Advancing forest management: Integrating genetic analysis and LiDAR for selecting superior trees in ginkgo plantations

Forests, as critical terrestrial ecosystems, play a central role in ecological balance and carbon sequestration, with plantations like Ginkgo (Ginkgo biloba L.) playing a significant role in environmental and economic domains. Recent shifts toward sustainable management underscore the importance of identifying trees with superior traits for long-term breeding, balancing genetic diversity and genetic gain. Despite the advent of advanced remote sensing technologies like LiDAR for efficient, non-destructive phenotypic data collection, the challenge remains in integrating these methods with genetic analysis to screen for elite trees in plantations lacking seed source information. This emphasizes the necessity of studying high-throughput phenotypic and genetic analysis for Ginkgo and similar species, which is crucial for maintaining diversity and enhancing plantation productivity.

In September 2023, Plant Phenomics published a research article entitled by “Eff-3DPSeg: 3D Organ-Level Plant Shoot Segmentation Using Annotation-Efficient Deep Learning ”.

In this study, researchers utilized Simple Sequence Repeat (SSR) markers for genetic analysis and high-density terrestrial laser scanning (TLS) to extract growth structural characteristics. The study aimed to explore the potential of remote sensing in forest breeding. Initially, the genetic diversity and population structure of 102 ginkgo trees were assessed using 14 SSR primers, revealing high genetic diversity and complex population structure. Trees were then grouped based on genetic distance. Subsequently, growth traits such as height, diameter at breast height, and crown measurements were accurately and nondestructively extracted using TLS, showing a wide variation in structural traits. Comprehensive evaluation identified 11 exceptional trees, combining phenotypic superiority and genetic diversity. The results confirmed the efficacy of TLS in providing detailed, nondestructive growth assessments. Statistical analysis revealed significant variation in growth characteristics, likely influenced by the  genetic diversity of the plantation and environmental factors. Principal Component Analysis (PCA) was employed to further refine the selection of superior trees, ensuring the retention of genetic diversity while emphasizing desirable growth traits.

Overall, this dual approach of combining genetic analysis with advanced remote sensing technologies like TLS represents a significant advancement in forest breeding strategies. The study demonstrates the utility of integrating genetic insights with detailed phenotypic data, offering a comprehensive method for selecting and breeding superior trees in plantations. The success of this method in ginkgo plantations suggests its potential applicability to other tree species, particularly in large-scale operations where information on the seed source is lacking. This innovative approach opens new pathways for efficient, informed forest management and breeding, combining the depth of genetic analysis with the precision of remote sensing technologies.

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References

Authors

Wen  Gao¹, Xiaoming  Yang¹, Lin  Cao¹, Fuliang  Cao¹, Hao  Liu¹, Quan  Qiu², Meng  Shen¹, Pengfei  Yu³, Yuhua  Liu⁴, and Xin  Shen^1*

Affiliations

¹Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, Jiangsu  210037,  PR  China.  

²College  of  Forestry  and  Landscape  Architecture,  South  China  Agricultural  University,  Guangzhou,  Guangdong  510642,  PR  China.  

³Suining  County  Runqi  Investment  Co.  Ltd.,  Xuzhou,  Jiangsu  221200,  PR  China.  

⁴Jiangsu  Vocational  College  of  Agriculture  and  Forestry,  Zhenjiang,  Jiangsu 212400, PR China.

About Xin Shen

Xin Shen currently works at the forestry, Nanjing Forestry University. Xin does research in Environmental Science, Forestry and Geostatistics.