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Why use DNA for monitoring?

With nature in a state of decline, responsive environmental management has never been more important; but this requires a constant stream of information about the living world. It is increasingly clear that the traditional set of tools and techniques for deriving are too low throughput, expensive, and slow to meet the data requirements for effective environmental management.

Ecologists are limited by a lack of biological data, usually as a result of a sampling bottleneck, a taxonomic bottleneck, or both. Combined these issues mean that (a) the true distribution and status of most species is unknown, and (b) total community level biodiversity is rarely assessed. 

Specific issues limiting environmental management include (Figure 1.1): 

  • Low detection rates
  • Low throughput
  • Inconsistency and inaccuracy among taxonomists

Low detection rates


Finding rare, elusive, or cryptic taxa is difficult by definition, and these taxa may be particularly important for management decisions (e.g. finding invasives before they establish, or conserving rare populations before they go extinct). Environmental DNA can be used to detect organisms present in the wider area without requiring their presence in the physical sample. Moreover, eDNA detection is independent of life stage, which may yield greater opportunities for detection (e.g. from the presence of eggs and larvae).

eDNA detection has been shown to be more sensitive than traditional survey methods across a wide variety of rare, invasive, and cryptic organisms.

Low throughput


Traditional taxonomy is not conducive to high throughput, which is vital for long-term and routine monitoring. The scalability of biotic surveys have traditionally been limited in scale or scope due to the demands of specimen sorting and species identification – particularly in the case of invertebrates.

Passive trapping and active sampling techniques can capture a vast number and diversity of organisms, but their extensive deployment is limited by the taxonomic bottleneck. Using DNA Metabarcoding, analysis of these diverse samples can be scaled up to allow hundreds of samples to be processed in parallel. 

Inconsistency and inaccuracy among taxonomists


Traditional taxonomy is difficult to standardise. Taxonomists vary in their experience and skill levels, while dichotomous keys also vary and can be based on subjective characters that may be interpreted differently. Consequently the same query materials can be identified differently by different taxonomists. These inconsistencies may be absent for highly trained taxonomists identifying large, easily identifiable species, but trained taxonomists are an endangered species in themselves. Inconsistencies and inaccuracies are a more significant problem for cryptic and neglected taxa such as the meiofauna. For example documenting the diversity of marine life is challenging because many species are cryptic, small, and rare, and belong to poorly known groups.

DNA sequences are digital and can be easily curated and databased. This information is easily auditable and third-party verifiable. 

The reduced reliance on expert taxonomists means that DNA taxonomy can be combined with citizen science programs to generate enormous datasets at large spatial and temporal scales. For example, a great crested newt monitoring programme run by Freshwater Habitats Trust has been conducted primarily by volunteers with limited training. A larger scale citizen science project in California (CALeDNA) aims to characterise aquatic sediment samples in and around California to build up detailed and complex distribution maps.


6 advantages

Many times faster

Generate data across thousands of species in just a few weeks

Morphology and life-stage independent

Species-level data for morphologically intractable groups

Reduced taxonomic bias

No need to select for body size or ease of recognition

Statistically powerful datasets

Large datasets can detect even subtle changes

Third-party verifiable

DNA can be stored for re-analysis

Reduced observer bias

No reliance on field ID skills

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