Juglans) species are economically important hardwood trees cultivated worldwide for both edible nuts and high-quality wood. Broad-scale assessments of species diversity, evolutionary history, and domestication are needed to improve walnut breeding. In this study, we sequenced 309 walnut accessions from around the world, including 55 Juglans relatives, 98 wild Persian walnuts (J. regia), 70 J. regia landraces, and 86 J. regia cultivars. The phylogenetic tree indicated that J. regia samples (section Dioscaryon) were monophyletic within Juglans. The core areas of genetic diversity of J. regia germplasm were southwestern China and southern Asia near the Qinghai-Tibet Plateau and the Himalayas, and the uplift of the Himalayas was speculated to be the main factor leading to the current population dynamics of Persian walnut. The pattern of genomic variation in terms of nucleotide diversity, linkage disequilibrium, single nucleotide polymorphisms, and insertions/deletions revealed the domestication and selection footprints in Persian walnut. Selective sweep analysis, GWAS, and expression analysis further identified two transcription factors, JrbHLH and JrMYB6, that influence the thickness of the nut diaphragm as loci under selection during domestication. Our results elucidate the domestication and selection footprints in Persian walnuts and provide a valuable resource for the genomics-assisted breeding of this important crop.Walnut ( Juglans) species are economically important hardwood trees. The Persian walnut as a woody nut crop is limited by an incomplete understanding of the diversity and spatial genetic structure of the species’ wild germplasm, and the relationship of the secondary germplasm pool of walnut  to improved cultivars. To better understand walnut’s breeding, we sequenced the genomes of 309 walnut accessions from around the world. The results revealed core areas of genetic diversity of Persian walnut germplasm were southwestern China and southern Asia near the Qinghai-Tibet Plateau and the Himalayas. The genome-wide scan for selective sweeps, genome-wide association studies (GWAS), and expression analysis identified JrbHLH and JrMYB6 transcript factors related to the thickness of the diaphragm that were under selection during domestication. Our results elucidate the genetic diversity of walnut and its evolution as a species and a crop. We identified centers of wild genetic diversity that could contribute to breeding and sustainable improvement.Walnut ( Juglans regia L.; Juglandaceae, Fagales) is a large, wind-pollinated, diploid (2n = 32), monoecious, heterodichogamous, long-lived tree. It is grown worldwide [J. regia originated as an ancient hybrid between American and Asian walnut lineages in the late Pliocene  [J. regia survived and grew in isolated stands in the foothills of the Western Himalayas from the Kashmir region to Tajikistan and Kyrgyzstan [J. regia is complicated by its long history of human cultivation and its use as a traditional nut crop in many Old World cultures. Because of its ancient association with humans and the movement of walnuts via regional- and even continental-scale trade routes, apparently wild or (possibly) feral populations of walnuts are now found in isolated favourable habitats from China to the mountains of the Iberian Peninsula [Persian or common walnut  . It is grgyzstan –13 and irgyzstan . Persianrgyzstan . C-datedrgyzstan ,14. Somergyzstan ,10,12,15rgyzstan –17. Indeeninsula ,10,12. Peninsula –21 and peninsula –25.th century, so modern cultivars have short pedigrees, although the selection and dispersal of walnuts by humans have been occurring in Asia and Europe for millennia [in situ in remote locations, and the relationship between landrace samples and already characterized populations is unclear. Wild or landrace walnut populations may be descendants of truly autochthonous local sources, feral trees, descendants of trees dispersed from distant locations by humans, or a combination of these sources. The origins of walnut populations and the effects of human selection during domestication may be revealed by a detailed genetic comparison of wild, landrace, and cultivar populations. The availability of single nucleotide polymorphism (SNP) markers for rapid and highly automated genotyping makes them ideal for genetic diversity studies, genomic selection analysis, and genome-wide association studies (GWAS).Programs to produce improved, clonal varieties of Persian walnut began in the mid-20illennia ,10,12,16illennia ,26–27. Lillennia –30. The illennia , most ofJuglans relatives, 98 (presumed) wild J. regia accessions, 70 J. regia landraces, and 86 J. regia cultivars. We provide genomic evidence for elucidating the intraspecific genetic relationships of walnut source populations, the historical dynamics of J. regia cultivation, and candidate loci under selection during Persian walnut domestication.Here, we sequenced 309 diverse walnut accessions from around the world, including 55 Juglans accessions, including wild walnuts, landrace walnuts, cultivars, and related species  than landrace walnuts (4.44 × 10− 3) and cultivated walnut (4.39 × 10− 3) genomes (ST) between wild walnuts and landrace walnuts was similar to that between wild walnuts and cultivars, but there was a relatively low FST value between landraces and cultivars based on SNPs and InDels, indicating that modern breeding has not substantially influenced the genome of walnuts beyond the initial effects of domestication.The nucleotide diversity (π) of walnut at the whole-genome level across all walnut accessions was 4.5 × 10− 3 . The pat− 3 . Wild wa genomes . The genr2 value of 0.225  distances and relatively rapid LD decay . The LD of 0.225 . The aveof 0.225 . Cultivaof 0.225 .J. regia compared to the cultivated and landrace samples include differences in the π value and LD distance, but similar levels between cultivated and landrace walnuts , mainly wild J. regia (98 samples), and cultivar and landrace walnuts (156 samples)  .J. regia accessions, we performed phylogenetic analysis, principal component analysis (PCA), and population structure analysis  than the first group  and the fourth group . The lowest nucleotide diversity was observed among the wild walnuts in the third group   and excluded all cultivated and landrace walnut accessions. The filtered SNP dataset of wild walnut accessions  was subjected to phylogenetic analysis, population structure analysis , and PCAulations , which wulations . The fir× 10− 3) . Our datNe) before 1.0 Mya (million years ago), but the shapes of the Ne curves diverged c. 0.4–0.5 Mya  and Guizhou Province (Gu) had different Ne curves during c. 10 Mya to c. 5 Mya  c. 10.0 Mya. During the Quaternary glacial (1–2 Mya) in Asia, all of the Persian walnut populations that we studied showed expected glacially induced decline  model indicated that the Persian walnut demes shared strongly similar trajectories of effective population size (–0.5 Mya . Most lic. 5 Mya . During  decline .FST = 0.33) value between XJ samples and samples from other populations; the lowest FST values were found between WA and SA and between WA and EU  on chromosome 6, and JrMYB6 was located 951 bp upstream of the associated SNP  on chromosome 10 based on GWAS for the thickness of the nut diaphragm (P < 0.01). Sequence comparison analysis indicated that the JrbHLH was homologs to that of Arabidopsis thaliana, Glycine max, Populus trichocarpa, and Malus domestica , while JrMYB6 was expressed increased during three walnut diaphragm developmental stages  adaptation to cultivation and local environments through human selection. Genome-wide selection analysis indicated that approximately 3% (16.4 Mb) of the walnut genome containing 866 genes were shaped by selection based on a comparison of wild versus cultivar+landrace groups . Among tiaphragm . These tiaphragm . The nutomestica . While Jchocarpa . The Expl stages . Thus, tJuglans) samples collected around the world, with a focus on J. regia, the most valuable nut crop in China. The study revealed that the core areas of genetic diversity of Persian walnut germplasm were southwestern China and southern Asia near the Qinghai-Tibet Plateau and the Himalayas. A comparison of the genomic diversity of wild, landrace, and improved Persian walnuts showed evidence of loss of diversity associated with domestication but a relatively weak additional loss of diversity from cultivar selection. The resources generated in this study provide a genomic framework for future germplasm use and walnut improvement.Here, we sequenced the genomes of 309 walnut  [When we began this research, we expected that close examination would show that breeders had reduced the genetic diversity of walnut as they advanced germplasm into the final stages of cultivar development. In general, this was not true; the genetic diversity, LD distance, and gene flow provided a relatively weak artificial selection in the evolution process, which is different from the main annual crops like rice and soybeans –20. Thiso Persia ,000 BCE 000 BCE) ,12,16, h000 BCE) –20,37–39000 BCE) , which lJuglans, the phylogenetic tree showed that the J. regia samples (section Dioscaryon) were monophyletic , five chloroplast DNA spacer sequences, a hypervariable matK, restriction fragment length polymorphisms (RFLPs), and whole chloroplast genomes [Dioscaryon is a topic of active research; however, it may be that J. regia and other members of Dioscaryon were derived from the hybridization of ancestors that became what is now sect. Rhysocaryon and sect. Cardiocaryon, a conclusion based on nuclear SNPs [Juglans may explain why the phylogeny of Persian walnut differs depending on whether it is based on nuclear SNPs or chloroplast and mitochondrial sequences [J. regia within the genus Juglans.Within phyletic . Distinc samples . The sys genomes ,15,41–44ear SNPs ,45. Diffequences –48. The equences –48. TherWe observed that the landraces and cultivars were mixed and showed signs of introgression, certainly because of the use of landraces for cultivar breeding, as mentioned above, but probably also because seeds or scion wood of cultivars was propagated by villagers, which permitted gene exchange through pollen . The genOur results are supported by previous genetic and ecological data showing that Persian walnut survived the LGM in glacial refugia spread across a wide geographic area from 30°N to 45°N latitude in the Balkans, western Europe, Xinjiang Province, northeastern China, central China, and southwestern China ,10,12. DThe presence of walnut habitat in the mountains of southern Asia was supported by a recent archaeological study of several new sites in northeastern India containing carbonized walnut samples ascribed to an earlier date (~4500 B.P.) than previously reported (~4000–3500 B.P.) . The widDioscaryon (J. sigillata) is found [Persian walnut is considered a relict species of the Tertiary Period ,10,12 nais found . The curis found ,59–60, ais found –63.JrbHLH and JrMYB6 were associated with the thickness of the diaphragm in walnut. The homologs of JrbHLH in poplar was related to cell wall development [JrMYB6 in Malus domestica is involved in relative lignin metabolism [JrbHLH and JrbMYB6 may regulate cell wall formation and lignin biosynthesis during xylem development in walnut. Additionally, expression analysis indicated that JrMYB6 was upregulated in walnut diaphragm development, but JrbHLH was downregulated during walnut diaphragm development. Collectively, JrbHLH and JrMYB6 might differentially express to participate in the xylem development to affect the thickness of the diaphragm in walnut. Thus, these two TFs provide potential targets for improving Persian walnut nut quality by molecular marker-assisted selection or genetic manipulations.To the best of our knowledge, the diaphragm is a typical domestication trait that protects the nuts during natural environmental selection, thereby maintaining the continuity of the species. In addition, the diaphragm affects the ease of kernel extraction, and a thin diaphragm shows the great commercial value and thus is attractive to breeders. In the study, selection analysis and GWAS revealed that elopment –65. The tabolism –67. It itabolism . Thus, JJ. nigra), 20 butternuts , 11 hybrids (J. hopeiensis), and Carya illinoinensis  chromosome-level genome as a reference [haplotypecaller to identify SNPs [To obtain clean reads, we filtered the reads with three features: 1) reads with adapters, 2) reads with low quality (<10) base percentage greater than 20% compared to the whole genome, and 3) reads with N percentages greater than 5%. After filtering, we mapped the clean reads to the reference genome using BWA V0.7.17 software with default parameters . Using teference , clean dify SNPs . FinallyJ. regia groups around the world, we wrote an in-house Perl script  to calculate the diversity parameters, including the SNP number, the average pairwise divergence within a population (θπ), Watterson’s estimator (θw) and Tajima’s D. Along the genome, a sliding window of 10 kb with a 5 kb step was used to estimate the θπ, θw, and Tajima’s D values. The population differentiation parameter FST and reduction of diversity (ROD) were computed in the same windows based on a pairwise SNP sequence [C. illinoinensis as the outgroup. Figtree was used to graph the phylogenetic tree. We performed PCA using PLINK software (version 1.90) with default settings [K) was set from 2 to 10, representing the assumed groups of the simulated ancestral population. The best K was inferred based on the delta K method using the Structure Harvester program [To compare the diversity of sequence . PHYLIP sequence . The algsettings . The topsettings . The popsettings  with a m program . LD was  program  with the“samtools mpileup -C 50 -Q 20 -u -v” and “vcfutils. pl vcf2fq -d 4 -D 100”. Second, the diploid consensus sequence was obtained by fq2psmcfa as an input of the PSMC, and the parameter was “fq2psmcfa -q20”. Third, PSMC V0.6.5 was run with the parameter “psmc -N25 -t15 -r5 -p 4+25*2+4+6”. To analyze the results, we used 30 years as the generation time, and the mutation rate was set as 2.09E-8 substitutions per site per generation [We used the PSMC model to infer the population size history of different groups. First, heterozygous sites were generated by SAMtools version 1.6 . The parC. illinoinensis) was set as the outgroup (X) for all four population tests. For any four populations , the gene flow occurred either between W and Y or between X and Z when the Z score was more than 0. Conversely, gene flow occurred between either W and Z or X and Y when the Z score was smaller than 0. A gene flow signal was considered significant when the Z score was more than 3 or less than -3. To study gene flow among the wild samples, landraces, and cultivars, we calculated two parameters: the D-statistic and Nm (estimate of gene flow). The D-statistics were generated by ADMIXtools software based on the fixation index (FST). Nm was calculated according to Wright’s (1931) formula Nm = (1- FST)/(4*FST) [To understand gene flow, we calculated the D-statistics of four populations with ADMIXtools version 4.1 software . The SNP/(4*FST) . TreeMix/(4*FST) .wild/πcultivar+landrace; domesticated). Genomic regions under selection often showed a decrease in genetic diversity. We identified genomic regions selected by domestication by comparing the FST and ROD for the pool of wild trees compared to the cultivar+landrace pool using a 100-kb sliding window with a step size of 10 kb using VCFtools software (v0.1.13) [FST>0.15 (the genetic differentiation between groups is large), 2) the top 10% of FST values, and 3) ROD>0.2. In the domestication-related genomic regions, we selected genes meeting these three criteria as candidate domestication genes [To identify evidence of selection in Persian walnut genomes associated with domestication and improvement, we screened for genomic regions with a sharp ROD between wild and domesticated walnut (cultivars + landraces) groups (πv0.1.13) ,79,81. Ton genes ,78.The SNPs with an MAF<0.05 and a missing rate<0.25 were used for the GWAS in SHAPEIT (4.0) –83. To oJrbHLH and JrMYB6), we collected common walnut diaphragms from one adult tree growing at Xi’an (Northwest University campus) on 95 days after flowering (DAF), 120 DAF, and 140 DAF, respectively. Total RNA from walnut diaphragms was processed using RNA Library Prep Kit . We then performed quantitative real-time PCR (qRT-PCR) reactions in a common walnut diaphragm during three development stages  (primers see details in To verify the expression patterns of two candidate transcription factors (S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S1 FigJuglans regia. (B) Gene density represented as a grayscale heatmap; i.e., blacker areas had a higher gene density  per 300 kbp window). (C) Number of SNPs in 300-kb windows. SNPs among wild walnuts (green), cultivars (light blue), and landraces (red) . (D) Insertions and deletions (indels) in wild walnuts (green), cultivars (light blue), and landraces (red). (E) The genomic nucleotide diversity (π) of the nuclear genomes of wild walnuts (green), cultivars (light blue), and landraces (red). (F) Heatmap of the genetic differentiation (FST) between wild walnuts and selected walnuts (cultivars and landraces). (G) Heatmap of the coverage of selected regions between wild walnuts and selected walnut (cultivars and landraces).(A) sixteen chromosomes of (TIF)Click here for additional data file.S2 FigJY = outgroup, Wild = wild walnuts, Landrace = landrace walnuts, Cultivated = cultivar walnuts.(TIF)Click here for additional data file.S3 FigPrincipal components plots for wild walnut accessions, including samples from Eastern Asian , Yunnan-Kweichow Plateau , Xinjiang Province ; southern Asia ; western Asia ; Europe .(TIF)Click here for additional data file.S4 Fig(A) Each color corresponds to a single population as noted. Each walnut accession is represented by a vertical bar. The Y-axis refers to the proportion of the genetic background, and the height of each line with different colors represents the probability of an accession belonging to a different genetic background. (B) Delta K showed a peak at 4, suggesting four clusters as the most appropriate option, which supports the phylogenetic tree and PCA result of “four major discrete clusters of Eastern Asian (EA), Yunnan-Kweichow Plateau (YG), Xinjiang province (XJ), and southern Asian (SA)+western Asia (WA)+ Europe (EU) groups were detected”.(TIF)Click here for additional data file.S5 FigJ. regia (XP_035546602.1), A. thaliana (AT1G69010.1), G. max (KRH03185), P. trichocarpa (PNT00429), and M. domestica (mRNA:MD06).The protein information of these plants as follows: (TIF)Click here for additional data file.S6 FigJ. regia (XP_018812236.1), Zea mays , Arabidopsis thaliana (AT3G47600.1), Solanum lycopersicum (Solyc06g069), Malus domestica (mRNA:MD06), Oryza sativa (Os12t0125000-01), Glycine max (KRH64960.1), and Populus trichocarpa (PNS90469).The protein information of these plants as follows: (TIF)Click here for additional data file.


Arabidopsis suecica and its parent species A. arenosa and A. thaliana were chosen as a model system to investigate relationships between phenotypic plasticity, fitness, and genetic variation. Particularly, we test if A. suecica is more plastic, show higher genetic diversity, and/or have higher fitness than its parent species. Wild Norwegian populations of each species were analyzed for phenotypic responses to differences in availability of nutrient, water, and light, while genetic diversity was assessed through analysis of AFLP markers. Arabidopsis arenosa showed a higher level of phenotypic plasticity and higher levels of genetic diversity than the two other species, probably related to its outbreeding reproduction strategy. Furthermore, a general positive relationship between genetic diversity and phenotypic plasticity was found. Low genetic diversity was found in the inbreeding A. thaliana. Geographic spacing of populations might explain the clear genetic structure in A. arenosa, while the lack of structure in A. suecica could be due to coherent populations. Fitness measured as allocation of resources to reproduction, pointed toward A. arenosa having lower fitness under poor environmental conditions. Arabidopsis suecica, on the other hand, showed tendencies toward keeping up fitness under different environmental conditions.Polyploid species possess more than two sets of chromosomes and may show high gene redundancy, hybrid vigor, and masking of deleterious alleles compared to their parent species. Following this, it is hypothesized that this makes them better at adapting to novel environments than their parent species, possibly due to phenotypic plasticity. The allopolyploid  Does polyploid species outrange their parents in phenotypic response range, genetic diversity, and fitness? We investigated the allopolyploid Arabidopsis suecica and its diploid parents A. thaliana and A. arenosa and found no indications of this. PolyploThe physiological effects of polyploidization are relatively little explored, but Soltis et al.  pinpointThe adaptive advantage of an allopolyploid may result from phenotypic plasticity and/or fitness homeostasis  Norrl. ex O.E. Schulz and its two parent species, A. thaliana (L.) Heynh. and A. arenosa (L.) Lawalrée. Arabidopsis suecica originates from a hybridization between the mostly diploid A. thaliana and the mostly autotetraploid A. arenosa .2.22 were harvested. Leaf harvesting was not standardized. Flow cytometry was performed, and DNA ratios were obtained by G. Geenen, Plant Cytometry Services . Diploid A. thaliana from the “Columbia” line was acquired from the University of Tromsø and provided as a control sample along with the experimental samples. For internal control Ilex crenata “Fastigiata” was used.In order to ensure that all populations of the study species had the expected chromosomal numbers and ploidy levels, DNA content was measured with flow cytometry. Seeds from each of the populations grown in the experiment were sown in pots and grown to a size where harvesting was permissible. For each population, three individuals were selected for harvesting. Leaves corresponding to a total area of 1–2 cm2.33 factorial design. These treatments consisted of all different combinations of dry and wet water conditions, rich and poor nutrient conditions, and high and low light conditions. Five replicates were grown per treatment combination. This adds up to 10 populations × 8 treatment combinations × 5 replicates = 400 plants grown in total. Pictures of the experimental design are shown in Figure Seeds from the 10 sampled populations were grown under controlled environmental conditions in a growth chamber. To assess whether different species react differently to varying environmental conditions, eight different treatments were applied in a 2Sphagnum peat, 10% sand and 4% granule clay. One trolley was assigned to each treatment combination. For each population, seeds from all sampled individuals were mixed on a white paper sheet, then several seeds were drawn randomly and sown in each pot. The different populations were distributed randomly within each trolley. 9 l of water were applied to each trolley after sowing.Eight trolleys with a size of 100 × 60 cm were covered first with plastic, and then with felt mats to transport the water evenly over the whole trolley. 50 circular 8C‐101 flowerpots with a diameter of 8 cm  were placed on each trolley. 400 flowerpots were prepared overall. Each flowerpot was filled with Gartnerjord soil  consisting of 86% ®‐BT 400W/d Pro Daylight E40  light bulbs in GAVITA GAN 400 AL lamps . The amount of light in the chamber was measured to 210–250 µmol m−2 s−1 with a LI‐189 quantum/radiometer/photometer . The seeds were allowed to germinate at similar conditions for all trolleys, and water was applied regularly to avoid desiccation. Almost all seeds from the T‐DRA3 population failed to germinate, and the population was excluded from the experiment. The flowerpots assigned to T‐DRA3 plants were left on the trolleys during the whole experiment, to keep the flowerpot pattern equal between all trolleys. Leaving out the T‐DRA3 population, a total number of 360 plants distributed on the remaining populations were grown for the experiment. Among these, four died during the experiment and were not included in the analyses.The seeds were stratified for four days in 4xC and 24 h darkness. Then, conditions were changed to 20°C/17°C day/night temperature and an 8/16 h light/dark cycle. Light was provided by OSRAM 400W Powerstar HQ1When the seedlings had reached the stadium where primary leaves started to become visible, they were thinned so that one plant remained in each flowerpot. For some populations, transplantations between pots were done. The plants were allowed one week of optimal growth conditions before treatments were applied.Nutrient treatment was applied by giving nutrient solution made from 1.25 ml Superba NPK 14–4–21 + mikro  and 1 l water to each of the rich nutrient trolleys once per week, while no nutrients were applied to the poor nutrient trolleys. The water used for making the nutrient solution was included in the total amount of water given to the plants, as described below.−2 s−1, equivalent to a reduction of 60–70%.Light treatment was applied by covering the low‐light treatment trolleys with XLS 17 Revolux light‐reducing fabric . The fabric is partly made from aluminum, and it does not change the spectral composition of the light that passes through. The amount of light below the fabric was measured to be 80–90 µmol mWater treatment was initially performed by applying 2 l of water three times a week to the wet condition trolleys, and 1 l of water three times a week to the dry condition trolleys. The light‐shading fabric was found to heavily reduce evaporation from the low‐light trolleys, so to obtain similarity in water conditions between the low‐light and the high‐light trolleys, the low‐light trolleys were watered once a week, applying 2 l of water to the wet condition trolleys and 1 l of water to the dry condition trolleys.−2 s−1 for the high light and low light treatments, respectively. For the high‐light trolleys, watering was done by applying 2 l of water once a week to the wet condition trolleys, and 1 l of water once a week to the dry condition trolleys. For the low‐light trolleys, watering was done by applying 2 l of water once every third week to the wet condition trolleys, and 1 l of water once every third week to the dry condition trolleys.Vernalization was initiated 39 days after sowing (35 days after germination conditions were initiated). Growth conditions were changed to 4°C constant temperature and an 8/16 h light/dark cycle. Since growth was low during vernalization, nutrients were applied on average every third week, in the same doses as described above. The amount of light in the growth chamber was reduced to avoid the plants dying from light stress. The amount of light was measured to be 125–135 and 27–32 µmol mBased on findings in Lewandowska‐Sabat et al. , vernaliDuring the whole experiment, the trolleys were moved around within the chamber, and pots were moved around on the trolleys periodically to avoid edge effects. This was performed haphazardly.2.3.1Phenotypic variables were measured at different times. At the initiation of vernalization, three different variables were measured: number of rosette leaves per plant, length of the longest rosette leaf for each plant (including both petiole and lamina), and length of the lamina on the longest rosette leaf. In cases where leaves were serrated, the length from the innermost serration to the leaf tip was measured and recorded as lamina length.At the end of the vernalization period, the days it took for each individual plant to bolt and to open the first flower were counted, with the last day of vernalization set as day zero. In addition, the number of rosette leaves was measured at bolting. For plants that bolted, but did not flower, the time to flowering was set as missing data. For plants that neither did flower nor bolt, rosette leaves were counted at the ending day of the experiment, and both time to bolting and time to flowering were set as missing data.At the end of the experiment, five different variables were measured for each plant: Plant height measured as the longest stem from root to tip for each plant, number of branches on the stem, total number of flowers and siliques (denoted as “number of flowers”—buds were not counted) and dry weight of the aboveground biomass. To measure the dry weight, the plants were harvested and dried at 60°C for 24 h in a TS8136 drying oven  before weighing them with AG ED224S scales .2.4EcoRI enzyme , and 0.125 units MseI enzyme (New England Biolabs) was adjusted with MilliQ water to a total of 40 µl and incubated at 37°C for 75 min. Adapters (Invitrogen) were annealed by mixing forward  and reverse adapters  to a concentration of 10 µM (EcoRI adapters) or 50 µM (MseI‐adapters) followed by incubation at 65°C for 10 min, 37°C for 10 min and 25°C for 10 min. 0.1 µM annealed EcoRI adapter, 1 µM MseI adapter, 0.2 µM ATP . 1xRL‐buffer, 0.05 µg BSA and 0.02 units T4 DNA ligase (Invitrogen) was added to the restricted DNA and adjusted with MilliQ water to a total of 50 µL and incubated at 37°C for three hours. The ligated DNA was diluted 10x with MilliQ water.For genetic analyses, seeds from each plant harvested during the fieldwork were sown in individual pots for the populations A‐GAU1, A‐NFRO4, A‐DRA1, S‐DRA2, S‐NFRO3, S‐DRA2, S‐EID3, T‐SFRO3, and T‐EID1. For T‐DRA3, seeds harvested from the plants grown in the growth chamber experiment were sown. A‐EID4 was not available for analysis, since there were very few viable seeds left. The plants were grown until large enough to permit harvesting. Fresh tissue was harvested from one individual per pot. No standardization was done when it came to harvesting. During the harvest, ~100 µg of fresh tissue per plant was cut in pieces with scissors and put into 2‐ml tubes (Eppendorf). The tubes were stored at −80°C. Before isolation of DNA, two 3 mm crushing beads were applied to each tube. The tubes were dipped into liquid nitrogen, before the tissue was crushed with a TissueLyser II  for 1 min at 20 r/s. DNA was extracted from the crushed tissue using a DNeasy Plant Mini Kit (QIAGEN). The quantity of DNA in each isolation was checked using a NanoDrop 8000 UV‐Vis Spectrophotometer . A modified AFLP protocol after Hayashi et al.  and Vos One E+1 primer (E01) and one M+1 primer (M01) was used for preamplification. MilliQ water, 1× PCR buffer (Applied Biosystems), 2 mM MgCl2 , 0.2 mM dNTP (Invitrogen), 0.3 μM E+1 primer (Invitrogen), 0.3 μM M+1 primer (Invitrogen) and 0.038 units of AmpliTaq DNA polymerase . The three underlined combinations yielded the best testing results based on the number of amplified fragments in the range 50–500 base pairs, and amount of polymorphism among the included individuals and were chosen for further analyses.MilliQ water, 1 μl 1× PCR buffer (QIAGEN), 0.5 mM MgCl2 (QIAGEN), 0.2 mM dNTP (Invitrogen), 0.625 μM fluorescently labeled E+3 primer (Invitrogen), 0.625 μM M+3 primer (Invitrogen), and 0.025 units of HotStarTaq DNA polymerase (QIAGEN) and 2.5 μl of diluted preamplified DNA was adjusted to a total volume of 10 μl. PCR was run using the following program: 95°C for 15 min, then 10 cycles of 94°C for 20 s, 66°C for 30 s with a reduction of 1°C per cycle and 72°C for 2 min, then 25 cycles of 94°C for 30 s, 56°C for 30 s and 72°C for 3 min, then 60°C for 30 min. Six different combinations of E+3(fluorescently labeled) and M+3 primers were tested for selective amplification  and 1 μL diluted amplified DNA was mixed and denatured for 3 minutes at 95°C. Electrophoresis was performed with an ABI PRISM 3730 DNA analyzer (Applied Biosystems).500 LIZ® ver. 5.0 (Life Technologies). Only single peaks clearly differentiated from background noise were scored as a band. Manual corrections were run on all samples. Individuals showing anomalous peak patterns on at least one of the three primer combinations were removed completely from the dataset. The number of replicated samples was 31  for primer combination E33xM37, 30  for primer combination E33xM38 and 23  for primer combination E42xM38. The genotyping error, due to for instance incomplete digestion or imperfect PCR, for each primer combination was calculated using the formula  × 100/(number of replicates) × (number of markers) , number of leaves at start of vernalization , number of branches , and length of lamina on longest leaf and its percentage of total leaf length . The remaining variables were superimposed onto a biplot of the first two NMDS axes.Descriptive multivariate analysis using non‐metric multidimensional scaling (NMDS) from the R package vegan , with 106 iterations and a burn‐in of 105 iterations. An admixture model was used; meaning that for each individual different parts of the genome are allowed to descend from different groups. Linkage between markers was not considered. A minimum of one population (K = 1) and a maximum of 9 populations (K = 9) was allowed per analysis. For each value of K, 10 independent runs were done. The results were assessed using the R functions in Structure‐sum  analysis was run on a distance matrix calculated with Dice's coefficient of similarity Dice, . The PCOTo assess and compare the diversity of the sampled populations and species, 95% confidence intervals for Nei's Genetic Diversity Nei,  was cons2.5.3To assess possible relationships between genetic diversity and phenotypic plasticity, a Mantel test was run to compare Euclidean distance matrices calculated from Nei's Genetic Diversity and Coefficients of variation for all phenotypic variables. The test was run on the eight populations where results from both growth experiments and genetic analyses were available. A corresponding test was also done with a phenotypic distance matrix calculated from coefficients of variation where each variable was scaled to unity. The scaling was done by dividing all values in the variables by the highest value in the variable.33.1A. thaliana, 32 chromosomes/tetraploid for A. arenosa and 26 chromosomes/tetraploid for A. suecica. There were two exceptions: One individual showed a lower chromosomal number than expected in A. arenosa. This might be due to aneuploidy, but it might also be due to errors in the measurement. One alleged individual A. thaliana showed a chromosome number that one would expect for A. arenosa. This is probably due to a confusion of samples. The samples used for flow cytometry were not used in the other experiments, hence this likely did not affect the rest of the results.The populations used in the experiment mainly showed the expected chromosomal numbers and ploidy levels: 10 chromosomes/diploid for 3.23.2.1A. thaliana was separated from the other two species , the species had similar values within the high light treatment .In the analysis of the comparison variable t Figure . Within 3.3A. arenosa, 27 were only present in A. suecica, 16 were only present in A. thaliana, 67 were present in A. arenosa and A. suecica, 45 were present in A. suecica and A. thaliana, 12 were present in A. arenosa and A. thaliana and 44 were present in all three species. The percentage of polymorphic markers was 95.2% in A. arenosa, 82.5% in A. suecica and 77.8% in A. thaliana. The genotyping error was calculated to be 3.30%.A total number of 136 individuals were analyzed for variation in 274 AFLP markers . 63 markers were only present in 3.3.1A. suecica was placed in the middle of the first axis between its parent species. On the population level, A. arenosa showed a clear population clustering both in Structure and PCO  exhibited the lowest genetic diversity. For T‐DRA3, the number of sampled individuals was so low that the total sample did not necessarily reflect the population diversity. More diversity was observed within A. suecica populations than within A. thaliana populations .The AMOVA showed that the between‐populations percentage of variation in the AFLP markers was 27.5% in 3.4The Mantel test showed a significant positive correlation between phenotypic plasticity measured as coefficients of variation and the measurements of Nei's Genetic Diversity Table . This in44.1Arabidopsis suecica is intermediate between A. thaliana and A. arenosa  and a high number of non‐synonymous and putatively deleterious mutations. Meanwhile, our results indicate that some of the A. thaliana populations were the least genetically diverse among the three species, possibly due to lower levels of diversity in A. thaliana in this geographic region compared to other regions  was chosen based on methods used by Davidson et al. . More fl5A. suecica had more genetic diversity, a higher degree of phenotypic plasticity and a higher degree of fitness than its parent species A. thaliana and A. arenosa. We were unable to reveal any advantages for the allopolyploid A. suecica in our experiment in neither genetic diversity nor phenotypic plasticity. On the contrary, we found that A. arenosa had the highest level of diversity and phenotypic plasticity, probably due to its outcrossing nature. Across all species, we did find a positive relationship between phenotypic plasticity and genetic diversity, but this was not related to ploidy. A. arenosa showed tendencies towards having the lowest degree of fitness homeostasis, showing that there is no clear relationship between phenotypic plasticity and the ability to keep fitness high under varying conditions.In an environment that is changing faster than ever, understanding the mechanisms involved in range expansion and adaptation is important. Both phenotypic plasticity, genetic diversity, and polyploidy has been suggested as driving forces for introduction and adaptations to novel environments. This led us to investigate if the allopolyploid species Torbjørn Kornstad: Conceptualization ; Formal analysis (lead); Investigation (lead); Methodology (lead); Visualization (lead); Writing – original draft (lead); Writing – review & editing . Siri Fjellheim: Conceptualization (lead); Funding acquisition (lead); Project administration (lead); Resources (lead); Supervision (lead); Writing – original draft (supporting); Writing – review & editing . Mikael Ohlson: Conceptualization (supporting); Supervision (supporting); Writing – original draft (supporting); Writing – review & editing .The authors declare no competing interests.


Continuous urban expansion transforms the natural land cover into impervious surfaces across the world. It increases the city’s thermal intensity that impacts the local climate, thus, warming the urban environment. Surface urban heat island (SUHI) is an indicator of quantifying such local urban warming. In this study, we quantified SUHI for the two most populated cities in Alberta, Canada, i.e., the city of Calgary and the city of Edmonton. We used the moderate resolution imaging spectroradiometer (MODIS) acquired land surface temperature (LST) to estimate the day and nighttime SUHI and its trends during 2001–2020. We also performed a correlation analysis between SUHI and selected seven influencing factors, such as urban expansion, population, precipitation, and four large-scale atmospheric oscillations, i.e., Sea Surface Temperature (SST), Pacific North America (PNA), Pacific Decadal Oscillation (PDO), and Arctic Oscillation (AO). Our results indicated a continuous increase in the annual day and nighttime SUHI values from 2001 to 2020 in both cities, with a higher magnitude found for Calgary. Moreover, the highest value of daytime SUHI was observed in July for both cities. While significant warming trends of SUHI were noticed in the annual daytime for the cities, only Calgary showed it in the annual nighttime. The monthly significant warming trends of SUHI showed an increasing pattern during daytime in June, July, August, and September in Calgary, and March and September in Edmonton. Here, only Calgary showed the nighttime significant warming trends in March, May, and August. Further, our correlation analysis indicated that population and built-up expansion were the main factors that influenced the SUHI in the cities during the study period. Moreover, SST indicated an acceptable relationship with SUHI in Edmonton only, while PDO, PNA, and AO did not show any relation in either of the two cities. We conclude that population, built-up size, and landscape pattern could better explain the variations of the SUHI intensity and trends. These findings may help to develop the adaptation and mitigating strategies in fighting the impact of SUHI and ensure a sustainable city environment. Urban expansion is one of the fundamental human-induced activities caused by the population growth . It is aTwo types of UHI studies were reported in the literature, such as Canopy Urban Heat Island (CUHI) and Surface Urban Heat Island (SUHI), depending on the technique at which the temperature was determined . CUHI isSUHI modulates the local climate  as they In Canada, several studies investigated both CUHI and SUHI in some of the major cities ,47,48,49We chose the city of Calgary and the city of Edmonton  as our study area, the two largest cities in the Province of Alberta, Canada see . The citWe employed nine different datasets in this study that are as follows. (i)MODIS monthly LST (MOD11C3 version 6) at 5.6 km from 2001 to 2020 acquired from the National Aeronautics and Space Administration (NASA) that was used for estimating the SUHI;(ii)Administrative boundary shapefiles obtained from the geospatial data repository of the city of Calgary and the city of Edmonton were used for the determination of urban and rural areas. We used administrative boundaries of 2001, 2005, 2007, and 2008 for Calgary; and 2001, 2018, and 2019 for Edmonton.(iii)Digital Elevation Model (DEM) of Alberta at 25 m acquired from Altalis that was used for standardizing the monthly and annual LST images;(iv)MODIS yearly land cover (MCD12Q1 version 6) at 500 m acquired from NASA. It was used for the removal of built-up and water pixels in the rural and urban areas, respectively, from the monthly and annual LST images;For mapping SUHI and its trends:(v)Landsat 5 TM and Landsat 8 OLI level 2 products were acquired from the United States Geological Survey (USGS), were used to map the built-up areas in the five-year interval from 2001 to 2020 for the cities. The image dates were 20 June 2001, 5 August 2006, 4 September 2011, 16 August 2016, and 26 July 2020 for Calgary, and 20 June 2001, 30 May 2006, 13 June 2011, 10 May 2016, and 26 July 2020 for Edmonton;(vi)For the accuracy assessment of the classified images, we used available very high-resolution satellite images from the Google Earth Pro engine that were having acquisition dates very close to the Landsat images;For mapping the built-up changes:(vii)A monthly anomaly of the teleconnection indices of the atmospheric oscillations, i.e., Niño 3.4 Sea Surface Temperature (SST), Pacific Decadal Oscillation (PDO), Arctic oscillation (AO), and Pacific North America (PNA), for North America, obtained from NOAA’s National Centers for Environmental Information (NCEI).(viii)Monthly average precipitation data of the available stations from Environment Canada; and(ix)Population census data for 2001, 2006, 2011, and 2016 from Statistics Canada, and the estimated population of 2020 were derived from the Regional Dashboard of the Government of Alberta.For finding the correlation of SUHI with the influencing factors:LST_Day_CMG and QC_day for daytime, and LST_Night_CMG and QC_Night for nighttime). Here, the QC layer was used to check for any cloud and aerosol infested pixels that were interpreted by the LST quality assurance with the aid of bit flags. The pixels with an average LST error higher than or equal to 3 K were removed from the images, which were about 0.005% for the daytime and 0.006% for the nighttime LST images in our study. In addition, the acquired surface reflectance images of Landsat 5 TM and Landsat 8 OLI having atmospherically and geometrically corrected by USGS were preprocessed to UTM 12N with NAD 83. The acquired MODIS monthly LST product was having 17 layers in HDF format with Geographic (Longitude and Latitude) projection. We preprocessed it to the Universal Transverse Mercator projection system at Zone 12 Northern hemisphere (UTM Zone 12N) with North America Datum 1983 (NAD 83). Next, we prepared the images by extracting the layers with LST values and Quality Control (QC) for day and nighttime , −1 K−1) and We performed four steps to estimate the SUHI and its trend. In the first step, we applied terrain corrections to the monthly day and nighttime LST images of both cities to derive the standardized LST images. It was performed using Equation (1) by calculating the atmospheric pressure at each LST pixel with its equivalent elevation point in the DEM to standard atmospheric pressure  at a temperature of 20 °C .(1)θs=TIn the second step, we determined the urban and rural areas of the cities for both corrected and uncorrected LST images. For this, we adopted the administrative boundary shapefiles of the cities during 2001–2020 as the urban area. Next, we performed a buffer through a distance outside of the city boundary to define the rural surrounding with approximately the same size as the urban. We opted for this method to avoid the situation where a smaller rural buffer zone or larger urban areas result in a surface urban cool island (SUCI) . Using turbanT = Mean of urban LST pixels and ruralT = Mean of rural LST pixels.In the third step, we calculated the SUHI of the monthly and annual LST images using Equation (3), both day and nighttime of the cities. First, we estimated the monthly global mean of all the urban and rural LST pixels from 2001 to 2020. Then, we determined SUHI intensity as the difference between the means of the urban and rural areas. We repeated the same processes using the annual images of both cities to determine their SUHI intensity.2) for both cities between the SUHI trends derived from both corrected and uncorrected LST images.Finally, we applied Mann–Kendall test ,57 and SWe performed an indices-based classification on the Landsat images to derive the built-up land cover class in the cities. The class included impervious surfaces such as roads, pavement, parking lots, and residential and industrial buildings. All the remaining land covers were considered as the other class that included vegetation , open water bodies , and agricultural lands (cultivated and uncultivated), open spaces, and non-impervious bare surfaces. To implement this classification method, we calculated three indices that included normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), and normalized difference built up index (NDBI) from the surface reflectance bands using Equations (4)–(6). These derived index images were then stacked together, and the spectral signatures for the built-up areas were generated and used to derive the land cover map. Note that we applied the indices-based classification method in this study because it performed better than the ISODATA clustering and random forest (RF) classification method in the separation of the built-up land cover of an urban area .(4)NDVIFor the accuracy assessment of the classified thematic images, we prepared a set of reference polygons for the built-up class on the Landsat images with the help of Google Earth Pro images. Next, we calculated a confusion matrix by comparing the thematic classes  of the classified images with the corresponding classes in the reference polygons for the overall accuracy and Kappa statistics. Further, we calculated the areas of built-up class in the classified images of 2001, 2006, 2011, 2016, and 2020 to determine the changes in the built-up area over the period 2001–2020. Finally, we assessed the relationships of SUHI  ,61, inclThe calculated annual and monthly day and nighttime SUHI based on the observational LST  for Calgary and Edmonton are shown in The average monthly day and nighttime SUHI of Calgary and Edmonton from 2001 to 2020 are presented in R2 > 0.97) during the day and nighttime for both cities. Such a very strong correlation, therefore, indicated that elevation had very little influence on the SUHI trends in the cities, and therefore, we presented a further analysis based on the SUHI derived from the observational  LST images.The comparison between the terrain corrected and uncorrected monthly and annual day and nighttime SUHI trends is presented in The monthly and annual day and nighttime SUHI trends of Calgary and Edmonton during 2001–2020 are presented in 2) and 70.05% in Edmonton (from 200.42 to 340.82 Km2), from 2001 to 2020.The expansions of the built-up area in Calgary and Edmonton from 2001 to 2020 are presented in The relationships among the built-up area, population, and annual day and nighttime SUHI over the period 2001–2020 at five-year intervals are presented in r), such as, unsatisfactory (r ≤ 0.4), acceptable (r = 0.40–0.60), satisfactory (r = 0.60–0.70), good (r = 0.70–0.85), and very good (r = 0.85–1.00) [r = 0.67) and very good (r = 0.82) positive correlations of the annual daytime SUHI with the population of Calgary and Edmonton, respectively. For the annual nighttime SUHI, we found acceptable (r = 0.41) and satisfactory (r = 0.61) positive correlation with the population of Calgary and Edmonton, respectively. We also noticed that precipitation has negative but unsatisfactory relationships with the annual daytime SUHI of Calgary (r = −0.04) and Edmonton (r = −0.15). In contrast, the annual nighttime SUHI showed positive and negative unsatisfactory relationships with the precipitation at Calgary (r = 0.06) and Edmonton (r = −0.12) respectively. The atmospheric oscillations of SST and PDO indices showed a negative and unsatisfactory relationship with the annual daytime SUHI for the two cities except for an acceptable relationship with SST for Edmonton (r = −0.47). In contrast, SST and PDO showed positive but unsatisfactory relationships with the annual nighttime SUHI for the cities, except SST for Edmonton (r = 0.46) with an acceptable relationship. In the case of the AO index, we found positive but unsatisfactory relationships with both annual day and nighttime SUHI for the cities apart from a negative relationship (r = −0.14) with the annual nighttime SUHI of Calgary. Furthermore, all the PNA correlation coefficients showed positive but unsatisfactory relationships with both annual day and nighttime SUHI for the cities except for a negative relationship (r = −0.12) with the daytime at Calgary. The correlation analysis of annual day and nighttime SUHI with the potential influencing factors  at Calgary and Edmonton are shown in 85–1.00) . For insThe strong relationship between the terrain corrected and uncorrected SUHI trends see  we obserIn the case of the SUHI trends, the observed continuous and significant increase in the annual daytime warming in both cities could probably be due to the continual urban expansion and anthropogenic forcing that resulted from the loss of agricultural land, vegetation, and green space within the urban corridor  during tThe continuous expansion of the built-up area in the two cities over the last 20 years has been due to an increase in population. It has changed the socioeconomic factors, such as income and occupation that caused the increase in impervious infrastructures such as car parking areas, roads, buildings, etc. These infrastructures were continuously expanded to compensate for the growing population that has encouraged the changes in thermal properties , high heIn the relationships of SUHI with potential influencing factors, the population showed a strong positive impact on the day and nighttime SUHI of the two cities which might be related to the large built-up size and its composition and configuration. Other studies have also shown similar results that the larger size of urban areas and the compact nature of housing and impervious surfaces were positively related to SUHI ,27,72. WIn this study, we used MODIS monthly LST to determine SUHI and its trend in the city of Calgary and the city of Edmonton during 2001–2020. We also determined the relationships of SUHI with seven different influencing factors, including the built-up and population in the two cities. We prepared a set of terrain corrected LST images by removing the effect of elevation. Then we determined SUHI as the difference between the mean LST of the urban and rural areas for both uncorrected and corrected sets of images. Next, we estimated the SUHI trends for both datasets and compared their trends. These two datasets showed highly correlated trends, and therefore, we used the observational SUHI trends, i.e., calculated from the uncorrected datasets, in finding the relationships between SUHI and influencing factors such as build-up, population, precipitation, SST, PNA, PDO, and AO. Our analysis revealed that a continuous increase in annual day and nighttime SUHI existed for both cities, where Calgary had the highest magnitude during the nighttime with a significant daytime warming in July. Moreover, SUHI of the two cities was greatly impacted by the increasing population, built-up expansion, and SST over the last 20 years. To combat the increasing SUHI trends and minimize the local warming due to ongoing climate change, more innovative designs in the composition and configuration of urban areas are required for further built-up expansion of the cities. This study would help to comprehend the pattern and drivers of SUHI related to local warming so that the adaptation and mitigation strategies could be developed to achieve sustainable cities and urban environments.


Alongside the use of fertilizer and chemical control of weeds, pests, and diseases modern breeding has been very successful in generating cultivars that have increased agricultural production several fold in favorable environments. These typically homogeneous cultivars (either homozygous inbreds or hybrids derived from inbred parents) are bred under optimal field conditions and perform well when there is sufficient water and nutrients. However, such optimal conditions are rare globally; indeed, a large proportion of arable land could be considered marginal for agricultural production. Marginal agricultural land typically has poor fertility and/or shallow soil depth, is subject to soil erosion, and often occurs in semi-arid or saline environments. Moreover, these marginal environments are expected to expand with ongoing climate change and progressive degradation of soil and water resources globally. Crop wild relatives (CWRs), most often used in breeding as sources of biotic resistance, often also possess traits adapting them to marginal environments. Wild progenitors have been selected over the course of their evolutionary history to maintain their fitness under a diverse range of stresses. Conversely, modern breeding for broad adaptation has reduced genetic diversity and increased genetic vulnerability to biotic and abiotic challenges. There is potential to exploit genetic heterogeneity, as opposed to genetic uniformity, in breeding for the utilization of marginal lands. This review discusses the adaptive traits that could improve the performance of cultivars in marginal environments and breeding strategies to deploy them. When coupled with the use of irrigation, fertilizers and pesticides, modern breeding has increased agricultural production several fold in favorable environments . HoweverThroughout evolutionary history, plants have had to adapt to a range of environmental conditions including adverse habitats. As a consequence, plant species including crop wild relatives typically exhibit a high level of genetic and phenotypic variation within their distribution range . Understde novo domestication of new crops  and subsequent fixation of domestication syndrome and post-domestication traits including cultural selection for local preference . MoreoveWild progenitors are subject to natural selection in response to environmental stressors, herbivory, pathogen attack and inter-plant competition from the same or other species occupying similar ecological niches in a variable climate. While viable seed production is the ultimate measure of ‘fitness,’ reproductive investment in CWR is usually lower than in their domesticated counterparts . This imConversely, crop plants are subject to a combination of natural and artificial selection giving rise to the domestication syndrome  and subsequent selection for local adaptation and food preferences . CompareL. angustifolius) and yellow lupin (L. luteus) represent the opposite extreme. Here the wild progenitors range widely around the Mediterranean basin (particularly L. angustifolius), being domesticated in the 18th century as spring-sown crops for limited sandy acid soil regions in central and eastern Europe  and conservative water use , and very short-term tolerance through osmotic adjustment . Given tchickpea . At thisjustment . Surpristifolius , ConversSelection attributes according to the crop objective are related to classical domestication traits, such as reduced seed and fruit dispersal, changes in plant structure, plant phenology, seed dormancy, palatability, or acquisition of modified fruit size and shape in vegetable crops . Much prCultivars with tolerance to drought, heat, salinity, soils with extreme pH  and low fertility, as well as various biotic factors are known to be required in marginal environments . Irrespe–1). Tall crops could favor competition with weeds, and the late-season genotype could prolong the duration of root growth, allowing the utilization of water and nutrients deeper in the soil profile (Vicia villosa)  . The adavillosa) . The lesThinopyrum intermedium)  . These prmedium) , is simirmedium) . For forrmedium) . The conrmedium) . While iDifferent drought scenarios  and heat-stress are the most common and important stress factors in marginal lands . This coThe phenotypic traits between drought postponement and tolerance may be overlapping in the same directions (co-gradient variation). Plant adjustments under stress conditions (plasticity) as opposed to tolerance as a constitutive trait of the species independent of water and/or heat stress are considered. Both the genetic differentiation in phenotypic traits and the plasticity of those traits are complementary mechanisms contributing to plant adaptation to environmental resource fluctuation . UniformRhizobium, or the Rhizobium-legume symbioses, decreasing the N-fixing activity  is important in legumes and should be considered . The peractivity . Marginaactivity . Screeniactivity .a priori, and the global plant functional trait databases may be used as a screening tool  represent useful germplasm , and theing tool .Clarkia pulchella, after the eighth generation of farm cultivation, the relative fitness of the wild plants was significantly greater than the farmed plants, especially under drought stress. Genetic diversity lost over generations may be mitigated by growing out a large number of unrelated individuals  adapted to the target marginal environments, with acceptable forage yield and nutrition. Secondly, to reach a successful commercial scale, it is important to have seed available for farmers . To imprdormancy . The demdormancy , 2019. Sdormancy . In a scdormancy . Thus, edormancy . NeverthCrop wild relatives are an untapped source of genetic diversity and a critical resource to meet food security needs and the challenges of new production systems, especially in response to climate change . However“choose first” or “cross first”  is used as donor and one elite cultivar, with good agronomic performance but lacking the trait of interest, is used as recurrent parent . CrossinorldClim , EnviremorldClim , and SoiorldClim , along w soybean , wheat  is an efficient approach that was pioneered in animal breeding, where complex, polygenic traits are the norm . Genomica priori knowledge  to identify the potential most useful accessions  while the “cross first” is more suitable for polygenic traits, which is expected for most traits associated to adaptation to marginal agricultural lands. Multiple parent populations, especially MAGIC, are well-suited for the cross first strategy. Such populations are useful to discover agronomically relevant loci, to improve genetic diversity of pre-bred populations, and to directly incorporate them to breeding schemes. However, we identified a lack of multiparent populations (MPPs) that include CWR as founders, meaning that current MPPs are exploiting only a small fraction of species’ genetic diversity. Therefore, we call for a systematic development of MAGIC populations, which should include CWR, elite cultivars, and landraces as founders. The outlined advantages of MAGIC populations for both basic science and plant breeding should facilitate the creation of public-private pre-breeding partnership programs aiming to develop cultivars well-adapted to marginal agricultural lands. Although universal MAGIC populations can be used to discover agronomically relevant loci by multiple research groups, we encourage (when possible) the development of MAGIC populations with local founders to exploit local adaptation.cis-genesis , transcription activator-like effector nucleases (TALENs), and clustered regularly interspaced short palindrome repeats associated protein 9 (Cas9), CRISPR/Cas systems . ZFNs anhey live . In addihey live . This ishey live . HoweverGenome editing (GE) can be used for several purposes like to activate or suspend the function of any gene. Helianthus, native to North America, comprises 52 species including cultivated sunflower, H. annuus L. which has emerged as a model for genetic studies of adaptation, hybridization, and speciation. In particular, H. annuus and H. petiolaris are chromosomally divergent species, differing by a minimum of seven translocations and three inversions  frameworks should address. Negotiations around ABS in treaties have increasingly focused on Digital Sequence Information (DSI) associated with CWR . Digital1  repository remains an essential database and data repository for discovery of species locations that includes most herbaria globally1 . Efforts1 (2  and one 1 (2 (3 . A numbeAs noted by ex situ collections are critical as access points. But ex situ conservation alone is unable to fully conserve crop diversity, particularly for CWR that have historically been smaller parts of germplasm collections and harbor orders of magnitude more genetic diversity than domesticated accessions (In situ conservation efforts for CWR have been growing over the last decade (ex situ collections. In situ genetic reserve conservation may be defined as “the location, designation, management and monitoring of genetic diversity in natural wild populations within defined areas designated for active, long-term conservation” (in situ conservation have lagged, particularly in Vavilovian centers of origin where CWR taxa are most diverse and abundant. Protecting CWRs in their natural habitats is an essential strategy to prevent extinctions, especially where ex situ conservation of sufficient genetic diversity is practically or economically unfeasible (in situ conservation, because it has the advantage of maintaining the dynamic evolution of the CWR diversity itself in relation to parallel biotic and abiotic changes. In the span of only three decades, for example, there was a shift in allelic diversity related to flowering time genes in wild barley (ex situ collections and in situ conservation areas fail to protect the full range of diversity in CWR (For those harnessing CWRs, cessions . In situt decade  and can rvation” . Some offeasible . There id barley  likely ad barley  has beeny in CWR .JR, JBr, CC, JBe, EW, MN, SU, FH, and PS contributed equally to writing and final editing. JR conceived the original idea. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Polioptila californica, is a small songbird found in desert and coastal scrub habitats from the southern end of Baja California Sur to Ventura County, California. Lack of congruence among morphological subspecies hypotheses and lack of measurable genetic structure found in a few genetic markers led to questions about the validity of subspecies within P. californica and the listing status of the coastal California gnatcatcher, P. c. californica. As a U.S. federally threatened subspecies, P. c. californica is recognized as a flagship for coastal sage scrub conservation throughout southern California. We used restriction site‐associated DNA sequencing to develop a genomic dataset for the California gnatcatcher. We sampled throughout the species' range, examined genetic structure, gene–environment associations, and demographic history, and tested for concordance between genetic structure and morphological subspecies groups. Our data support two distinct genetic groups with evidence of restricted movement and gene flow near the U.S.‐ Mexico international border. We found that climate‐associated outlier loci were more strongly differentiated than climate neutral loci, suggesting that local climate adaptation may have helped to drive differentiation after Holocene range expansions. Patterns of habitat loss and fragmentation are also concordant with genetic substructure throughout the southern California portion of the range. Finally, our genetic data supported the morphologically defined P. c. californica as a distinct group, but there was little evidence of genetic differentiation among other previously hypothesized subspecies in Baja California. Our data suggest that retaining and restoring connectivity, and protecting populations, particularly at the northern range edge, could help preserve existing adaptive potential to allow for future range expansion and long‐term persistence of the California gnatcatcher.Understanding genetic structure and diversity within species can uncover associations with environmental and geographic attributes that highlight adaptive potential and inform conservation and management. The California gnatcatcher,  Genetic data can help to determine appropriate evolutionary and geographic boundaries for management , and systematic sampling of the species' range. We applied clustering and landscape genetic methods to describe population structure and habitat and climate drivers of genetic differentiation. To assess putative adaptive responses to climate, we tested for genetic‐environment associations using climatic variables and examined the spatial structure in a subset of climate‐associated outlier loci. We explored the demographic history inferred from the genomic dataset, and estimated contemporary effective population size and genetic diversity across the range. Finally, we formally tested for concordance between genetic structure and the two most comprehensive morphological subspecies hypotheses of Mellink and Rea  and Atwo22.1Our study area included the full species' range. Samples north of the international border  were collected between Spring 2012 and Fall 2013. Samples south of the international border were collected in Fall 2018 and Spring 2019, with locations chosen to equally represent the full species range throughout the Baja Peninsula . We used established techniques to capture adults in mist nets strategically placed within territories, using song playbacks to attract birds to nets. Pin feathers were collected and placed in 500 μl of either Qiagen ATL Buffer  or 1X DNA/RNA Shield Buffer (in Baja) and stored on ice until samples could be transferred to a −20° freezer. All birds were handled in accordance with protocols approved by the USGS Western Ecological Research Center Animal Care and Use Committee and permitted under U.S. Master Banding Permit 22372, U.S. Federal Permit 829554, and a Memorandum of Understanding with the California Department of Fish and Wildlife held by B. E. Kus, and Mexico collection permits of R. Rodriguez‐Estrella (Centro de Investigaciones Biológicas del Noroeste S.C.).g and 4°C. For samples with low yields, the same protocol was performed on the feather storage buffer. Feathers collected in Baja were extracted with the DNA Tissue Extraction Kit (Qiagen) according to manufacturer's protocol, with an extended 48 h digestion step in the presence of DTT. For samples with low yields, additional DNA was extracted from the storage buffer, using a standard salt precipitation method  according to manufacturer's protocol with minor modifications, including cell lysis in the presence of Proteinase K and dithiothreitol (DTT), an overnight DNA precipitation step, and 5–30 min centrifugation steps at 21,194 2.2EcoRI and MseI (New England Biolabs) and used Solid Phase Reversible Immobilization (SPRI) beads (Beckman Coulter) to purify the digestions prior to ligating uniquely bar‐coded adapters with T4 ligase (New England Biolabs). We quantified all ligation products on the Qubit fluorometer, pooled across index groups in equimolar concentrations, and then size selected fragments between 250 and 400 base pairs (bp) using a Pippin Prep size fractionator (Sage Science). We amplified the recovered fragments from each pool using 5–10 ng of the recovered DNA, Phusion High‐Fidelity Taq (New England Biolabs), and Illumina's primers . Polymerase chain reaction (PCR) products were then cleaned with SPRI beads and quantified using the Qubit fluorometer (Life Technologies) before being pooled for sequencing (100 bp single end reads) on two lanes of an Illumina HiSeq 4000 at the Genomics and Cell Characterization Core Facility at the University of Oregon. Samples from different sampling locations and regions were mixed across libraries and sequencing lanes to avoid confounding batch effects.In total 230 samples were prepared for sequencing at the Western Ecological Research Center, San Diego Field Station laboratory. We quantified DNA on a Qubit fluorometer (Life Technologies™), and 125–500 nanograms (ng) of DNA were used for next‐generation sequencing (NGS) library preparation. We followed the double‐digest restriction‐associated DNA (ddRAD) sequencing protocol developed in Peterson et al.  for NGS 2.3process_radtag program within STACKS. Cleaned raw reads were aligned to the reference genome of the closest available relative, Polioptila caerulea,  and were then exported and sorted as .bam files using samtools  < 0.05. The latter dataset was used for range‐wide genomic structure and distance analyses, while the full dataset was used for demographic and gene–environment associations. Including low frequency alleles can increase the number of non‐synonymous mutations in the dataset and detection of true positive loci under selection  describing overall genetic structure and assessing relationships among genetic, habitat and climate distances; (2) testing for climate adaptation and identifying outlier loci; (3) exploring demographic history of the species and contemporary patterns of genetic diversity and effective population size; (4) testing for genetic distinctiveness in relation to two previous morphological subspecies hypotheses Atwood, .2.4.1RADpainter module of fineRADstructure to create the input coancestry matrix, which is defined as a summary of nearest neighbor haplotype relationships. We used the finestructure module to assign individuals to populations using the default parameters of 100,000 Markov chain Monte Carlo (MCMC) iterations with a burn‐in of 100,000 iterations and sampling occurring every 1000 iterations. Finally, a tree was constructed with 10,000 hill‐climbing iterations, and a cladogram and heatmap were used to visualize the results using the provided R scripts (available at http://cichlid.gurdon.cam.ac.uk/fineRADstructure.html).We used two clustering methods to assess structure. First, we applied the maximum‐likelihood approach of ADMIXTURE . We used maximum temperature of the warmest month , minimum temperature of the coldest month , precipitation seasonality , precipitation of wettest quarter , and precipitation of driest quarter , where the number of dimensions used in models refers to the species or distinct group, and a folded JSFS because we lacked out‐group information. Using the demographic modeling workflow from Portik et al.  that was estimated for another small passerine  using the linkage disequilibrium method  of 0.05. We applied a standard correction for physical linkage based on the number of chromosomes as suggested by Waples et al.  and P. c. margaritae south of 28°N, but did not comment on any breaks farther south. Following Zink et al.  differences were followed with post‐hoc tests between group pairs. McCormack and Maley , using either hypothesis 1 or hypothesis 2 as a priori groupings. To minimize overfitting, an initial DAPC was used to find the a‐score, which was used to select the number of principal components to retain in the subsequent re‐analysis  were aligned to the reference genome with the remainder discarded due to insufficient mapping quality or excessive soft‐clipping. The average number of aligned reads per sample was 3.3 million. From these reads, a total of 724,435 loci were built and effective per‐sample coverage ranged from 5.0x to 57.2x, mean 18.1x . After quality filtering and dropping samples with read coverage lower than 8x and missing data greater than 30 percent, the final genomic dataset included 184 individuals from 48 aggregations across the California gnatcatcher range, with 84 individuals from the US and 100 from Baja California retained Figure . The ful.4% were 3.2Admixture analysis marginally supported two clusters over a single cluster . When analyzed separately, different patterns became apparent in the California and Baja groups. In the California group, Euclidean geographic distance and human modified habitat cost distance were both positively correlated with genetic distance, as were TMax, TMin, PrecipS and PrecipWQ . In contrast, there was little detectable pattern in the Baja subgroup. PrecipS was the only variable associated with genetic distance in the Baja subgroup in the Mantel Test and MRM . All climate variables had inflation factors <10 and were not highly cross correlated (r ≤ 0.7) and were retained. There were 4 significant RDA axes (p ≤ 0.001).The full RDA model [genotypes ~ TMax + TMin + PrecipS + PrecipWQ +PrecipDQ] explained 1.2% of the total observed genetic variation  SNPs that loaded ±3 SD from the mean loading on one or more of the four significant RDA axes. Based on the strongest correlation between each outlier locus and climate variable, 1961 of the outlier SNPs were most strongly associated with PrecipWQ, 1466 with PrecipDQ, 1459 with TMax, 1046 with PrecipS, and 407 with TMin. Patterns across PC axes 1 and 2 in the climate outlier loci and the remaining climate neutral loci were relatively similar, with most Baja birds tightly clustered and southern California birds extending across PC 1 Figure  than in 3.4Tajima's D was estimated to be −1.59, consistent with a population expansion or a strong selective sweep (Table We found highest support for demographic models that involve a history of population expansion across the range of the gnatcatcher. The optimum model (highest likelihood score) was a three‐epoch model, which models multiple instantaneous size changes Table  that occep Table . We alsoComparisons of most genetic diversity indices among sites, subspecies and groups were similar across the entire range, with the exception of sites in northern Baja  and at the southern tip of Baja  where diversity indices were much higher Figure . In addi3.5P. c. californica was strongly discriminated on DA1  assignment to any one of the southern groups, and the majority were substantially admixed  and ΦSC (among populations within subspecies) estimates were obtained with the set of climate‐associated loci than with all loci. A slightly larger proportion of variation was partitioned among subspecies using hypothesis 1 groupings  than hypothesis 2 groupings . Only one other subspecies, P. c. atwoodi, was significantly different from geographically neighboring subspecies, and only in the climate‐associated loci. For hypothesis 2, pairwise tests were significant between P. c. californica and both P. c. margaritae and P. c. abbreviata in the climate loci, but only P. c. margaritae in the full set of loci. (Table Hierarchical AMOVA tests suggested that a small but statistically significant amount of genetic variation was partitioned among subspecies for both hypothesis 1 and hypothesis 2 Table . Generali. Table .4P. c. californica and all other subspecies in the AMOVA tests. A small number of recent migrants, and higher genetic diversity in northern Baja are consistent with recent dispersal and admixture between these groups in San Diego County and northern Baja California. Patterns of differentiation were stronger in climate outlier loci than the remainder of climate neutral loci which could be indicative of adaptive differences in the two groups.Our data support two genetic groups within the California gnatcatcher, a southern California group and a Baja group with admixture, suggesting either a recent evolutionary origin or a stable tension between zones of adaptive differentiation countered by occasional gene flow (Barton & Hewitt, 4.1Patterns of genetic differentiation were different in the two regional genetic groups. In Baja, high admixture across large geographic distances was apparent, and only seasonality in precipitation was associated with genetic differentiation. In southern California, differentiation was strongly associated with both habitat fragmentation and with clines in climate, particularly in precipitation and minimum winter temperature. Previously, high genetic connectivity and evidence of occasional long‐distance dispersal throughout much of southern California were found with microsatellites (Vandergast et al., Gnatcatchers in southern California are associated with generally lower temperatures and higher precipitation than in Baja. In addition, some of the northern‐most populations contain unique genetic variation associated with drier and/or warmer summers and more seasonal precipitation. These differences may be of adaptive significance and warrant further investigation. Range limitations in California gnatcatchers are associated with some combination of low winter temperatures and winter rainfall, suggesting physiological limitations in cold tolerance in this species Mock, . Differe4.2Previously published genetic and species distribution analyses were suggestive of a northward range expansion in gnatcatchers (Zink et al., Contemporary effective population sizes, based on linkage disequilibrium, are much lower than historical estimates derived from the JSFS, with 95% confidence intervals ranging from the low hundreds to low thousands. The large difference in size estimates obtained with these two methods could reflect the evolutionary time scale of the model estimates versus the very recent time scale of linkage disequilibrium, which dissipates over a few generations Waples, . Previou4.3P. c. californica and all other birds sampled in Baja but did not strongly support other hypothesized subspecies breaks in Baja. Applying a definition of a subspecies as a breeding population or collection of populations that occupies a distinct segment of the geographic range of the species and that is measurably distinct in phenotype, genotype or a combination of these traits (e.g., Haig & D'Elia, P. c. californica as a distinct subspecies. We found stronger support for a genetic boundary or contact zone between californica and its neighboring subspecies near 32°N as hypothesized by Mellink and Rea (P. c. californica at 32°N, while only the two pairwise AMOVA tests were significant at 30°N. The lack of specificity in the AMOVA may be due to the smaller number of individuals sampled in the region between 30°–32°N compared to north of the border, while the two more spatially explicit approaches resolve this boundary with greater precision. If the few mis‐assigned birds in San Diego County and northern Baja are recent migrants, this could suggest a relatively broad contact zone with at least some recent movement and gene flow.Our four analyses found genomic differences between  and Rea , rather  and Rea . All fouConversely, our data indicate high genetic similarity and connectivity across the remainder of the Baja Peninsula, despite the large geographic area and range of climatic conditions encompassed therein. However, as previously noted, genetic patterns are not always congruent with morphological traits. In cases where morphological adaptation has occurred recently or in the presence of gene flow, the majority of the genome is unlikely to show high differentiation (Fitzpatrick, 4.4Based on the shallow divergences concordant with areas of highest habitat loss, patterns of evolutionarily recent range expansions, and indications here and in previous studies that gnatcatchers are able to disperse long distances (Bailey & Mock, The authors declare that there is no conflict of interest.Tables S1‐S5 and Figures S1‐S5Click here for additional data file.


Advances in breeding practices during the ‘Green Revolution’ in the 1960s have helped to maintain crop yields in the face of population growth. However, finding opportunities for further improvements is increasingly challenging, particularly due to the negative impacts of climate change . Solutions to this challenge must be found in new technologies, including new genetic engineering strategies, high-throughput phenotyping approaches, and greater exploitation of the available genetic variation within crop germplasms. Traditional breeding practices have yet to be exploited efficiently for the production of significant improvements in crop photosynthetic efficiencies. Achieving this in concert with new technologies is now a major goal.Genetic variation between or within species provides a record of selection pressure over evolutionary time scales. Climatic conditions form an important component of such environmental selection pressures. Although photosynthetic traits can acclimate to prevailing climatic conditions during growth and development, innate ‘hard-coded’ differences based on genomic sequence diversity can greatly aid species adaptation to a particular environment. Thus, characterizing genetic diversity in photosynthetic traits can help to enhance our understanding of factors involved in adaptation, as well as providing a wealth of resource diversity that can be explored for industrial or agronomic applications.Cyanobacteria are an evolutionary ancient group of highly diverse and environmentally adaptable microorganisms, also of notable fame for their engulfed group member that formed the evolutionary ancestor of the eukaryotic chloroplasts , based on a comparison of 26 winter wheat cultivars spanning 60 years of wheat breeding in China.Genetic variation is the key ingredient for improving crop performance via selective breeding. In some crops, comparisons between historical cultivars sorted by release date have provided evidence of selection on photosynthesis traits as an inadvertent side effect of plant breeding efforts. For example, Vitis vinifera (grapevine) genotypes, which contributed significantly to differentiation in heat tolerance and water use efficiency. Sorghum bicolor, identifying several genomic regions associated with variation in gas exchange and chlorophyll fluorescence traits, which might be used to further enhance already substantial drought tolerance of this C4 food, feed, and bioenergy crop. Whereas genetic variation in photosynthetic traits is significant in many major crop species (reviewed by Oryza glaberrima (African rice) by O. sativa indica and japonica. Having been domesticated independently from O. sativa, African rice germplasm harbours many traits that help to sustain plant growth under challenging conditions. Indeed, Cowling and colleagues show that under common growth conditions, extensive variation in many steady-state and dynamic photosynthetic traits across a reference panel of 155 accessions clustered in line with the prevailing climate conditions of their collection sites, in particular water availability.Although the germplasm of most crops already appears to show significant variation for photosynthetic traits, genetic diversity within a crop’s germplasm is expected to be significantly less than that of its wild progenitors. The negative effect of domestication on genetic diversity is well known, although the often-quoted ‘domestication bottleneck’ typically blamed for this phenomenon has recently become the subject of renewed scrutiny (et al. recommend a ‘genome to phenome’ approach, using accession-specific sequence information to find haplotypes for key candidate genes in photosynthesis. Models to integrate physiological and accession-specific genetic information are a key ingredient of this approach. Linking of model parameters directly to underlying genetic variation can be very powerful to assess the potential impact of allelic variants across a range of genetic loci on crop performance across the TPE. A striking example of such an approach is provided by seminal work from 3 crop species. Using their GECROS model, they go on to show the relative importance of genetic variation in model parameters describing photosynthetic electron transport, relative to parameters controlling the light-saturated rate of CO2 assimilation, such as maximum Rubisco activity. Their simulations also predict that breeding programmes incorporating selection for traits to improve electron transport, nitrogen uptake, and sink capacity would provide the highest impact on yield.As emphasized by Overall, the new strategies and technologies highlighted in the Special Issue are poised to provide a step change in our capacity to breed new crop varieties with improved photosynthetic traits and greater resilience. Such new tools should provide timely support to an agricultural industry under huge pressure to meet future food security demands.