Impact of grazing on germination trait selection in an alpine grassland on the Tibet Plateau

Previous studies indicated that grazing can cause significant changes in abiotic and biotic environment in grassland.However,how these changes impact germination trait selection in grassland has not been well studied.Thus,we aimed to explore whether grazing can significantly change germination trait diversity and composition of grasslands community.We measured the germination traits of species in the laboratory,and compared their performance in grazed and nongrazed grasslands.Then,we compared the community-weighted means of germination traits and functional diversity of grazed and nongrazed grasslands based on these germination traits to know whether grazed and nongrazed grasslands differed in their germination trait structures.At the species level,we found that the changes of abundance in grazed and nongrazed grasslands were not related to species’germination traits.However,at the community level,compared with nongrazed grasslands,species in the grazed grasslands generally exhibited a higher seed germination percentage.Moreover,seed germination response in grazed grasslands was more positively related to alternating temperature than in nongrazed grasslands,and breadth of the germination temperature niche was narrower in grazed than in nongrazed grasslands.Compared with nongrazed grasslands,seed germination trait diversity was increased and germination trait evenness decreased in grazed grasslands.Grazing can change microhabitat conditions,thereby changing germination trait selection by environmental filtering,resulting in a significant difference in germinate trait composition at the community level.

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.

Rice molecular markers and genetic mapping:Current status and prospects

Dramatic changes in climatic conditions that supplement the biotic and abiotic stresses pose severe threat to the sustainable rice production and have made it a difficult task for rice molecular breeders to enhance production and productivity under these stress factors. The main focus of rice molecular breeders is to understand the fundamentals of molecular pathways involved in complex agronomic traits to increase the yield. The availability of complete rice genome sequence and recent improvements in rice genomics research has made it possible to detect and map accurately a large number of genes by using linkage to DNA markers. Linkage mapping is an effective approach to identify the genetic markers which are co-segregating with target traits within the family. The ideas of genetic diversity, quantitative trait locus（QTL） mapping, and marker-assisted selection（MAS） are evolving into more efficient concepts of linkage disequilibrium（LD） also called association mapping and genomic selection（GS）, respectively. The use of cost-effective DNA markers derived from the fine mapped position of the genes for important agronomic traits will provide opportunities for breeders to develop high-yielding, stress-resistant, and better quality rice cultivars. Here we focus on the progress of molecular marker technologies, their application in genetic mapping and evolution of association mapping techniques in rice.

Expanding the phenotypic plasticity paradigm to broader views of trait space and ecological function

Foundational concepts of trait spaces, including phenotypic plasticity and function of traits, should be expanded and better integrated with ecological theory. This article addresses two areas where plasticity theory can become further integrated with ecological, evolutionary, and developmental thinking. First is the idea that not only trait means within environments and plasticity of trait means across environments is optimized by selection, but that the entire shape of phenotype distributions such as variance or skew should be optimized within and across environments. In order for trait distribution shape to evolve into adaptations, there must be a genetic basis for and selection upon variation in distribution shapes and their plasticities. I present published and new data demonstrating genetic control and selection for higher moments of phenotype distributions; though, plasticity in these values has not yet been tested. Genetic control of phenotype distribution moments is shown for Neurospora crassa ascospore size and shape. Selection on trait distribution moments is shown for Eurosta solidaginis gall size. Second, there is a tradition in modeling plasti- city as an adaptive strategy that pits it as an alternative to ecological specialization or generaliza- tion. However, these strategies need not be considered alternatives. Rather, with environmental fluctuation within generations plasticity may produce additive or non-additive intermediate （gener- alist） phenotypes, or something new altogether. I present published and new data on the snail Physa virgata and fish Gambusia affinis that show plasticity produces partly intermediate （general- ist） and partly unique phenotypic elements in mixed and fluctuating environments. Plasticity can thus be viewed in the context of a broader trait space and as having broader ecological roles than currently is conceived.