Order kits
Submit samples
Login & Order Kits

TICI Explained

The Taxon-Independent Community Index (TICI)

Background

Biodiversity and habitat quality worldwide are declining due to over-exploitation and intensive land use. While high-profile ecological disasters often capture attention, gradual ecosystem changes frequently go unnoticed until critical tipping points or irreversible damage occur. Traditional monitoring relies on selected ‘indicator’ species, a process that is time-consuming, costly, and limited in scope.

What is TICI?

TICI is a novel ecological health index developed by Wilderlab in partnership with New Zealand’s regional councils. It uses environmental DNA (eDNA) and machine learning to assess whole-ecosystem health without depending on taxonomic identification of specific species. This approach provides a more comprehensive and unbiased view of biodiversity, including many organisms that traditional methods often miss.

How TICI Was Developed

Between 2020 and 2021, 53 river and stream sites across New Zealand were extensively sampled using eDNA metabarcoding assays. A total of 848 samples were analysed, focusing on the 3,000 most common DNA sequences, which were assigned indicator values through a novel ranking process. Unlike conventional methods that focus solely on macroinvertebrates or fish, TICI assigns scores directly to DNA sequences, capturing a wide range of taxa.

Background

Biodiversity and habitat quality worldwide are declining due to over-exploitation and intensive land use. While high-profile ecological disasters often capture attention, gradual ecosystem changes frequently go unnoticed until critical tipping points or irreversible damage occur. Traditional monitoring relies on selected ‘indicator’ species, a process that is time-consuming, costly, and limited in scope.

Advantages of TICI

Taxon-independent: does not require species-level identification
Utilises a broad spectrum of genetic data, including unassigned sequences
Cost-effective and scalable for frequent monitoring
Provides detailed, reproducible health scores to support ecosystem management