Premise: The Mediterranean region is experiencing increasing aridity, affecting ecosystems and plant life. Plants exhibit various anatomical changes to cope with dry conditions, including anatomical changes. This study focused on five co-occurring Mediterranean plant species namely Quercus calliprinos, Pistacia palaestina, Pistacia lentiscus, Rhamnus lycioides, and Phillyrea latifolia in wet and dry sites, investigating anatomical differences in leaves and xylem. Methods: Leaf analysis involved stomatal density, stomatal length, Leaf Mass Area (LMA), lamina composition, quantification of leaf intercellular air spaces (IAS), and mesophyll cell area exposed to these spaces. Xylem anatomy was assessed through vessel length and area in branches. Results: In the dry site, three species showed increased stomatal density and decreased stomatal length. Four species exhibited increased palisade mesophyll (PM) and reduced air space volume. In contrast, the phenotypic change in the xylem was less pronounced, with vessel length remaining unaffected by the site conditions. Furthermore, vessel diameter decreased in two species. Intercellular air spaces (IAS) proved to be the most dynamic anatomical feature. Quercus calliprinos demonstrated the highest anatomical phenotypic changes, while Rhamnus lycioides exhibited minor changes. Conclusions: This study sheds light on the variation in anatomical responses among co-occurring Mediterranean plant species and identifies the most dynamic traits. Understanding these adaptations provides valuable insights into the ability of plants to thrive under changing climate conditions. Methods Histological preparations The samples were collected in June 2021, at the beginning of the dry season. For stem anatomy, one cm long segments of 0.5 to 1 cm in diameter were taken from the terminal branches of new growth. The same branches were used for leaf anatomy, where a rectangle of 1 x 2 cm was cut along the lamina while avoiding the midrib. All samples were fixed immediately after cutting in a formaldehyde-acetic acid–alcohol solution (FAA, 10:5:50 in double-distilled water) for 48 h. Following gradual dehydration in an ethanol series (70, 80, 90, 95, and 100%, for 30 min each), the samples were subjected to a gradual Histoclear solution (25, 50, 75, and 100%). The samples were incubated overnight at room temperature with Paraplast chips (Leica, Wetzlar, Germany, Paraplast Plus) followed by several hours of incubation at 42 °C. The dissolved pure paraffin was changed twice a day for four days at 62 °C before the samples were embedded in blocks. Following embedding, stem samples were immersed in water for a few days and then sectioned using a microtome (Leica RM2245, Leica Microsystems Ltd. , Nussloch, Germany) into 12 μm sections which were mounted on slides, incubated overnight at 40 °C, and stained with Fast Green and Safranin (Ruzin and others, 1999). Images were captured using a light microscope (Leica DMLB, Leica Microsystems Ltd. , Nussloch, Germany) with a Nikon DS-fi1 camera (Nikon Corporation, Japan). Image analysis was done using ImageJ software (Rasband, W.S., ImageJ, US National Institutes of Health, Bethesda, MD, USA, http:// imagej.nih.gov/ij/, 1997–2015). Leaf anatomy analyses The leaf parameters (Table 3) were measured in eight samples from each of the five studied species at each site. Leaf mass area (LMA) was computed by dividing the leaf dry mass (g) by the leaf area (cm²). Leaf area was determined through the analysis of RGB-scaled photos using ImageJ software. Subsequently, the leaves were dried at 70°C for 5 days, followed by measurement of the dry weight. Stomatal density was measured from adaxial and abaxial epidermal imprints, which were made using a dental impression gel (CounterFit II, Clinician's Choice), followed by an impression of clear nail polish, which was removed using adhesive tape and mounted on a microscope slide. Stomata were counted on an area of 0.0837 mm2 which represented the whole image size at the corresponding magnification (x40). Lamina anatomy was analyzed from leaf cross sectional images using the ImageJ software to obtain thickness values in microns for the different leaf organs: adaxial (Ad) and abaxial (Ab) epidermis layers, palisade mesophyll (Pal) and spongy mesophyll (SM) as well as total leaf thickness (T). Cuticle thickness assessment was available only on the adaxial side (Ac) as the abaxial cuticle was indistinct. All parameters were measured at three different locations on a cross section. The Midrib vessel area was assessed by measuring the ten largest vessels using the ImageJ software. Intercellular airspaces were evaluated from the mesophyll surface area exposed to intercellular airspace per unit leaf area , which was calculated according to (Evans et al., 1994):: Where is the total length of mesophyll cells facing the intercellular air space, is the section width and F is the curvature correction factor, which depends on the shape of the cells and was calculated as the weight average of the palisade and spongy mesophyll according to (Thain, 1983). The fraction of the intercellular air space (%IAS) was calculated as Where ΣSs is the sum of the cross-sectional areas of the mesophyll cells and is the thickness of the mesophyll between the two epidermal layers. Stem anatomy analyses The stem parameters (Table 3) were measured in eight samples from each of the five studied species at each site. Vessel length distribution was measured by the "air injection method" (Cohen et al., 2003), with some modifications according to Wang et al. (2014). Briefly, fresh long shoots were cut using a sharp razor blade. The basal end of the stem segment was attached to a flexible silicone tube (clamped to it) and connected to an air compressor which injected air into an old dial manometer and a digital pressure sensor (MPX5100 IC, NXP Semiconductors, Netherlands) wired to a datalogger (Campbell Sci. CR1000 datalogger, Campbell Scientific, Inc., Utah, United States), along with a "bleed" valve. Pressure was adjusted to 0.08-0.15 MPa and logged during the measurements. The distal end of each shoot was immersed in water. Stem segments (2 cm long) were cut back until bubbling was observed, and the length of the remaining stem was taken as the maximum vessel length (in some cases, bubbles appeared immediately before cutting, in which case the maximum vessel length was longer). Then, the stem was cut back consistently to measure air flow rate at several lengths. For each stem length, the bubbles flowing out from the distal end were collected in a volumetric cylinder by the water displacement method according to (Wang et al., 2014). The airflow rate [Q (mL/min)] was computed as follows: Q =(Wi − Wf)/(ΔTρ) Where Wi and Wf are the initial and final weights of the volumetric cylinder respectively, ΔT is the time interval for the water displacement by the bubbles and ρ is the density of water displaced by the air. Air conductivity (C) was calculated according to equation [4] at (Cohen et al., 2003) as follows: Where L is the length of the wood segment (m), P is the distal pressure (kPa) at which the flow rate Q was measured at the distal end is the average pressure in the segment and ΔP is the pressure difference across the segment. According to Cohen et al. (2003) C should decrease exponentially as:
Where is the limiting conductivity as x approaches zero, k is the extinction coefficient and x is the stem length. The plot of the natural log of C versus x resulted in a linear plot, from which k was evaluated from the slope. The most common of mode vessel length (Lmode) was −1/k. The mean vessel length was calculated from Lmean = 2Lmode. The probability density function (PDF) of vessel length was calculated as described in (Cohen et al., 2003) and (Sperry et al., 2005) was: Where is the probability of vessels of length x and k (negative value) is the slope of the linear plot. The vessel area/diameter was evaluated from the most two outer rings of the stem cross sections (described above), which were marked and measured manually by "tracking tool" by Image J software. The vessel diameter (D) was calculated from the vessel area as follows: Statistical analyses The individual data for each anatomical trait are presented as boxplots. To test the effect of site, species, and their interaction on the anatomical traits, a two-way ANOVA was conducted using Python software (Python Software Foundation, Wilmington, Delaware, United States; package: statsmodel).Traits for which variances were non-homogeneous underwent logarithmic transformation before analysis. To compare the two sites for each species, contrast t-tests were performed. To quantify the degree of the difference between the two sites for each species, the effect size was measured using Cohen's d method for each anatomy trait. The formula used for calculating Cohen's d is: Cohen's d = (M1 - M2) / pooled standard deviation where M1 - M2 is the difference between means, i.e., the absolute value of the difference between the mean values of the wet and arid sites, and the pooled standard deviation was calculated as follows: pooled standard deviation = sqrt[(SD1^2 + SD2^2)/2] where SD1 and SD2 are the standard deviations for the wet and dry sites, respectively.

Wheat leaf rust, also known as wheat stalk rust, stripe rust. Wheat leaf rust mainly occurs in Hebei, Shanxi, Inner Mongolia, Henan, Shandong, Guizhou, Yunnan, Heilongjiang and Jilin. It mainly damages wheat leaves and produces herpes-like lesions, rarely occurring in leaf sheath and stem. Summer spore heap is round to long ellipse, orange-red, smaller than stem rust, larger than stripe rust, and scattered irregularly. Several secondary summer spore heaps occur around the primary summer spore heap, usually on the front of the leaves, and a few can penetrate the leaves. After maturation, the epidermis cracks in a circle, and the orange summer spores are scattered out. The winter spore heap mainly occurs on the back of leaves and leaf sheaths. It is round or oblong, black, flat and scattered, but it does not break up when it is mature. [Control methods] Variety selection: Resistant and resistant varieties such as Shaannong 7859, Ji5418, Lumai 1, Xiaoyan 6 and Xuzhou 21 were planted in Huanghuaihai region. In addition, in recent years, newly bred Winter Wheat Varieties with leaf rust resistance are: Jingdong 1, 8, Jinghe 3 (Jinghe 931), Jing411, Beinongbai, Wanmai 26, 27, 28, Mianyang 26, Bainong 64, Zhoumai 9-Aiyou 688, Xinbaofeng (7228), Yumai 39 (Yunong 8539), Zaomai 5, Jinsong 49, Xingmai 17, Yunmai 19, Qinmai 12, Jimai 48, 40, Ji 92-3235, 6. New lines 021, etc. Spring wheat varieties are Ken 95, Longmai 23, Longfumai 7, Mengmai 30, Jingyin 1, Longchun 8139, Dingfeng 3, etc. [Agricultural control] Strengthen cultivation and disease prevention measures to sow timely, eliminate weeds and self-growing wheat seedlings, timely drainage in rainy season, retention of humidity. [Drug control] Drug seed dressing with 0.03% - 0.04% (active ingredient) leaf rust or 20% Triazolone EC with 0.2% seed weight. The seeds coated with 15% Baofeng No. 1 seed coating agent (active ingredients are pink rust Ning, carbendazim, phoxim) are automatically solidified into film shape, and a protective ring is formed after sowing, with a long lasting effect. Four grams per kg of seeds were used to control wheat leaf rust, powdery mildew and total rot, and also to control underground pests. Spraying 20% Triazolone EC 1000 times at the initial stage of disease can treat stripe rust, stalk rust and powdery mildew, once every 10-20 days, and prevent 1-2 times.

Dataset on functional traits of plants in the Desert area of the Tibetan Plateau: A field survey was carried out on 86 sites in the desert area of the Qinghai-Tibet Plateau from 2019 to 2021. Five dominant plants (from) were selected from each site to measure their crown width and plant height. At the same time, the aboveground parts of plants were sampled from 3 quadrats in each plot and brought back to the laboratory to measure the carbon and nitrogen content in leaves and stems of 529 plants. Canopy width and plant height represent interception of precipitation and light. The content of carbon, nitrogen and phosphorus in plant leaves and stems is closely related to forage quality, leaf life, leaf elongation rate, maximum stomatal conductance, leaf tissue density, lignin content and thickness. The Excel table for submitting data includes two forms. The "Canopy width and plant height" form records the longitude and latitude, altitude, sample size, biomass, canopy width and plant height of each sampling site for scientific investigation. The "Plant CARBON and Nitrogen" sheet provides measurements of carbon, nitrogen and phosphorus in plant species, leaves and stems collected at each expedition sampling site.

Premise Hydraulic segmentation, caused by the difference in embolism resistance across plant organs, provides a sacrificial layer of cheaper plant organs, like leaves, to protect more costly organs, such as stems, during drought. Within-leaf hydraulic segmentation has been observed in two compound-leaved tree species, with leaflets being more vulnerable than the rachis or petiole. Many herbaceous species have compound leaves, and some species have leaflets that are associated with pulvini at the base of the lamina, which could provide an anatomical means of preventing embolism from spreading within a leaf due to the high concentration of vessel-endings in the pulvinus. Methods We assessed embolism resistance across the leaf in three diverse palmately and pinnately compound-leaved herbaceous species, one of each with a pulvinus. We used the optical vulnerability method to simultaneously investigate whether hydraulic segmentation occurred across the leaves of six compound-leaved herbaceou..., Plants were cultivated in the Lilly Greenhouses at Purdue University (West Lafayette, Indiana, USA) in 5-L pots containing commercial potting mix and were maintained under greenhouse conditions for 4 to 7 months, except Eranthis hyemalis, which was gathered from Horticulture Park on the Purdue campus in February 2023. Plants in the greenhouse were watered daily and received weekly applications of liquid fertilizer (Miracle-Gro, Scotts Company LLC, OH, USA). Conditions in the glasshouse were maintained under a natural photoperiod and controlled day:night temperatures of 28:22°C.  Samples of Ailianthus altissima (“Tree of Heaven†), were collected in June of 2024 from Horticulture Park on the Purdue campus (n=3) and immediately bagged and brought back to the lab to rehydrate. Vulnerability curves of the rachis and lamina were conducted using the optical vulnerability method and subsequent image analysis outlined by (Brodribb et al., 2016). Three plants from each species were brought to the..., , # Within-leaf variation in embolism resistance is not a rule in compound-leaved angiosperms

https://doi.org/10.5061/dryad.8pk0p2nxc

Hydraulic segmentation, caused by the difference in embolism resistance across plant organs, provides a sacrificial layer of cheaper plant organs, like leaves, to protect more costly organs, such as stems, during drought. Within-leaf hydraulic segmentation has been observed in two compound-leaved tree species, with leaflets more vulnerable than the rachis or petiole. Many herbaceous species have compound leaves, and some species have leaflets associated with pulvini at the base of the lamina, which could provide an anatomical means of preventing embolism from spreading within a leaf. To determine if hydraulic segmentation is also a rule in herbaceous angiosperms, we assessed embolism resistance across the leaf in three diverse palmately and pinnately compound-leaved herbaceous species (n=3), one of each with a pu...

Functional trait data for 27 temperate grassland speciesTraits were measured on plants grown in a common garden in Tartu, Estonia (58°22´N, 26°41´E) using seeds collected from seven grasslands that differed in species richness and composition, soil fertility, pH, and management history (seed origin coordinates supplied). Individual plants of each focal species were subjected to treatments that manipulated a) neighbour identity (surrounded by either conspecifics or heterospecifics), and b) neighbour density (0, 1, 2, 3, 4, 6 or 8 neighbours). Each neighbour identity by density combination was replicated twice. In the heterospecific treatment, each focal species was grown together with a species that it frequently encountered in the field as its nearest neighbour. Five aboveground traits and total aboveground dry mass of focal and neighbour plants were recorded. The maximum vegetative height of the focal plants was measured as the highest point reached by stem leaves (or rosette leaves in the absence of a leafed stem). Leaf water content was calculated by dividing the difference between fresh and dry mass by the fresh mass of two newly produced but fully expanded leaf blades (four leaves for Carex ornithopoda, Juncus gerardii, Veronica chamaedrys; five leaves for Antennaria dioica; ten leaves for Lotus corniculatus; 25 leaves for Galium verum). Specific leaf area (SLA), was calculated as the ratio of leaf area and leaf dry mass of leaves used for water content measurements. The dry mass of the supportive structures was found by summing the dry mass of stems (including stolons), leaf petioles and leaf sheaths (in the case of graminoids). All above-ground parts of each focal plant and its neighbours were oven-dried at 70C for 48 h and weighed separately as necessary for calculations.Abakumova_etal_NEWPHY2016_morphology_data.txt Plant functional traits can vary widely as a result of phenotypic plasticity to abiotic conditions. Trait variation may also reflect responses to the identity of neighbours, although not all species are equally responsive to their biotic surroundings. We hypothesized that responses to neighbours are shaped by spatial community patterns and resulting variability in neighbour composition. More precisely, we tested the theoretical prediction that plasticity is most likely to evolve if alternative environments (in this case, different neighbour species) are common and encountered at similar frequencies. We estimated the frequencies of encountering different neighbour species in the field for 27 grassland species and measured the aboveground morphological responses of each species to conspecific vs heterospecific neighbours in a common garden. Responses to neighbour identity were dependent on how frequently the experimental neighbours were encountered by the focal species in their home community, with the greatest plasticity observed in species that encountered both neighbours (conspecific and heterospecific) with high and even frequency. Biotic interactions with neighbouring species can impose selection on plasticity in functional traits, which may feed back through trait divergence and niche differentiation to influence species coexistence and community structure.

ICON Introducing non-flooded crops in rice-dominated landscapes: Impact on CarbOn, Nitrogen and water budgets Above ground foliar, stem and panicle injury observation and root nematode observation data collected for the ICON project. The relevant excerpt from the proposal, also included in .doc format, follows. Project 4 (Disease epidemics in rice-based systems affected by changes in water management; IRRI, Savary – no funding requested) will monitor disease progress - in particular sheath blight - in relation to the physical environment of the soil and of the canopy (microclimate), in both the rice and the maize crops (Project 3). The shift from flooded to non-flooded cropping systems directly affects the physical environment and occurrence of natural enemies of the soil-borne pathogens and this, indirectly, affects the physical environment of the canopy, where non soil-borne pathogens may develop (H.1). Rhizoctonia species are soil-borne fungi causing sheath blight in rice, a major disease in rice production, and there is indication that some of the R. solani sub-species can infect maize as well. In this project emphasis will be given to identify the responses of Rhizoctonia as well as other pathogens to (i) crop rotation and (ii) water management regime in order to develop functional relationships between cropping system and crop management and disease progress (H.3). Change in water management is a prerequisite for adaptation of rice-based agroecosystems in a context of climate change. While water-saving technologies, including supply of agricultural water (the largest user of water in tropical Asia), but also tillage and crop establishment is necessary, singignificant, and possibly considerable changes are to be expected with respect to the entire guild of yield-reducing organisms of rice, including pathogens (bacteria, fungi, and viruses), as well as insects (Savary et al., 2005). It is worth noting here that this work is congruent with large scale work IRRI has engaged in South Asia, under the umbrella of the Cereal System Initiative for South Asia. This project, among a series of objectives, aims at improving the performances of environmentally constrained – especially, water constrained – intensive cereal systems that must develop to feed South Asia for the decades to come; and this includes a series of heavily instrumented platforms where work similar to what is described below will be conducted. Over the years, IRRI has developed a set of methodologies – coupled standardized acquisition methods of injuries (IP) due to diseases and insects, as well as weeds; characterization of production situations (PS), including the physiological status of the crop; statistical multivariate, non-parametric methods to link IPs and PSs; and simulation modeling methods to analyze the effects of individual yield reducing organism of the guild within a community. A recent publication summarizes these methods and their applications (Savary et al, 2006). Project 4 of ICON will look at a series of attributes that will be changed with evolving water supply to rice crops: meso-climate (which will be monitored in the overall experiment); micro-climate, and I particular, leaf temperature and leaf wetness duration. We intend to implement the above methodology at successive development stages, including at least: Maximum tillering Booting Early dough where the levels of leaf diseases (esp., bacterial blight, sheath blight, blast, brown spot, narrow brown spot, bacterial leaf streak) tiller diseases (esp. sheath blight, sheath rot, stem rot) panicle diseases (esp. grain discoloration, false smut, bakanae) whole-plant diseases (esp. rice tungro) insect leaf injuries (esp. leaf folders, whorl maggots) insect tiller injuries (esp. stem borers – “dead hearts”) insect panicle injuries (esp. stem borers – “white heads”) sucking insect populations (brown plant hopper, white-back planthopper, and green leaf hopper) will be monitored. Groups a and e – leaf injury; b and f – tiller injury; c and g – panicle injury; d – systemic injury; and h – sucking injury represent the framework of the “sub-guilds” developed in the above approach to characterize yield-reducing yields. These also are the basis of RICEPEST (Willocquet et al., a generic, mechanistic, crop physiology-based simulation model which enables to explore the individual impact of specific yield-reducer, and their combined effects on systems’ performances. RIRCEPEST has been parameterized, tested, and validated in China, India, and the Philippines during several cropping seasons. IRRI’s inputs in Project 4 should thus be seen twofold. Quantification of the effects of varying levels of water management on the entire guild of yield-reducing organisms This component will make use of field data acquisition procedure that have been heavily tested and validated in China, India, Vietnam, and the Philippines, as well as in Laos and Cambodia. The main approach to analyze the...

Recently, significant die-back of nonnative common reed, Phragmites australis, has been reported in the Mississippi River Delta (MRD), Louisiana, USA. This dieback has been attributed to an invasive scale insect, Nipponaclerda biwakoensis. We test whether fungi are involved in the recent infestation by this insect and subsequent die-offs of Phragmites australis. Several haplotypes of P. australis occur in the MRD, and the European (M) and Delta (M1) haplotypes appear to experience differing levels of N. biwakoensis infestation. We tested whether these haplotypes differed in their fungal microbiomes in both their leaf and stem tissues, and whether differences in fungal community composition were linked to the level of infestation using a metabarcoding Internal Transcribed Spacer (ITS) amplicon sequencing approach. Our analyses showed differences in fungal community composition and diversity between haplotypes and tissue types, but none of these differences were directly correlated with N. biwakoensis infestation severity. However, we did find that the European haplotype hosted higher putative pathogen loads in stem tissues compared to the Delta haplotype, which may confer resistance to herbivory, though it is possible that differences in infestation between haplotypes are due to morphology.

Methods Site and sample collection

On September 17, 2018, shortly after peak biomass, we traveled to the Passe-a-Loutre Wildlife Management Area of the Mississippi River, Louisiana, USA. Passe-a-Loutre is a coastal freshwater marsh with a mean salinity of less than 1 ppt. P. australis is the dominant vegetation in the region (Knight et al., 2020). 20 samples of each haplotype (European and Delta) were collected at two separate sites (Table 1) for a total of 80 individuals. The paired-haplotype plots at each site were identified by researchers at Louisiana State University on May 31, 2017 (Knight et al., 2020). Individuals of the European haplotype and Delta haplotype are easily differentiated in the MRD due to morphological and phenological differences, with the European haplotype exhibiting a much shorter, more gracile stature, earlier flowering time (all 40 European haplotype individuals collected were in flower at the time of sampling), and sparse or absent ligule hairs. The Delta haplotype is much taller, thicker-stemmed and possess dense ligule hairs (Hauber et al., 2011). No Delta haplotype individuals were in flower at the time of sampling. Culms of each haplotype were cut at the surface of the water if the plot was flooded (because scale insects do not occur under water) or at the soil in dry plots and placed individually in bags on wet ice for transport. Mean water depth at Site 1 was 19.8 cm and 2.6 cm at Site 2. Information on stand density and number of dead and living culms was determined for each individual using a circular quadrat which covers an area of ¼ m2 placed with the selected culm in the center. On the day of collection, samples were placed in a 4°C refrigerator for holding and small subsamples of internodal (hereafter, “stem”) and leaf tissue from each of the 80 individual culms collected were placed in a -80°C freezer for metabarcoding analysis.

Using the remaining portions of the individuals, the number of N. biwakoensis insects per plant was determined by counting individuals every third internode and extrapolating to the entire culm. This was done by finding the average number of scales per node and multiplying by the number of internodes on the culm. Because the internode lengths differ within one individual, as well as between haplotypes, this value was standardized by the overall above-water height of the culm to determine scale density per centimeter of height. While the native scale insect Aclerda holci also occurs on P. australis in Louisiana, N. biwakoensis can be identified by its uniformly sclerotized and rounded abdomen (Knight et al. 2018) and was the only scale species present on the collected P. australis samples.

Surface sterilization

For metabarcoding, 10 culms from each plot were selected by arranging all collected culms within that plot in order of lowest scale density to highest. Starting with the lowest, every-other individual was chosen for analysis. This ensured we had a range of infestation represented for each plot and each haplotype. A 10cm section of healthy leaf tissue and a 4cm internodal section of stem from each sample were selected and placed in a tea strainer. Plant tissue was surface sterilized following protocol developed for Spartina patens (Lumibao et al., 2018). Tissues were sequentially soaked in 95% ethanol for 10 seconds, 0.5% sodium hypochlorite solution for 2 minutes, 70% ethanol for 2 minutes, and rinsed in sterilized deionized water for 2 minutes. Samples were then dried on UV sterilized KIMTECH Kimwipes before placing in gamma-sterilized cryovials for storage at -80°C.

DNA extraction

Following surface sterilization, samples were homogenized using liquid nitrogen and a mortar and pestle sterilized between samples. Genomic DNA extractions for the leaf samples were done following Qiagen DNeasy ® PowerPlant® Pro Kit protocol using 50mg of tissue, Phenolic Separation Solution and 250µl of Solution IR. Leaf samples were eluted using 100µl of Solution EB. Due to low yields for stem tissue, 75mg of stem tissue was used and elution was done using 50µl of Solution EB. Extraction concentrations were determined using a ThermoFisher Scientific Qubit fluorometer and all samples above a concentration of 10ng/µl were standardized to 10ng/µl by mixing extraction product with additional Solution EB.

PCR

To allow for maximum flexibility in choosing region-specific primers and dual-indexing barcode combinations, sequencing libraries were created in a two-step process following U’ren and Arnold (2017). PCR1, or amplification and primer ligation, was done in duplicate with an annealing temperature of 54.0° C. The primer sequences used were ITS1F 5’ – CTT GGT CAT TTA GAG GAA GTA – 3’ and ITS2R 5’ – GCT GCG TTC TTC ATC GAT GC – 3’ (Gardes & Bruns, 1993; White et al., 1990). Sample duplicates were then pooled and dual indexing primers were added so that no two samples contained the same combination of indexing barcodes. Five nanograms of DNA from each sample were pooled into a common library along with four negative controls which were included for identification of possible contaminant sequences. The pooled library was then purified and concentrated using Agencourt AMpure XP Beads with Dynamag-2 Magnet and sent to Duke University for Illumina MiSeq v3 sequencing (300 bp paired reads).

Sequence analysis

    Amplicon sequence variants (ASV) were identified following the DADA2 ITS pipeline (version 1.8) (Callahan et al., 2016) in R (R Core Team, 2019). ASVs are “phylotypes” that are a single DNA sequence (not clustered by a sequence similarity threshold like operational taxonomic units, OTUs). Primers and adapters were trimmed using the Biostrings package in R (Pagès et al., 2020) and data was denoised and paired reads joined using DADA2. No further sequence trimming was done, because DADA2 is robust to low-quality sequences through the incorporation of read quality information into its error model. Seven possible contaminant sequences were identified using the negative controls and removed using the decontam package in R (Davis et al., 2018). One sample was dropped due to very low reads. Taxonomy was assigned using UNITE 7.2 with a minimum bootstrap confidence of 70% (Nilsson et al., 2019) and functional guild assignments were obtained using FUNGuild (Nguyen et al., 2016).

The dataset of LAI measurements was obtained by LI-3000, the protractor and the ruler in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on May, 20, 24, 25, 28 and 31, Jun. 6, 11, 12, 14, 16, 21 and 27, Jul. 2 and 9, 2008. The maximum leaf length and width of maize and wheat, the leaf angle, length and width of each section (one leaf was divided into 3 sections) were measured. And also the plant height, leaf base height, the crop spacing, the canopy height, row spacing and ridge spacing were measured. Two representative plants would be taken back for indoor observation for the stem length, stem width, stem circumference, and leaf area by LAI3000. Data were archived in Excel format.

Invasive species provide an opportune system to investigate how populations respond to new or changing environments. While the impacts of invasive species increase annually, many gaps in our understanding of how these species invade, adapt, and thrive in the areas they are introduced to remain. Using the perennial forb Gypsophila paniculata as a study system, we aimed to investigate how invasive species respond to different environments. Baby’s breath (Gypsophila paniculata) was introduced to North America in the late 1800’s and has since spread throughout the northwestern United States and western Canada. We used an RNA-seq approach to explore how molecular processes may be contributing to the success of invasive G. paniculata populations that are thought to share similar genetic backgrounds across distinct habitats. Transcription profiles were constructed for root, stem, and leaf tissue from seedlings collected from a sand dune ecosystem in Petoskey, MI (PSMI) and a sagebrush ecosystem in Chelan, WA (CHWA). Using these data we assessed differential gene expression between the two populations and identified SNPs within differentially expressed genes. We identified 1,146 transcripts that were differentially expressed across all tissues between the two populations. GO processes enriched by genes displaying higher expression in PSMI were associated with increased nutrient starvation, while enriched processes in CHWA were associated with abiotic stress. Only 7.4% of the differentially expressed genes across all three tissues contained SNPs differing in allele frequencies of at least 0.5 between the populations. In addition, common garden studies found the two populations differed in germination rate and seedling emergence success, but not in above- and below-ground tissue allocation. Our results suggest that the success of invasive G. paniculata across these two environments is likely the result of plasticity in molecular processes responding to different environmental conditions, although some genetic divergence may also be contributing to these differences.

Methods RNA Extraction. We collected 16 G. paniculata seedlings from CHWA (June 8, 2018) and 15 seedlings from PSMI (June 1, 2018). We then dissected seedlings into three tissue types (root, stem, and leaf), placed tissue in RNAlater™ (Thermo Fisher Scientific, Waltham, MA), and flash-froze them in an ethanol and dry ice bath. We extracted total RNA from frozen tissue using a standard TRIzol® (Thermo Fisher Scientific) extraction protocol (https://assets.thermofisher.com/TFS-Assets/LSG/manuals/trizol_reagent.pdf). We resuspended the extracted RNA pellet in DNase/RNase free water. The samples were then treated with DNase to remove any residual DNA using a DNA-Free Kit (Invitrogen, Carlsbad, CA). We assessed RNA quality with a Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA) and NanoDrop™ 2000 (Thermo Fisher Scientific). RNA Integrity Number (RIN) values for individuals used in this study ranged from 6.1-8.3. However, because both chloroplast and mitochondrial rRNA can artificially deflate RIN values in plant leaf tissuewe deemed these values to be sufficient for further analysis based upon visualization of the 18S and 28S fragment peaks (see Babu & Gassmann, 2016). This resulted in high quality total RNA from 10 PSMI leaf, 10 PSMI stem, 10 PSMI root, 10 CHWA leaf, 9 CHWA stem, and 10 CHWA root samples.

cDNA Library Construction and Sequencing. Prior to sequencing, all samples were treated with a Ribo-Zero rRNA Removal Kit (Illumina, San Diego, CA). cDNA libraries were constructed using the Collibri Stranded Library Prep Kit (Thermo Fisher Scientific) before being sequenced on a NovaSeq 6000 (Illumina) using S1 and S2 flow cells. Sequencing was performed using a 2 x 100 bp paired-end read format and produced approximately 60 million reads per sample, with 94% of reads having a Q-score >30.

Transcriptome Assembly. Prior to transcriptome assembly, read quality was assessed using FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Adapters and bases with a quality score less than 20 were first removed from the raw reads using Trim Galore (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). Next, rRNAs were identified using SortMeRNA (mean rRNA percent content of 5.31%) (Kopylova, Noé, & Touzet, 2012). A reference transcriptome was then assembled de novo using non-rRNA reads from all samples and Trinity v2.8.2 (Grabherr et al. 2011; Haas et al. 2013), with a normalized max read coverage of 100, a minimum k-mer coverage of 10, and k-mer size set to 32. The assembled transcriptome was annotated using Trinotate v3.1.1. Trinotate was given open reading frames (ORFs) predicted from TransDecoder and transcript homology (blastx and blastp) to the manually curated UniProt database (Bryant et al., 2017). The final assembly consisted of 223,810 putative genes and 474,313 putative transcripts (N50 = 3,121) from the 59 samples.

Differential Expression. To quantify transcript expression, reads were mapped back to the assembly using bowtie and quantified using the RSEM method as implemented in Trinity. Counts were generated for genes and transcripts. We then tested for differential gene expression using edgeR v3.22.5 in R v3.5.2 (Robinson, McCarthy, & Smyth, 2010; R Development Core Team, 2017). First, however, the count data was filtered and only transcripts with greater than 10 counts in at least 10 samples were included. Following filtering, 111,042 genes (49.61%) and 188,108 transcripts (39.66%) remained. Considering tissue type, 127,591 transcripts remained in the data from 20 root samples (26.90%), 125,261 transcripts remained in the 19 stem tissue samples (26.41%), and 112,499 transcripts remained in the 20 leaf tissue samples (23.72%). For differential expression testing, the data were stratified by tissue and filtered transcripts were then fit to the negative binomial (NB) model and tested using the quasi-likelihood F test with TMM (trimmed mean of M values) normalization. To be considered significantly differentially expressed, transcripts needed to have an adjusted p-value (BH method) below 0.05 and a log2 fold change greater than 2. For transcripts that were differentially expressed, we identified Gene Ontology (GO) biological processes that were either over- or under-represented using the PANTHER classification system v14.1, where transcripts were assessed against the Arabidopsis thaliana database (http://pantherdb.org/webservices/go/overrep.jsp). In addition, for those transcripts that were differentially expressed across all three tissues, we converted the UniProt IDs of the transcripts to GO biological process IDs using the online database bioDBnet (https://biodbnet-abcc.ncifcrf.gov/db/db2db.php), and used the metacoder package v0.3.3 (Foster, Sharpton, Grünwald, 2017) in R v3.6.0 to construct heat trees to visualize the relationship of our differentially expressed transcripts across GO biological process hierarchies.

Single Nucleotide Polymorphism (SNP) Variant Calling. We used the HaplotypeCaller tool from GATK4 to identify potential SNPs that were present in transcripts that were differentially expressed between populations (McKenna et al., 2010; DePristo et al. 2011). The bowtie mapped files were used to jointly genotype all 59 samples simultaneously with a minimum base quality and mapping quality of 30. Variant data was visualized using the vcfR package v1.8.0 (Knaus & Grünwald 2017). We identified variants associated with non-synonymous SNPs, synonymous SNPs, 5’ and 3’ UTR SNPs, 5’ and 3’ UTR indels, frame-shift and in-frame indels, premature or changes in stop codons and changes in start codons, and calculated population diversity estimates for all SNP types. The effect prediction was done using custom scripts (which can be found in the Dryad repository) and the Transdecoder predicted annotation in conjunction with the base change. We set a hard filter for the SNPs so that only those with QD scores > 2, MQ scores > 50, SOR scores < 3, and Read Post Rank Sums between -5 and 3 passed. We then calculated the allele frequencies for each SNP within PSMI and CHWA. For the subsequent evaluation, we focused on SNPs that had potential functional effects (i.e., they were not listed as ‘synonymous’ or ‘unclassified’), were in transcripts differentially expressed between PSMI and CHWA across all three tissues, and that exhibited differences in SNP allele frequencies between the populations by at least 0.5. We used the R package metacoder v0.3.3 to visualize the GO biological process hierarchies associated with transcripts containing these SNPs.

Germination Trial. On August 11, 2018 we returned to our sample sites in CHWA and PSMI and collected seeds from 20 plants per location. This date was chosen because Rice, Martínez-Oquendo, & McNair (2019) previously determined that this collection time can yield over 90% seed germination for G. paniculata collected from Empire, MI. To collect seeds, we manually broke seed pods off and placed them inside paper envelopes in bags half-filled with silica beads. We stored bags in the dark at 20 to 23˚C until the germination trial began one month later, We counted one hundred seeds from twenty plants per population and placed them in a petri dish lined with filter paper (n = 2,000 seeds per population). We established a control dish using 100 seeds from the ‘Early Snowball’ commercial cultivar (G. paniculata) sold by W. Atlee Burpee & Co in 2018, known to have germination percentages in excess of 90%. Incubators had a 12:12h dark:light photoperiod and growth chamber conditions were set at 20˚C with 114 μmol m-2 s-1 photosynthetically active radiation from fluorescent light bulbs. Each day we randomized petri dish locations within the incubator to avoid bias in temperature or light regimes. We conducted this study for fourteen days,

All data in this data set are original data, including meteorological and soil moisture content, stem sap flow, water potential of plant tissue, isotope characteristics of atmospheric and humidified water vapor, fluorescence tracer image, plant photosynthetic fluorescence, and basic data of five desert plants, Tamarix chinensis, Haloxylon ammodendron, Bawang, Nitraria tangutorum and red sand, which are related to field and indoor control experiments Because of the data of expression regulation. 1. Isotopic data of Tamarix chinensis. After humidifying for 1 hour, 2 hours and 3 hours, the tissue samples of indoor and outdoor plants of plexiglass were collected at the same time. The samples were put forward and processed by low-temperature vacuum distillation glass water extraction system, and then used euro The isotopic data were measured by ea3000 element analyzer and isoprime gas stability mass spectrometer. Tamarix Tamarix samples were collected from Sitan village, Jingtai County, including humidification and control samples. The variation data of isotopic composition can be used to determine the way and amount of water vapor absorbed by plant leaves. 2. Fluorescence section photo data: all the data in this data set are original data, including the structural photos under high-power microscope of Tamarix, Haloxylon ammodendron, Nitraria, Bawang, Hongsha and other desert plant leaves in Sitan village of Jingtai County and Ejin Banner. The specific method is as follows: apply fluorescent dye to the surface of desert plant leaves before humidification, collect plant leaves and stems after humidification for 1 hour, 2 hours and 3 hours, put them in liquid nitrogen, take them back to the laboratory, observe and take photos with fluorescence microscope. It can be used to analyze the tissue and organs of water absorption by desert plant leaves and the direction and path of water migration in plants. 3: Gene transcription and expression data: transcription and expression data of Tamarix chinensis, data collection time: May 25, 2014, location: Sitan village, Jingtai County, Gansu Province, data analysis platform: lllumina hisep TM 2000 platform, obtained by transcriptome analysis of baimaike company. 4. Photosynthetic and fluorescence data: photosynthetic and fluorescence parameters measured by photosynthetic apparatus in the field (Sitan village and Ejin Banner, Jingtai County). 5. Sap flow and environmental data: all data are original data. Sap flow data of desert plants measured by stem flow meter, including Tamarix chinensis, Haloxylon ammodendron, Nitraria tangutorum, red sand and other desert plants (Sitan village, Jingtai County and Ejin Banner), and environmental data monitored by automatic weather station, including temperature and humidity.