Abstract
ABSTRACTEnvironmental DNA (eDNA) is increasingly used in biodiversity assessments, but there remain uncertainties regarding its congruence with data based on traditional approaches involving habitat sampling and morphological‐based taxonomy. Using eDNA for biomonitoring has several advantages, including improved processing efficiencies and precision of taxonomic identification. In contrast, traditional biomonitoring is time‐consuming and expensive, often limiting the number of sites monitored. Establishing that eDNA‐derived metrics are congruent with their traditional equivalents on a national scale would support its wider use in biomonitoring. Our study compared ecosystem health assessments made by traditional biomonitoring techniques to those using eDNA from 53 sites throughout Aotearoa New Zealand. Because eDNA sampling was not done concurrently with benthic sampling at most sites, we used the average community composition at each site based on previous sampling occasions. We also allocated species identified by eDNA to the traditional level of identification to allow comparisons with eDNA data identified to broader taxonomic groups. We assessed similarities between the three datasets and found a high degree of correlation and convergence between biotic indices calculated from the different methods. eDNA did, however, appear to under‐represent some taxa, reflecting challenges in matching barcodes with an often‐incomplete sequence library. eDNA data did not always perform better in terms of showing the effects of land use on invertebrate community composition, but all datasets produced similar patterns. Multivariate analyses (redundancy analysis and variation partitioning) identified congruent relationships between environmental and spatial variables with the invertebrate community structure described by the three methods. eDNA data replicated the environmental responses and showed the same overall patterns in community composition as the traditionally collected data. We suggest that eDNA biomonitoring can complement traditional methods, and will perform at least as well as traditional data at detecting patterns in invertebrate community composition and ecosystem health at a national scale.
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