There are two main life cycles in plants—annual and perennial1,2. These life cycles are associated with different traits that determine ecosystem function3,4. Although life cycles are textbook examples of plant adaptation to different environments, we lack comprehensive knowledge regarding their global distributional patterns. Here we assembled an extensive database of plant life cycle assignments of 235,000 plant species coupled with millions of georeferenced datapoints to map the worldwide biogeography of these plant species. We found that annual plants are half as common as initially thought5–8, accounting for only 6% of plant species. Our analyses indicate that annuals are favoured in hot and dry regions. However, a more accurate model shows that the prevalence of annual species is driven by temperature and precipitation in the driest quarter (rather than yearly means), explaining, for example, why some Mediterranean systems have more annuals than desert systems. Furthermore, this pattern remains consistent among different families, indicating convergent evolution. Finally, we demonstrate that increasing climate variability and anthropogenic disturbance increase annual favourability. Considering future climate change, we predict an increase in annual prevalence for 69% of the world’s ecoregions by 2060. Overall, our analyses raise concerns for ecosystem services provided by perennial plants, as ongoing changes are leading to a higher proportion of annual plants globally.

Water scarcity is the primary environmental constraint affecting wheat growth and production and is increasingly exacerbated due to climatic fluctuation, which jeopardizes future food security. Most breeding efforts to improve wheat yields under drought have focused on above-ground traits. Root traits are closely associated with various drought adaptability mechanisms, but the genetic variation underlying these traits remains untapped, even though it holds tremendous potential for improving crop resilience. Here, we examined this potential by re-introducing ancestral alleles from wild emmer wheat (Triticum turgidum ssp. dicoccoides) and studied their impact on root architecture diversity under terminal drought stress. We applied an active sensing electrical resistivity tomography approach to compare a wild emmer introgression line (IL20) and its drought-sensitive recurrent parent (Svevo) under field conditions. IL20 exhibited greater root elongation under drought, which resulted in higher root water uptake from deeper soil layers. This advantage initiated at the pseudo-stem stage and increased during the transition to the reproductive stage. The increased water uptake promoted higher gas exchange rates and enhanced grain yield under drought. Overall, we show that this presumably ‘lost’ drought-induced mechanism of deeper rooting profile can serve as a breeding target to improve wheat productiveness under changing climate.

In the mass production systems of microalgal species, it is important to ensure the safety and quality of the biomass and product. This requires effective monitoring tools that are sensitive, rapid and simple to use. In this study, hyperspectral transmittance spectroscopy (HTS) was applied for the detection, cell density quantification and classification of algal and cyanobacterial species. A database of HTS data was assembled from samples of seven algal and cyanobacterial species at different cell densities and used for quantifying and classifying the species, using chemometric and machine learning algorithms. The results obtained showed the ability to quantify the species with a detection limit of 104 cells/mL for the support vector machine models applied, and classify the species at concentrations >105 cells/mL. The current study suggests that HTS is applicable for cell density quantification. HTS was used to distinguish between cell cultures of cyanobacteria and algae and was further able to distinguish between cyanobacteria species as well as algal species. In addition, reducing the dimensions (number of spectral bands) of HTS data using feature selection and aggregation improved the classification accuracy. Thus, HTS is recommended as an effective tool for monitoring and management of microalgal bioreactors.

SUMMARY Tomato (Solanum lycopersicum) is a prominent fruit with rich genetic resources for crop improvement. By using a phenotype-guided screen of over 7900 tomato accessions from around the world, we identified new associations for complex traits such as fruit weight and total soluble solids (Brix). Here, we present the phenotypic data from several years of trials. To illustrate the power of this dataset we use two case studies. First, evaluation of color revealed allelic variation in phytoene synthase 1 that resulted in differently colored or even bicolored fruit. Secondly, in view of the negative relationship between fruit weight and Brix, we pre-selected a subset of the collection that includes high and low Brix values in each category of fruit size. Genome-wide association analysis allowed us to detect novel loci associated with total soluble solid content and fruit weight. In addition, we developed eight F2 biparental intraspecific populations. Furthermore, by taking a phenotype-guided approach we were able to isolate individuals with high Brix values that were not compromised in terms of yield. In addition, the demonstration of novel results despite the high number of previous genome-wide association studies of these traits in tomato suggests that adoption of a phenotype-guided pre-selection of germplasm may represent a useful strategy for finding target genes for breeding.

Controlled population development and genome-wide association studies have proven powerful in uncovering genes and alleles underlying complex traits. An underexplored dimension of such studies is the phenotypic contribution of nonadditive interactions between quantitative trait loci (QTLs). Capturing of such epistasis in a genome-wide manner requires very large populations to represent replicated combinations of loci whose interactions determine phenotypic outcomes. Here, we dissect epistasis using a densely genotyped population of 1,400 backcross inbred lines (BILs) between a modern processing tomato inbred (Solanum lycopersicum) and the Lost Accession (LA5240) of a distant, green-fruited, drought-tolerant wild species, Solanum pennellii. The homozygous BILs, each harboring an average of 11 introgressions and their hybrids with the recurrent parents, were phenotyped for tomato yield components. Population-wide mean yield of the BILs was less than 50% of that of their hybrids (BILHs). All the homozygous introgressions across the genome reduced yield relative to recurrent parent, while several QTLs of the BILHs independently improved productivity. Analysis of two QTL scans showed 61 cases of less-than-additive interactions and 19 cases of more-than-additive interactions. Strikingly, a single epistatic interaction involving S. pennellii QTLs on chromosomes 1 and 7, that independently did not affect yield, increased fruit yield by 20 to 50% in the double introgression hybrid grown in irrigated and dry fields over a period of 4 y. Our work demonstrates the power of large, interspecific controlled population development to uncover hidden QTL phenotypes and how rare epistatic interactions can improve crop productivity via heterosis.Controlled population development and genome-wide association studies have proven powerful in uncovering genes and alleles underlying complex traits. An underexplored dimension of such studies is the phenotypic contribution of nonadditive interactions between quantitative trait loci (QTLs). Capturing of such epistasis in a genome-wide manner requires very large populations to represent replicated combinations of loci whose interactions determine phenotypic outcomes. Here, we dissect epistasis using a densely genotyped population of 1,400 backcross inbred lines (BILs) between a modern processing tomato inbred (Solanum lycopersicum) and the Lost Accession (LA5240) of a distant, green-fruited, drought-tolerant wild species, Solanum pennellii. The homozygous BILs, each harboring an average of 11 introgressions and their hybrids with the recurrent parents, were phenotyped for tomato yield components. Population-wide mean yield of the BILs was less than 50% of that of their hybrids (BILHs). All the homozygous introgressions across the genome reduced yield relative to recurrent parent, while several QTLs of the BILHs independently improved productivity. Analysis of two QTL scans showed 61 cases of less-than-additive interactions and 19 cases of more-than-additive interactions. Strikingly, a single epistatic interaction involving S. pennellii QTLs on chromosomes 1 and 7, that independently did not affect yield, increased fruit yield by 20 to 50% in the double introgression hybrid grown in irrigated and dry fields over a period of 4 y. Our work demonstrates the power of large, interspecific controlled population development to uncover hidden QTL phenotypes and how rare epistatic interactions can improve crop productivity via heterosis.

The soil seed bank is a major component of plant communities. However, long-term analyses of the dynamics of the seed bank and the ensuing vegetation are rare. Here, we studied the dynamics in plant communities with high dominance of annuals in Mediterranean, semiarid, and arid ecosystems for nine consecutive years. For annuals, we hypothesized that the density of the seed bank would be more stable than the density of the standing herbaceous vegetation. Moreover, we predicted that differences in temporal variability between the seed bank and the vegetation would increase with aridity, where year-to-year rainfall variability is higher. We found that the temporal variability at the population level (assessed as the standard deviation of the loge-transformed density) of the nine dominant annuals in each site did not differ between the seed bank and the ensuing vegetation in any of the sites. For the total density of annuals, patterns depended on aridity. In the Mediterranean site, the temporal variability was similar in the seed bank and the vegetation (0.40 vs. 0.40). Still, in the semiarid and arid sites, variability in the seed bank was lower than in the vegetation (0.49 vs. 1.01 and 0.63 vs. 1.38, respectively). This difference between the population-level patterns and the total density of annuals can be related to the lower population synchrony in their seed bank. In contrast, for the herbaceous perennials (all species combined), the seed bank variability was higher than in the vegetation. Overall, our results highlight the role of the seed bank in buffering the annual vegetation density with increasing climatic uncertainty typical in aridity gradients. This role is crucial under the increasing uncertainty imposed by climatic change in the region.

Chickpea (Cicer arietinum L.) is a key legume crop grown in many semi-arid areas. Traditionally, chickpea is a rainfed spring crop, but in certain countries it has become an irrigated crop. The main objective of this study was to evaluate the ability of Unmanned Aerial Systems (UAS) imaging platform with an integrated RGB camera to provide estimations of leaf area index (LAI), biomass, and yield for chickpea during the irrigation period. Two field trials were conducted in 2019 and 2020, in which chickpea plants were subjected to five and six irrigation regimes, respectively. Eight vegetation indexes (VIs) and three morphological parameters were estimated from the RGB images. In parallel, biomass was determined, LAI was measured manually, and yield was determined at full maturity. In total, 294 plant samples were acquired and analyzed over the two years. Firstly, each of the VIs and morphological parameters were correlated separately against the two biophysical parameters and yield. Then, all the VIs and morphological parameters were analyzed together, and two statistical models, partial least squares regression (PLS-R) and support vector machine (SVM); were used to predict biomass and LAI. The yield was predicted using multi-linear regression (MLR). When each index or morphological parameter was analyzed separately, plant height and some of the VIs provided adequate predictions of the biophysical parameters in 2019 (R2 values ≥ 0.50) but failed (R2 values ≤ 0.25) in 2020. The integration of the VIs with the morphological parameters and the use of PLS-R and SVM models increased the accuracy level for both biophysical parameters (R2 ranged from 0.31 to 0.96) and mitigated the lack of consistency between the years. The SVM model was superior to the PLS-R model in both biophysical parameters. The R2 values for the combined 2019 and 2020 biomass model increased, at the model-testing stage, from 0.62 to 0.96 and the RMSE values dropped from 1778 to 490 kg ha−1. The ability of the SVM model to estimate chickpea biomass and LAI can provide convenient support for different management decisions, including timing and amount of irrigation and harvest date.