The two main challenges of bioassessment are (1) the detection of species and (2) their correct taxonomic identification. DNA-based monitoring tools can help address both aspects. For small-bodied and species-diverse groups such as benthic macroinvertebrates and diatoms, the monitoring challenge lies not so much in species detection but in the need for rapid, cost-effective and accurate identification of taxa. The best-validated approach for DNA-based biomonitoring of these groups is to follow established sample collection protocols (as outlined in existing standards e.g. ISO 16665:2014; ISO 10870:2012; ISO 10870:2012; ISO 10870:2012; CEN/EN 13946:2014; CEN/EN 14407:2014), substituting morphological identification of taxa with metabarcoding and DNA-based taxonomy (Hering et al. 2018). A Technical Report on sampling benthic diatoms has been adapted for metabarcoding and published by the European Committee for Standardisation⁷, and in 2019 a new working group was established within the CEN Technical Committee on Water Analysis (CEN/TC 230) for the development of standards for DNA-based assessment of aquatic biodiversity. This represents a clear marker of the appetite for uptake of these tools.

For fish and invasive non-native species, the challenge for monitoring lies principally in detection rather than identification. Conventional fish survey methods (e.g., electrofishing and netting) are labour-intensive, inefficient for community assessment, and often cause harm or stress for the fish (Snyder 2003), while surveillance for invasive species lacks the sensitivity to detect target species at low population levels (e.g., when first introduced and not yet established) when a rapid management response could minimise the overall cost and impact of the invasion. This creates a strong driver for new sampling methods that increase detection sensitivity for these groups while lessening the physical impact on fish. Therefore, for these target groups we focus on surveys using aquatic eDNA.

We first consider field sampling and preservation methods for each of four sample types:

• aquatic eDNA
• bulk invertebrates
• benthic periphytic diatoms
• soils/sediments

We then outline key quality control checks to be applied to DNA extracts and a framework for positive and negative controls to be integrated into the workflow.

Next we give a detailed overview of the laboratory steps, decisions and trade-offs associated with the two broad approaches to sample analysis:

• Targeted species detection using qPCR and allied methods.
• Community assessment using metabarcoding

For completeness, we give a brief overview of the major choices and considerations in bioinformatics processing, focusing on those that materially affect the results obtained.

Finally, we summarise the key factors that influence methodological decision-making and outline key practical recommendations for DNA-based biomonitoring (See Figure 1 for an overview of the document contents).

Figure 1.

Practical Guide overview.

DNA can be captured in various states, and the state in which it is captured influences how it needs to be handled, processed and interpreted. In particular, we make a key distinction between organismal DNA, which is captured in the form of whole organisms, and extra-organismal DNA, which is captured in the absence of the organism they originated from.

While various definitions have been proposed and employed, we define environmental DNA (eDNA) as genetic material that has been isolated from environmental samples such as water, soil or air (Taberlet et al. 2012a, Pawlowski et al. 2020b). A key feature of eDNA is the presence of both organismal DNA from microscopic organisms such as protists and bacteria (organismal DNA) and extra-organismal DNA from larger organisms (Pont et al. 2018, Rodriguez-Ezpeleta et al. 2021).

Spatial and temporal interpretations based on detection of species from extra-organismal DNA are complex because the DNA may have travelled away from the point at which it was released from the organism. Extra-organismal DNA is also typically present at very low concentrations in a sample, which makes it highly vulnerable to contamination. Special precautions need to be taken both in the field and the lab in order to mitigate this risk (Goldberg et al. 2016). The inference challenge and low concentrations have particular implications for the design and validation of methods used for the detection of DNA from environmental samples (i.e., eDNA assay) and for the level of replication required to overcome inherent stochasticity associated with very low target concentrations. We cover these implications in depth below, but briefly outline the main principles here.

In the field, key considerations in working with eDNA include:

• Avoiding situations where the same sampling equipment comes into direct contact with multiple independent samples, unless the equipment is decontaminated in between.
• If sampling from a boat, collecting water from the bows to minimise the risk of contamination from the boat itself. In rivers, start sampling at the most downstream point and travel upstream.
• In lentic water bodies (non-flowing water), avoid entering the sampling area prior to or during sample collection in order to avoid transfer of DNA from footwear or clothing. In lotic (flowing) water bodies where it is necessary to enter the water, standing downstream of the water you collect.
• Wearing disposable gloves to avoid introducing your own DNA to the sample and to reduce the risk of cross-contamination between samples.
• Frequent use of negative field controls, especially where equipment is being decontaminated and reused (see section 6.3 for more details).

In the lab, key aspects of working with eDNA include:

• Dedicated clean-room facilities for working with low quantity DNA.
• Physical separation of the spaces used for different stages of the analysis, and implementation of a unidirectional workflow, with particular attention to separating pre- and post-PCR processes.
• Frequent decontamination of surfaces and equipment.
• Diligent use of gloves, which are regularly changed to reduce the risk of cross-contamination between samples, and face masks to avoid introducing human DNA where this could interfere with the analysis (e.g. if analysis targets a taxonomic group that includes humans such as primers amplifying mammals, vertebrates, and even general eukaryotic primers).
• Frequent use of negative lab controls at each new step of the process (i.e., filtration, extraction, PCR and indexing samples if pooled onto a single sequence run).

A variety of different water collection strategies provide robust data with good detection probabilities. These range from sampling continuously across the area of the waterbody for a set period of time, or pooling subsamples from different point locations into single merged sample (Pont et al. 2019), to taking multiple discrete samples spread out across the area of the waterbody (Hänfling et al. 2016). The latter provides more information about the spatial distribution of species and enables analysis of occupancy, but the greater number of samples will increase overall cost.

As with any ecological survey, robust sampling design prior to field collections is essential to ensure the data obtained are fit for the purpose required. A significant advantage of an eDNA approach is that biological replication - crucial for robust statistical analysis - is easily incorporated into survey design.

Environmental DNA sampling design needs to account for (1) the physical and chemical properties of the matrix from which it is isolated, (2) environmental variability, and (3) the ecology of the target species to be surveyed. While many studies have calculated sampling effort for particular species in given environments using occupancy modelling and allied methods (e.g. Erickson et al. 2019), conclusions are often difficult to extrapolate to other environments, species groups and analysis workflows, which can vary in efficiency. Here, we outline some general principles that can be applied to identify circumstances when greater sampling effort is likely required for species detection.

Environmental DNA persistence in space and time is influenced by a multitude of factors. These include season, waterbody size and depth, temperature, stratification, connectivity, substrate, water chemistry, and flow. It is often difficult to tease apart specific effects, especially in natural settings, since combinations of factors will work synergistically or antagonistically to directly or indirectly facilitate degradation of eDNA (Stewart 2019). We do not attempt to provide a complete review of these factors in this guide (see Harrison et al. 2019 and Torti et al. 2015 for reviews), but instead highlight those most likely to impact species detectability.

One of the most important aspects of eDNA is its spatial distribution in the environment, which integrates how far eDNA travels from its “point of release” and how well mixed it is in the water column (Deiner and Altermatt 2014, Deiner et al. 2016, Hänfling et al. 2016, Jerde et al. 2016, Shogren et al. 2017, Macher and Leese 2018, Pont et al. 2018). This is worth considering in some detail for each of the major types of water body (summarised in Table 1 and Figure 2):

Figure 2.

Water eDNA sampling design. Sampling locations should consider the spatial distribution of eDNA within the environment being sampled, i.e., lentic, lotic or marine conditions. This figure indicates possible sample collection points in each of these environments.

• In ponds and lakes, eDNA is patchily distributed at small-scale intervals (Brys et al. 2020a), meaning that water needs to be sampled (or sub-sampled) from multiple points to maximise detection probability. Seasonal circulation patterns can influence vertical distribution of eDNA in the water column of large lakes. This is largely driven by water temperature, which determines the extent to which the water column is mixed or stratified (e.g. thermal stratification), and seven different thermomix groups have been described (Thomas et al. 1996). Environmental DNA is well mixed during periods of thermal circulation, meaning that surface samples are representative of the whole water column, while during stratification periods the water is layered and eDNA needs to be collected from the different layers for complete sampling. For instance, studies from lakes on both sides of the Atlantic ocean have demonstrated that in monomictic lakes eDNA is stratified during the summer months but homogeneously distributed in the water column in winter (Handley et al. 2019, Littlefair et al. 2020). In practical terms, this means cold-water species occupying the deeper water can be detected in surface water or shoreline samples collected in these lakes during the winter, but only from water collected below the thermocline in summer. Many Scandinavian lakes are dimictic, meaning that they undergo two circulation periods (spring and autumn), and two thermal stratification phases (summer and winter).
• In rivers, eDNA is more evenly mixed in the water column but sampling design needs to consider downstream transportation and tributary dilution of eDNA from its point of release. This means deciding whether to sample upstream or downstream of tributaries or potential sources of environmental contamination, depending on the aim of the monitoring. For instance, sampling immediately downstream of inhabited areas carries a risk of detecting species whose DNA in the water originates from food items via wastewater rather than the presence of the species itself in the local aquatic ecosystem. Modelling the spatial distribution of eDNA in rivers is challenging (Harrison et al. 2019) and it is not yet clear how results from particular studies can be transformed into general models that are applicable across catchments. Thus, some element of spatial uncertainty must usually be accepted when sampling eDNA in rivers. As a guiding principle, the greater the flow rate, the larger the upstream area represented by a sample. This may vary from a few hundred metres in slow-flowing lowland streams and rivers to tens of kilometers in fast-flowing systems (Pont et al. 2018, Seymour et al. 2018). When evaluating the impact of barriers in rivers, eDNA is powerful for indicating lack of upstream movement, but caution should be applied when interpreting results relating to downstream movement. Robust statistical approaches should be integrated into the design of monitoring programmes, particularly where downstream movement of species is of relevance to the aims of the monitoring, and the specific hydrology of the catchment area should be taken into consideration. Detailed discussion of sampling strategy in rivers is given by (Carraro et al. 2020).
• In oceans, less is known about how hydrological systems affect eDNA transportation and distribution and a lot more research is required to fully understand how to optimise marine sampling strategies and spatially interpret results. However, several studies have shown that communities obtained from eDNA metabarcoding are surprisingly representative of the immediate local habitat where the sample was collected (e.g. Port et al. 2016, Yamamoto et al. 2017, Jeunen et al. 2019, Djurhuus et al. 2020). Like in lakes, vertical stratification in the water column (e.g. due to thermoclines and haloclines) restricts mixing of DNA, meaning that water samples should be collected from each different depth zone to fully characterise marine communities at a particular point location (Jeunen et al. 2020).

Chemical, physical and biotic factors influence the persistence of eDNA in the environment by affecting the rate at which it is degraded. Faster degradation reduces the time window for species detection, which carries advantages and disadvantages. On the one hand, it gives less opportunity for the DNA to travel long distances from the point of release, giving somewhat greater precision in temporal and spatial inference. On the other hand, sampling may need to occur more frequently or with greater spatial sampling effort to fully characterise communities.

Factors that affect the rate of eDNA degradation include:

• Low pH is generally associated with a faster rate of degradation (Strickler et al. 2015, Seymour et al. 2018). A mechanistic assessment of pH-related degradation of eDNA has yet to be conducted, but some speculation can be derived from classical molecular biology. Degradation of DNA is particularly likely when positively charged enzymes, indicative of acidic conditions (i.e., low pH), are present, which is why preservation buffers for DNA extracts are typically alkaline (e.g., Tris, EDTA buffer at pH 9). Moreover, extracted DNA will degrade if left in water due to acid hydrolysis, particularly below pH 7.5 (Torti et al. 2015).
• eDNA degrades faster in warmer water because of increased microbial activity, which is a strong driver of eDNA degradation (Zulkefli et al. 2019). However, in practice the faster degradation of eDNA in warmer waters will usually be offset by increased rate of eDNA production, linked to greater activity under these conditions (discussed below). Oxygenation levels may also play a role in degradation rate, and this is also linked to water temperature, with oxygen saturation decreasing as water temperature rises (Bozinovic and Pörtner 2015). DNA structure is highly stable in dry anoxic conditions, but decays rapidly via hydrolysis in oxygenated environments (Torti et al. 2015).
• High nutrient loading is often associated with increased microbial activity, which is expected to negatively impact eDNA detection through increased consumption or absorption of the freely available genetic material (Barnes and Turner 2015). This means that eDNA may degrade faster in environments with high nutrient inputs including agricultural run-offs, though this has yet to be tested.

It is also important to note that different subcellular components degrade at different rates, so detectability may vary according to the gene region targeted for analysis. Most commonly-used mitochondrial gene fragments (e.g. COI, 12S and 16S) will persist in the environment longer than ribosomal DNA fragments (e.g. 18S) due to the more resilient structure of the mitochondria once cells start to degrade. However, the higher abundance of ribosomal genes may offer a better alternative for localized monitoring under certain conditions (Jo et al. 2019b, Moushomi et al. 2019).

Factors related to the biology and ecology of the target organism(s) can also affect detection probability. These include:

• Habitat preferences and patterns of seasonal activity and movement, which affect the probability a particular species will be present or detectable in a given water body at a given time of year.
• Aspects of life-history mean that some lifestages may be more detectable than others. For instance, some species or groups of organisms shed very little DNA as adults (e.g. crayfish; Rusch et al. 2020) but are readily detected during breeding season when adults are more active, eggs and sperm provide an abundant source of eDNA, and juveniles are growing and moulting.
• Biological and physiological traits affect eDNA detectability in several ways:

• Some animal forms shed more DNA than others. For instance, animals with exoskeletons seem to shed less DNA (Allan et al. 2020), as do those with dry, scaly skin such as reptiles. Conversely, those that produce lots of mucous or shed skin / scales, are more readily detectable.
• Faeces represent a major source of eDNA, meaning that toilet habits are a key predictor of detectability. Some semi-aquatic species that use latrines on land tend to be underrepresented in eDNA surveys (e.g. otters), while terrestrial animals that commonly defecate in or above the water may be overrepresented (e.g. tapirs). This also means that frequency of feeding and defecation (i.e. energy use) are linked to detectability of aquatic species (Klymus et al. 2015). It therefore follows that ectothermic (cold-blooded) organisms are expected to shed less DNA than endothermic (warm-blooded) ones.

Behavioural factors interact with life-history and physiology to affect the amount of DNA released by a given species at any one time. Animals have been documented to release more eDNA when they are stressed, when they are active, and when they are warm (Jo et al. 2019a, Thalinger et al. 2021a).

• Thus, if animals become less active during cold weather, they may be expected to release less eDNA and therefore to become less detectable during these times, although note that this may be offset to some extent by slower degradation of eDNA at lower water temperatures, as mentioned above.

Taking these factors into account allows estimation of how detection probability is likely to vary temporally and spatially for a given species, and more intensive sampling regimes may be required when conditions or timing are not optimal for detection, or when the species’ behavioural or physiological traits mean that it is likely to be underrepresented in aquatic eDNA. Table 2 summarises factors that could be expected to reduce detection probability and recommends how to adjust sampling strategies to account for these. Note that some of these factors have not yet been studied in great depth and they will not affect all target species to the same extent in all environments. However, where several of these factors apply, it should be considered that greater sampling effort may be required in order to achieve a high detection probability.

Two principal methods have been used for capture of eDNA from water.

• Ethanol precipitation involves adding water to an ethanol and salt (usually sodium acetate) solution. At low temperature, the salt changes the charge of dissolved DNA so it becomes hydrophobic and comes out of solution (precipitates). Precipitated DNA is then forced into a pellet at the bottom of the tube through centrifugation and the ethanol solution discarded. DNA extraction is applied to the pellet.
• Filtration involves passing water through a fine porous membrane. Cellular and subcellular material is captured on the membrane and preserved ready for DNA extraction.

Although early studies (e.g. Ficetola et al. 2008) used ethanol precipitation as the primary capture method for eDNA, there is now broad consensus that filtration is a more effective approach for detection of aquatic species in most environments (Tsuji et al. 2019; see Text Box 1 for more details), and we recommend that this is the first method to consider for aquatic eDNA monitoring programmes until different or new knowledge is gained about the benefits of other methods. Henceforth in this guide, we focus on the various decisions that one must make when using filtration-based capture methods.

Note that in certain environments where cellular breakdown happens very fast (e.g. environments of extreme heat or acidity), precipitation may be a more effective method because a greater proportion of the available DNA will be extracellular.

A wide variety of equipment is used for filtration-based capture of eDNA from water, including different filter membrane materials, pore sizes, and filtration mechanisms, different transportation, storage and preservation methods, and different DNA extraction protocols. Due to the number of variables in a given workflow, it is difficult to robustly assess the importance of the choices made at any particular step. Moreover, there are almost certainly interactions between different elements of the process; for instance, certain membrane materials or filter designs may be best suited to use with particular pore sizes or DNA extraction methods, while others work optimally with a different combination of choices (Deiner et al. 2018).

Our overarching message is that the first step should be to identify the main constraints for the project (e.g. time, remoteness, budget, availability of equipment), the characteristics of the study system, and the key aspects of the methodology that they affect. Next, optimisation should be carried out to determine the most effective combination of choices in the rest of the workflow, given the identified constraints. Reassuringly, many studies now show that with appropriate workflow optimisation, eDNA analyses are highly robust to different choices made (e.g. Li et al. 2019, Di Muri et al. 2020).

We identify three distinct categories of filters used for eDNA capture (See Figure 3):

Figure 3.

Water eDNA filter types. 1: Open filters are exposed to the air during filtration either in the field or lab (a and b). 2: Housed filters are a membrane placed in a solid unit during filtration (c). Filters from Open and Housed units need to be removed from the filtration unit and stored in a petri dish or eppendorf tube until extraction (c and d). 3: Enclosed filters are systems in which the membrane is enclosed within the outer housing (e and f). Extraction is carried out directly from the enclosed filtration unit.

1. Open filters are membranes that are exposed to the air during filtering. These are commonly used with vacuum pumps when filtering in a laboratory setting, and also include cup-type filters such as the Analytical Test Filter Funnel from Nalgene, which is commonly used with peristaltic pumps in the field. These filters carry the greatest risk of contamination because not only are they exposed during filtering, but they must usually be handled during the preservation process and usually again during DNA extraction. This handling must be carried out with the utmost care to avoid cross-contamination, and the use of negative filter controls is highly recommended (Goldberg et al. 2016).
2. Housed filters describe systems in which a filter membrane is protected within a solid housing during the filtration process. Their advantage is these filters can easily be opened and the filter removed for preservation and later processing. When opened in the field, care must be taken not to contaminate the samples. Preservative solutions can be added directly to the filter membrane inside the housing and transported to the laboratory, cleaned on the outside and opened in the laboratory in clean room conditions in order to avoid contamination. As with the open filters, transferring the filter carries a risk of contamination and must be carried out with great care and appropriate negative controls to check for contamination. However, this type of housing allows for the greatest flexibility of filter types and extraction methods, including those that involve bead beating.
3. Enclosed filters are systems in which the membrane is enclosed within an outer housing from which it is not removed, and no handling of the filter membrane is required during the process. Preservative solutions can be added directly to the filter membrane inside the housing. Examples include Millipore’s Sterivex units and various varieties of disc filters. These systems are the most robust to field contamination and are likely to be the favoured option for large-scale surveys where samples are being collected by contractors or volunteers who are not molecular biologists. However, limited combinations of pore sizes and membrane materials are available, and the units themselves tend to be more expensive than open or housed filters. Enclosed filters are incompatible with mechanical lysis (the physical breakdown of cell membranes), which is needed for DNA extraction from diatoms captured in the water sample. Therefore, open or housed filters should be used if you aim to assess pelagic diatom from the eDNA samples.

The combination of membrane material and pore size is important for determining how much volume can be filtered through a single unit. A membrane must be hydrophilic for water to easily pass through, and note that some membrane materials (e.g. PVDF) can be purchased in both hydrophilic and hydrophobic versions.

Commonly used materials include Cellulose Nitrate (CN), Polyethersulphane (PES), Polyvinylidene Difluoride (PVDF), glass fiber (GF) and Polycarbonate Track Etched (PCTE). Some membrane material may introduce constraints in relation to other parameters; for instance, GF membranes cannot be produced with consistent pore sizes due to the matrix-like nature of the material, so the stated pore sizes are nominal only and are rarely below 0.7 μm. PCTE membranes are difficult to incorporate into enclosed filters because the material is less strong than others and cannot withstand the same amount of pressure during filtration without additional support. Cellulose nitrate filter membranes have been found to disintegrate when stored for long periods in ethanol.

Pore size selected for eDNA capture varies substantially but is most commonly below 1 μm. The volume of water that can be passed through a filter membrane generally increases with pore size due to a reduced rate of clogging, but the trade-off is that the smallest particles containing eDNA may pass through the filter. Experiments on fish eDNA (Turner et al. 2014, Wilcox et al. 2015) have shown that most eDNA-containing particles are in the range of 1-10 μm, so eDNA loss is expected to be minimal for pore sizes up to 1 μm and even a little larger (Deiner et al. 2018). Several studies targeting macrofauna have shown that filtering more water with a larger pore size is more efficient than filtering less water with a smaller pore size (Hosler 2017, Sepulveda et al. 2019), and even very large pore sizes 64 μm (Schabacker et al. 2020) have been used with success to capture fish eDNA. However, smaller pore sizes (<1 μm) should be used to target species such as macroinvertebrates, which produce smaller quantities of eDNA than fish (Moushomi et al. 2019).

The most commonly used pore sizes in published studies are 0.22 μm and 0.45 μm. While there are often good reasons for choosing these smaller pore sizes, this is also partly a function of the limited range of commercially available filters with larger pore sizes. For instance, the widely-used Sterivex filters are only available in 0.22 μm or 0.45 μm. Note that if you aim to capture total microbial diversity as well as eDNA from larger organisms, a smaller pore size is advised (generally around 0.2 μm; Lee et al. 2010).

In particularly turbid waters, or where a small pore size is required, pre-filtering through a membrane with a larger pore-size can be an effective way of maximising sample volume by removing larger particles of sediment and plant material before passing the water through the main filter for capturing eDNA. This carries a risk of losing some eDNA particles during pre-filtering so it is recommended that DNA is extracted from the pre-filter and processed alongside that from the main filter, at least until it can be determined that results from the main filter are not negatively impacted by the prefiltration.

Many published studies describe collecting water in sealed containers and transporting it to a clean laboratory for filtration using vacuum pumps (Jerde et al. 2011, 2013, Hänfling et al. 2016). This approach is attractive for speed and simplicity in the field. However, since eDNA degrades quickly, the water must be kept refrigerated during transportation and either filtered on the same day as collection or frozen for storage. This can be impractical and expensive, especially for large-scale sampling campaigns.

The alternative is to perform filtration on-site, either manually with syringes or hand pumps, or with the aid of a powered pump (vacuum or peristaltic) (Figure 4). Manual filtration represents an inexpensive and universally applicable solution, but it can be hard physical work and time consuming depending on the targeted volume of water per sample, the particle load in the water and number of sites to be completed. Filtration with powered pumps makes it easier to process larger volumes of water, but may be unfeasible in situations where sampling is carried out by multiple field teams in parallel, or for use in remote areas where it is not possible to carry in the equipment and power supply needed. Sampling equipment is an area of rapid innovation and more portable and fully integrated eDNA sampling systems are now starting to become available (e.g. the ANDe eDNA sampling backpack; Thomas et al. 2018).

Figure 4.

Water eDNA collection and filtration methods. Water collection should cause minimum disruption to the substrate of the water body, either by collecting the sample from the bank (a and b) without entering the water or by collecting the sample upstream of the sampler (c and d). Pairing filtration or sampling in the field with an enclosed filter is recommended to minimise potential contamination. Filtration in the field can be carried out with a peristaltic pump (e) or by using a disposable syringe (f, g and h).

Note that as filtration pressure increases (whether using pump or syringe filtration), there is some evidence of reduced DNA retention on the filter membrane, as more molecules are forced through. However, this effect seems to be offset by the benefits of processing higher water volumes (Thomas et al. 2018).

The volume of water filtered in published studies ranges from as little as 15 ml to over 100 l, but the most common volumes are between 500 ml and 5 l. There is little consensus on the minimum viable filtration volume, which will depend to some extent on other factors, such as:

1. The sampling strategy employed, including number of sampling replicates and spatial representativeness of each sample.
2. Practical constraints, such as turbidity of the water, which causes filters to clog.
3. The size of the waterbody, which affects the concentration of eDNA in the water, and therefore detection probability (McClenaghan et al. 2020).
4. The aim of the monitoring and the management cost of failure to detect low-abundance species, which may be high in contexts such as biosecurity but less serious for establishing community metrics.
5. The efficiency of the downstream processes for DNA extraction and analysis.

While for any given sampling system the volume of water filtered tends to correlate positively with the amount of DNA recovered and detection probability of rare species (McClenaghan et al. 2020), this is by no means a linear relationship, and community composition can be well recovered even when relatively small volumes of water are filtered (Mächler et al. 2016, Muha et al. 2019). It is likely that the sampling strategy - including the spatial representativeness of the filtered water - is of greater importance than volume per se, particularly in lentic systems (standing waters such as lakes or ponds), where DNA can be patchily distributed (Li et al. 2019). Note that in marine environments, where eDNA concentration is often extremely low, it is generally recommended to process larger volumes of water.

In general, as detection probabilities decrease due to environmental, physical or biological factors, sampling effort should be increased. This can be achieved through both increasing the number of samples collected and increasing the volume of water filtered per sample. Increasing the number of samples is often likely to be more effective in increasing detection probability, and has the added benefit of enabling assessment of frequency or occupancy of species across the replicates. It is also usually easier to achieve given that volume may be restricted by filter clogging, although there are some high-volume sampling systems that enable filtration of much larger volumes of water in a single sample (Cilleros et al. 2019, Schabacker et al. 2020).

eDNA on filters is preserved for transportation and storage by either freezing, drying, or adding liquid preservative to the filter.

• Freezing is effective but requires immediate access to cooling equipment and the ability to keep the samples cooled or frozen during transportation to the laboratory. Freezing may have a positive effect on eDNA recovery compared with samples that are extracted immediately after filtering, possibly due to a lysis effect resulting from cell bursting (Mauvisseau et al. 2021), but multiple freeze-thaw cycles should be avoided. This is likely to be most applicable to samples that have been filtered in the laboratory.
• Drying the filter membrane requires either silica gel, a desiccator, or paper that absorbs water. This is a less commonly used preservation approach, but is attractive in its ease and simplicity as it allows storage at room temperature for several weeks or even months (Thomas et al. 2019). It is challenging to completely dry the membrane within an enclosed, disc-shaped filter capsule, so alternative approaches are advisable if working with this type of filter.
• Preservation liquids can be broadly assigned to two different categories: pure preservatives such as ethanol and RNAlater, or lysis agents, including Longmire’s buffer (Longmire et al. 1997, Wegleitner et al. 2015) and Sarkosyl buffer (Civade et al. 2016), which release DNA into solution at the same time as preventing degradation. It is critical to know whether or not the buffer you use is lysing the cells or organelle membranes, since this affects how you approach the early stages of the DNA extraction process in the laboratory. It is also important to consider if you will analyse the microbial portion of diversity collected in the sample. If microbes are targeted then it will be important to arrest microbial growth at the time of preservation, and not all preservative solutions will achieve this. The properties of four commonly-used preservative solutions are summarised in Table 3 below. An advantage of using preservative solutions is that positive control DNA can be incorporated into the solution and used to check DNA preservation and extraction.

Exogenous internal positive control (IPC) DNA can be added to the sample to check that DNA has been adequately preserved. This DNA, which can be purchased commercially along with the qPCR primers and probes needed for amplification, can be added, in a well defined concentration to the filter capsule shortly after filtering. If a liquid preservative is used then the IPC can be pre-mixed into the preservative and efficiently added this way. Note that IPC added to the water prior to filtering may not be captured effectively in the filter since it is not in the same state as eDNA (predominantly cellular, subcellular or particle bound).

After DNA extraction, IPC concentration can be quantified using qPCR or ddPCR to check that it is recovered at the expected concentration. Testing should be carried out using the specific sampling and DNA extraction method to be employed, to ensure IPC recovery, and to determine the concentration to be added to the sample and the expected results in the absence of DNA degradation and inhibition.

We recommend that commercial IPCs are used where possible, and this should always be the case for analyses carried out in a regulatory or management context. If custom controls are to be used in a research setting, the DNA used as IPC should be completely absent in the study system, and should not interfere with the DNA of target organisms and/or downstream applications. If the IPC is designed to be analysed alongside the target group in a metabarcoding analysis, it should be ensured that there are no primer mismatches, as this could reduce efficiency of recovery in some circumstances.

eDNA extraction protocols from filters can be based on a number of different commercial DNA extraction kits (e.g. Qiagen DNeasy PowerWater or PowerSoil kits, Macherey Nagel NucleoMag Water kits), custom column-based methods (Sellers et al. 2018), or liquid phase methods (Renshaw et al. 2015, Deiner et al. 2018). For routine monitoring applications in regulatory or industry contexts, the use of reagents from commercial DNA extraction kits is generally advised, since they are expected to be standardised and certified as DNA-free. Protocols such as liquid phase extractions with phenol-chloroform-isoamyl are effective and produce greater DNA yields (Deiner et al. 2015), but they carry significant health and safety concerns that most commercial laboratories will seek to avoid.

Note that this is not to say that commercial kits are without health and safety concerns and must be handled appropriately and need proper disposal of waste. For example, the most widely used kits contain guanidine thiocyanate or guanidine hydrochloride which are reactive with sodium hypochlorite (i.e., bleach) to produce chloramines, chlorine and hydrogen cyanide gases. Due to the common use of bleach as a decontamination agent in most laboratories, this presents a major health risk if laboratory personnel are not properly trained to avoid contact between commercial extraction kit liquids and bleach (e.g. they mistakenly wipe the bench top with bleach first instead of with a mild detergent after an extraction procedure).

Initial steps in extracting DNA from filters must be optimised according to the type of filter and preservation strategy used and the biological targets for analysis.

First, the target group(s) must be considered in the selection of a lysis method. Chemical lysis is sufficient for extraction of animal DNA, but mechanical lysis is required for disrupting cell walls of some unicellular groups such as diatoms. Since mechanical lysis cannot easily be applied to enclosed filters, open or housed filters are recommended if you plan to target such groups in your samples, and a DNA extraction that includes bead beating (a type of mechanical lysis using beads to break down cell walls) or equivalent is required.

Second, the preservative solution and filter type will influence the lysis procedure. It is vital to know whether the storage solution has lysed the cells.

• Pure preservatives such ethanol and RNAlater can be discarded, leaving the material containing DNA on the filter membrane ready for subsequent lysis (Spens et al. 2017).
• Ethanol should be completely evaporated otherwise this can cause inhibition of later stages in the extraction. To maximise DNA recovery from samples contained in ethanol it is possible to carry out a precipitation step on the ethanol in addition to extraction from the filter membrane, although this will increase costs.
• Lysis buffers used for preservation (e.g. Longmire’s or Sarkosyl buffer) cause much of the DNA to be in solution by the time extraction begins in the lab. In this case, discarding the storage solution will result in catastrophic loss of DNA. Instead, the filter should be incubated in the storage solution, which is then used as lysate for the next stages of the DNA extraction. Note that some DNA extraction kits require particular salt concentrations in the lysis buffer, so it is important to check compatibility between the buffer solution and kit chosen.

An additional consideration is that not all extraction methods will release DNA bound to particles such as clay. If it is suspected that much of the DNA in a sample is particle bound (e.g. in highly turbid waters), then using kits optimized for soil extraction (e.g. Qiagen PowerSoil) or lysis buffers containing trisodium phosphate are needed to release adsorbed DNA (Ogram et al. 1987, Sellers et al. 2018). If not, then adsorbed DNA will not be extracted.

Organic compounds co-extracted from the sample along with the DNA may inhibit downstream PCR reactions. This particularly affects samples from turbid waters and small water bodies containing lots of rotting leaves, which introduce tannins and other dissolved organic compounds to the water. Other sources of organic material that may cause inhibition include faeces from livestock (e.g. cattle; Wilson 1997, Rapp 2010). Extraction kits designed for use on water, soil or faecal samples often incorporate inhibitor removal as part of the standard process. Others - including those designed for use of pure biological samples of tissue or blood - do not, and a separate step may be required to remove inhibitors post-extraction using kits such as the Zymo OneStep PCR inhibitor removal kit or Qiagen PowerClean kit, or with custom protocols (Abbaszadegan et al. 1993). Inhibition can also sometimes be overcome by dilution of the DNA extract. This reduces the concentration of inhibitors but also reduces the concentration of target DNA, which can affect detection probability. A common approach is to run a dilution series to determine the minimum level of dilution that can counteract the inhibition. Note that the use of clean-up kits also results in some DNA loss, so these measures should only be applied to eDNA samples that are affected by inhibition.

Extraction efficiency and the presence of inhibitors can be assessed by including internal positive control DNA in the lysis buffer and checking via qPCR that it is recovered in the expected quantity after DNA extraction. This is discussed in more detail in Section 6.2 below.

The first point at which DNA- and morphology-based assessment processes for bulk samples differ is in the preservation of bulk samples for storage and transportation. However, there is currently no clear consensus on the ideal preservation and storage strategy. Fixation in formaldehyde, which is commonly used to preserve bulk samples for morphological processing, is incompatible with DNA-based analyses and should be avoided at all cost.

Research studies commonly use ethanol for sample preservation. Ethanol works as a preservative by replacing water in biological tissues, but water drawn out from the tissues serves to dilute the ethanol, reducing its effectiveness for long-term storage. The dilution effect decreases as the ethanol to sample volume ratio increases so it is important to add ethanol in at least double the sample volume and it is standard practice to replace the ethanol for long-term storage of samples. However, while ethanol is certainly an effective preservative, it also poses considerable difficulties for use in routine biomonitoring. Pure, undenatured ethanol can be expensive to purchase in countries where alcohol duties are applied, requires special storage conditions, and is difficult to transport because it is a flammable liquid that is subject to dangerous goods regulations. This also raises health and safety concerns for many organisations that carry out fieldwork. Moreover, the large volumes of ethanol required for preserving bulk macroinvertebrate samples are expensive to dispose of correctly, and the sample needs to be completely dried prior to DNA extraction because ethanol residues interfere with the extraction chemistry.

The cost and accessibility barrier can be overcome through the use of denatured alcohols such as industrial denatured alcohol (IDA), industrial methylated spirits (IMS), and isopropanol. However, some denatured alcohols (e.g. IMS) seem to be unreliable as preservatives for DNA because of their capacity to gradually degrade dsDNA (Carter et al. 1997). They may also act as inhibitors in downstream steps. Moreover, denatured alcohols vary substantially in their chemical composition and to date there has been a lack of systematic testing that would enable general principles to be outlined regarding the suitability of use for DNA preservation. Like ethanol, they are also highly flammable and subject to regulations for transport, storage and disposal.

Non-flammable preservation solutions include:

• Propylene glycol (Robinson et al. 2021, Weigand et al. 2021), which can be purchased in large volumes and at low cost as an antifreeze. Note that it can be viscous in some forms, which complicates laboratory processing.
• RNAlater, which is prohibitively expensive to purchase commercially at the volumes needed for preserving bulk invertebrate samples but potentially represents a good option if it can be made up in the laboratory (see Table 3 for details). It is non-hazardous, making it more logistically viable than ethanol in many cases. Testing is still required to demonstrate use of RNAlater for preservation of this sample type.

Note that for collection of terrestrial invertebrates using passive trapping methods, the fluid serves as a collection and killing agent as well as a preservative. In some types of traps (e.g. Malaise traps), ethanol currently remains the preferred solution for this reason, and any alternative should be tested to check that it does not reduce trapping efficiency. If ethanol is used for collection, it can subsequently be filtered off for dry transportation of samples if necessary, but will still need to be disposed of correctly.

An alternative approach is to store the sample in a lysis buffer, which effectively maintains DNA integrity during storage. These solutions are usually non-hazardous and easy to transport. There are commercially available buffers as well as those that can be made up in the laboratory, such as Longmire’s Solution (Longmire et al. 1997, Wegleitner et al. 2015). However, note that soft-bodied organisms will disintegrate if stored in lysis buffers, making it impossible to morphologically confirm species identity or archive voucher specimens.

Non-liquid-based preservation strategies include immediate freezing or crushing of organisms to isolate mitochondria (Macher et al. 2018a). These strategies require in-field equipment that may not always be readily available or easily transported to site, so they do not currently represent realistic options for standard protocols. A more practical option may be to dry the samples at 40-50^oC as soon as possible after collection, as this will stabilise the DNA for short- or medium-term storage and transportation at the same time as reducing the weight and volume of the sample. This requires further testing to fully understand its effectiveness in preserving DNA, and how it affects downstream processes. For instance, when a large amount of debris is collected along with the organisms it may be harder to separate this from a dried sample.

DNA extraction from bulk samples typically starts with homogenisation, which can be achieved either using bead beating or blending (Figure 5). Organisms must be ground extremely finely to facilitate unbiased extraction. Specimens can be homogenised either dry or in a preservation solution e.g. (Pereira-da-Conceicoa et al. 2021), but this will depend on your choice of preservation strategy and homogenisation equipment. This can also happen in ethanol, but fire risk should be considered if grinding samples in ethanol as significant heat can be created, particularly if samples contain stones and other hard objects, and ethanol has a very low flash-point of 37°C.

Figure 5.

DNA homogenisation methods. DNA sample homogenisation protocols vary depending on equipment available, here we present a number of lab set-ups which show differing equipment for the homogenisation of tissue samples (dry and wet) , IKA Tube-Mill 100 (a) with blending blades (b), dry malaise trap sample before and after homogenisation with the IKA Tube-Mill 100 (c and d), Qiagen TissueLyser II with plate attachment (bead mill also available) (e), and stainless steel wet-grinding blender (f and g).

A practical challenge to the large-scale operationalisation of this process is that bulk samples are often of considerable volume - much larger than can be accommodated in most tissue homogenisers or bead mills, and especially those that can process multiple samples in parallel. Therefore, large samples must either be homogenised one-by-one in a large-volume blender (which requires decontamination between samples) or split into smaller subsamples for this stage, which introduces additional consumables cost and reduces the capacity for parallel processing of large numbers of samples.

Moreover, it is usually necessary to clean the sample prior to homogenisation, removing organic and inorganic detritus (and organisms with thick calcium carbonate shells) to reduce the volume of the sample prior to homogenisation and improve homogenisation efficiency (although see See Pereira-da-Conceicoa et al. 2021 and Buchner et al. 2021 who successfully blended samples without cleaning). This also removes potential sources of inhibition that could affect PCR efficiency downstream. Like with size-sorting, this manual sample processing step reduces overall time and cost efficiency of the DNA-based approach, while also risking the loss of small organisms if due care is not taken to retain them.

Lastly, variation in body size presents a major challenge when working with bulk samples. This is particularly relevant to benthic macroinvertebrates, which vary in biomass by orders of magnitude. If DNA is extracted from bulk samples without size sorting, this leads to the DNA of very small organisms being overwhelmed by that of larger ones, resulting in detection bias weighted towards large-bodied taxa. Size sorting the organisms prior to extraction helps to fully recover the diversity of a sample, but it also requires a significant time investment as well as multiple DNA extractions per sample, which adds substantially to the cost, so there is a trade-off between taxonomic completeness and processing time/cost. Different sorting schemes have been proposed, ranging from very strict (separate into multiple size classes, extract DNA from each separately, and then pool the DNA extracts proportionally) to very coarse (remove large individuals, leaving only a leg or other body part in the bulk sample) (Elbrecht et al. 2017a, 2021) or even sorting according to taxonomic group (Moriniere et al. 2016, Beentjes et al. 2019). An alternative to size sorting is to greatly increase sequencing depth during metabarcoding to improve recovery of small organisms. These options carry significant costs, which may in some cases negate the economic advantage of employing a molecular approach over a morphological one.

As discussed above, various aspects of the sample-handling and homogenisation process limit the throughput of the DNA-based approach for bulk samples (specifically the time taken in drying, cleaning and size-sorting the samples, and capacity issues introduced by the need to divide up large samples). Furthermore, a potential barrier to the uptake of DNA-based methods for macroinvertebrate monitoring under the Water Framework Directive is the requirement to retain voucher specimens (a preserved specimen that serves as a verifiable and permanent record), which is incompatible with sample homogenisation.

An alternative to homogenisation is to retrieve DNA non-destructively by extracting it from the liquid solution in which the sample has been stored. Although frequently used for DNA extraction from individual museum specimens, this has only recently been widely applied to bulk samples in metabarcoding studies (e.g., Hajibabaei et al. 2012, Linard et al. 2016, Carew et al. 2018, Erdozain et al. 2019, Ji et al. 2019, Martins et al. 2019, Zizka et al. 2019b, Zenker et al. 2020).

Many tests of this approach (e.g. Hajibabaei et al. 2012, Martins et al. 2019, Zenker et al. 2020) have focused on retrieving DNA from samples stored in ethanol, with varied levels of success. More recent studies have worked with samples stored in a lysis buffer, with extremely promising results (Ji et al. 2019, Nielsen et al. 2019). However, different types of organisms have been observed to contribute DNA into the preservation solution at different rates, with soft bodied organisms shedding DNA more readily than those with an exoskeleton or operculum (Carew et al. 2018, Zizka et al. 2019b). This, together with differential amplification efficiencies, will mean that quantitative information will almost certainly be lost during the process and data should be analysed based on presence-absence only. The concentration of DNA extracted from a storage solution will also be lower than that extracted directly from homogenised tissues and it will be necessary to account for amplification stochasticity and increased vulnerability to contamination.

Since this method is in the early stages of development, a number of fundamental questions remain regarding the relative effectiveness of different buffer solutions and how best to maximise release of DNA from the organisms into the solution without completely destroying the specimens (if they are to be retained as voucher specimens or for subsequent morphological identification). For instance, it may be possible to greatly increase extraction efficiency through agitation of the sample or the addition of proteinases, but this will destroy soft-bodied organisms. It is still unclear how long a sample should be left in the preservative solution and what volume of solution should be used for DNA extraction (Martins et al. 2019, Zizka et al. 2019b). This approach therefore requires further research attention, but has the potential to increase efficiency and facilitate high-throughput monitoring of macroinvertebrate communities.

A wide variety of different commercial kits or liquid phase extraction methods are used for DNA extraction from homogenised macroinvertebrate samples. Thus, there are no clear recommendations as to which kit or method is preferable. However, due to health and safety concerns of working with toxic chemicals such as Phenol, commercial kit-based extraction approaches are generally preferred in commercial or routine monitoring laboratories. For DNA extraction from lysis buffer solutions, kits that enable a large volume of lysate to be processed may be preferred (e.g. QiAamp DNA Blood Maxi Kit, which uses 3-10 ml of lysate; Fonseca et al. 2011).

Early stages of the DNA extraction process are influenced by choice of preservation strategy, since some solutions, including ethanol, can interact with the chemistry during initial extraction stages, causing reduced extraction efficiency. Thus, samples preserved in ethanol must be fully dried prior to lysis, and this can pose a cross-contamination risk as electrostatic charges can cause small dried fragments to jump considerable distances. Using a lysis buffer (without SDS or other surfactants) or wet grinding in ethanol (Buchner et al. 2021) for sample preservation avoids this issue and can streamline the extraction process.

The volume of material used for extraction is also an important consideration. If the sample has been homogenised, using too much of the sample can cause PCR efficiency to be reduced through inhibition. Assuming that homogenisation has been sufficiently complete, representative communities can be described from as little as 0.3 g of the total homogenate (Hajibabaei et al. 2019). If DNA is extracted from a storage solution, there is a decision to be made regarding the volume of solution that is processed in the extraction. Using a larger volume may increase detection of rare species, but will significantly slow down the extraction process when large numbers of samples are being analysed. Further testing is required to optimise this.

DNA-based assessment of benthic diatom communities uses the same sampling methodology as morphology-based assessments applied for monitoring under the WFD, for which standard protocols have been set (NF EN 13946 - April 2014). These documents describe how to collect biofilm samples (number and type of substrate collected, habitat, biofilm surface collected), while a Technical Report from the European Committee for Standardisation (CEN/TR 17245, 2018) provides recommendations to ensure that biofilm samples are collected and stored in such a way as to be compatible with molecular analysis (Figure 6).

Figure 6.

Biofilm sampling. Biofilm samples are collected from submerged stones (a) in a waterbody using toothbrush (b). Diatom sub-samples are then collected from larger samples (c.) and then pelleted using centrifugation prior for DNA extraction (d).

This sampling strategy described in the standards was statistically optimized to obtain the best ecological assessment of evaluated rivers or lakes for minimal sampling effort. In the scope of routine monitoring and WFD requirements, this method is easy to apply and the same sample can be used for both DNA- and morphology-based assessment.

Diatom community DNA is dominant in the samples, so the vulnerability to contamination is low, which means that precautions can be lighter than for aquatic eDNA sampling: gloves are not mandatory, and collection of substrates generally requires entering the water (walking against the water flow), see the technical report (CEN/TR 17245, 2018) for more details. However, if biofilm samples are used to characterise prokaryotic communities or for analyses of extra-organismal DNA (e.g. from fish), considerations described in section 1.3 should be carefully applied to limit contamination.

Historically, biofilm samples were preserved using formaldehyde or Lugol solutions. These are compatible with morphological processing of samples, since they preserve the silica walls surrounding the diatom cells (frustules), which are used for identification with light microscopy. Formaldehyde solution is known to be incompatible with DNA preservation, and the use of Lugol solution for such application is still under debate (Mäki et al. 2017).

The CEN technical report (CEN/TR 17245, 2018) recommends the use of pure and undenatured ethanol solution with a final concentration >70% to preserve biofilm samples collected in lakes and rivers. Several studies have applied this protocol successfully for benthic diatom ecological assessment in lakes and rivers using DNA metabarcoding (Vasselon et al. 2017a, Rivera et al. 2018, Bailet et al. 2019, Mortágua et al. 2019, Apothéloz-Perret-Gentil et al. 2020, Pérez-Burillo et al. 2020, Rivera et al. 2020). This choice is mainly driven by the fact that ethanol preservation remains easy to apply and enables the use of the same biofilm sample for both morphology- and DNA-based approaches. However, further experiments are required to evaluate the stability of ethanol preserved samples over time and the effect of different storage conditions on DNA preservation.

Alternative preservation approaches that have been used for benthic diatom samples include RNA-later (Hamilton et al. 2015, Kelly et al. 2018) and direct freezing (Apothéloz-Perret-Gentil et al. 2017, Mora et al. 2019). An ongoing experiment, conducted as part of the DNAqua-Net COST Action, is evaluating the performance of these 2 approaches in comparison to ethanol over a period of one year. Results will help to update guidelines and recommendations.

Growth of aquatic biofilms can be affected by a wide variety of environmental factors including the nature of the substratum (Mora-Gómez et al. 2016). This means that biofilms collected with the same protocol may vary in physicochemical properties and biological composition, resulting in different structure and biomass development. Thus, biofilm samples are usually homogenized using a vortex and subsampled in smaller aliquots using 1 - 2 ml of preserved biofilm. This provides enough biological material to characterise diatom community structure without reaching maximum starting sample amount for most DNA extraction protocols. For biofilm samples with low biomass, higher volumes could be used to increase starting material amount.

The subsampled biofilm aliquot is pelleted using centrifugation (a separation process that relies on the action of centrifugal force to separate particles in a solid–liquid mixture) and the supernatant (i.e. the preservative solution) is discarded.

Like other environmental samples, biofilm samples are characterized by the presence of organic matter, humic acids and polyphenols that are known to inhibit molecular methods like PCR, so this needs to be accounted for during or after the DNA extraction process. In addition, diatom cells are protected by a frustule made of silica that is hard to break, and this can reduce the efficiency of DNA extraction if the cell lysis step is insufficient (Hamm et al. 2003).

A wide variety of DNA extraction methods from benthic biofilms are found in the literature (e.g. Apothéloz-Perret-Gentil et al. 2017, Vasselon et al. 2017a, Mora et al. 2019, Rivera et al. 2020, Kelly et al. 2018). Studies have compared the efficiency of different elements of these extraction protocols (Nguyen et al. 2011, Vasselon et al. 2017b), and shown that although extraction method does affect the relative quantification of diatom taxa obtained after metabarcoding, results were similar in terms of ecological quality scores based on diatom indexes.

One way to deal with large volumes of soil or sediment samples is to separate the organisms (macrofauna and/or meiofauna) from the soil or sediment itself, which is usually achieved through a series of flotation, decanting and sieving steps (Creer et al. 2010, Fonseca et al. 2011, Creer et al. 2016, Haenel et al. 2017).The organisms can then be processed as bulk samples, as described above. If this approach is taken, it is important to consider the risk of cross-contamination from the equipment used to clean and sort the sample. It has also been shown that the method used for separating the organisms from the sediment significantly affects community composition of the sample (Haenel et al. 2017), so whatever method is chosen should be maintained throughout the monitoring programme.

Separating the organisms from the soil or sediment allows a larger volume of sediment to be processed, but this needs to be balanced against the consideration that it is a labour-intensive process, and this may limit the scope of monitoring programmes in terms of the number of samples that can be handled.

Thus, extracting DNA directly from the soil or sediment itself is preferable in many ways: the process is more readily standardised and scalable, and requires less handling of the sample, which reduces contamination risk. DNA can be directly extracted from soil or sediment in volumes of up to 10 g using commercial DNA extraction kits (e.g. DNeasy PowerMax Soil Kit). Further work is needed to establish the optimal combination of sample size and replication to account for spatial heterogeneity in sediment communities, although various studies have examined this in certain environments (Nascimento et al. 2018).

Samples must be preserved for transportation to the laboratory and storage prior to DNA extraction. Rapid and effective preservation of soil/sediment samples is particularly important if either eDNA, RNA, or microorganisms are targeted. Common preservation strategies include freezing at -80^oC, and the use of preservative solutions such as ethanol or Qiagen’s LifeGuard Soil Preservation Solution.

Freezing is widely accepted to be an effective way of preserving samples (Domaizon et al. 2013, Agasild et al. 2018) but relies on having immediate access to the necessary equipment and the logistical capabilities to keep the samples frozen during transport to the lab. This may make it an impractical choice for large-scale monitoring programmes in which a range of different parties will be collecting samples, and not all may have ready access to freezers. Note that a recent paper suggests that microbial communities are preserved with less extreme cooling (Delavaux et al. 2020).

Preservation using liquid preservative solutions may represent a more practical option, but choice of preservative solution is critical.

• It is important to consider whether or not the microbial component of biodiversity is being targeted (or could be in the future), since not all preservative solutions will arrest the growth of microbial communities. If these continue to grow and develop after sampling, the community will cease to reflect that of the environment in which it was sampled.
• Analysis of RNA requires specific preservation solutions, such as LifeGuard ^TM, which is very expensive for use with samples of the volume recommended for meiofaunal assessment.
• Ethanol is a good preservative, but for use with sediment samples (which have a high water content), it should ideally be changed once, and the final concentration should be > 80% for effective DNA preservation. As mentioned elsewhere, ethanol also presents logistical and safety challenges when applied to routine or industrial use and interferes with the chemistry of the DNA extraction process if not completely removed from the sample.
• Salt-based lysis solutions such as DESS (Yoder et al. 2006, Tatangelo et al. 2014) are effective for preservation of metazoan DNA, but require the addition of sodium azide or similar chemicals to arrest growth of some microbial groups, and this brings health and safety concerns both in the field and in the laboratory. Further systematic testing of the effectiveness of different solutions for preserving communities of various groups of organisms in sediments is required.
• RNAlater is not recommended as a preservative for soil and sediment samples because storage in this medium has been shown to lead to changes in composition of microbial communities and loss of diversity (Nilsson et al. 2019, Delavaux et al. 2020).

Initial steps in DNA extraction will depend on the volume of soil or sediment collected, the target group for analysis, and the preservation method used. If a preservation liquid such as ethanol or LifeGuard^TM has been used for sample preservation then this must first be removed from the sample, usually by centrifugation and discarding the supernatant. Subsequent wash steps may be needed to ensure that all traces of preservative are removed since they may interfere with the chemistry of the extraction kit. This is especially important with ethanol. If a salt-based buffer such as DESS has been used to preserve DNA, then the sample can sometimes be introduced more directly into an extraction process (Fonseca et al. 2011, van der Loos and Nijland 2020).

The maximum volume of soil or sediment that can be directly extracted in commercial kits is currently 10 g. Where a larger volume has been collected, thorough mixing prior to subsampling for extraction will help to ensure that the extracted DNA is representative of the sample as a whole. It may be worth carrying out multiple extractions per sample in this case, at least until it is clear to what extent a single extraction is representative of the whole sample. Soils and sediments typically contain PCR inhibitors, and heavily polluted sediments are often associated with particularly high levels of inhibition. Therefore, most extraction protocols need to include an inhibitor removal step. This is incorporated into commercial kit protocols designed for soils and sediments, but a specific clean-up step will almost certainly need to be incorporated where custom extraction protocols are used (e.g. Sellers et al. 2018).

DNA from very large volume sediment samples are typically extracted using a phosphate buffer approach as outlined by (Taberlet et al. 2012b), although note that this specifically targets extracellular DNA.

The most commonly-used platforms for DNA quantification are the Qubit fluorometer (Invitrogen, Carlsbad CA) and the Nanodrop spectrophotometer (Thermo Scientific, Waltham MA).

• Nanodrop concentration usually gives an inflated estimate of DNA concentration because it measures even single nucleotides, so it is not recommended for this purpose. However, the 260/280 ratio given by the Nanodrop can be useful for indicating RNA and protein contamination (values between 1.8 and 2. denote pure DNA; below 1.8 indicates phenol, salt, protein or polysaccharide contamination; Olson and Morrow, 2012).
• The Qubit uses an intercalating dye that binds directly to DNA and thus measures the quantity of double or single stranded DNA precisely even at very low concentrations, although it provides no estimate of DNA purity. Users should be aware that readings from the Qubit are affected by temperature, so care needs to be taken to ensure consistency in this regard. Where both instruments are widely available, it is recommended to use Qubit for quantifying DNA concentrations, while the Nanodrop or other spectrophotometer readings can be useful for assessing DNA purity.

When targeting extra-organismal DNA extracted from environmental samples (water and sediment), it is important to bear in mind that a significant portion of the DNA extracted can belong to non-target organisms (Capo et al. 2020). Thus, even when a relatively high total DNA concentration is obtained, the concentration of target DNA can be extremely low. Where it is necessary to quantify target DNA in preparation for metabarcoding, this can be achieved by running a qPCR assay or equivalent with the chosen metabarcoding primers, although the accuracy of this analysis will depend on primer specificity to the target group. Quality control analyses such as running agarose or digital gels (e.g. FragmentAnalyzer, Agilent, Santa Clara CA) can also be useful to assess potential DNA degradation and the fragment size distribution of the DNA.

A common cause of false negative results is PCR inhibition (Jane et al. 2015, McKee et al. 2015), which is caused by chemicals and compounds in the sample that interact with the PCR and either reduce its efficiency or cause it to fail completely, even when target DNA is present. Inhibitors are usually present in soil, sediment and faecal samples, and often in water samples. In soils and water, complex humic substances are the main known inhibitor of PCR (Braid et al. 2003), although much research remains to be done to complete our understanding of this. They tend to be more prevalent in eutrophic waters than in oligotrophic ones and are also associated with high sediment loads, shallow waters that contain a lot of organic material such as faeces from livestock, heavily polluted waters, and samples containing a high concentration of calcium carbonate (e.g. DNA extracted from samples that contain bivalves and gastropods; Schrader et al. 2012). Thus, it is important to test extracted DNA for inhibition in order to avoid false negative results.

This can be achieved through the use of an exogenous internal positive control (IPC) added at known concentration to the extracted DNA (Hoorfar et al. 2004; Furlan et al. 2016; Brys et al. 2020b). These non-target standardised IPC DNAs can be purchased commercially along with the primers and probe required for the qPCR/ddPCR reaction, and they can be multiplexed with the tests for the target species (i.e. tested for in the same reaction to save time and cost). If amplification of the IPC fails or is delayed (i.e. Ct value is higher than expected), the sample should be assumed to be inhibited (Hartman et al. 2005). The difference between expected and observed Ct value can give some indication of the strength of the inhibition.

Inhibition can often be overcome by using additional purification steps (using commercial kits such as the Zymo OneStep PCR Inhibitor Removal Kits or Qiagen PowerClean kits), use of chemical enhancers such as bovine serum albumin (BSA) and dimethyl sulphoxide (DMSO) in the PCR reaction, or by dilution of the DNA. The inhibition test should then be repeated to check that the inhibition has been overcome.

While dilution of the DNA reduces the chance of one type of false negative result (inhibition) it can also increase the chance of false negatives occurring due to stochasticity by reducing the concentration of target DNA. This can be especially consequential when working with eDNA, where target DNA is already likely to be at very low concentrations. Dilution should therefore be compensated by increasing the number of PCR replicates performed or the volume of extracted DNA added to each reaction.

Data generated from eDNA analyses could be used as evidence for species presence (or when rigorously understood, absence) in contexts including permits for new development or construction of infrastructure, the designation of protected areas, protected species licensing, assessing illegal species introductions, and decisions to remove connectivity barriers. Many of these applications sit within a regulated framework of requirements, with legal consequences for environmental mismanagement, and this means that data derived from DNA-based analyses may have to be defended in court. Moreover, management responses to detection of certain species can be expensive and disruptive, so confidence in data quality is paramount.

Thus, the use of DNA-based monitoring methods to determine species presence or absence at a given location for environmental management purposes requires rigid quality assurance protocols, similar to those applied in other high-consequence sectors that use molecular methods, such as forensics (Board 2000, Brandhagen et al. 2020) and medicine (Precone et al. 2018). This typically goes beyond the main principles of sound field and laboratory conduct (Griffiths et al. 2015, Goldberg et al. 2016), and involves the use of analytical controls at every step of the workflow, from field planning to reporting and interpretation frameworks. The controls are particularly important as the analyses move out of the research sphere and into commercial and operational use where the field, laboratory, data analysis, and interpretation may be undertaken by different actors. In this scenario, the decision-maker must have access to evidence that each stage of the process has performed as expected, that there is a system in place to prove chain of custody such that a judgement can be made as to the accuracy and authenticity of the results, and any errors can be traced back to a specific point in the workflow. We do not discuss chain of custody issues, but a rigorous quality assurance document can lay out this process. For example, the US Fish and Wildlife Service has established such a quality assurance document for the monitoring of the economically important invasive asian carps (Woldt et al. 2020). Here we present a summary of the controls necessary for such cases in the application of DNA-based monitoring (Table 4).

While various technology platforms can be used, the basic principle is that species presence is inferred based on successful amplification using a taxon-specific primer set. The same primer set can be adapted for use with different technologies, the most commonly-used being:

• Conventional PCR (cPCR; also referred to as endpoint or diagnostic PCR),
• Quantitative PCR (qPCR)
• Digital droplet PCR (ddPCR).

Note that other methods exist and are being developed, but have yet to be fully integrated into mainstream eDNA applications. Examples include loop-mediated isothermal amplification (LAMP), nucleic acid sequence based amplification (NASBA), self-sustained sequence replication (3SR), rolling circle amplification (RCA), and CRISPR-based assays (Williams et al. 2017, 2019).

The benefits of these single-taxon screening methods lies primarily in the simplicity of the laboratory and data analyses, and potentially in their sensitivity compared to metabarcoding approaches for some taxonomic groups (Simmons et al. 2016, Blackman et al. 2020). Tests can be carried out fast - even to the point of being able to conduct them in the field - which is useful where management decisions need to be taken quickly. Principal disadvantages include the need for extensive and costly validation studies to ensure specificity of the assay prior to operational use (Thalinger et al. 2021b), and the limited information obtained, which relates only to the target species. Thus, cost mounts rapidly as the number of target species increases.

LOD and LOQ should be determined after an assay has been optimized and must be reported in order for an assay’s results to be interpreted correctly (Klymus et al. 2020). The method for determining LOD and LOQ varies across platforms.

For qPCR, estimating target DNA concentration requires normalization against standard curves. These should consist of at least 5 different concentrations of DNA that contains primer and probe binding sites identical to the target species. Because high-concentration positive control DNA poses a contamination risk (especially when working with low template samples such as eDNA), the standard DNA should ideally be created using purified amplicons (e.g. Currier et al. 2018), or ideally double-stranded synthetic amplicons (e.g. gBlocks™; Langlois et al. 2020). For calculation of LOD and LOQ, the number of technical replicates per sample should be as high as possible (5-12) in order to properly assess the mean and associated variance per sample, with 3 replicates being the absolute minimum required to calculate a standard deviation. LOD and LOQ can be defined in various ways (e.g. Agersnap et al. 2017, Hunter et al. 2017), but a recent effort to standardize these definitions ultimately defined LOD as being the lowest concentration at which 95% of the technical replicates of the standard amplify, while LOQ was defined as being the lowest concentration for which the coefficient of variation (CV) value is < 35% for the used standard DNA (Klymus et al. 2020); also adopted by (Thalinger et al. 2021b). The CV represents the ratio of the standard deviation to the mean, and allows the amount of variation to be compared between replicates of low versus high target concentration samples. Based on these definitions, it is still possible (indeed common) to detect target DNA at concentrations below the theoretical LOD, especially when multiple technical replicates are run for each environmental sample. Detections below the LOD may be attributed a lower confidence level in interpretation of results. Since definitions of LOD and LOQ are still being established insofar as they relate to eDNA, it is important to report not only the actual values of these two parameters for each assay, but also the way in which they have been defined and calculated.

• For cPCR with capillary electrophoresis, the LOD is selected to reliably separate target bands from background fluorescence and is commonly around 0.08 Relative Fluorescence Units (RFU) (Thalinger et al. 2019). A positive PCR result can typically be generated from 10-30 target DNA double strands present at the beginning of PCR (Deprez et al. 2016, Hunter et al. 2017, Thalinger et al. 2021c).
• For ddPCR, the lowest concentration that can theoretically be detected and measured is one target molecule per reaction (Deprez et al. 2016, Hunter et al. 2017, Thalinger et al. 2021c), and each reaction can include up to 10 µl of DNA template because of the reduced impact of inhibitors compared with in qPCR. The same practical definition of LOD as used in qPCR can be applied to ddPCR (95% positive detections of standard DNA) meaning that here too it is still possible to obtain valid detections below the LOD. Note that when calculating the mean target concentration of samples, it is important to include non-amplifications as zero-estimates.

Good design of species-specific primers and probes is critical for any targeted species approach because interpretation of the results often depends solely on whether or not amplification occurs. Non-target amplification can therefore lead to species presence being wrongly inferred, with potentially costly consequences for environmental management. The challenge with the use of target-based approaches for routine monitoring at large geographic scales lies in designing and validating species-specific primers that are reliable across diverse ecological systems. This can be achieved, but it involves significant investment of time and effort, along with resources to fully test and validate assay performance in the intended environment.

As a guiding principle, the design and implementation of PCR primers should follow the established guidelines for the Minimum Information for the Publication of Quantitative Real-Time PCR Experiments, which have been adapted for use in eDNA applications (Bustin et al. 2011, Goldberg et al. 2016, Langlois et al. 2020). However, in practice the degree to which published eDNA research follows these guidelines is highly variable, since research study objectives often do not require the high level of robustness and quality assurance needed for routine biomonitoring. When assays are implemented outside the geographic region for which they were originally developed, the validation status of the primer pair should be considered with great care and additional tests for specificity and sensitivity are likely to be required (Thalinger et al. 2021b) recently carried out an extensive meta-analysis of published assays used for targeted eDNA detection, and proposed a set of validation levels along with clear guidelines for how to both develop new assays and implement previously developed assays, taking into account their limitations. This extensive set of guidelines can be used as the basis on which to standardize reporting guidelines for targeted eDNA assays, facilitating their use for the entire scientific community and environmental managers (https://edna-validation.com).

The concentration of target DNA in the DNA extract (as measured by qPCR and ddPCR) or fluorescence strength of the target band (in cPCR) has been linked to species abundances under controlled experimental settings, but this is extremely complex to extend to natural systems. Use of IPCs for internal normalisation of quantitative estimates, together with models based on allometric scaling of species’ body sizes (Yates et al. 2020) can help to calibrate estimates, but caution should still be applied given the multitude of factors that affect the amount of DNA shed by a given number of individuals and captured using a given sampling and capture strategy (see section 2.1 above).

The goal of any metabarcoding analysis is to record and identify all species within a particular taxonomic group from a sample. To accomplish high taxonomic resolution (i.e. species level assignments) and maximise detection probability for rare species, designing or selecting appropriate primers is crucial.

Primers can be designed to target narrow taxonomic groups through to very broad ones. At the narrowest end of the spectrum, primers can also be designed to target a single species, using fast-evolving DNA regions that allow identification of intraspecific (population level) genetic diversity (e.g. Tsuji et al. 2020). At the other extreme, primers can be designed to encompass entire kingdoms or domains (e.g. Eukaryotes or bacteria). Between these extremes, commonly used metabarcoding assays target intermediate-level groups, such as animals (Metazoa), vertebrates, fish, diatoms, etc. Some of those commonly used in aquatic biomonitoring are listed in Table 5.

To target broad taxonomic groups, degenerate primers are often used, which incorporate some level of flexibility in the priming sequence via so-called ‘mixed’ or ‘wobble’ bases. This allows for some sequence variation in the primer binding region of the target organisms so a more diverse group can be targeted. Degenerate primers will increase the number of amplified taxa, but can lead to increased amplification of non-target organisms when applied to environmental samples (e.g. Deiner et al. 2016, Macher et al. 2018b, Wangensteen et al. 2018).

The most challenging aspect of primer design often relates to intermediate taxonomic groups such as Metazoa or paraphyletic groups such as ‘macroinvertebrates’ when they are targeted in environmental samples. In this case, primers have to be sufficiently broad to capture a taxonomically diverse group of target organisms, while not being so permissive as to amplify non-target groups such as bacteria and algae, which may dominate in terms of quantity of DNA isolated from environmental samples. High levels of non-target amplification in environmental samples are often mitigated by increasing sequencing depth. The increased read depth allows for sequences from non-target groups to be discarded during bioinformatic filtering, leaving a sufficient amount of sequence data derived from the target group. While this can work, it is an inefficient solution increasing overall costs and careful primer design is preferred where possible, with the aim of increasing target specificity and minimising non-target amplification.

There is an inherent trade-off in that as the target group broadens, a wider cross-section of biodiversity is obtained, but this comes at the expense of the completeness of the data in each of the different groups (Macher et al. 2018b, Hajibabaei et al. 2019, Gleason et al. 2020). For example, a eukaryote assay applied to marine sediment samples will profile biodiversity across metazoans, algae including diatoms, protists (e.g. foraminiferans), and marine fungi, yielding an integrated biodiversity signal that is likely to reflect overall ecosystem status (Grey et al. 2018). Nonetheless, a metazoan-specific primer set would more comprehensively characterise the animal diversity contained in the samples. Similarly, a vertebrate assay applied to aquatic eDNA samples is attractive for its ability to reveal both fish and mammals in a single assay, but applying two separate assays that target fish and mammals, respectively, will usually recover more diversity in each group. Narrower assays are particularly useful for detecting rare species and those present at low population densities (e.g. Boussarie et al. 2018). A multi-marker approach is usually required for obtaining comprehensive species data across a wide range of taxonomic groups (Hajibabaei et al. 2019, Ficetola et al. 2020, Martins et al. 2020). The number and combination of primer sets selected will depend on the aims of the monitoring and the available budget, since cost scales with the number of primer sets to be used.

The potential for primer bias (also referred to as amplification bias) is an important consideration in primer design and selection. Primers that target a narrow taxonomic group can often be designed to have an exact or near-exact match to all target taxa, which ensures approximately equal amplification efficiency across taxa. This means that sequence read counts will usually correlate well with the relative concentrations of eDNA captured for each species in the sample. Conversely, those targeting a very broad group will vary in amplification efficiency across taxa, which reduces the potential to make (semi-)quantitative inferences from sequence read count data (Bista et al. 2018). Inclusion of positive controls and spike-ins can help with understanding stochasticity and potentially even quantification of metabarcoded data (Ji et al. 2019).

Before ordering metabarcoding primers, it is first important to decide which metabarcoding labelling strategy will be employed. This is important because the primers need to be ordered with the corresponding 5’ nucleotide additions required for building sequencing libraries. A thorough review of metabarcoding labelling strategies can be found in Bohmann et al. 2021.

In the one-step PCR approach, sequence adapters and (typically) nucleotide tags are incorporated directly to the synthesis of the forward and reverse primers so that amplification and library preparation is achieved in a single PCR step, see Figure 7a (Elbrecht and Leese 2017, Vamos et al. 2017). This provides a highly streamlined workflow and reduces the risk of cross-contamination between amplicons, with the caveat of increasing materials cost for projects with large sample sizes and multiple primer sets. Although usually performed using conventional thermocycling machines, this can also be carried out on a qPCR platform (e.g. Bessey et al. 2020), providing simultaneous quantification of the product and eliminating yet another step that would otherwise be carried out separately.

The trade-off with this approach is that the primer sequences become very long when up to 60 nucleotides (sequence adapters and indexes) are added to the primers. This makes them costly to buy, while the long overhangs can decrease efficiency, thereby reducing the detection probability of rare species and the consistency between replicates (Zizka et al. 2019a).

The one-step approach is sometimes referred to as the ‘fusion primer approach’, but we avoid this terminology because all metabarcoding approaches involve primers that are to some extent a fusion of multiple components.

In the two-step PCR approach, shorter and potentially more efficient primers are used for the initial amplification of the DNA extracts, see Figure 7b, c and d (Bourlat et al. 2016). However, note that the additions to the primers can still be quite long - typically around 30 nucleotides. In the two-step PCR approach, the primer in the first round of PCR includes at minimum the target-specific primer sequence and an adaptor ‘tail’ for the second-round PCR primers to bind onto. These most minimal metabarcoding primers are attractive for maximising efficiency (by minimising length) and cost-effectiveness (the same primer is used for all samples). Nonetheless, these advantages need to be weighed against risk of cross-contamination between amplicons, which can easily occur during two-step library preparation and is untraceable if these minimal primers are used. A more robust modification of this approach is to incorporate a short, sample-specific 5’ nucleotide tag sequence in first round PCR primers, such that each sample is amplified with a different set of primers incorporating a unique sequence of bases between the primer sequence and the adaptor tail. This has higher up-front costs since a separate primer set needs to be purchased for each unique index used for each sample.

The second stage of the two-step PCR process consists of a short second-round PCR (typically 8-10 cycles; Bourlat et al. 2016) performed with primers that bind to the tail of the first-round primers and incorporate Illumina library indexes and adaptors. If primers with sample-specific tags have been used during the first-round PCR then multiple samples can be combined within a single library index in the second round.

The third of the three main strategies for amplicon labelling in metabarcoding studies is ‘adapter ligation’ or ‘ligation-based approach’, see Figure 7e (Leray et al. 2016). Note that this is sometimes referred to as the ‘tagged primer approach’, but we avoid this terminology since most library preparation approaches incorporate some element of tagging within the primer construction.

In the ligation-based approach, DNA extracts are PCR amplified with primers carrying short 5’ nucleotide tags, typically just 6-10 nucleotides in length and unique to each sample. The additions to primers are thus the shortest among the three approaches, which in theory should cause the least reduction in PCR efficiency. Following PCR, the tagged PCR products are pooled, and sequencing adapters are added using a ligase enzyme that covalently links the amplified DNA fragments to the adaptors⁸. Library indexes can be included with the adaptors or added separately via a PCR step (reviewed in Bohmann et al. 2021). Detailed protocols for the robust design and implementation of ligation based library preparation include the Tagsteady protocol (Carøe and Bohmann 2020), and commercial library preparation kits include the Illumina TruSeq DNA PCR free library kit.

In all three approaches, heterogeneity spacers can be added to the primer sequence to improve sequencing performance by increasing the diversity at each base position, see Figure 7c and d (De Barba et al. 2014, Elbrecht and Steinke 2018). These usually take the form of a series of degenerate bases that vary in length between samples.

Regardless of sample type and analysis method, DNA-based monitoring of biodiversity is a complex process made up of many interdependent steps, each of which requires optimisation and incorporates choices and trade-offs. It is important to emphasize that given sufficient optimisation, highly reliable and replicable results can be obtained, and this is often robust to different choices being made at certain steps.

Choices made at each step will be influenced by multiple factors, including:

• Data requirements for the intended use-case. This is the single most important determinant of methodological choices, since the first priority is to ensure that the data are fit for purpose. Different applications carry different management costs of failure to detect rare species, and this will influence decisions from sampling design and intensity to technical replication in DNA extraction and PCR, stringency of the quality control framework, sequencing depth and bioinformatic filtering stringency.
• Practicality and logistics. This will primarily affect sample collection and preservation, with knock-on implications for DNA extraction. Important factors include the accessibility of the sampling locations and number of individual field teams sampling in parallel, the availability of cold-storage facilities, and the ability to safely handle and transport flammable liquids.
• Cost. Budget is a major consideration in almost all monitoring programmes. It principally affects the number of samples that can be collected and analysed, and places the emphasis on generating the maximum amount of information from a given number of samples. At first glance, there are many ways in which the costs of a DNA-based monitoring programme can be reduced, but many of these are ultimately false economies and all have tradeoffs that need to be evaluated in the context of the specific programme.

In applied DNA-based biomonitoring, it is vital to provide consistent documentation of methodological choices made and the results of quality control tests conducted throughout the workflow. This helps to provide confidence in the results obtained or to flag results that may be less reliable, and enables assessment of the comparability of results obtained using different workflows.

The exact data to report from any given workflow covered in this guide will vary depending on sample and analysis type. Minimum reporting standards will also vary depending on the goal of the work, which may range from exploratory surveys to publication in scientific journals and increasingly the provision of evidence meant to stand up in a court of law.

For the laboratory processes covered in this guide, our starting point should be to draw from standards that have already been established in other industries that use the same types of analyses. These include MIMARKS (minimum information about a marker gene sequence) and MIxS (minimum information about any “x” sequence) specifications (Yilmaz et al. 2011) as well as the MIQE guidelines (dMIQE Group and Huggett 2020) for qPCR. The FAIRsharing Collections represents a valuable repository of standards and databases, and includes a standard on the minimum information for biological and biomedical investigations (https://fairsharing.org/collection/MIBBI).

Several efforts have been made to adapt these broader molecular standards for eDNA applications, including the establishment of minimum information criteria for eDNA analysis from water samples (Goldberg et al. 2016), determination of LOD and LOQ for eDNA qPCR assays (Klymus et al. 2020) and the development of a framework for validating single species assays, which covers the entire workflow from field to data interpretation (Thalinger et al. 2021b). More broadly, the ten simple rules for reproducible computational research (Sandve et al. 2013) have been advocated as best practice standards in DNA based species monitoring (Deiner et al. 2017b).

For field sampling steps, however, there is little in the way of standardised reporting requirements beyond best practice for reporting in scientific publications. Goldberg et al. (2016) provided a list of field variables that should be reported as standard for detection of species’ DNA from aquatic samples, but these require adaptation for other environments and sample types and will need to be tailored for routine monitoring contexts as opposed to scientific research.

In Text Box 4 we provide an example of key information that should be recorded for all aquatic eDNA samples at the field collection stage and during analysis with a metabarcoding pipeline.

This practical guide to DNA-based biomonitoring was a collaboration of many people across the spectrum of basic research to applied contexts and represents the state of advice and knowledge at the time of writing. We acknowledge that at the time of your reading this resource, some of the hurdles and challenges we highlight may have been overcome and there may be new evidence for ways forward that may even contradict the guidance provided here. We intend to update this practical guide periodically, but recommend that readers and users should keep in mind that rapid advancements will be made and it is always a good idea to discuss the current trends and read the newest studies. Please consider the authors of this guide as a resource and do not hesitate to reach out and discuss with us!

Thus, we view the practical advice collected here as to the best of our collective knowledge at time of publication. The chosen publication style as an electronic book format will allow us to update the guide to new versions with ease. We welcome further input from the broader scientific and applied communities of practice in this area to help us keep this document as up to date as possible and contribute to future editions so that it remains relevant and useful.