TICI: a taxon-independent community index for eDNA-based ecological health assessment

Abstract

Global biodiversity is declining at an ever-increasing rate. Yet effective policies to mitigate or reverse these declines require ecosystem condition data that are rarely available. Morphology-based bioassessment methods are difficult to scale, limited in scope, suffer prohibitive costs, require skilled taxonomists, and can be applied inconsistently between practitioners. Environmental DNA (eDNA) metabarcoding offers a powerful, reproducible and scalable solution that can survey across the tree-of-life with relatively low cost and minimal expertise for sample collection. However, there remains a need to condense the complex, multidimensional community information into simple, interpretable metrics of ecological health for environmental management purposes. We developed a riverine taxon-independent community index (TICI) that objectively assigns indicator values to amplicon sequence variants (ASVs), and significantly improves the statistical power and utility of eDNA-based bioassessments. The TICI model training step uses the Chessman iterative learning algorithm to assign health indicator scores to a large number of ASVs that are commonly encountered across a wide geographic range. New sites can then be evaluated for ecological health by averaging the indicator value of the ASVs present at the site. We trained a TICI model on an eDNA dataset from 53 well-studied riverine monitoring sites across New Zealand, each sampled with a high level of biological replication (n = 16). Eight short-amplicon metabarcoding assays were used to generate data from a broad taxonomic range, including bacteria, microeukaryotes, fungi, plants, and animals. Site-specific TICI scores were strongly correlated with historical stream condition scores from macroinvertebrate assessments (macroinvertebrate community index or MCI; R² = 0.82), and TICI variation between sample replicates was minimal (CV = 0.013). Taken together, this demonstrates the potential for taxon-independent eDNA analysis to provide a reliable, robust and low-cost assessment of ecological health that is accessible to environmental managers, decision makers, and the wider community.

Keywords: Biodiversity; Biotic index; Ecological health; Ecology; Taxon-independent analysis; eDNA.

Figure 1. Geographic spread and habitat type of 53 monitoring sites included in the Summer 2020/2021 high-replicate eDNA survey.

Hard-bottomed streams are shown as yellow circles and soft-bottomed streams are shown as orange circles. 40 of the sampling locations were long-term Regional Council monitoring sites and had at least five years of historic MCI data available through the LAWA website (https://www.lawa.org.nz/). Map layer by Google (https://www.google.com) via the ‘ggmap’ R package (Kahle & Wickham, 2013).

Figure 2. Taxon IDs for 3,000 TICI indicator ASVs.

The 3,000 most commonly encountered ASVs across the 8 metabarcoding assays are shown at superkingdom (inner ring) and phylum (outer ring) levels. Missing segments indicate the number of indicator ASVs that could not be identified at each taxonomic rank, and asterisks show lower-ranked taxa whose phyla are not specified in the NCBI taxonomy database.

Figure 3. Validation of the TICI index against existing five-year median kick-net MCI values for the 40 sites where historic MCI data were available.

TICI values from 640 individual samples (40 sites × 16 replicate samples) are plotted against their kick-net MCI site-medians. The linear regression of five-year median kick-net MCI versus site-averaged TICI values (shown here as black crosses) was significantly correlated with a p-value of less than 0.001 and an adjusted R² value of 0.825.

Figure 4. Example distributions of TICI and eMCI indicator values.

The TICI indicator values (A–C) follow a smoother beta distribution than the eMCI (D–F) indicator values due to the comparatively larger size of the indicator set. Shown here are representative eDNA samples taken from poor (A, D), average (B, E) and pristine (C, F) sites. Example sites shown in the figure are the Papanui Stream in the Hawke’s Bay (site-averaged TICI = 75.52), Are Are Creek in Marlborough (97.16), and Manganui Stream in the Southern Waikato Region (120.04), respectively.

Figure 5. Ridgeline plot showing a positive relationship between the number of indicator ASVs per sample and the precision of the site-averaged TICI.

A total of 99.7% of samples containing 100 or more indicator ASVs yielded TICI values within four units of the site-average (average over all 16 site replicates), while 94% of samples containing fewer than 100 TICI ASVs were within a similar level of precision. Plot produced using the ‘ridgeline’ R package (Soage González & Koncevicius, 2023).