Farrow, A. A. | Search bioRxiv

bioRxiv2026

Extraction workflow determines marker-specific recovery andreproducibility in leaf-litter eDNA metabarcoding

Forest-floor leaf litter is a dynamic and structurally complex ecological transition zone and thus a promising substrate for terrestrial eDNA metabarcoding. Yet, extraction workflows for this heterogeneous matrix remain poorly standardized, especially in tropical systems, making it largely impossible to compare ecological functions across space, time, and taxa. To guide workflow selection across a series of selection criteria, including biological target, research question and practical considerations, we compared DNA extraction workflows for leaf-litter eDNA collected from 42 biological samples across seven different forest sites on Oahu, Hawaii. We evaluated four DNA extraction workflows: (1) Two low-volume approaches, with DNA extracted directly from 200 mg of homogenized litter using (i) CTAB or (ii) DNeasy PowerSoil(R); and (2) two high-volume approaches using PBS wash-based from 10 g of litter followed by (i) Centrifugation or (ii) Filtration. Taxonomic recovery from each workflow was evaluated with two COI primer sets targeting arthropods (ANML and shorter NoPlant), and one ITS marker targeting fungi. Results show that eDNA workflows tested here recovered site-level differences among forest-floor communities, but biodiversity recovery depended strongly on extraction workflow and marker. For low volumes, PowerSoil recovered the highest fungal richness (with ITS marker), and produced the most reproducible PCR-replicate profiles across markers, and required the least hands-on time, while CTAB was less expensive but required handling hazardous chemicals. For high volumes workflow, Centrifugation recovered higher arthropod diversity with ANML primer. Differences in community composition were nonetheless recovered by each method. At the same time, sampling sites explained more ASV-level compositional variation than extraction workflow across markers, showing that all workflows retained site-level ecological signals. Together, these results support a workflow framework in which extraction choice depends on target organism group, DNA state, reproducibility needs, and practical constraints.

ecology↗

bioRxiv2026

Topography structures of arthropod communities revealed by leaf-derived environmental DNA on Oahu, Hawaii

Arthropod communities on oceanic islands are shaped by spatial isolation, environmental gradients, and biological invasions, yet their structure remains difficult to resolve due to incomplete taxonomic coverage. In particular, it remains unclear (i) how non-native arthropods can influence community composition and (ii) how they interact with native and non-native plants. To answer the first question, we used leaf-derived environmental DNA (eDNA) to characterize arthropod communities across elevational gradients on five ridges on Oahu, using the native tree Metrosideros polymorpha as a standardized plant. To understand the second question, we compared leaf-derived eDNA from Metrosideros polymorpha (Native), Acacia koa (native), and Psidium cattleianum (invasive), co-occurring in two ridges on Oahu. Additionally, to overcome limitations of reference databases, we applied NIClassify to infer native versus introduced status without requiring species-level identification. Across 96 leaf samples (with 851 Arthropod ASVs), we found arthropod richness increased with elevation, while the proportion of introduced taxa declined significantly. Community composition was primarily structured by ridge, with strong distance-decay relationships indicating high spatial turnover in both native and non-native assemblages. In contrast, plant species effects were context dependent and did not show a consistent native versus invasive signal. Threshold analyses identified a community transition (native vs introduced) near 500 m elevation. These results show that plant-derived eDNA can resolve spatial and environmental structuring of arthropod communities while capturing invasion dynamics under incomplete taxonomic knowledge. Classifier-based inference enables community-level ecological interpretation beyond reference-limited taxa, providing a scalable framework for biodiversity monitoring in data-poor systems.

ecology↗