The TripleLock™ Advantage

Environmental DNA (eDNA) methods offer several advantages over conventional species survey methods, especially when carried out with the right amount of expertise at each step of the workflow. Such advantages include time and cost savings, especially when on-site detection is used. eDNA offers higher sensitivity and specificity reducing observer bias compared to conventional methods. eDNA also reduces disturbances to the species and its natural habitat.

Although eDNA science is relatively young, it is maturing rapidly, with many aspects of survey design, sampling methods, and laboratory analysis being accepted as best practices – and one day industry standards [1,2]. However, eDNA surveys that fail to comply with such a set of best practices can quickly fall into a trap of poor detection probability and erroneous results. While eDNA surveys can appear relatively simple to conduct, one cannot simply grab a water sample in a Nalgene bottle and go to a lab, hoping to achieve optimal detection probability or have high confidence in their results. Quality eDNA surveys are conducted with careful consideration of all aspects of survey design and sample collection and processing.

Here at Precision Biomonitoring Inc. we developed the TripleLock™ Platform which at its core is based on the widely accepted best practices for eDNA surveys, placing us at the forefront of the eDNA survey industry. Conducting robust eDNA work is especially important at this early stage if eDNA methods are to be accepted so that the advantages of eDNA can be employed for environmental assessments and conservation biology across the world. The focus of this blog will be to outline the advantages of the TripleLock™ Platform and how they compare to other methods in the industry.

Platform Core I: TripleLock™ qPCR Assays

The first core of the TripleLock™ platform consists of high-quality, rigorously validated qPCR assays for the detection of a target species. At Precision Biomonitoring Inc. we have conceived and developed a proprietary method for stringent assay development, which allows us to consistently design and validate species specific and highly sensitive qPCR assays. We developed our method meeting and exceeding The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines. The MIQE guidelines outline several standards that should be adhered to for reliable and interpretable qPCR analyses, and by building our platform based on such rigorous standards we ensure replicable results while minimizing errors. Our TripleLock™ assays are also verified with highest level of international standards by a 3rd party ISO 17025 accredited laboratory. The advantages of following standardized methods are clear as they produce consistency in results. Assays need to conform to such rigorous standards and cannot be simply collected from different sources of literature and 3rd party reporting, since that would result in inconsistent standardization across the catalogue.

We take pride in developing our own assays in-house, as opposed to obtaining published assays in literature, as it allows us to optimize assays using our trusted methods and equipment. While the quality of assays in the eDNA literature may vary, one must remember that an assay consists of more than just a primer and probe set, it encompasses the exact type of qPCR reagents and instrumentation with which it was validated. This means that replicating the exact sensitivity of published assays on different equipment can entail extra costs and time, while also moving away from the MIQE standards if the published assay parameters can’t be replicated in vitro.

Additionally, our assays are designed with an internal positive control (IPC) to detect PCR inhibition, an absolute necessity to avoid false negative results. Many experts in eDNA science concur that checking for PCR inhibition is part of the foundation of good eDNA work [2].

Platform Core II: Optimal Survey Design

The second core of the TripleLock™ Platform is providing optimal survey designs to maximize the probability of detection for a target species. It is widely understood that the distribution of eDNA is not homogenous and depends on several variables including species ecology, water quality, pH, and turbidity, to name a few. It is for this reason that understanding the ecology of eDNA, and how best to sample for it, is so crucial. Our platform brings together optimal sampling designs, together with sophisticated sampling methods to conduct eDNA surveys with the utmost confidence.

We take into account the critical environmental variables, species biology and ecology, and site specific considerations that directly relate to the purpose of the study. At Precision Biomonitoring we use a proprietary method based on statistical analysis, including habitat occupancy modelling, to determine sampling optimality for a given eDNA survey. We are currently developing a software tool (patent pending) that will allow for customized survey designs based on the most up to date eDNA data available. This survey design tool adds another level of consistency and standardization to our workflow.

The foundation of a good sampling scheme is how the samples are collected to begin with. Our field sampling methods are far more advanced compared to other offerings. Precision Biomonitoring makes use of the ANDe™ sampling backpack, the first purpose-built aquatic eDNA sampling system [4]. The ANDe™ affords increased sampling versatility, allowing us to sample more effectively at depth, across transects, with increased throughput, and higher water volumes. For example, we can sample for species that typically reside on the bottom of a lake, at 30 feet deep, very easily using the ANDe™ system, which greatly increases the effectiveness of such a survey. This is something that cannot be done using small plastic bottles to sample surface water, a low fidelity practice offered by others in the industry. Our survey design methods along with our advanced sampling techniques let us conduct superior eDNA surveys compared to others in the eDNA industry.

Platform Core III: On-site Detection

The third core of the TripleLock™ platform is on-site detection which enables us to obtain rapid results in the field. On-site detection brings together several components of our field sampling methods including the ANDe™ filtration system, as well as on-site DNA extraction and qPCR analysis. The advantages of on-site detection come when obtaining results is time sensitive and a decision is pending. By making use of the Biomeme eDNA sample prep kit and Biomeme Three9™, a portable qPCR device, we can obtain results in the field and communicate them rapidly, via built in data connectivity, to a decision maker. This means no waiting on samples to be transported and analysed back at a central lab. Another advantage of these methods is that by filtering, extracting, and eluting DNA into a pH and thermostable buffer, in the field we minimize the potential for sample degradation during transit, thus decreasing the possibility of false negatives.

Removing the potential for sample degradation is an inherent advantage of our field methods compared to methodologies offered by others. By quickly extracting DNA from a filter into a stable buffer we preserve those valuable fragments of eDNA for analysis.

There are several eDNA analysis companies that offer sampling “kits” consisting of plastic bottles which are used to grab a small surface sample which then must be shipped all the way back to a lab before any analysis can start. Using handheld plastic bottles places the technician right where they need to sample, causing disturbances in the water as well as increasing the potential for contaminant transmission by said technician between sampling points, not to mention contamination stemming from the wet exterior of a bottle. By using the ANDe™ system we enable our technicians to be up to 3.6 meters from the point where the sample is being taken, and contain our sample in an enclosed and sterile, single-use filter cartridge, greatly reducing the potential for contamination.

Besides the serious disadvantages that stem from being limited to surface samples due to using bottles, other disadvantages and concerns arise from transporting water samples back to a lab for analysis. Transporting water samples introduces significant opportunity for eDNA to degrade. Transporting water samples on ice can potentially decrease degradation but does not eliminate it. Additionally, delays in shipment, especially during warm weather, make keeping water samples cold logistically difficult and at times impossible. By extracting and analysing DNA in the field we remove the potential for DNA to decay, and eliminate these significant logistical challenges.

Additionally by minimizing DNA degradation we eliminate the need for superfluous, ambiguous, and costly additional qPCR tests for so called total viable eDNA. These tests are often based on plant/algal DNA. Plant/algal cells are surrounded by resilient cell walls that prevent osmotic lysis, but animal cells are not. In theory this means that more plant/algal DNA may be preserved inside cells caught on filters prior to eDNA extraction, while animal cells easily lose integrity and release DNA, making it less long-lived compared to plant based eDNA. Inferences about the integrity of a target animal species DNA that are based upon the results of a plant based test are likely suspect. Furthermore, tests for total eDNA of a family of organisms, all fish for example, are also superfluous. Target DNA can be in extremely low abundance compared to total eDNA, shown to be > 0.0004% of total eDNA in some cases [5]. In theory, tests for total fish or amphibian eDNA most likely don’t give pertinent information about the integrity of a single target species eDNA, especially in cases where samples are taken from an isolated location or depth where other organisms in a family may not inhabit. A theoretical example of such a case is when an anthropogenically constructed habitat, i.e. a remediated river, is home to only a select few pioneer or reintroduced species. In these cases, a test for total fish eDNA may be interpreted as a false negative due to low abundance, not low integrity.

Precision Biomonitoring’s TripleLock™ Platform brings together multiple aspects of molecular biology, environmental survey design and sampling, and leading innovations to produce gold standard eDNA surveys. The development of our platform is anchored in the best practices for eDNA surveys as well as standards such as the MIQE guidelines, making Precision Biomonitoring an industry leader for eDNA surveys.


1.            Wilcox TM, Carim KJ, Young MK, McKelvey KS, Franklin TW, Schwartz MK. Comment: The Importance of Sound Methodology in Environmental DNA Sampling. North Am J Fish Manag. 2018; 1–5. doi:10.1002/nafm.10055

2.            Goldberg CS, Turner CR, Deiner K, Klymus KE, Thomsen PF, Murphy MA, et al. Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods Ecol Evol. 2016;7: 1299–1307. doi:10.1111/2041-210X.12595

3.            Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments. Clin Chem. 2009;55: 611–622. doi:10.1373/clinchem.2008.112797

4.            Thomas AC, Goldberg CS, Howard J, Nguyen PL, Seimon TA, Thomas AC. ANDe TM : A fully integrated environmental DNA sampling system. 2018;2018: 1379–1385. doi:10.1111/2041-210X.12994

5.            Turner CR, Barnes MA, Xu CCY, Jones SE, Jerde CL, Lodge DM. Particle size distribution and optimal capture of aqueous macrobial eDNA. Gilbert M, editor. Methods Ecol Evol. 2014;5: 676–684. doi:10.1111/2041-210X.12206

Exploring MIQE-Like Standards for qPCR-Based Molecular Detection of eDNA

Exploring MIQE-Like Standards for qPCR-Based Molecular Detection of eDNA

In the previous blog, we briefly touched on the MIQE standards – guidelines published to promote minimal standards of reporting qPCR data from clinical trials,  used to detect small relative changes in gene expression within cells or tissues, or to quantify the amount of pathogen(s). As much of contemporary environmental DNA work is conducted using qPCR approaches, it is only sensible that similar standards be developed for eDNA work, that should – it will be argued – extend beyond what is acceptable for clinical applications.

This is somewhat of a reverse of convention in scientific circles, as it is normally the case that clinical applications need an elevated burden of evidence to support any conclusions drawn from experimental data. However, because of the ecology of eDNA, its distribution in natural systems with myriad potential sources of generation and decay, allied to the hierarchical nature of its sampling, I believe eDNA presents a special case whereby more rigorous standards should be applied than clinical settings so that confidence in our results are trustworthy.

What are the chief differences in using qPCR to detect changes in gene expression or viral load, for instance, with using qPCR to detect eDNA? Figure outlines the workflow for A) performing a gene expression qPCR analysis alongside B) a generic eDNA workflow. In A) an investigator has a sample of tissue, within which there is guaranteed nucleic acid content. Imagine that they want to test for the expression levels of a gene that is hypothesized to play a positive role in alleviating stress in plants subject to an environmental stressor. In such studies, expression levels are compared against so-called reference genes, which are always expressed, so that any changes in the target gene can be compared after normalization of expression levels. Although plants in a control plot should show little expression of the target gene, it would still be expected to be found, albeit at reduced levels, in the plant tissue subject to nucleic acid extractions (in this case, messenger RNA, which is converted to DNA (complementary DNA or cDNA)) in a process called reverse transcription, so that the target is amenable to DNA-based qPCR. As such, one expects a lot of cDNA, if it were to be visualized using standard laboratory gel techniques. There will always be much more – and stable – expression of the reference gene’s mRNA, and by extension cDNA. In short: there is a reliable source of cDNA, which forms the template for the qPCR assays for both the target gene and the reference gene(s).

Generic workflows for conducting A) gene expression qPCR and B) eDNA qPCR

Consider then, environmental DNA. Leaving aside the complex ecology of eDNA to one side (subject to another blog post but see excellent review by Barnes and Turner [2]), the distribution of eDNA in a water body is largely ephemeral and at much lower concentrations, with no guarantee that sampling will entrain DNA molecules or cellular debris into sampling tubes or onto filter papers. There is a significant source of observational error at this stage, which is largely absent in gene expression studies, although both share procedural errors that can impact downstream qPCR success. Depending on factors including the volume of water sampled and pore size of the filters, the amount of total eDNA collected may vary substantially, although if visualized on a gel, is less likely to contain as much target as tissue-extracted mRNA turned cDNA.

Performing qPCR for either A) or B) requires taking an amount of total cDNA or total eDNA for use as template for the reactions. The probability of subsampling this extract and not getting detection is higher for eDNA, due to the extra rarity of the target in both the water sample – which may not have been collected at all – and by the relative rarity of the target molecule in the soup of total eDNA molecules.  For eDNA we have two uncertainties in sampling due to the two sampling events – of the water body and of the total eDNA – that act to increase underlying statistical error.

Bottom-line: eDNA needs to adopt more sensitive and, arguably, more specific assays than clinical applications. More specific? Well, whilst many genes evolve during evolution by way of duplication and subsequent divergence, these events are localized to gene families so whilst when developing a qPCR assay for a gene with a number of evolutionary similar orthologs and paralogs, the nucleotide divergence between these genes and all others in the genome of a single tissue type is huge, a dn thus non-specific amplification of a non-target gene attenuated. However, when using an eDNA marker, that same stretch of DNA (e.g., COI locus) is present in all non-targets, as it has been inherited by a common ancestor. However, evolution will increase the number of nucleotide differences between species as generations pass. However, more recently diverged taxa may not display enough between species variation to design an appropriate assay – but see the previous blog for an in-depth treatment of assay design and target specificity.

In the next blog, I shall discuss exactly how we estimate LOD, LOQ – further adopting MIQE standards – and how we optimize an assay to befit a rigorous, repeatable and reproducible eDNA assay. To do so, not only do we need to optimize the target, but also identify and countenance factors that input variance into the system, most nefariously PCR inhibitors. I shall describe how we use MIQE-like guidance and synthetic internal positive control elements to determine the reliability of eDNA results. We shall also discuss in the future, how the ecology of eDNA and assay performance in pilot trials can be used to optimize detection (and the potential quantification) of targeted eDNA detection studies.

[1] Bustin et al. (2009). The MIQE guidelines: minimum information for publication of qPCR experiments. Clinical Chemistry 55,

[2] Barnes and Turner (2016). The ecology of environmental DNA and implications for conservation genetics. Conservation Genetics, 17, 1-17.

[3] Doi et al. (2017). Environmental DNA analysis for estimating the abundance and biomass of stream fish. Freshwater Biology, 62,

 [4] Nevers et al. (2018). Environmental DNA (eDNA): a tool for quantifying the abundant but elusive round goby (Neogobius melanostomus). PLoS One, 13,

[5] Evans et al. (2016). Quantification of mesocosm fish and amphibian species diversity via eDNA metabarcoding. Molecular Ecology Resources, 16, doi/10.1111/1755-0998.12433.

[6] Hunter et al. (2016). Detection limits of quantitative and digital PCR assays and their influence in presence-absence surveys of eDNA. Molecular Ecology Resources, 17, doi/10.1111/1755-0998.12619.

[7] Forootan et al. (2017). Methods to determine limit of detection and limit of quantification in quantitative real-time PCR (qPCR). Biomolecular Detection Quantification, 12, 1-6.

***cDNA vs eDNA (diagram of how each is made and detected)? – largely deterministic range of signal vs. stochastic ephemeral signal; # orthologs/paralogs intragenomically vs. interspecifically; targets are constant within cells vs. ephemeral and temporal-spatial of species distributions and predictors of shedding rate and eDNA decomposition in ecosystems. ***

Detecting eDNA – The Importance of Assay Specificity and Sensitivity Part I: An Introduction to the MIQE Guidelines and DNA Sequence Database Generation & Curation for qPCR Assay Design

MIQE – a Brief Introduction

In 2009, Bustin and colleagues[1] codified a set of minimal requirements for the publication of quantitative real-time polymerase chain reaction (qPCR) data, ostensibly for use in the fields of clinical medicine and molecular biology, as a means of ensuring rigorous datasets that enable investigators to quantify subtle changes in the expression of particular genes or in the estimation of viral load, for instance. In gene expression studies, fractional changes in the production of intracellular messenger molecules, called mRNAs, which convey genetic information encoded in genes to their proteinaceous or final form, need to be perceived so as to test crucial hypotheses of physiological, medical and even evolutionary and ecological import. Bustin et al. address some of the minimal mandatory standards that need to be reported to ensure continuing robust detection of nucleic acids at low concentrations. These standards were termed the MIQE guidelines: Minimal Information for the publication of Quantitative PCR Experiments.

We will discuss these guidelines and how they compare with eDNA standards in future posts, but for now we will divulge the chief difference between the studies that invoke MIQE and those that involve environmental DNA quantification using qPCR: that concentrations of eDNA, depending upon the circumstances in which it is collected, is likely to be several orders of magnitude lower than many observed changes in the level of mRNA expression or absolute levels of viral genomes present within tissues. Consequently, we need to modify MIQE standards to ensure that eDNA surveys are protected from accusations of too lax standards that may result in the significant incurrence of Types I and II error (false positive and negatives, respectively). Like ancient DNA (aDNA) before, eDNA detection has to elevate these standards to a higher level given the much more ephemeral nature of the target molecules in contemporaneous natural systems.

The Importance of Specificity and Sensitivity

Shared with aDNA and biomedical applications of qPCR assays, is a vital dependency on two touchstones of molecular-based detections: specificity (or: what is the likelihood of incorrectly detecting a non-target, which may lead to Type I error if not wholly specific to the target(s)?) and sensitivity (or: what is the likelihood of detecting extremely low concentrations of the target species, which, if unquantified, may lead to Type II error?)[2]. The very first step to minimize these potential sources of error is to design extremely robust assays in silico, which depends, crucially, on having substantial genomic data from target and sympatric (co-distributed) non-target organisms with which to design highly discriminative assays. To do so, genomic information is paramount; one must collect and curate a significant body of genetic sequence information.

Information is Key – An Evolutionary and Population Genetics Rationale for Data Generation and Curation

How much genomic information is enough? And how can we account for high levels of within-species genetic variation, and low levels of genomic sequence divergence between closely-related, sister and/or cryptic species? All excellent questions, and each needs to be answered satisfactorily, or one cannot with good conscience publish or make available a generic assay for the target species in question.


In this post, we have broadly discussed gathering and curating data for designing a species-specific assay to be generally deployed across a species’ range, or at least in the populations from which sequence data were generated. Here at PBI, we are also developing novel ways to tease apart even extremely closely-related species with low levels of nucleotide variation. In a similar vein, we also hope to design population-specific assays that may be able to target individual evolutionary significant units (ESUs) and management or conservation units (M/CUs)[9].

In the next blog, we shall develop further the required minimal standards of a reliable, robust and reputable eDNA qPCR assay, and how these requirements extend and embellish those of the MIQE guidelines. We shall treat in some detail the concepts LOD (limit of detection) and LOQ (limit of quantification) and ask: what are the crucial assay requisite parameters (CARP) for designing, optimizing and validating eDNA assays?

[1] Bustin et al. (2009). The MIQE guidelines: minimum information for publication of qPCR experiments. Clinical Chemistry

[2] Lahoz-Monfort et al. (2015). Statistical approaches to account for false positive errors in environmental DNA samples. Molecular Ecology Resources16, doi: 10.1111/1755-0998.12486.

[3] Hale et al. (2012). Sampling for Microsatellite-Based Population Genetic Studies: 25 to 30 individuals per population Is enough to accurately estimate allele frequencies.  PloS One, doi: 10.1371/journal.pone/0045170

[4] Luo et al. (2018). Biparental inheritance of Mitochondrial DNA in humans. Proceedings of the National Academy of Sciences, doi: 10.1073/pnas.1810946115

[5] Benasson et al. (2001). Mitochondrial pseudogenes: evolution’s misplaced witnesses. Trends in Ecology and Evolution16, 314-321.

[6] Smith & Smith (1996). Synonymous nucleotide divergence: what is “saturation”? Genetics142, 1033-1036.

[7] Felsenstein J (2003). Inferring Phylogenies, Sunderland (Sinauer).

[8] Crête-Lafrenière et al. (2012). Framing the Salmonidae family phylogenetic portrait: a more complete pictire from increased taxon sampling. PloS One, doi: 10.1371/journalpone.0046662.

[9] Palsbøll et al. (2006). Identification of management units using population genetic data. Trends in Ecology and Evolution22, 11-16.

Using Environmental DNA (eDNA) to Monitor Aquatic Ecosystems

Environmental DNA monitoring – eDNA – is at the vanguard of a new wave of technologically advanced monitoring efforts. With roots in soil and paleoecology, eDNA was first used to detect a multicellular aquatic organism– the invasive American bullfrog Lithobates catesbeianus– in a landmark scientific paper by Ficetola et al. in 2008[1]. By applying the widely used, yet highly sensitive, polymerase chain reaction (PCR) – a reaction that ‘amplifies’ a specific DNA sequence in a sample, only if the species’ DNA is present in however little amount – Ficetola and colleagues detected the frogs’ DNA in sediment filtered out of the water column. To understand why, a current working definition of eDNA – adopted by most ecologists – will illuminate: eDNA is “…genetic material obtained directly from environmental samples (soil, sediment, water, etc.) without any obvious signs of biological source material” (Thomsen & Willerslev 2015[2]). Examples of how eDNA is shed by an organism – e.g., here a midland painted turtle Chrysemys picta – are illustrated in the figure below.

Blog pic 1

In the figure, DNA-containing cells are constantly or periodically shed from the internal linings of the turtle’s gut, reproductive system, through regurgitation of food, the replacement of skin cells and mucus, the egress of waste materials, and through the release of sex cells (i.e., sperm and eggs). Once in the water, DNA is somewhat protected within cells. Eventually, cells are broken down and DNA is released into the aqueous environment whereupon it is to be found in solution. Although eDNA is depleted through a number of biological, chemical and physical processes, it will keep being replenished if the organism is still to be found living in the vicinity. That is to say, the signal of eDNA will be stronger the closer it is to its source, and will also increase when there are more individuals in a local population, if the volume of water remains the same. Therefore, an eDNA signal can be a reliable indicator of the target species’ presence in a given habitat.

What are the chief benefits of eDNA monitoring? First of all, there is no need to physically sample the species, thus minimising disturbance associated by the targeted monitoring of live creatures that are sensitive to stress. Furthermore, as only water samples are taken, and by few people, there will be a decrease in the environmental footprint associated with monitoring efforts per se. Because PCR is a highly sensitive molecular biological assay, eDNA surveys tend to have much higher sensitivities to be able to detect rare or cryptic species than conventional methods (e.g., Schmelzle & Kinziger 2016[3]). As a result, species-specific eDNA surveys are also potentially much more cost-effective than conventional techniques. Furthermore, anyone can take a water sample, following simple instructions, democratising and facilitating citizen science projects across the globe. Indeed, the current crop of companies that offer eDNA detection services are predicated on a model of water samples being collected by lay and technical personnel for processing back in a central laboratory.

However, as eDNA is a nascent technology, uncertainty exists over some of the conclusions drawn from early eDNA studies. However, these issues (i.e., sources of ‘error’) are under ongoing scrutiny by scientists, including here at Precision Biomonitoring, to minimise their impacts. As such, all potential sources of error, as they are currently understood, must be acknowledged and incorporated into any technical development (i.e., design, production and validation of species-specific PCR assays) or standard operating protocols for field surveys. For example, organisms are liable to move throughout their lifetimes. Seasonality has shown to be a strong factor in eDNA detection success (de Souza et al. 2016[4]). It is imperative that surveys are conducted with a thorough knowledge of a species’ ecology, including insight into current distributions and habitat preferences, otherwise inadequate surveying will lead to a false negative result, i.e., inferring a target to be absent when it actually is present; just undetected. Failure to account for false negatives can result in severe financial repercussions if infrastructure projects are subsequently halted, put on-hold or abandoned due to the rediscovery of the target by an intrepid ecologist or member of the public. False negatives can also result from improper assay development, the underestimation of within-species genetic diversity at PCR amplification sites, and by the current disjointed process by which eDNA samples are processed by the majority of eDNA practitioners.

As noted previously, eDNA will decay if left exposed to natural world processes. Therefore, collected eDNA is at risk of post-sampling decay, as there would be no mechanism for eDNA replenishment in the collection vessel, reducing the eDNA signal and potentially failing to garner a positive PCR result. Therefore the risk of eDNA degradation during sampling – particularly on hot, sunny days – and in transit from the field to the laboratory, is highly significant. Inappropriate storage may also destroy eDNA (e.g., water crystal formation during freezing may ‘shred’ DNA molecules). To compound the status quo further, despite the best efforts of contemporaneous laboratories, there remains a significant risk of false positive PCR results mediated by the transportation of aerosolised DNA particles among labs within buildings through ventilation pathways. Most eDNA practitioners seek to physically separate the processing of eDNA samples (e.g., filter papers or precipitated water samples) with downstream PCR detection, but even that is far from fool-poof.

Here at Precision Biomonitoring, we are set to unveil a state-of-the-art platform that will seek to eliminate, or minimise, these sources of eDNA analytical and sampling error, through the eradication of transit stages to a central laboratory and the application of standard operating procedures. Moreover, we will further the cause of a democratised biomonitoring field in which no technical specialty is required to conduct sophisticated PCR-based species-specific assays. Our system, using bespoke PCR assays, will yield PCR eDNA results in real-time (< 2 hours from water sampling to PCR read-out), which can then be immediately disseminated to colleagues via the cloud.

It is our aim to give those working at the coalface of biodiversity monitoring (from professional ecologists to local citizen science projects), the power to conduct highly rigorous, and potentially highly coordinated, targeted eDNA surveys to better vouchsafe our world’s biodiversity heritage for all generations to come.

[1] Ficetola et al. (2008). Species detection using environmental DNA from water samples. Biology Letters4, 423-425.

[2] Thomsen & Willerslev (2015). Environmental DNA – An emerging tool in conservation for monitoring past and present biodiversity. Biological Conservation183, 4-18.

[3] Schmelzle & Kinziger (2016). Using occupancy modelling to compare environmental DNA to traditional field methods for regional-scale monitoring of an endangered aquatic species. Molecular Ecology Resources16, 895-908.

[4] de Souza et al. (2016). Environmental DNA (eDNA) detection probability is influenced by seasonal activity of organisms. PLoS One, doi: 10.1371/journal.pone.0165273