Seed dispersers shape the pulp nutrients of fleshy-fruited plants.

The dispersal-syndrome hypothesis posits that fruit traits are a product of selection by frugivores. Although criticized as adaptationist, recent studies have suggested that traits such as fruit or seed size, colour and odour exhibit signatures that imply selection by animal mutualists. These traits imply nutritional rewards (e.g. lipid, carbohydrate), attracting frugivores; however, this remains incompletely resolved. Here, we investigated whether fruit nutrients (lipid, sugar, protein, vitamin C, water content) moderate the co-adaptation of key disperser-group mutualisms. Multivariate techniques revealed that fruit nutrients assembled non-randomly and grouped according to key dispersal modes. Bird-dispersed fruits were richer in lipids than mammal-dispersed fruits. Mixed-dispersed fruits had significantly higher vitamin C than did mammal- or bird-dispersed fruits separately. Sugar and water content were consistently high irrespective of dispersal modes, suggesting that these traits appeal to both avian and mammalian frugivores to match high-energy requirements. Similarly, protein content was low irrespective of dispersal modes, corroborating that birds and mammals avoid protein-rich fruits, which are often associated with toxic levels of nitrogenous secondary compounds. Our results provide substantial over-arching evidence that seed disperser assemblages co-exert fundamental selection pressures on fruit nutrient trait adaptation, with broad implications for structuring fruit-frugivore mutualism and maintaining fruit trait diversity.

Genome-wide association analysis of nutrient traits in the oyster Crassostrea gigas: genetic effect and interaction network.

BACKGROUND: Oyster is rich in glycogen and free amino acids and is called "the milk of sea". To understand the main genetic effects of these traits and the genetic networks underlying their correlation, we have conducted the whole genome resequencing with 427 oysters collected from the world-wide scale. RESULTS: After association analysis, 168 clustered significant single nucleotide polymorphism (SNP) loci were identified for glycogen content and 17 SNPs were verified with 288 oyster individuals in another wide populations. These were the most important candidate loci for oyster breeding. Among 24 genes in the 100-kb regions of the leading SNP loci, cytochrome P450 17A1 (CYP17A1) contained a non-synonymous SNP and displayed higher expressions in high glycogen content individuals. This might enhance the gluconeogenesis process by the transcriptionally regulating the expression of phosphoenolpyruvate carboxykinase (PEPCK) and glucose 6-phosphatase (G6Pase). Also, for amino acids content, 417 clustered significant SNPs were identified. After genetic network analysis, three node SNP regions were identified to be associated with glycogen, protein, and Asp content, which might explain their significant correlation. CONCLUSION: Overall, this study provides insights into the genetic correlation among complex traits, which will facilitate future oyster functional studies and breeding through molecular design.

Construction of a high-density genetic map and lint percentage and cottonseed nutrient trait QTL identification in upland cotton (Gossypium hirsutum L.).

Upland cotton plays a critical role not only in the textile industry, but also in the production of important secondary metabolites, such as oil and proteins. Construction of a high-density linkage map and identifying yield and seed trait quantitative trail loci (QTL) are prerequisites for molecular marker-assisted selective breeding projects. Here, we update a high-density upland cotton genetic map from recombinant inbred lines. A total of 25,313 SSR primer pairs were screened for polymorphism between Yumian 1 and T586, and 1712 SSR primer pairs were used to genotype the mapping population and construct a map. An additional 1166 loci have been added to our previously published map with 509 SSR markers. The updated genetic map spans a total recombinant length of 3338.2 cM and contains 1675 SSR loci and nine morphological markers, with an average interval of 1.98 cM between adjacent markers. Green lint (Lg) mapped on chromosome 15 in a previous report is mapped in an interval of 2.6 cM on chromosome 21. Based on the map and phenotypic data from multiple environments, 79 lint percentage and seed nutrient trait QTL are detected. These include 8 lint percentage, 13 crude protein, 15 crude oil, 8 linoleic, 10 oleic, 13 palmitic, and 12 stearic acid content QTL. They explain 3.5-62.7 % of the phenotypic variation observed. Four morphological markers identified have a major impact on lint percentage and cottonseed nutrients traits. In this study, our genetic map provides new sights into the tetraploid cotton genome. Furthermore, the stable QTL and morphological markers could be used for fine-mapping and map-based cloning.

Leaf nutrient traits of planted forests demonstrate a heightened sensitivity to environmental changes compared to natural forests.

Leaf nutrient content (nitrogen, phosphorus) and their stoichiometric ratio (N/P) as key functional traits can reflect plant survival strategies and predict ecosystem productivity responses to environmental changes. Previous research on leaf nutrient traits has primarily focused on the species level with limited spatial scale, making it challenging to quantify the variability and influencing factors of forest leaf nutrient traits on a macro scale. This study, based on field surveys and literature collected from 2005 to 2020 on 384 planted forests and 541 natural forests in China, investigates the differences in leaf nutrient traits between forest types (planted forests, natural forests) and their driving factors. Results show that leaf nutrient traits (leaf nitrogen content (LN), leaf phosphorus content (LP), and leaf N/P ratio) of planted forests are significantly higher than those of natural forests (P< 0.05). The impact of climatic and soil factors on the variability of leaf nutrient traits in planted forests is greater than that in natural forests. With increasing forest age, natural forests significantly increase in leaf nitrogen and phosphorus content, with a significant decrease in N/P ratio (P< 0.05). Climatic factors are key environmental factors dominating the spatial variability of leaf nutrient traits. They not only directly affect leaf nutrient traits of planted and natural forest communities but also indirectly through regulation of soil nutrients and stand factors, with their direct effects being more significant than their indirect effects.

Functional trait database for Nova Scotian coastal barren, green roof, and ruderal flora.

This is the first plant functional trait database for Nova Scotia, Canada. The data contained here were collected between 2016 and 2019 from locations around Halifax, Nova Scotia. The species selected for trait collection were chosen based on species inventories taken across Nova Scotian coastal barrens and from green roofs at Saint Mary's University. The purpose of the coastal barren trait data was to understand community assembly in this understudied ecosystem. The green roof inventory was included as coastal barren species are known to succeed on green roofs in Nova Scotia. The green roof trait data was used to answer questions surrounding coexistence and trait divergence, and community assembly and spatial heterogeneity. In total, this database contains 14,341 trait values from 203 species comprising 130 genera and 53 families. The majority of species are commonly found on coastal barrens (84 species), disturbed sites (48 species), and forests (27 species). Additionally, this database contains trait data for 30 species that have been successfully established (survival for >1 year) on green roofs in Nova Scotia and ruderal species that commonly colonize both green roofs and coastal barrens. This database contains 12 plant functional traits: leaf thickness (203 species), leaf area (203 species), specific leaf area (203 species), leaf dry matter content (203 species), plant height (203 species), canopy width (203 species), seed mass (79 species), seed shape (61 species), root radius (22 species), leaf phosphorus content (3 species), leaf nitrogen content (30 species), and leaf carbon content (30 species). The species in this database can be subdivided into 10 growth forms, with the majority of species characterized as forbs (75 species), shrubs (56 species), or graminoids (33 species). This data set is freely available for scientific use; when used in published analyses, this paper should be referred to as the data source.

Differing Trade-Off Patterns of Tree Vegetative Organs in a Tropical Cloud Forest.

Functional trait ecology demonstrates the significance of the leaf economics spectrum in understanding plants' trade-off between acquisitive and conservative resource utilization. However, whether trait variations of different vegetative organs are coordinated and whether the plant economics spectrum is characterized by more than one vegetative organ remain controversial. To gain insights into these questions, within a tropical cloud forest in Hainan Island, a total of 13 functional traits of 84 tree species were analyzed here, including leaf, stem and root traits. By using standardized major axis (SMA) regression and principal components analysis, we examined the trait variations and correlations for deciphering plants' trade-off pattern. We found decreases of leaf phosphorus content, leaf nitrogen content and specific leaf area and increases of leaf mass per unit area (LMA), wood density and leaf thickness along the first principal component, while there were decreases of specific root length and specific root area and increases of root tissue density along the second principal component. Root phosphorus and nitrogen contents were significantly positively associated with the phosphorus and nitrogen contents of both stem and leaf. Wood density was significantly positively associated with LMA and leaf thickness, but negatively associated with leaf thickness and specific leaf area. Our results indicate that, in the tropical cloud forest, there is a "fast-slow" economic spectrum characterized by leaf and stem. Changes of nutrient trait are coordinated, whereas the relationships of morphological traits varied independently between plant above- and below-ground parts, while root nutrient traits are decoupled from root morphological traits. Our findings can provide an insight into the species coexistence and community assembly in high-altitude tropical forests.

Spatial variation in leaf nutrient traits of dominant desert riparian plant species in an arid inland river basin of China.

Understanding how patterns of leaf nutrient traits respond to groundwater depth is crucial for modeling the nutrient cycling of desert riparian ecosystems and forecasting the responses of ecosystems to global changes. In this study, we measured leaf nutrients along a transect across a groundwater depth gradient in the downstream Heihe River to explore the response of leaf nutrient traits to groundwater depth and soil properties. We found that leaf nutrient traits of dominant species showed different responses to groundwater depth gradient. Leaf C, leaf N, leaf P, and leaf K decreased significantly with groundwater depth, whereas patterns of leaf C/N and leaf N/P followed quadratic relationships with groundwater depth. Meanwhile, leaf C/P did not vary significantly along the groundwater depth gradient. Variations in leaf nutrient traits were associated with soil properties (e.g., soil bulk density, soil pH). Groundwater depth and soil pH jointly regulated the variation of leaf nutrient traits; however, groundwater depth explained the variation of leaf nutrient traits better than did soil pH. At the local scale in the typical desert riparian ecosystem, the dominant species was characterized by low leaf C, leaf N, and leaf P, but high leaf N/P and leaf C/P, indicating that desert riparian plants might be more limited by P than N in the growing season. Our observations will help to reveal specific adaptation patterns in relation to the groundwater depth gradient for dominant desert riparian species, provide insights into adaptive trends of leaf nutrient traits, and add information relevant to understanding the adaptive strategies of desert riparian forest vegetation to moisture gradients.