# From anarchy to clarity, data pre-processing and statistical choices influence quantitative environmental DNA (eDNA) analyses

Jonas Bylemans, Teun Everts, Rein Brys, Richard P Duncan

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Jonas Bylemans, Teun Everts, Rein Brys, Richard P Duncan. From anarchy to clarity, data pre-processing and statistical choices influence quantitative environmental DNA (eDNA) analyses. Methods in Ecology and Evolution, 2025, 16 (7), pp.1322-1333. 10.1111/2041-210X.70064. hal-05447262

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# From anarchy to clarity, data pre-processing and statistical choices influence quantitative environmental DNA (eDNA) analyses

Jonas Bylemans<fnref n="1" /> ID

Teun Everts<fnref n="2" />,<fnref n="3" /> ID

Rein Brys<fnref n="2" /> ID

Richard P. Duncan<fnref n="4" /> ID

**Correspondence**

Jonas Bylemans

Email: jonas.bylemans@inrae.fr

**Funding information**

AnaEE France and the Pôle R&D Écla; Research Foundation Flanders, Grant/Award Number: S01822N

Handling Editor: Chloe Robinson

## Abstract

1. Environmental DNA (eDNA) analyses hold great potential for increasing species detection sensitivities and estimating species abundances. The rapidly growing user base, continuous method development and optimisation have led to diverse approaches for capturing and analysing eDNA. While significant efforts have been made to standardise field and laboratory protocols, a notable gap remains in understanding the consequences of data pre-processing and statistical choices on the final results obtained, particularly in quantitative eDNA analyses. These insights are crucial for developing best-practice guidelines that can harmonise analytical workflows.

2. To address this gap, we conducted an extensive literature review focusing on quantitative species-specific eDNA studies. We assessed the diversity of data pre-processing and statistical choices made to evaluate the correlation between eDNA concentrations and species' abundance or biomass, and collected the raw datasets when available. We then applied commonly used data analysis strategies to the datasets to formulate general recommendations for improving the reliability and reproducibility of quantitative eDNA analyses.

3. Our results indicate that, within the available literature, statistical methods are not always clearly described and raw data are rarely made publicly available. Furthermore, the choice of data pre-processing strategies and statistical tests used to assess quantitative correlations can significantly influence the likelihood of detecting positive correlations and the effect sizes.

4. Overall, we recommend the following: (i) increase transparency in method descriptions and data availability; (ii) assess correlations using mixed-effect models that can account for data characteristics; (iii) avoid pre-processing quantitative eDNA data, especially when combined with sub-optimal statistical tests. Implementing these guidelines should enhance the accessibility and transparency of quantitative eDNA data and ultimately their use for managers and policy makers.

<footnote n="1">¹University of Savoie Mont Blanc, INRAE, CARRTEL, Thonon-les-Bains, France</footnote>

<footnote n="2">²Genetic Diversity, Research Institute for Nature and Forest (INBO), Geraardsbergen, Belgium</footnote>

<footnote n="3">³Plant Conservation and Population Biology, KU Leuven, Leuven, Belgium</footnote>

<footnote n="4">⁴Centre for Conservation Ecology and Genomics, Institute for Applied Ecology, University of Canberra, Bruce, Australian Capital Territory, Australia</footnote>
KEYWORDS

abundance, biomass, digital PCR (dPCR), environment DNA, qPCR, statistical guidelines

1 | INTRODUCTION

Analysing DNA present in environmental samples, commonly re-
ferred to as environmental DNA or eDNA, has emerged as a pow-
erful, cost-efficient, and non-invasive tool for species detection
(Ficetola et al., 2008; Pilliod et al., 2013; Takahara et al., 2012) and
community monitoring (Bista et al., 2017; Creer et al., 2016; Hänfling
et al., 2016). The explosive growth in the user base, continuous
method development, and widespread adoption of eDNA analyses
in recent years have led to a diverse array of methods for captur-
ing and analysing eDNA (Hakimzadeh et al., 2023; Tsuji et al., 2019).
This rapid expansion has prompted the need for clear guidelines
and standardised protocols (Goldberg et al., 2016; Loeza-Quintana
et al., 2020; Mathon et al., 2021). Although considerable attention
has been given to field and laboratory methods, a similar level of
emphasis has not been placed on the impact of data pre-processing
and the possible statistical choices on the final conclusions.

Accurately modelling eDNA data requires careful consideration
of the inherent characteristics of the data. First, environmental
DNA survey data are typically hierarchically structured, with often
multiple samples collected from numerous sites, and multiple PCR
replicates for each sample (Buxton et al., 2021; Ficetola et al., 2015;
Furlan et al., 2016). Analyses need to accommodate this hierarchi-
cal data structure as samples from the same sites and PCR repli-
cates from the same samples will not be independent of each other.
Second, surveys generate data in the form of counts (i.e. copies L⁻¹
(C/L) or read numbers) which theoretically can only take positive
integer values (e.g. one litre of sampled water cannot contain half
a copy of DNA). A common approach for analysing such data is to
assume the counts are Poisson distributed. However, the variance
of count data is often significantly larger than the mean, resulting
in overdispersion (Bliss & Fisher, 1953) which needs to be accom-
modated in the analysis. Third, zero values are common and may
represent both false (e.g. related to errors in sampling or laboratory
protocols) or true zeros (i.e. a species is not detected because it is not
present at a sampling location). A higher than expected frequency
of zero counts will cause zero-inflation (Blasco-Moreno et al., 2019;
Heilbron, 1994), which also needs to be accommodated in the analy-
sis. Consequently, accounting for the data structure, overdispersion,
and zero-inflation is crucial to drawing sound statistical conclu-
sions but remains a challenging endeavour (Arnqvist, 2020; Blasco-
Moreno et al., 2019; Ives, 2015; O'Hara & Kotze, 2010; St-Pierre
et al., 2018; Warton et al., 2016).

Environmental DNA surveys are widely used for species detec-
tion, but can be used to estimate species' abundances or biomass as
it can be expected that, under ideal conditions, estimates of DNA
copy numbers obtained from quantitative species-specific eDNA
surveys (i.e. using quantitative real-time PCR (qPCR) or digital PCR

(dPCR)) will be positively correlated with species abundance (A) or
biomass (B). A wide range of data pre-processing strategies and
statistical methods have been used to estimate these relationships,
raising the question of whether different approaches could lead to
varying outcomes and conclusions. In some cases, the hierarchical
structure of the data (i.e. sampling multiple sites, mesocosms or ex-
perimental tanks; collecting multiple samples per site; performing
multiple PCR replicates) is included in the statistical model speci-
fications (Eichmiller et al., 2016; Hinlo et al., 2018; Lacoursière-
Roussel et al., 2016). In other cases, the hierarchical structure is
overlooked or the data are simplified by averaging over sample
and/or PCR replicates (Doi et al., 2017; Kutti et al., 2020; Skinner
et al., 2020; Thalinger et al., 2019). Many studies have dealt with
the issue of over dispersed counts by log transforming eDNA con-
centration data, which often stabilises the variance (Doi et al., 2015;
Dougherty et al., 2016; Thomsen et al., 2012). However, the impact
of data transformations on statistical analyses remains contentious
(Ives, 2015; O'Hara & Kotze, 2010). To address the issue of zero-
inflation in eDNA data, one approach is to establish a lower bound
for eDNA quantification. This can entail filtering out zeros or val-
ues below the limit of detection (LOD) or quantification (LOQ; Dunn
et al., 2017; Takahara et al., 2012; Takahashi et al., 2020), although
this approach ignores true zeros (Blasco-Moreno et al., 2019;
Klymus et al., 2020). Another common approach to reduce zero-
inflation is to average over replicates (i.e. taking mean values per
sample or site) prior to analyses (Dougherty et al., 2016; Spear
et al., 2021). However, averaging removes variation inherent to the
data generating processes. Additionally, a wide range of statistical
tests have been applied to assess the correlation between eDNA
concentrations and abundance or biomass (A/B) including Pearson
or Spearman correlations (Jo, 2023; Plough et al., 2018), linear or
generalised linear models (Eichmiller et al., 2016; Pilliod et al., 2013)
and Bayesian models (Erickson et al., 2016). These different sta-
tistical choices may strongly influence the resulting conclusions
(Arnqvist, 2020; Gould et al., 2025).

To our knowledge, two studies have assessed the impact of dif-
ferent data pre-processing strategies (Jo, 2023) or statistical tests
(Chambert et al., 2018) on quantitative species-specific eDNA data.
However, a thorough evaluation of the combined impacts of the most
common data pre-processing strategies and statistical tests is lacking.
To bridge this gap, we performed a systematic literature search for
quantitative species-specific eDNA studies that assessed the correla-
tion between eDNA concentrations and species A/B. We evaluated
the variety of data pre-processing strategies and statistical choices
in the current literature and checked for the availability of raw data
(i.e. where measurements are available for all levels of replication).
We subsequently identified common pre-processing strategies and
statistical tests and applied them to re-analyse the available empirical
<figure><img src="image_2.png" /></figure>

datasets. Specifically, we assessed (i) the common analytical choices made during the analyses of quantitative eDNA data and the extent of variability in these approaches and (ii) how these analytical choices influence the ability to detect eDNA-abundance correlations and their effect size. Finally, we used our results to identify key considerations and best-practice guidelines that can improve the reliability and reproducibility of quantitative eDNA analyses.

## 2 | LITERATURE REVIEW AND DATA COLLECTION

We conducted a literature search on the 16th of March 2023 using Publish or Perish v 8.8.4275 (Harzing, 2007) to identify studies examining the relationship between quantitative and species-specific eDNA measurements and species A/B (Figure 1). A subset of 500 published studies was retrieved from Google Scholar, using search criteria that included “environmental DNA” or “eDNA” in the title, along with additional keywords such as {“biomass” OR “abundance”} AND “quantitative” AND {“qPCR” OR “ddPCR”}. We also considered studies referenced in a recent review (Rourke et al., 2021) and a

meta-analysis (Carvalho et al., 2022) on the quantitative nature of species-specific eDNA surveys, as well as studies encountered prior to finalising the dataset (i.e. June 2023).

We performed a first evaluation of the retained publications by assessing if they reported correlation analyses between eDNA concentrations and species A/B, and at least three A/B measurements were documented (Figure 1). For all retained publications, we extracted details of the study system and focal species as well as choices made throughout the analytical workflow. These choices included the use of site, sample and PCR replicates during sampling and molecular analyses; methods for capturing and quantifying eDNA concentrations; the approaches used for summarising and/or transforming the raw data; statistical tests applied for correlation analyses and the availability of the associated data. We obtained quantitative estimates of potential observer biases through an independent assessment of a random subset of twenty-two published studies by two of the authors. Publications with publicly available raw data (i.e. eDNA concentrations reported for all replication levels) were compiled into a standard data format by a single author (see author contributions statement). Data compilation consisted of recording, for each publication (or individual regression if multiple correlations were performed within a

<figure><img src="image_3.png" /><figcaption>**FIGURE 1** Schematic overview of the data collection and data analysis process. Research questions used to formulate guidelines on quantitative eDNA analyses are given (Q1 and Q2). Briefly, a literature review was used to identify studies reporting correlation analysis between environmental DNA concentrations ([(eDNA)] and species abundance (A) or biomass (B). Relevant studies were used to assess study trends and the variety of analytical choices used during the analyses of quantitative eDNA data. The available raw data from these studies was subsequently compiled and pre-processed using strategies that were commonly found in literature. Generalized linear (mixed-effect) models (gl(m)m) with a negative binomial distribution, and including a zero-inflation model where relevant, were used to determine the optimal model structure (e.g. models highlighted in blue for illustrative purposes) based on Akaike Information Criterion (AIC) values. Subsequently, models with alternative distributions were fitted and alternative statistical tests were used to assess correlations and the impact of the various choices on regression outcomes. (additional legend: C/L=eDNA copies per litre, A/B=species abundance or biomass, zi=zero-inflation model, s=sample replicate, S=site replicate, l(m)m=linear (mixed-effect) model).</figcaption></figure>
<figure><img src="image_4.png" /></figure>

study), characteristics of the molecular assay used; limits of detection (LOD) and/or quantification (LOQ) if available; unique site, sample and/or PCR replicates; measures of eDNA concentrations in a single PCR; standardised eDNA concentration measurement (i.e. C/L or ng/L); and measurements of species A and B (Bylemans et al., 2025). In some cases, temporal surveys were conducted in one or a limited number of sites, rendering sampling events within a single site no longer independent of one another. Temporal sampling (i.e. sampling one site at different time points as species A/B change), however, also results in a hierarchical data structure similar to spatially explicit eDNA surveys. Therefore, we considered both spatial and temporal sampling surveys when compiling the data. Site level replicates were thus considered to be (i) spatially distinct locations, (ii) temporally distinct sampling of one or a few locations and (iii) experimental units (i.e. ponds, mesocosms or aquaria) sharing the same species A and/or B.

Visualisation of the data was conducted in R version 4.2.3 (R Development Core Team, 2010) using the R packages tidyverse (Wickham et al., 2019) and ggsankey (R package by David Sjoberg). To quantify the degree of observer bias during data extraction, we determined the overall congruence between observers using the percentage similarity for seven study characteristics (i.e. eDNA capture and quantification method, replication levels, type of data filtering and transformation, approach used to summarise the data, and the statistical test used) from the twenty-two independently assessed studies. We visualised trends in the number of publications and the data availability using bar plots while the variety of methodological choices were visualised using Sankey diagrams.

A total of 110 peer-reviewed publications published between 2011 and 2023 reported correlation analyses between some measure

of eDNA concentration and species A/B. The overall congruence between independent observers carrying out the study assessments was 80.5% with the similarity for all seven study characteristics ranging from 65.9% to 90.9% (Figure S1) suggesting that the effects of observer biases on data collection is likely limited. The number of studies reporting correlation analyses between eDNA concentrations and species abundance/biomass increased over time (Figure 2). While the percentage of studies making their data publicly available increased from 2015 onwards, only a limited number of studies published the full raw data (i.e. 26 out of 106 published studies or 24.5% of studies between 2011 and 2022; Figure 2). Most studies to date were conducted in natural freshwater ecosystems with lentic and lotic systems being approximately equally represented, and marine and terrestrial ecosystems being underrepresented (Figure S2). Most studies focussed on fishes and used filtration and a quantitative real-time PCR approach with fluorescent probes to capture and quantify eDNA concentrations, respectively (Figure S2).

Across the selected studies, key choices throughout their analytical workflow were visualised in Figure 3. Most studies use replication throughout the eDNA analysis workflow (ca. 67%) (i.e. multiple sites sampled with multiple samples taken per site and multiple PCR replicates per sample). While most studies do not apply any data filtering prior to statistical analyses (ca. 74%), commonly encountered pre-processing strategies involve setting a lower bound to eDNA quantification results (i.e. filtering out zeros or values below the assay's LOD or LOQ) (ca. 19%) (Figure 3). Summarising eDNA quantification data prior to analysis is a commonly used strategy, with about one third of all published studies calculating the mean eDNA concentrations per sample (ca. 36%) or per site (ca. 32%) prior to

<figure><img src="image_5.png" /><figcaption>FIGURE 2 Yearly trends in the number of publications reporting a correlation between environmental DNA concentrations and species abundance/biomass (a) and the data availability for published studies over the years (b). Data availability categories are: Not publicly available (NPA), publicly available but the data is summarised (Ps), publicly available but the data is incomplete (Pi) or the complete and unmodified data is publicly available (Pr).</figcaption></figure>
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>**FIGURE 3** Sankey diagram visualising the key analytical choices found in the literature review of species-specific quantitative environmental DNA studies.</figcaption></figure>

applying statistical tests (Figure 3). Most studies do not use a data transformation (ca. 55%) but the second most popular strategy is to log transform eDNA concentrations prior to statistical analyses (ca. 44%) (Figure 3). Finally, multiple statistical tests have been used in the current literature, falling into 15 different categories (Figure 3). Overall, linear and generalised linear models, with or without random effects, appear to be most popular (ca. 53%). Amongst the others, Spearman (ca. 15%) and Pearson (ca. 7%) correlations are frequently used to statistically test correlations (Figure 3).

# 3 | EVALUATING COMMON ANALYTICAL CHOICES

## 3.1 | Distributional assumptions

We considered several approaches for modelling the distribution of quantitative eDNA data. These data typically have the following count characteristics: (i) consisting of positive values (e.g. number

of copies per L); (ii) exhibiting overdispersion; and (iii) often having an excess of zeros. Accounting for excess zeros in statistical modelling can be achieved through the use of zero-inflated models (Brooks et al., 2017). The added benefit of these models is that they effectively use a qualitative modelling for all zero values which has previously been recommended when eDNA concentrations fall below the LOQ (Klymus et al., 2020). We assume that a Negative Binomial model would be the 'optimal' model as it can account for both overdispersion and, when including a zero-inflation model, the excess of zeros in count data (Blasco-Moreno et al., 2019). Additional distributions frequently used to model quantitative eDNA were compared to this benchmark. For instance, a zero-inflated Log-Normal distribution (i.e. applying a log(y + 1) transformation of the response variable) can accommodate overdispersion and excess zeros but only when overdispersion is limited (O'Hara & Kotze, 2010). Alternatively, a zero-inflated Poisson distribution can account for excess zeros but not overdispersion. Finally, normal distributions have previously been used to model quantitative eDNA data but we did not consider this in the current analyses
<figure><img src="image_8.png" /></figure>

given the obvious inability to adequately model data with the earlier described characteristics.

## 3.2 | Data structure and pre-processing

Appropriately incorporating the hierarchical data structure can be done through: (i) summarising the data by averaging over sample or PCR replicates or (ii) using appropriate hierarchical models. While summarising the data prior to analyses effectively removes the data structure and also reduces the impact of excess zeros, it may reduce statistical power (Jo, 2023). In contrast, mixed-effect models can accommodate a hierarchical data structure and current statistical packages can fit such models using a wide range of distributions while also accounting for zero-inflation (Arnqvist, 2020; Brooks et al., 2017). Finally, setting lower bound threshold values for quantitative results and thus removing all zeros is a common data filtering approach which effectively removes zero-inflation but may remove potentially relevant data.

## 3.3 | Re-analyses of empirical datasets

Models fitting was done in R version 4.2.3 using the R packages glmmTMB (Brooks et al., 2017), performance (Lüdecke et al., 2021) and model performance was assessed using the packages broom, mixed (Bolker & Robinson, 2019) and emmeans (Lenth, 2024). Final analyses were conducted on a subset of studies where replication was present across all hierarchical levels (i.e. 16 studies reporting 81 individual regressions) and eDNA concentrations were measured as C/L (i.e. 12 out of 16 studies reporting 60 individual regressions). Studies reporting quantitative eDNA measures as ng/L were excluded (5 out of 16 studies reporting 21 individual regressions) as they contain zero-inflated positive decimal values which are notoriously challenging to model correctly. For final analyses, some studies or subgroups were excluded due to the presence of a high number of zero values (Plough et al., 2018) or limited variation in species A/B estimates (Takahashi et al., 2020) (Figures S3-S6). Removal of these data alleviated initial convergence problems when fitting models and resulted in a final dataset of 11 studies reporting 55 individual regressions (i.e. C/L ~ A: 10 studies reporting 45 regressions, C/L ~ B: 3 studies reporting 15 regressions; Figure 1).

Commonly used data summarising (i.e. no summary or taking means per sample or per site) or filtering approaches (i.e. no filtering or removal of zeros) were applied prior to model fitting thus generating six different datasets for each individual regression. We fitted models separately for each individual regression between eDNA copy numbers and species A or B and each of the data pre-processing strategies. Firstly, a Negative Binomial regression model was used to determine optimal model structure (see Figure 1). Copy numbers per litre were modelled using the log transformed A or B estimates as a fixed effect. Random effects (i.e. samples nested

within sites when no data pre-processing was used) and/or a zero-inflation model (i.e. considering only A/B as fixed effect or including both fixed and random effects) were subsequently included to account for the hierarchical data structure and zero-inflation (see Supporting Information for full model descriptions). Random effects and zero-inflation models were adjusted according to data pre-processing strategies (e.g. sample effects were removed from the random effects if means were taken per sample and zero-inflation models were excluded if zeros were removed prior to model fitting). Akaike Information Criterion (AIC) values (Akaike, 1974, 1978) were used to select the optimal model structure, which was subsequently used to assess model fit using two commonly used alternative distributions, namely, a Log-Normal (i.e. applying a log[C/L + 1] transformation) and Poisson distribution. Model parameter estimates, associated p-values and AIC values (i.e. corrected in the case of the Log-Normal regression) were extracted. A Wilcoxon signed-rank test for paired samples was used to assess if the Negative Binomial distribution provides a better fit compared to the alternative distributions (i.e. lower AIC values) for all combinations of data pre-processing strategies. Finally, Spearman and Pearson tests were performed to assess the correlations between eDNA concentrations and species A/B for all individual regressions and the different combinations of data pre-processing strategies. Pearson correlations were also performed after log transforming response and predictor variables (i.e. log-log transformation). This transformation was not considered for the Spearman correlations as this is a non-parametric rank-based test and more robust to potential outliers.

For all individual regressions, explanatory variables (A/B), data pre-processing steps (i.e. filtering and summarising) and statistical tests; the ability to detect a significant positive correlation between eDNA concentrations and species A/B at $α=0.05$ was encoded as binomial data. For this, we only considered the conditional, and not the zero-inflation, part of the models as the latter effectively models eDNA detection probabilities. This approach also facilitated comparisons between different data pre-processing strategies, which may exclude the need for a zero-inflation model. We subsequently used a generalised linear mixed-effect model to assess the impacts of the different choices in the analytical workflow. The probability to detect a significant positive correlation was modelled as a function of the statistical tests and the two main data pre-processing steps used. Individual regressions nested within studies were included as random effects to account for between-study variance. AIC-based forward model selection was used to evaluate whether the inclusion of two-way interactions between fixed effects improved model fit. Pairwise comparisons using estimated marginal means were conducted to assess the significance of between-group effects relative to the “optimal” model, after applying a false discovery rate correction (Benjamini & Hochberg, 1995). The above analyses were rerun using an $α$-value of 0.01 to determine the significance of regression slope estimates to assess if more conservative thresholds would influence the results. Finally, a similar approach was used to evaluate the impact of the statistical choices on model regression slope estimates as a proxy for effect size using a linear mixed-effect
<figure><img src="image_9.png" /></figure>

model and an ordered quantile normalisation transformation of the
response variable to ensure normality of the data.

3.3.1 | The neglected impact of distributional assumptions

Comparisons of AIC values revealed that a Negative Binomial dis-
tribution best describes raw quantitative eDNA data as AIC values
are significantly lower when compared to models assuming a Log-
Normal or Poisson distribution (p < 0.0001; Figure 4). When plotting
the predicted versus observed values for a random subset of 50 re-
gressions, we found that the Negative Binomial distribution provided
a good fit to the data (Figure S7). Pairwise comparisons of the proba-
bility to detect significant positive correlations and the estimated re-
gression slopes were conducted using the Negative Binomial model
without any prior data modifications as a benchmark. Comparisons
between different statistical tests based on the raw data revealed
an increase in type I errors (i.e. false positives) when using Spearman

and Pearson regressions, and more conservative significance esti-
mates only enhanced these patterns (Figure 5a,b). No significant ef-
fects on the probability to detect a significant positive correlation
and the estimates of regression slopes were observed for the Log-
Normal and Poisson models, and the use of more conservative alpha
values reduced the occurrence of type I and II (i.e. false negatives)
errors (Figure 5).

3.3.2 | Data structure and pre-processing affect model inferences

Comparison of AIC values showed that a Log-Normal distribu-
tion fitted the quantitative eDNA data better when mean values
are taken per sample or site, while the Negative Binomial model
outperformed the Poisson model when applying common data
pre-processing strategies (p < 0.0001; Figure 4). Considering the
pairwise comparisons between mixed-effect model parameters,
the general observed pattern was a decreasing ability to detect

<figure><img src="image_10.png" /></figure>

FIGURE 4 Delta Akaike Information Criterion (AIC) values for the alternative distributions used in the mixed-effect models (i.e. the log-normal (AIC<sub>ln</sub>) for the upper panels and Poisson distribution (AIC<sub>p</sub>) for the lower panels) relative to the negative binomial distribution (AIC<sub>nb</sub>). Results are given for different regression types (x-axis and different colours) and the different possible strategies for summarising (horizontal panels) and filtering (i.e. darker colours: No filtering, lighter colours: Removal of zeros) the data.
<figure><img src="image_11.png" /><figcaption>FIGURE 5 Influence of different combinations of statistical choices, relative to “optimal” model using a Negative Binomial distribution and without prior data filtering or summarising, on the probability of detecting a significant positive correlation (a: $\alpha=0.05$, b: $\alpha=0.01$) and the estimates of the regression slope ($\alpha=0.05$) (c). Commonly used statistical test are given on the x-axis while the different strategies for summarising and filtering the data prior to statistical analyse are given on the y-axis. Colours indicate the relative decrease (blue) or increase (red) in mean estimates relative to the “optimal” model for each regression type (vertical panels). When relative differences where statistically significant ($\alpha=0.05$), p-values are given.</figcaption></figure>

significant positive correlations and lower slope estimates when removing zero values prior to analyses (Figure 5). Taking the means per sample or site resulted in an overestimation of significant positive correlations and regression slope estimates, with these effects being most pronounced when using a Poisson model (Figure 5). Applying pre-processing strategies prior to Spearman and Pearson correlations tended to reduce the impact of type I errors, especially when

taking mean values per sample or site (Figure 5). In particular, applying Pearson analyses to the untransformed data and taking the mean values per sample or site may result in an increase in type II errors. While the use of more conservative alpha values to assess the significance of correlations reduces the prevalence of type II errors, it increased type I errors when using a Poisson model distribution or Spearman and Pearson correlations (Figure 5).
<figure><img src="image_12.png" /></figure>

## 4 | GENERAL RECOMMENDATIONS

Since 2011, the number of quantitative species-specific eDNA studies has steadily increased. This growth resulted in a wide variety of data pre-processing approaches and statistical methods that have been applied to assess potential correlations between eDNA concentrations and species A/B. Surprisingly, a formal evaluation of the impact of these different strategies and formulating some general guidelines to handle such data is lacking. A re-analysis of available empirical datasets shows that the choice of pre-processing strategy or statistical test can have profound effects on type I and II error rates and estimated effect sizes (i.e. regression slope). Harmonising analytical workflows is urgently needed to increase the reliability of quantitative eDNA studies and to ensure that sound conclusions can be drawn from future studies, literature reviews and/or meta-analyses (e.g. Carvalho et al., 2022; Rourke et al., 2021). Several key recommendations arise from our study, which can significantly improve the reliability and credibility of quantitative eDNA analyses, and thus, their application in conservation programmes and policy making.

### 4.1 | Increased efforts are needed to provide clear descriptions of statistical workflows and make raw unmodified datasets publicly available

The first standard step that should be promoted is providing full and detailed descriptions of statistical analyses and making raw data publicly available (Reichman et al., 2011). Unfortunately, many current studies lack appropriate (meta-)data standards, making it a challenging endeavour to understand the available data or reproduce the analysis. Moreover, despite the availability of multiple free data repositories for storing raw research data and the requirement for a data accessibility statement by an increasing number of journals, only a quarter of the studies reviewed here published their raw data. During our search we came across instances where the data was not available through the provided links, the metadata were unavailable or too limited to fully understand all reported measures, the data was available but in a summarised format or only a subset of the data was available. While the unavailability of raw data remains an issue across various fields (Miyakawa, 2020), we urge the eDNA community to make an extra effort to store unmodified data in public repositories and ensure there is well-documented metadata, even if this is not required by the journal.

### 4.2 | Negative binomial mixed-effect models that account for both the hierarchical data structure and zero-inflation should be preferred to assess relationships between eDNA concentrations and species A/B

The use of mixed-effect models, particularly those with a Negative Binomial distribution, is the most appropriate standard for modelling eDNA copy numbers. Our study confirms the findings by Chambert

et al. (2018) that a Negative Binomial distribution better fits quantitative eDNA data. However, alternative modelling distributions do not seem to have a significant impact on model estimates as long as data were not modified and appropriate measures are taken to account for the hierarchical data structure and the impact of zero-inflation. Patterns in the pairwise comparisons are generally consistent between the Negative Binomial and Log-Normal models, thus indicating that Log-Normal models can be a valuable alternative to Negative Binomial models when the latter prove challenging to fit. This aligns with Ives (2015) who found that, when testing for the significance of regressions, a log transformation of count data can be used. Our review indicates that a wide variety of statistical tests have been used to assess correlations between eDNA concentrations and species A/B, highlighting the need for standardisation of statistical tests to facilitate more direct comparisons between studies. Overall, we recommend against the use of Spearman and Pearson correlations due to the increased risk of type I errors and the inability to account for data structure and zero-inflation in the analyses.

### 4.3 | Pre-processing of quantitative eDNA data should be avoided, especially in combination with sub-optimal statistical tests

Data pre-processing strategies should, as much as possible, be avoided. Our results indicate that common pre-processing strategies can increase type I and type II errors and affect effect size estimates. The impact of these pre-processing strategies is especially profound when combined with sub-optimal statistical analyses (i.e. Poisson models, Spearman and Pearson correlations). Our literature review revealed that most studies applied at least one data pre-processing step. While filtering out values (e.g. based on LOD, LOQ, outliers, etc.) was relatively uncommon, many studies averaged data across sample or site replicates. Summarising data prior to statistical analyses was found to increase type I errors especially when using mixed-effect Poisson models for regression analyses. While taking mean values across PCR and/or sample replicates is common and likely to give a value closer to the 'true' eDNA concentrations, especially for low concentrations (Ellison et al., 2006), this ignores the variability inherent to the data generating processes which can be effectively accounted for through mixed-effect models. Earlier work has also indicated that pooling measurements reduces the likelihood of detecting significant correlations and attributed this to a reduced sample size (Jo, 2023). Due to the limited availability of LOD and LOQ estimates, we did not assess the effects of discarding results below the LOD or LOQ. However, previous research has recommended that eDNA concentration below the LOQ should only be considered qualitatively (Klymus et al., 2020). In this regard, it is noteworthy that the use of zero-inflated mixed-effect models effectively addresses this issue for all zero values. Overall, given the range of pre-processing choices, the use of models that can account for the data characteristics (i.e. counts, excess zeros and hierarchical structure) should be preferred (Arnqvist, 2020; St-Pierre et al., 2018; Stroup, 2015).
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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**Table S1.** Full descriptions of the Negative Binomial models used to model the relationship between environmental DNA (eDNA) concentrations and species abundance (A) or biomass (B).

**Figure S1.** The percentage of congruence based on the twenty-two independently assessed publications.

**Figure S2.** Sankey diagrams highlighting the key study systems and target taxa used to study the correlation between environmental DNA (eDNA) concentrations and species abundance/biomass (A) and temporal trends in the methods used for capturing (B) and quantifying (C) eDNA.

**Figure S3.** Overview of the raw data for all studies (top panel names) and individual regressions (lower panel IDs) reporting correlations

analyses between environment DNA copies L⁻¹ and species abundance.

**Figure S4.** Visualisation of the distribution of the environmental DNA concentration measures for all studies (top panel names) and individual regressions (lower panel IDs) reporting correlations analyses between environment DNA copies L⁻¹ and species abundance.

**Figure S5.** Overview of the raw data for all studies (top panel names) and individual regressions (lower panel IDs) reporting correlations analyses between environment DNA copies L⁻¹ and species biomass.

**Figure S6.** Visualisation of the distribution of the environmental DNA concentration measures for all studies (top panel names) and individual regressions (lower panel IDs) reporting correlations analyses between environment DNA copies L⁻¹ and species biomass.

**Figure S7.** Verification of the fit of the Negative Binomial models without any prior treatment of the data by plotting the predicted versus observed values. Plots are created for a random subset of 50 regressions with panel labels indicating the individual regressions and the predictor variable given in between brackets (i.e. species abundance (A) or biomass (B)).

**Figure S8.** The error rate in function of the *p*-value of the reference model (i.e. model with negative binomial distribution without prior data filtering/summarizing) for regressions between eDNA concentration and species abundance.

**Figure S9.** The error rate in function of the *p*-value of the reference model (i.e. model with negative binomial distribution without prior data filtering/summarizing) for regressions between eDNA concentration and species biomass.

**How to cite this article:** Bylemans, J., Everts, T., Brys, R., & Duncan, R. P. (2025). From anarchy to clarity, data pre-processing and statistical choices influence quantitative environmental DNA (eDNA) analyses. *Methods in Ecology and Evolution*, 16, 1322-1333. https://doi.org/10.1111/2041-210X.70064