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bioRxivEcologyPreprint

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

Sven WeberiD, Leke HutchinsiD, Pritam BanerjeeiD, Willow CallaghaniD, Alex A FarrowiD, Jeremy C. AnderseniD, Rosemary Grace GillespieiD, George K. RoderickiD

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Abstract

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.

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