Experimental teaching of facility soil nematode community structure and function based on environmental DNA

To meet the demand for interdisciplinary talent under the Emerging Agricultural Education initiative, we designed an inquiry-based practical teaching program using environmental DNA (eDNA) metabarcoding as a key tool. By tracking the full-cycle dynamics of soil nematode communities in a greenhouse-based watermelon cropping system, the program aimed to equip students with strong competencies in high-throughput biomonitoring and ecological data analysis. Simultaneously, it aimed to foster a deep understanding of the ecological rationale for selecting the “peak plant growth period” as a critical sampling window for soil biodiversity monitoring. This design bridges the gap between applied ecological assessments and advanced molecular techniques, which is essential for training the next-generation agricultural professionals.

Soil samples were collected in three biological replicates at six time points across one complete watermelon growth cycle: before fertilization, the seedling, vine elongation, flowering and fruit-setting, early maturity, and late maturity stages. Nematode communities were assessed using eDNA metabarcoding targeting the 18S rRNA gene with the specific primers NF1 and 18Sr2b, followed by Illumina sequencing and bioinformatics-based annotation using the QIIME2 framework and SILVA database. Alpha diversity indices, including Shannon diversity and observed species richness, as well as ecological functional indices including the maturity, enrichment, and channel indices, were calculated. One-way ANOVA and Pearson correlation analysis were employed for statistical comparisons.

Nematode community dynamics were closely coupled with watermelon phenology. Alpha diversity exhibited a unimodal pattern, peaking at the vine elongation stage with observed species richness and the Shannon diversity index reaching 42.7 and 1.84, respectively, followed by a marked decline after the flowering and fruit-setting stage (p < 0.05). At the early maturity stage, the Shannon diversity index dropped to 0.71, indicating drastic community-level simplification. Functional group analysis revealed that the gap in relative abundance between bacterivores and omnivores/predators expanded from less than 20% before the vine elongation stage to 83.87% at the early maturity stage, indicating a shift from a “dual-core” food web structure to a “bacterivore-dominated energy channel.” The maturity index declined significantly from ~3.20 to 2.17 (p < 0.05), demonstrating the quantitative degradation of soil ecosystem stability under intensive management. Correlation analysis showed that the maturity index represented an independent diagnostic dimension, largely unassociated with resource enrichment status or decomposition pathways, and that Shannon diversity and observed species richness indices suffice to capture most diversity variations without redundancy. After completing the full research pipeline, students successfully acquired core skills in eDNA metabarcoding, bioinformatics analysis, and ecological interpretation of complex community data; they also reported enhanced confidence in linking ecological theory with real-world agricultural soil management.

This teaching program effectively integrated cutting-edge molecular technology with ecological theory. Through systematic “field-to-data” training, it enhanced students’ scientific reasoning and practical innovation capabilities, providing a scalable pedagogical model for graduate experimental education in Resource Utilization and Plant Protection under the Emerging Agricultural Education initiative, with potential applicability to other cropping systems and soil organism groups. Crucially, students learned to critically interpret eDNA data by applying ecological filtering to distinguish contemporary functional signals from historical or exogenous DNA inputs, and to recognize that statistical non-significance does not necessarily imply ecological irrelevance, thereby developing a nuanced interpretation of data beyond mere p-values. By revealing the ecological mechanisms behind the critical monitoring window, this program enables students to understand why the “peak plant growth period” captures the most information on soil food web structure and function, thereby reinforcing the scientific foundation of nationwide soil sampling protocols.