Multiple QTLs linked to agro-morphological and physiological traits related to drought tolerance in potato.

Dissection of the genetic architecture of adaptation and abiotic stress-related traits is highly desirable for developing drought-tolerant potatoes and enhancing the resilience of existing cultivars, particularly as agricultural production in rain-fed areas may be reduced by up to 50 % by 2020. The “DMDD” potato progeny was developed at International Potato Center (CIP) by crossing the sequenced double monoploid line DM and a diploid cultivar of the Solanum tuberosum diploid Andigenum Goniocalyx group. Recently, a high-density integrated genetic map based on single nucleotide polymorphism (SNP), diversity array technology (DArT), simple sequence repeats (SSRs), and amplified fragment length polymorphism (AFLP) markers was also made available for this population. Two trials were conducted, in greenhouse and field, for drought tolerance with two treatments each, well-watered and terminal drought, in which watering was suspended 60 days after planting. The DMDD population was evaluated for agro-morphological and physiological traits before and after initiation of stress, at multiple time points. Two dense parental genetic maps were constructed using published genotypic data, and quantitative trait locus (QTL) analysis identified 45 genomic regions associated with nine traits in well-watered and terminal drought treatments and 26 potentially associated with drought stress. In this study, the strong influence of environmental factors besides water shortage on the expression of traits and QTLs reflects the multigenic control of traits related to drought tolerance. This is the first study to our knowledge in potato identifying QTLs for drought-related traits in field and greenhouse trials, giving new insights into genetic architecture of drought-related traits. Many of the QTLs identified have the potential to be used in potato breeding programs for enhanced drought tolerance

Design decisions for a real time, alcohol craving study using physio- and psychological measures

The current study was a pilot for an alcohol craving monitoring study with a biosensor (E4 wristband) and ecological momentary assessment (EMA) smartphone app. The E4 wristband was evaluated on compliance rates, usability, comfort and stigmatization. Two EMA methodologies (signal- and interval-contingent design) were compared on data variability, compliance and perceived burden. Results show that both EMA methodologies captured variability of craving and compliance rates were between medium to low. The perceived burden of the designs was high, in particular for the signal-contingent design. Participants wore the wristband ranging from occasionally to often and the usability was rated good. Many participants reported frequent questioning about the bracelet, which they indicated as positive. However, addicted individuals are expected not to appreciate this attention, we therefore propose to provide them with coping strategies. Efforts should be made to increase compliance, we therefore propose the interval contingent design with micro incentives

Natural variation of potato allene oxide synthase 2 causes differential levels of jasmonates and pathogen resistance in Arabidopsis

Natural variation of plant pathogen resistance is often quantitative. This type of resistance can be genetically dissected in quantitative resistance loci (QRL). To unravel the molecular basis of QRL in potato (Solanum tuberosum), we employed the model plant Arabidopsis thaliana for functional analysis of natural variants of potato allene oxide synthase 2 (StAOS2). StAOS2 is a candidate gene for QRL on potato chromosome XI against the oömycete Phytophthora infestans causing late blight, and the bacterium Erwinia carotovora ssp. atroseptica causing stem black leg and tuber soft rot, both devastating diseases in potato cultivation. StAOS2 encodes a cytochrome P450 enzyme that is essential for biosynthesis of the defense signaling molecule jasmonic acid. Allele non-specific dsRNAi-mediated silencing of StAOS2 in potato drastically reduced jasmonic acid production and compromised quantitative late blight resistance. Five natural StAOS2 alleles were expressed in the null Arabidopsis aos mutant under control of the Arabidopsis AOS promoter and tested for differential complementation phenotypes. The aos mutant phenotypes evaluated were lack of jasmonates, male sterility and susceptibility to Erwinia carotovora ssp. carotovora. StAOS2 alleles that were associated with increased disease resistance in potato complemented all aos mutant phenotypes better than StAOS2 alleles associated with increased susceptibility. First structure models of ‘quantitative resistant’ versus ‘quantitative susceptible’ StAOS2 alleles suggested potential mechanisms for their differential activity. Our results demonstrate how a candidate gene approach in combination with using the homologous Arabidopsis mutant as functional reporter can help to dissect the molecular basis of complex traits in non model crop plants

Understanding and simulating cropland and non-cropland burning in Europe using the BASE (Burnt Area Simulator for Europe) model

Fire interacts with many parts of the Earth system. However, its drivers are myriad and complex, interacting differently in different regions depending on prevailing climate regimes, vegetation types, socioeconomic development, and land use and management. Europe is facing strong increases in projected fire weather danger as a consequence of climate change and has experienced extreme fire seasons and events in recent years. Here, we focus on understanding and simulating burnt area across a European study domain using remote sensing data and generalised linear models (GLMs). We first examined fire occurrence across land cover types and found that all non-cropland vegetation (NCV) types (comprising 26 % of burnt area) burnt with similar spatial and temporal patterns, which were very distinct from those in croplands (74 % of burnt area). We then used GLMs to predict cropland and NCV burnt area at ∼9×9 km and monthly spatial and temporal resolution, respectively, which together we termed BASE (Burnt Area Simulator for Europe). Compared to satellite burnt area products, BASE effectively captured the general spatial and temporal patterns of burning, explaining 32 % (NCV) and 36 % (cropland) of the deviance, and performed similarly to state-of-the-art global fire models. The most important drivers were fire weather and monthly indices derived from gross primary productivity followed by coarse socioeconomic indicators and vegetation properties. Crucially, we found that the drivers of cropland and NCV burning were very different, highlighting the importance of simulating burning in different land cover types separately. Through the choice of predictor variables, BASE was designed for coupling with dynamic vegetation and Earth system models and thus enabling future projections. The strong model skill of BASE when reproducing seasonal and interannual dynamics of NCV burning and the novel inclusion of cropland burning indicate that BASE is well suited for integration in land surface models. In addition to this, the BASE framework may serve as a basis for further studies using additional predictors to further elucidate drivers of fire in Europe. Through these applications, we suggest BASE may be a useful tool for understanding, and therefore adapting to, the increasing fire risk in Europe.</p