptera, Chamaemyiidae). Insect density and honeydew production in the Australian GPS population studied varied among locations and between generations. Although insect activity was well explained by climate, conditions recorded inside infested bark fissures often provided the weakest explanation of GPS activity. Our findings suggest that GPS activity is strongly influenced by climate, and this may in part be related to changes in host quality. An improved understanding of how our changing climate is influencing the phenology of phloem feeding insects such as GPS will help with predictions as to where these insects are likely to flourish and assist with management programs for pest species.

Marine-terminating glaciers play a critical role in controlling Greenland's ice sheet mass balance. Their frontal margins interact vigorously with the ocean, but our understanding of this interaction is limited, in part, by a lack of bathymetry data. Here we present a multibeam echo sounding survey of 14 glacial fjords in the Uummannaq and Vaigat fjords, west Greenland, which extends from the continental shelf to the glacier fronts. The data reveal valleys with shallow sills, overdeepenings (>1300 m) from glacial erosion, and seafloor depths 100-1000 m deeper than in existing charts. Where fjords are deep enough, we detect the pervasive presence of warm, salty Atlantic Water (AW) (>2.5°C) with high melt potential, but we also find numerous glaciers grounded on shallow (

Problem: Brain imaging studies of mental health and neurodevelopmental disorders have recently included machine learning approaches to identify patients based solely on their brain activation. The goal is to identify brain-related features that generalize from smaller samples of data to larger ones; in the case of neurodevelopmental disorders, finding these patterns can help understand differences in brain function and development that underpin early signs of risk for developmental dyslexia. The success of machine learning classification algorithms on neurofunctional data has been limited to typically homogeneous data sets of few dozens of participants. More recently, larger brain imaging data sets have allowed for deep learning techniques to classify brain states and clinical groups solely from neurofunctional features. Indeed, deep learning techniques can provide helpful tools for classification in healthcare applications, including classification of structural 3D brain images. The adoption of deep learning approaches allows for incremental improvements in classification performance of larger functional brain imaging data sets, but still lacks diagnostic insights about the underlying brain mechanisms associated with disorders; moreover, a related challenge involves providing more clinically-relevant explanations from the neural features that inform classification.Methods: We target this challenge by leveraging two network visualization techniques in convolutional neural network layers responsible for learning high-level features. Using such techniques, we are able to provide meaningful images for expert-backed insights into the condition being classified. We address this challenge using a dataset that includes children diagnosed with developmental dyslexia, and typical reader children.Results: Our results show accurate classification of developmental dyslexia (94.8%) from the brain imaging alone, while providing automatic visualizations of the features involved that match contemporary neuroscientific knowledge (brain regions involved in the reading process for the dyslexic reader group and brain regions associated with strategic control and attention processes for the typical reader group).Conclusions: Our visual explanations of deep learning models turn the accurate yet opaque conclusions from the models into evidence to the condition being studied.