Environmental DNA
Environmental DNA or eDNA is DNA that is collected from a variety of environmental samples such as soil, sediment, freshwater, seawater, snow or air, rather than directly sampled from an individual organism. As various organisms interact with the environment, DNA is expelled and accumulates in their surroundings from various sources. Such eDNA can be sequenced by environmental omics to reveal facts about the species that are present in an ecosystem — even microscopic ones not otherwise apparent or detectable. This approach enables low-cost, large-scale, and standardized biodiversity inventories, all without negatively impacting ecosystems.
In recent years, eDNA has been used as a tool to detect endangered wildlife that were otherwise unseen. In 2020, human health researchers began repurposing eDNA techniques to track the COVID-19 pandemic.
Example sources of eDNA include, but are not limited to, feces, mucus, gametes, shed skin, carcasses and hair. Samples can be analyzed by high-throughput DNA sequencing methods, known as metagenomics, metabarcoding, and single-species detection, for rapid monitoring and measurement of biodiversity. In order to better differentiate between organisms within a sample, DNA metabarcoding is used in which the sample is analyzed and uses previously studied DNA libraries, such as BLAST, to determine what organisms are present.
[|eDNA metabarcoding] is a novel method of assessing biodiversity wherein samples are taken from the environment via water, sediment or air from which DNA is extracted, and then amplified using general or universal primers in polymerase chain reaction and sequenced using next-generation sequencing to generate thousands to millions of reads. From this data, species presence can be determined, and overall biodiversity assessed. It is an interdisciplinary method that brings together traditional field-based ecology with in-depth molecular methods and advanced computational tools.
The analysis of eDNA has great potential, not only for monitoring common species, but to genetically detect and identify other extant species that could influence conservation efforts. This method allows for biomonitoring without requiring collection of the living organism, creating the ability to study organisms that are invasive, elusive, or endangered without introducing anthropogenic stress on the organism. Access to this genetic information makes a critical contribution to the understanding of population size, species distribution, and population dynamics for species not well documented. Importantly, eDNA is often more cost-effective compared to traditional sampling methods. The integrity of eDNA samples is dependent upon its preservation within the environment.
Soil, permafrost, freshwater and seawater are well-studied macro environments from which eDNA samples have been extracted, each of which include many more conditioned subenvironments. Because of its versatility, eDNA is applied in many subenvironments such as freshwater sampling, seawater sampling, terrestrial soil sampling, aquatic soil sampling, or other environments where normal sampling procedures can become problematic.
On 7 December 2022 a study in Nature reported the recovery of two-million year old eDNA in sediments from Greenland, which is currently considered the oldest DNA sequenced so far.
Environmental DNA is applied using a wide range of sampling and analytical protocols, and the reliability of results strongly depends on the robustness of the methods used. In aquatic environments, sampling strategy is a key determinant of detection success. Larger filtered water volumes generally yield greater amounts of DNA, increasing the likelihood of detecting organisms present at a site. Because eDNA is often spatially heterogeneous and locally distributed, filtering larger volumes of water along integrated transects provides a more representative and accurate snapshot of biodiversity.
Laboratory procedures also play a critical role, particularly the number of polymerase chain reaction replicates performed. PCR replication refers to the repeated amplification of the same DNA extract in independent reactions. Increasing the number of replicates improves detection probability, as PCR is subject to stochastic variation and a given species' DNA may fail to amplify in some reactions but succeed in others. For example, using 12 PCR replicates involves amplifying the sampled DNA in 12 independent subsamples, reducing the risk of false negatives.
Sequencing depth is another major factor influencing detection power. It refers to the total number of DNA sequences generated and analyzed from a sample. Higher sequencing depth increases the probability of detecting rare or low-abundance DNA fragments. Sequencing hundreds of thousands of reads in freshwater systems or up to around one million reads in marine systems can substantially enhance detection compared to lower-depth protocols, particularly for species present at low concentrations.
When robust sampling, replication, and sequencing strategies are applied, eDNA approaches are especially effective for detecting rare, elusive, or cryptic species, including protected, threatened, and invasive taxa.
Overview
Environmental DNA or eDNA describes the genetic material present in environmental samples such as sediment, water, and air, including whole cells, extracellular DNA and potentially whole organisms. The analysis of eDNA starts with collecting an environmental sample of interest. The DNA in the sample is then extracted and purified. The purified DNA is then amplified for a specific gene target so it can be sequenced and categorised based on its sequence. From this information, detection and classification of species is possible.eDNA can come from skin, mucous, saliva, sperm, secretions, eggs, feces, urine, blood, roots, leaves, fruit, pollen, and rotting bodies of larger organisms, while microorganisms may be obtained in their entirety. eDNA production is dependent on biomass, age and feeding activity of the organism as well as physiology, life history, and space use.
Despite being a relatively new method of surveying, eDNA has already proven to have enormous potential in biological monitoring. Conventional methods for surveying richness and abundance are limited by taxonomic identification, may cause disturbance or destruction of habitat, and may rely on methods in which it is difficult to detect small or elusive species, thus making estimates for entire communities impossible. eDNA can complement these methods by targeting different species, sampling greater diversity, and increasing taxonomic resolution. Additionally, eDNA is capable of detecting rare species, but not of determining population quality information such as sex ratios and body conditions, so it is ideal for supplementing traditional studies. Regardless, it has useful applications in detecting the first occurrences of invasive species, the continued presence of native species thought to be extinct or otherwise threatened, and other elusive species occurring in low densities that would be difficult to detect by traditional means.
It is important to note that absolute quantification of species abundance is only feasible under very specific conditions. In practice, this is largely restricted to wadeable rivers, where a narrow set of controlled environmental parameters can be met and where reference methods such as electrofishing are applicable. Outside of these limited contexts, both conventional ecological survey techniques and environmental DNA approaches generally provide only semi-quantitative information. In semi-quantitative frameworks, eDNA methods performoften better than traditional monitoring techniques. They reliably deliver ecological information such as species presence–absence, relative abundance patterns, and spatial and temporal trends. These data types are widely used in biodiversity assessment and long-term monitoring. Environmental DNA approaches also offer several practical advantages over many conventional methods. These include higher detectability, particularly for rare, cryptic, or low-density species; improved standardization across sites, seasons, and operators; lower overall operational costs; and non-invasive sampling that avoids disturbance or mortality of target organisms.
Degradation of eDNA in the environment limits the scope of eDNA studies, as often only small segments of genetic material remain, particularly in warm, tropical regions. Additionally, the varying lengths of time to degradation based on environmental conditions and the potential of DNA to travel throughout media such as water can affect inference of fine-scale spatiotemporal trends of species and communities.
Studies show that in both freshwater sites and oceans, DNA traces last only a few days, sometimes up to a week. That's why eDNA is like a snapshot: it captures which species were present, at that site and exact moment in time.
Despite these drawbacks, eDNA still has the potential to determine relative or rank abundance as some studies have found it to correspond with biomass, though the variation inherent in environmental samples makes it difficult to quantify. While eDNA has numerous applications in conservation, monitoring, and ecosystem assessment, as well as others yet to be described, the highly variable concentrations of eDNA and potential heterogeneity through the water body makes it essential that the procedure is optimized, ideally with a pilot study for each new application to ensure that the sampling design is appropriate to detect the target.
When comparing results obtained using different filters or when monitoring biodiversity changes over time, across seasons, or between years, data standardization is required. Standardization corrects for methodological biases introduced throughout the analytical process, as DNA from different species can amplify and sequence with varying efficiencies. This calibration is typically based on estimates of the initial number of DNA molecules present in the sample prior to amplification, often referred to as DNA copy numbers, combined with relative abundance metrics derived from sequencing data. Standardized datasets enable more reliable comparisons across samples, sites, and time periods.