Poplar and willow species in the Salicaceae are dioecious, yet have been shown to use different sex determination systems located on different chromosomes. Willows in the section Vetrix are interesting for comparative studies of sex determination systems, yet genomic resources for these species are still quite limited. Only a few annotated reference genome assemblies are available, despite many species in use in breeding programs. Here we present de novo assemblies and annotations of 11 shrub willow genomes from six species. Copy number variation of candidate sex determination genes within each genome was characterized and revealed remarkable differences in putative master regulator gene duplication and deletion. We also analyzed copy number and expression of candidate genes involved in floral secondary metabolism, and identified substantial variation across genotypes, which can be used for parental selection in breeding programs. Lastly, we report on a genotype that produces only female descendants and identified gene presence/absence variation in the mitochondrial genome that may be responsible for this unusual inheritance.

. Shrub patches influence soil fertility and vegetation, impacting species composition and diversity. The Eastern Qilian Mountains' unique context provides insights into alpine ecosystems' response to environmental challenges. This study aimed to evaluate the physical characteristics and soil nutrient contents of shrub patches for four different shrub species. Four alpine shrub species (Salix oritrepha, Spiraea alpina, Rhododendron capitatum, and Potentilla fruticose) were studied, assessing their patch characteristics and soil nutrients at different depths within three patch microsites. Soil samples were collected and analyzed for organic matter, nitrogen, phosphorus, and potassium content. Statistical analyses were conducted to evaluate differences among shrub species and locations within the shrub patches. Our results showed that the shrub species exhibited variations in patch characteristics and soil nutrient distribution. Soil nutrient content varied by depth and location within the shrub patches, with higher concentrations at the center. The relative interaction intensity (RII) showed nutrient aggregation or dispersion trends. The study highlighted the complex interactions between shrub characteristics and soil nutrients, emphasizing their influence on nutrient cycling, vegetation dynamics, and soil properties. These findings contribute to our understanding of alpine ecosystem dynamics and inform conservation and management strategies.

Cratylia argentea is a leguminous shrub with a great potential for livestock feeding in tropical areas. However, time consuming and labor-intensive methods of chemical analysis limits the understanding of its nutritive value. Near infrared spectroscopy (NIRS) is a low-cost technology that has been widely used in forage crops to fasten the assessment of its chemical composition. The objective of this study was to develop prediction models to assess crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF) and dry matter (DM) of Cratylia based on NIRS and partial least square analysis. A total of 155 samples were harvested at different maturity levels and used for model development, whereas 107 were used for calibration and 48 for external validation. The cross-validation presented Root Mean Square Error of Prediction of 0.77, 2.56, 3.43, and 0.42; a Ratio of Performance to Deviation of 4.8, 4.0, 3.8, and 3.4; an R2 of 0.92, 0.92, 0.87, and 0.84 for CP, NDF, ADF, and DM, respectively. Based on the obtained results, we conclude the ability to predict chemical parameters of Cratylia with the current model was accurate. This way, livestock producers and researchers may use it to fasten the assessment of Cratylia’s nutritive value.

Accurately estimating shrub biomass in arid and semi-arid regions is critical for understanding ecosystem productivity and carbon stocks at both local and global scales. However, capturing the shrub biomass accurately by satellite observations is challenging due to the short and sparse features of shrubs. In this study, we presented a framework to estimate shrub biomass at a 10m spatial resolution by integrating ground in-situ data, unmanned aerial vehicle (UAV), Sentinel-2, and Sentinel-1 observations. The pilot study was conducted in the Helan mountains of Ningxia province, China. First, the spatial distribution map of shrubland and other land cover types was generated in 2023. Then, a prediction model of shrub biomass was developed in a Random Forest Regression (RFR) approach driven by different predicted variable datasets based on in-situ measurements, UAV, and satellite images. Finally, the developed model was used to produce the biomass map of shrubland in 2023. The uncertainty was characterized by creating a standard deviation (SD) map using the Leave-One-Out Cross-Validation (LOOCV) method in the shrub biomass estimates. The resultant shrubland distribution map in 2023 has an F1 score of 0.92. The shrub biomass model driven by spectral bands and vegetation indices (R2= 0.62) demonstrated superior performance than that only driven by spectral bands (R2=0.33) or vegetation indices (R2=0.55). The uncertainty of the estimated biomass was lower than 4%, with the lowest values (<2%) happening in the high shrub coverage (>30%) and biomass production (>30kg/m2) regions. Furthermore, the findings show that the magnitude of shrub biomass was affected by the aridity and precipitation significantly in the study region. This study provides a workflow to accurately monitor the biomass of shrublands in a complicated environment based on remote sensing images from multi-platform observations.