# Comparison of Short Generalist and Specific Markers to Assess the Diversity of Arthropods Using eDNA Metabarcoding

Gentile Francesco Ficetola, Alexis Carteron, Silvio Marta, Aurelie Bonin, Isabel Cantera, Pierre Taberlet, Alessia Guerrieri

► To cite this version:

Gentile Francesco Ficetola, Alexis Carteron, Silvio Marta, Aurelie Bonin, Isabel Cantera, et al. Comparison of Short Generalist and Specific Markers to Assess the Diversity of Arthropods Using eDNA Metabarcoding. Environmental DNA, 2025, 7 (6), 13 p. 10.1002/edn3.70225. hal-05392299

HAL Id: hal-05392299

https://hal.inrae.fr/hal-05392299v1

Submitted on 2 Dec 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

ORIGINAL ARTICLE

OPEN ACCESS

# Comparison of Short Generalist and Specific Markers to Assess the Diversity of Arthropods Using eDNA Metabarcoding

Gentile Francesco Ficetola<fnref n="1" />,² ID | Alexis Carteron<fnref n="1" />,³ | Silvio Marta<fnref n="1" />,⁴ | Aurelie Bonin<fnref n="1" />,⁵ | Isabel Cantera<fnref n="1" /> | Pierre Taberlet² | Alessia Guerrieri<fnref n="1" />,⁵ ID

**Correspondence:** Gentile Francesco Ficetola (francesco.ficetola@gmail.com)

**Received:** 28 March 2025 | **Revised:** 8 October 2025 | **Accepted:** 10 November 2025

**Funding:** This work was supported by NextGenerationEU, Biodiversa+ (Grant 101052342), and Horizon Europe European Research Council (Grant 772284).

**Keywords:** Arthropoda | Collembola | glacier retreat | Insecta | primers | soil biodiversity | universality

## ABSTRACT

A large number of marker typologies have been developed for the metabarcoding of environmental DNA (eDNA). Generalist markers have been advocated for their ability to amplify many taxa simultaneously and produce exhaustive biodiversity assessments, while more specific markers have been proposed for their higher ability to detect and discriminate taxa within a more restricted group. Quantitative comparisons between generalist and specific markers are needed to assess their relative efficiency depending on study targets. Here we compared the performance of one generalist (Euka02, amplifying all eukaryotic groups), one intermediate (Arth02, amplifying arthropods), and two specific markers (Coll01 and Inse01, amplifying springtails and insects, respectively) for the assessment of springtails and insects diversity using eDNA metabarcoding. The four markers were used to analyze eDNA extracted from > 1200 soil samples collected in recently deglaciated terrains. We then assessed whether the different markers were able to detect community responses to key environmental drivers (soil temperature, time since glacier retreat, plant productivity, and topographic wetness). The two specific markers detected more taxa of both springtails and insects than the generalist markers; still the taxonomic richness and community dissimilarity were well correlated between generalist and specific markers. There was good overlap between the taxa identified by the specific markers and those identified by the generalist ones, but the specific markers often detected the taxa identified by the generalist markers, plus several additional ones. Both generalist and specific markers detected the impact of key environmental drivers on arthropods; still the specific markers showed the strongest power to detect relationships, and the most generalist marker was unable to identify the weaker relationships. Generalist markers efficiently provide an overall view of soil biodiversity; nevertheless complementing them with specific markers allows more detailed biodiversity measures, and provides a clearer picture of its response to environmental stressors.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *Environmental DNA* published by John Wiley & Sons Ltd.

<footnote n="1">¹Departement of Environmental Science and Policy, University of Milan, Milan, Italy | ²CNRS, LECA, Université Grenoble Alpes, Université Savoie Mont Blanc, Grenoble, France | ³École d'Ingénieurs de Purpan, UMR INRAE-INPT DYNAFOR, Université de Toulouse, Toulouse, France | ⁴Institute of Geosciences and Earth Resources, CNR, Pisa, Italy | ⁵Argaly, Bâtiment CleanSpace, Sainte-Hélène-du-Lac, France</footnote>
A large number of marker typologies have been developed for the metabarcoding of environmental DNA (eDNA) in the last years. Metabarcoding markers can differ for a broad range of features, including the length of the amplified region, the target taxa, and the taxonomic resolution, all of which can influence downstream results. As a consequence, researchers can select different markers, or combinations of markers, depending on the study objectives (Ficetola and Taberlet 2023; Taberlet et al. 2018; Yao et al. 2022).

A broad taxonomic coverage or “universality” (Fahner et al. 2016; Ficetola et al. 2010; Marquina et al. 2019) is a key feature of metabarcoding markers. Generalist primers (also named universal) may be extremely useful because they can amplify a very broad range of organisms. With a generalist marker, one single metabarcoding analysis may provide information over entire domains of life (e.g., all animals, all eukaryotes, all bacteria), potentially enabling an exhaustive assessment of the whole tree of life with just a few markers, (e.g., Capra et al. 2016; Holman et al. 2021; Martinez-Almoyna et al. 2019). However, universality generally comes at a cost. First, no marker is truly “universal” for one domain of life, as even the most generalist primers show preferential amplification of one or some taxa, mainly because of base variations in the priming region of the marker. For instance, the Fung01 marker (Epp et al. 2012) shows an excellent amplification of fungi, but does not properly amplify Glomeromycota due to a mismatch on the reverse primer region (Epp et al. 2012; Taberlet et al. 2018). Degenerated primers have been designed to overcome the issue of base variation in the priming region; nonetheless highly degenerated primers are not necessarily a good solution, given the risk of unspecific amplification (i.e., the amplification of non-target taxa, such as bacteria using COI primers) (Collins et al. 2019; Fahner et al. 2016; Hintikka et al. 2022). Second, generalist markers often have limited taxonomic resolution. In fact, if a marker is able to amplify all eukaryotes, it is unlikely that it will exhibit enough variation between closely related species for fine-scale discrimination (see Jamy et al. 2022 for an attempt using long metabarcodes, which can have limitations with eDNA). Third, even in the absence of mismatches in the priming regions, the most abundant taxa (in terms of biomass, number of DNA copies and so on) can be preferentially amplified, potentially leaving undetected some rare taxa that might be of interest for their ecological function, conservation status, or other reasons (Garrido-Sanz et al. 2022; Jurburg et al. 2021).

Over the years, a considerable number of specific metabarcoding markers have been developed, targeting taxonomic groups such as individual phyla (e.g., Echinodermata, Nematoda, Arthropoda), classes (e.g., Gastropoda, Oligochaeta, Insecta, Collembola, Elasmobranchii) or even families (e.g., Poaceae, Enchytraeidae, Culicidae) (Andújar et al. 2018; Baamrane et al. 2012; Clarke et al. 2014; Elbrecht et al. 2016; Leese et al. 2021; Marquina et al. 2019; McCartin et al. 2024; Pedro et al. 2023; Romahn et al. 2024; Taberlet et al. 2018). With specific markers, amplification efficiency and taxonomic resolution are expected to be optimized for the targeted taxa. Furthermore, targeting a narrow group (e.g., one family) reduces the number of involved species, thus minimizing the risk of overlooking key

taxa. However, this approach would require an unrealistic number of markers to analyze the astonishing biodiversity present in a single environmental sample (Ficetola and Taberlet 2023). Researchers should thus optimize the selection of markers based on study aims and resources, avoiding the use of too many specific markers if a satisfactory, more generalist primer is available. Accurate comparisons of the results obtained by different markers are thus needed to identify the most appropriate marker combinations and optimize the outcome of metabarcoding studies (Ficetola and Taberlet 2023). Such comparisons are particularly urgent for highly diverse taxonomic groups and for taxa used as indicators of environmental health and quality (Li et al. 2023), and would be useful for studies where researchers or stakeholders are interested in obtaining a broad picture of biodiversity while keeping the focus on key taxa.

The aim of this study was to compare the performance of both generalist and more specific primers, for the assessment of biodiversity using eDNA metabarcoding in two key groups of arthropods: insects (Insecta) and springtails (Collembola). Arthropods are the most biodiverse phylum of animals and include key groups such as crustaceans, arachnids, insects and springtails. Among them, insects are the most species-rich class and have a fundamental role in most terrestrial and freshwater ecosystems. Springtails have a lower described species richness but are extremely abundant in the soil and other underground environments, where they play multiple key roles (Bardgett and van der Putten 2014; Le Cadre et al. 2024; Pedro et al. 2023; Potapov et al. 2020). Several specific markers have been designed for the analysis of these organisms and have helped to investigate community responses to environmental changes (e.g., Andújar et al. 2018; Clarke et al. 2014; De Barba et al. 2014; Elbrecht et al. 2016; Janssen et al. 2018; Leese et al. 2021; Marquina et al. 2019; Pedro et al. 2023; Takenaka et al. 2024). Nevertheless, different generalist primers exist and these are expected to have good performances on springtails and insects (Andújar et al. 2018; De Barba et al. 2014; Ficetola et al. 2021; Guardiola et al. 2015). If generalist primers were able to retrieve biodiversity patterns consistent with those of specific primers, their use might have the advantage of providing information on other organisms as well, such as arachnids or crustaceans. Generalist primers could also be used in a standardized way across studies, ensuring greater consistency in the results and facilitating comparisons. We took advantage of > 1200 soil samples where eDNA was extracted and analyzed with four markers, including two generalist markers (a very generalist marker amplifying all eukaryotes (Euka02; Guardiola et al. 2015) and a marker amplifying all arthropods (Arth02; Taberlet et al. 2018)) and two more specific markers, targeting insects and springtails (Clarke et al. 2014; Janssen et al. 2018; Taberlet et al. 2018). Specifically, we tested: (1) if generalist markers retrieve levels of taxonomic diversity (alpha-diversity) comparable to those of the specific ones; (2) if generalist and specific markers yield overlapping information on the taxonomic composition of communities of the taxa targeted by the specific markers; (3) if, whatever the differences in detected communities, patterns of dissimilarity in species composition (beta-diversity) across plots are consistent between markers; and (4) if generalist and specific markers show similar power to detect relationships between the diversity of target taxa and environmental features. If hypotheses 1-4 are verified, a generalist marker could be preferable, in some cases,
<table><caption>TABLE 1 | Characteristics of the four markers used in this study.</caption><thead><tr><th>Universality</th><th>Name</th><th>Target gene</th><th>Forward primer/reverse primer</th><th>Minimum and maximum length in silico (bp)</th></tr></thead><tbody><tr><td>High</td><td>Euka02</td><td>V7 region, 18S rDNA gene</td><td>TTTGTCTGSTTAATTSCG/ CAAGAGATCCGTTGYTGAAAGTK</td><td>36–892</td></tr><tr><td>Medium</td><td>Arth02</td><td>16S mitochondrial rDNA gene</td><td>GATAGAAACCRACCTGGYT/ AARTTACYTTAGGGATAACAG</td><td>76–168</td></tr><tr><td>Low</td><td>Coll01</td><td>16S mitochondrial rDNA gene</td><td>ACGCTGTTATCCCTWAGG/ GACGATAAGACCCTWTAGA</td><td>76–192</td></tr><tr><td>Low</td><td>Inse01</td><td>16S mitochondrial rDNA gene</td><td>RGACGAGAAGACCCTATARA/ ACGCTGTTATCCCTAARGTA</td><td>75–265</td></tr></tbody></table>

considering their ability to deliver ecologically robust information across a broader range of taxonomic groups.

## 2 | Methods

### 2.1 | Sample Collection, Molecular and Bioinformatic Analyses

We collected soil samples from 1251 plots located in 46 proglacial landscapes from high-latitude and mountain regions of Europe, North America, South America, Asia and New Zealand (Ficetola, Marta, et al. 2024). On average, we collected soil samples in 31 plots per landscape (SD = 16.7), located in different environmental topologies including early deglaciated terrains with sparse vegetation, grasslands, shrublands, coniferous and broad-leaved forests (Cantera et al. 2024; Ficetola, Marta, et al. 2024). Samples consisted of 15 g of soil. Each sample was obtained by pooling five subsamples collected within a 1 m radius; samples were immediately desiccated using silicagel to ensure eDNA preservation (Guerrieri et al. 2021). Full details on the sampling protocol and DNA extraction are provided in Ficetola, Marta, et al. (2024) and Guerrieri et al. (2023).

Extracted eDNA was amplified using four primer pairs: Euka02, Arth02, Coll01, and Inse01 (Table 1, names after Taberlet et al. 2018). Euka02 is a primer pair amplifying a ~123 bp region of the 18S rDNA (V7) (Guardiola et al. 2015; Taberlet et al. 2018). It is a very generalist primer pair designed to amplify all eukaryotes. In vitro and in silico analyses suggest that Euka02 has a good ability to amplify arthropods, but it shows lower resolution compared to more specific markers (Ficetola et al. 2021). Arth02 amplifies a ~142 bp region of the 16S mitochondrial rDNA (Taberlet et al. 2018). It aims at amplifying all arthropods but has undergone limited empirical testing so far (Brun et al. 2022; Rota et al. 2020). Coll01 and Inse01 amplify ~132 and ~155 bp fragments, respectively, of the 16S mitochondrial rDNA (Clarke et al. 2014; Janssen et al. 2018; Taberlet et al. 2018). Despite the short amplified fragments, empirical and in silico tests suggest that these specific markers have a good ability to assess the diversity of insects and springtails from eDNA data, making them potentially suitable to assess the impact of environmental stressors (Calderón-Sanou et al. 2024; Clarke et al. 2014; Ficetola et al. 2021; Lunghi et al. 2022; Martinez-Almoyna et al. 2019; Meyer et al. 2021; Ohlmann et al. 2018).

The extracted DNA was amplified in four PCR replicates. For Euka02, Arth02, and Coll01 DNA was diluted 1:10 before PCR. For Euka02 and Arth02, we ran 45 PCR cycles, while for Coll01 and Inse01 we used 55 PCR cycles (Ficetola, Marta, et al. 2024; Guerrieri et al. 2023). Differences in the number of PCR cycles and DNA dilutions were related to differences in eDNA concentration and amplification rates, assessed through preliminary qPCR assays to avoid overamplification and minimize PCR errors. Amplified DNA was sequenced with the Illumina HiSeq 2500 platform with a sequencing depth of ~10,000 reads per PCR replicate. Rarefaction curves confirmed that this sequencing depth was appropriate for all the soil samples and markers (Ficetola, Marta, et al. 2024). Bioinformatics treatment was performed using the OBITools software (Boyer et al. 2016). Bioinformatics treatment, taxonomic assignment, and preliminary data cleaning followed the protocol described in previous studies (Ficetola, Marta, et al. 2024; Guerrieri et al. 2023). The obiclean program was run to detect potential PCR or sequencing errors, and we only kept the sequences tagged as “heads” in at least one PCR, which ideally correspond to true sequences (Boyer et al. 2016). These amplified sequence variants were clustered using thresholds optimized for each marker (Arth02: 99%; Euka02, Inse01: 96%; Coll01: 85%), in order to minimize both the risk of splitting the same species into different units, and the risk of lumping multiple species into the same taxon (Bonin et al. 2023). For Coll01 and Inse01, we only considered the sequences assigned by the ecotag program to springtails and insects, respectively.

### 2.2 | Comparison of Taxonomic Richness

For each taxonomic class (i.e., springtails and insects), we considered three datasets (hereafter sequence tables): the sequence variants detected using the specific marker (i.e., Coll01 or Inse01 for springtails and insects, respectively), and the sequence variants belonging to that class detected by each one of the generalist markers (i.e., by Arth02 and Euka02). Therefore, for our analyses we considered six sequence tables: (1) springtails detected by Coll01; (2) springtails detected by Arth02; (3) springtails detected by Euka02; (4) insects detected by Inse01; (5) insects detected by Arth02; (6) insects detected by Euka02.

As an estimate of taxonomic richness (alpha diversity), we calculated the number of sequence variants detected at each plot in
each table and assessed whether specific and generalist markers retrieved the same number of sequence variants, or if there were differences in richness for each target group. We used the Wilcoxon signed-rank test to assess whether specialist and generalist markers produce similar levels of alpha diversity across the 1251 plots. We compared the richness estimates of each class (springtails or insects) obtained using the specialist markers, with the one obtained by each of the two generalist markers (Euka02 and Arth02). Subsequently, we used the Pearson's correlation to assess whether the richness of each class detected by the two generalist markers is correlated with the richness estimated by the specialist markers. All the measures of taxonomic richness were transformed using $log(x+1)$ to improve normality and reduce heteroscedasticity. We also tested whether the total richness (springtails + insects) detected by each generalist marker was correlated with the combined richness of the two specific markers (Coll01 + Inse01). Values of richness obtained using Hill's numbers $>0$ can provide better estimates of the actual richness (Alberdi and Gilbert 2019; Calderón-Sanou et al. 2020; Mächler et al. 2021), thus we repeated the correlation analyses using the Hill's number $Q=1$, to account for variation in relative abundance of sequence variants. We did not run analyses with $Q>1$ because in several plots we detected zero taxa.

## 2.3 | Comparison of Community Composition

First, we calculated the overlap in detected taxonomic units between markers. For both insects and springtails, we focused on sequence variants identified at the family level or more precisely, as our data also covered remote and tropical regions where the coverage of reference databases is particularly poor (De Santiago et al. 2025; Ficetola et al. 2021; Taberlet et al. 2018). For each plot, we counted the number of families identified (i) by the specific marker only; (ii) by each of the generalist markers; (iii) by both the generalist marker and any of the specific markers; and (iv) by each of the two generalist markers but not by the specific one. A family was considered detected by a given marker if at least one sequence variant was attributed to that family. We then calculated the proportion of families for each category, as the mean value across plots, weighted by the taxonomic richness at that plot (i.e., plots with higher species richness had a higher weight). This analysis only considered the plots with at least one family, and was repeated separately for springtails and insects.

Subsequently, we assessed whether different markers returned a similar estimation of compositional dissimilarities among communities inhabiting the different plots (beta-diversity). Before running this analysis, we removed plots with zero detected taxa for any marker and calculated the relative abundance (proportion of reads) of each taxon in each sample, as this reduces sensitivity to the occurrence of rare sequence variants, which could potentially represent contaminants (Calderón-Sanou et al. 2020; Mächler et al. 2021). Values of relative abundance were transformed using the Box-Cox-chord transformation (Legendre and Borcard 2018). We then used Procrustes-based correlations to assess the multivariate correlation between the composition of communities obtained by the different markers on the same class of arthropods (Coissac and Gonindard-Melodelima 2019; Legendre and Legendre 2012). Procrustes correlation was assessed using the ProcMod R package, which allows assessing the

amount of covariation between matrices that is appropriate to evaluate relationships between datasets obtained via eDNA metabarcoding. Procrustes analyses can overestimate the strength of relationships; thus we used a corrected version of Procrustean correlation that is suitable for the analysis of highly-dimensional data and correctly estimates the amount of shared variation between data sets (Coissac and Gonindard-Melodelima 2019). Procrustean correlations were run using the *corls* function; we used 1000 randomizations to assess the significance of observed correlations (Coissac and Gonindard-Melodelima 2019).

## 2.4 | Measuring Relationships Between Community Diversity and Environmental Variables

We considered five features that can be used to describe environmental variation in proglacial landscapes: time since glacier retreat; diversity of plant communities (estimated using eDNA metabarcoding); normalized difference vegetation index, which is a measure of primary production; average soil temperature during the growing season; and the topographic wetness index (TWI). High TWI values indicate sites that can receive high water supply from upslope and with limited downslope water drainage such as valley bottoms, thus indicating potentially wetter soils (see Cantera et al. 2024; Ficetola, Marta, et al. 2024; Kopecký et al. 2021; Marta et al. 2021; Marta et al. 2023 for a complete description of the methods used to produce these variables). These variables show complex relationships between them (Table S1), so they cannot be considered as independent variables. We thus used a Principal Component Analysis (PCA) to summarize them into a smaller number of uncorrelated variables. Using the broken-stick method (Jackson 1993), we extracted two principal components that altogether explained 65% of the variability of environmental variables. PC1 showed a strong positive relationship with time since glacier retreat, plant diversity, primary production and soil temperature, while it was uncorrelated to wetness. This axis indicated a gradient from early- to late-successional plots. PC2 showed a strong positive relationship with topographic wetness, a moderate relationship with temperature, and a weaker relationship with the other variables. Therefore, high values of PC2 mostly correspond to plots in valley bottoms (i.e., areas that can receive high water supply from upslope and with limited downslope water drainage), while low PC2 values mostly correspond to sites in topographic ridges (Kopecký et al. 2021) (Table S1, Figure S1).

We then used general Linear Mixed Models (LMM) to test the relationships between the diversity of both springtails and insects, estimated from the different markers using Hill's number $Q=1$, and the two PCA components. LMM included landscape identity as a random factor to account for the non-independence of plots from the same landscape. For each class and marker, we ran a separate model (Euka02, Arth02 and the respective specialist marker). As a measure of the effect size of the relationships, we calculated the marginal $R^2$ of the overall LMMs (Nakagawa et al. 2017) and the Fisher's *z* of the relationship between diversity and each of the two PCA components (Rosenthal 1994). All statistical analyses were run in the R environment using the ade4, hillR, visreg, vegan, lme4, and lmerTest packages (Breheney and Burchett 2017; Kuznetsova et al. 2017; Li 2018; Oksanen et al. 2022; Thioulouse et al. 2018).
<figure><img src="image_3.png" /><figcaption>**FIGURE 1** | Comparison of taxonomic richness detected using specific and generalist markers in 1251 soil samples. (a) Violin plots showing the number of taxa of springtails (left panel) and of insects (right panel) detected by specific (Coll01 and Inse01) and more generalist (Arth02 and Euka02) markers. (b) Frequency of samples in which specific markers (Coll01 and Inse01) detected more (blue), less (orange) or the same number of taxa (gray), compared to generalist markers (Arth02 and Euka02). Figure S2 shows the frequency of samples with similar number of taxa in specific and generalist markers (difference < 2).</figcaption></figure>

3 | Results

With the Coll01 marker, we detected 483 sequence variants of springtails, while with the Inse01 marker we detected 1566 sequence variants of insects. With the Arth02 generalist marker, we detected 238 Collembola and 150 Insecta, while with Euka02 we detected 44 Collembola and 33 Insecta. Besides insects and springtails, the generalist markers also detected several additional taxa. Arth02 also detected 10 Arachnida, seven Malacostraca, one sequence identified as Pycnogonida, and 244 sequence variants identified at taxonomic levels higher than the class (e.g., Hexapoda, Mandibulata, or Pancrustacea). Pycnogonida is a fully marine class, but the

sequence identified as Pycnogonida showed a very low match
with known sequences of this class (85.5%), thus this is prob-
ably a misidentification due to the incompleteness of the data-
base. Additional arthropods detected by Euka02 included 107
Arachnida, four Diplopoda, three Protura, two Hexanauplia,
one Ostracoda and one Chilopoda.

3.1 | Patterns of Species Richness

The generalist markers almost always recorded lower richness
values compared to the specific markers; the strongest dif-
ferences were observed for insects (Figure 1a, Table S2). For
springtails, the mean richness detected by the specific marker was 2.64 sequence variants per plot (range: 0–20), while it was about four times smaller for both generalist markers (Euka02: 0.83 springtail sequence variants per sample; Arth02: 0.89; Figure 1b; Wilcoxon signed-rank tests: both *p* < 0.0001, Table S2). For insects, the mean richness detected by the specific marker was 7.38 sequence variants per plot (range: 0–59), while it was more than one order of magnitude smaller for both generalist markers (Figure 1b; Wilcoxon tests: both *p* < 0.0001, Table S2).

Taxonomic richness estimated using generalist markers was positively related to the richness estimated with the specific markers (Figure 2), but the pairwise correlations were not high (0.19 ≤ *r* ≤ 0.6; Table 2). The relationship was particularly weak for the richness of insects estimated using the Euka02 marker. The relationship between Arth02 and the richness of both springtails and insects was higher than what was observed for Euka02 (Table 2). Relationships were nearly identical if we used Hill's numbers with *Q*=1 to estimate taxonomic richness (Table S3).

<figure><img src="image_4.png" /><figcaption>**FIGURE 2** | Relationship between the number of taxa detected by generalist and specific markers. In blue, the trend lines obtained by regressing the number of taxa obtained through specific vs. generalist markers, along with their 95% confidence bands (gray); values of taxonomic richness are log-transformed. a–c: relationships between the Euka02 generalist marker and the specific ones. d–f: relationships between the Arth02 generalist marker and the specific ones.</figcaption></figure>

<table><caption>**TABLE 2** | Pearson's correlations between the richness of insects and springtails, estimated using specific markers Inse01 and Coll01, respectively, and the richness of insects and springtails estimated using generalist markers (Euka02 and Arth02).</caption><thead><tr><th rowspan="3"></th><th colspan="6">Richness estimated using specific markers</th></tr><tr><th colspan="2">Coll01</th><th colspan="2">Inse01</th><th colspan="2">Coll01 + Inse01</th></tr><tr><th>r</th><th>p</th><th>r</th><th>p</th><th>r</th><th>p</th></tr></thead><tbody><tr><td>Richness estimated using Euka02</td><td>0.46</td><td>&lt;0.001</td><td>0.19</td><td>&lt;0.001</td><td>0.50</td><td>&lt;0.001</td></tr><tr><td>Richness estimated using Arth02</td><td>0.52</td><td>&lt;0.001</td><td>0.45</td><td>&lt;0.001</td><td>0.60</td><td>&lt;0.001</td></tr></tbody></table>

Note: N=1249 for all the correlations.
## 3.2 | Comparison of Species Composition

There was good overlap between the taxa identified by the specific markers, and the ones identified by the generalist ones but, in the majority of cases, the specific markers detected the taxa identified by the generalist markers, plus several additional ones (Figure 3). For springtails, 39.3% of families detected within each plot were identified by Coll01 only, 27.4% by both Coll01 and at least one generalist marker, while 33.2% were detected by at least one generalist marker but not by the specific one. Euka02 was more efficient than Arth02, as it generally detected more springtail families (Wilcoxon signed-rank tests: $V=33,152, p<0.0001$). Nevertheless, variation across plots was strong, as shown by the large associated variances (Figure 3).

For insects, 79.9% of families were identified by Inse01 only, 10.2% by both Inse01 and at least one generalist marker, while 9.9% were detected by at least one generalist marker but not by the specific one (Figure 3). Among the generalist markers, Arth02 was more efficient than Euka02, as it generally detected more families of insects (Wilcoxon signed-rank tests: $V=19,776,$

$p<0.0001$). Still, variation across plots was strong also for insects, as shown by the large associated variances (Figure 3).

The matrices of species dissimilarity between plots were positively related across the different markers (Figure 4). For springtails, we detected a good correlation between the dissimilarity obtained with the Coll01 marker, and the dissimilarity obtained using the springtails detected with Arth02. The correlation between Coll01 and Euka02 was significant but much weaker. For insects, the correlations between the dissimilarities of all the markers were moderate to good (Figure 4); also in this case, the correlation between Inse01 and Arth02 was stronger than the one between Inse01 and Euka02.

## 3.3 | Ability of Different Markers to Capture Relationships Between Diversity and Environmental Features

With the Coll01 marker, the richness of springtails showed a very strong relationship with PC1 (Figure 5; Table S4). This

<figure><img src="image_5.png" /><figcaption>**FIGURE 3** | Venn diagrams showing the percentage of taxa identified at the family level or better detected by different markers for springtails (left) and insects (right). Values represent the weighted average (±SD) across all the samples where at least one family of each taxon was detected.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**FIGURE 4** | Procrustes correlations between community dissimilarities, obtained from the metabarcoding of soil samples amplified using both specific and generalist markers. All correlations are significant at $p<0.001$ (tested with 1000 permutations).</figcaption></figure>
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

**FIGURE 5** | Effect size of relationships between the taxonomic diversity (Q=1) of springtails and insects and environmental features of proglacial landscapes. For each taxon, diversity was estimated using three markers: one specialist marker (either Coll01 or Inse01), one intermediate marker (Arth02) and one generalist marker (Euka02). Effect size was measured as (a) the marginal R² of the linear mixed model relating diversity to both PC1 and PC2; (b) Fisher's z of the relationship between diversity and PC1; (c) Fisher's z of the relationship between diversity and PC2. PC1 and PC2 are the two principal components extracted from five environmental features describing plot time since glacier retreat, vegetation, primary production and microclimate. In (b) and (c) we also show the significance of relationships. ***p < 0.001; **p < 0.01; *p < 0.05; n.s.: *p > 0.05; error bars indicate 95% CI of Fisher's z.

indicates that the richness of springtails was highest in late-
successional stages, characterized by higher primary produc-
tion, richer plant communities and warmer microclimate.
Furthermore, we detected a negative relationship with PC2,
indicating that communities are slightly richer in soils located
in topographic ridges. Environmental features explained a
good proportion of the variability of community richness
(R²<sub>M</sub> = 0.333; Figure 5; Table S4). Using Arth02, we detected
similar relationships with both PC1 and PC2, but the effect
sizes were smaller (Figure 5, Table S4). With Euka02, we de-
tected a positive relationship between springtail richness and
PC1, while the relationship between richness and PC2 was
negative but not significant. Overall, the strength of the re-
lationships between springtail diversity and environmental
variation was weaker with generalist markers, as shown by
decreasing values of R²<sub>M</sub> (Figure 5, Table S4).

For insects, we detected an analogous pattern to that observed
for springtails (Figure 5). With Inse01, the richness of insects
showed a very strong positive relationship with PC1, indicat-
ing that insect richness was highest in late-successional stages.
Furthermore, we observed a negative relationship with PC2,
indicating richer communities in topographic ridges. With
Arth02, we detected similar relationships but, again, the effect
sizes of both relationships were smaller (Figure 5, Table S4).
With Euka02, we detected a significant relationship between
insect richness and PC1, but no relationship with PC2. Also for
insects, the use of generalist markers revealed a weaker overall
relationship between taxonomic richness and environmental
variation (Figure 5).

4 | Discussion

Selecting the markers to be used is one of the first, critical deci-
sions that need to be made in DNA metabarcoding studies. This
choice depends on multiple factors, including the study aims
and budget, and will have cascading effects on the outcomes
(Zinger et al. 2019). In principle, the use of multiple specific
markers targeting several taxa should increase the amount of
extracted information, but this also increases laboratory labor.
Therefore, studies aiming at extensive biodiversity analyses
often rely on generalist markers to retrieve comprehensive in-
formation (Ficetola and Taberlet 2023). Our analysis of >1200
soil samples identified trade-offs between generalist and spe-
cific markers. Generalist markers retrieved biodiversity pat-
terns that are largely consistent with those of specific markers
and detected similar responses to main environmental gradi-
ents. Nevertheless, shifting from specific to more generalist
markers decreased the number of detected taxa (i.e., sequence
variants) and reduced the power of analyses testing ecological
relationships.

**4.1** *Explaining the Differences Between Markers*

In principle, the usefulness of generalist markers relies on their
ability of simultaneously assessing a large number of taxa, thus
obtaining exhaustive biodiversity data without the need of
analyzing several metabarcodes. The number of taxa detected using specific markers corresponded well to known diversity values of pro-glacial environments (Devetter et al. 2021; Ficetola, Marta, et al. 2024; Hågvar 2010), while generalist markers retrieved fewer taxa, suggesting that they can underestimate the actual diversity values when applied to a given taxonomic group. However, it is important to recognize that generalist and specific markers have distinct properties, and their performance should be interpreted in light of these differences and the specific goals of each study.

The lower diversity observed using generalist markers can occur for several reasons. First, Euka02 amplifies all eukaryotes, including plants, small animals, fungi and protists, that can be much more abundant in soil than insects and springtails. It is thus possible that the limited diversity arose because of the competition of the DNA of many organisms during the amplification and sequencing processes. This hypothesis is supported by the observation that, for both springtails and insects, the richness detected using the generalist marker Euka02 was negatively related to the proportion of reads attributed to other taxa (Spearman's correlations; springtails: $ρ = -0.686, p < 0.0001$; insects: $ρ = -0.558, p < 0.0001$). Second, the markers target different genes (18S rRNA and 16S rRNA), each of which shows distinct evolutionary rates (Bartoš et al. 2024; Yeh et al. 2021), and the level of conservation and the taxonomic resolution can even be variable between regions of the same gene (Bukin et al. 2023). Third, we decided to analyze short metabarcodes because eDNA is often degraded (Jo et al. 2017; Taberlet et al. 2018). However, short metabarcodes frequently show limited taxonomic resolution because some sequences are shared between multiple taxa; this issue is exacerbated when short metabarcodes are designed to amplify and distinguish a very large number of taxa. For instance, Euka02 amplifies short fragments (mean length 123 bp, shorter than Inse01) and different species of insects within a genus and even different genera within a family can share the same metabarcode and thus cannot be discriminated. In fact, when tested in silico on sequences from the GenBank database, Euka02 and Arth02 were only able to discriminate ~72% of insect species (Table S5). The number of species merged into the same taxonomic unit is generally lower for specific markers (Bonin et al. 2023; Ficetola et al. 2021). For example, Inse01 can discriminate 90% of insect species (Table S5). These processes explain why specific markers consistently detected more taxa and can provide a more robust estimation of the actual species composition (Martins et al. 2021). Nevertheless, advances in long-read DNA sequencing technologies can partially solve issues associated with short metabarcodes, at least for the least degraded DNA (Chang et al. 2024; Heeger et al. 2018; Jamy et al. 2022; Krabberød et al. 2024). Fourth, in our study the amplification of specific markers required a larger number of PCR cycles. It is possible that primers requiring more PCR cycles could generate a larger number of errors that might inflate diversity estimates. Finally, we highlight that our study was conducted in peri-glacial environments, that host a relatively small number of cold-adapted species. The underestimation of diversity by generalist markers might be even stronger in species-rich environments that frequently are cradles of biodiversity (e.g., tropical regions) and can host impressive numbers of related species (Laursen 2013; Leandro et al. 2024). Additional studies, analyzing samples with known composition (e.g., mock

communities containing the DNA of a number of known taxa), will be essential to validate the performance of different markers, and identify the conditions under which each of them provides optimal results (Meyer et al. 2021; Yeh et al. 2021).

## 4.2 | Selecting Appropriate Markers for Different Aims

The selection of metabarcodes depends on multiple factors, as each marker, including the highly specific ones, suffers its own limitations. For instance, despite a generally good performance, even the Inse01 marker is not always able to tease apart related species (particularly species within a genus), and in vitro tests showed that it is not able to amplify all the insect species that are present (Ficetola et al. 2021; Martins et al. 2021). The use of multiple markers amplifying different genomic regions can thus provide more complete estimates of the actual diversity, as each of them may be able to detect some target taxa undetected by other markers (Brunetti et al. 2024; Martins et al. 2021; Meyer et al. 2021). Our analysis confirms this hypothesis. For springtails, generalist markers (particularly Euka02) detected some taxa that remained undetected by the specific one (Figure 3), and Arth02 detected several insects undetected by Inse02. The added value of generalist markers was generally limited. For both springtails and insects, the majority of taxa detected by generalist markers were also detected by the specific ones (Figure 3). Nevertheless, the extent to which a generalist marker complements a specific one probably depends on the taxa. For instance, Euka02 detected several springtails not detected by Coll01, while the added value of Arth01 was much smaller (Figure 3a). A further advantage of adding generalist markers is the amplification of other taxa that are not targeted by the specific ones. Such complementary information can be useful for stakeholders, or if researchers are interested in obtaining an overall view of biodiversity response to environmental gradients using limited resources. In this respect, Euka02 seems to be particularly efficient, as it detected many other arthropods (particularly Arachnida), and also other soil organisms that are not arthropod but have a key role in soil food webs (e.g., nematodes and protists, not assessed here; Ficetola, Guerrieri, et al. 2024; Giachello et al. 2024; Guerrieri et al. 2024).

The more generalist the marker, the lower its similarity to the results obtained with specific markers. Arth02 is a marker with intermediate generality (it amplifies a hyperdiverse phylum), and provided biodiversity estimates that correlated relatively well with those of specific markers, while these relationships were weaker for the more general Euka02 (Table 2; Figure 4). A similar pattern occurred for the relationships between diversity and environmental features, as specific markers provided the highest effect sizes for the target taxa (Figure 5). Arth02 showed more moderate effect sizes; still it allowed us to clearly detect the relationships. Euka02 was able to identify the relationship between diversity and the successional gradient, but the effect size of this relationship was much lower than for the specific markers. Interestingly, Euka02 did not detect the relationship with PC2, which represents an index of soil microclimate (particularly wetness) related to the topographic position of points. The decreasing match between specific and generalist markers probably occurs because an increasing number of taxa remain undetected by the
generalist markers, either because they are not amplified, and/
or because close taxa are not resolved. Both these factors are
problematic and, besides the underestimation of the actual bio-
diversity, they can obscure ecological relationships. This occurs,
for instance, when different species belonging to the same family
show specializations for distinct environmental conditions and
microhabitats but cannot be discriminated. The use of specific
markers can thus be particularly important when the aim is the
detection of subtle ecological relationships, or the assessment of
the impact of environmental stressors on one biodiversity compo-
nent (Martins et al. 2021; Meyer et al. 2021; Thomsen et al. 2024).
On the other hand, the full power of generalist markers can be ex-
ploited to detect divergent responses to environmental gradients
between taxonomic or functional groups. For instance, studies on
different groups of eukaryotes using Euka02 showed that the di-
versity of some groups (e.g., nematodes) quickly grows with time
since glacier retreat and is strongly affected by local microclimate
(Guerrieri et al. 2024). The diversity of other groups (e.g., con-
sumer protists) increases through time but shows weak relation-
ships with climate, and there are even groups (e.g., phototroph
protists) in which biodiversity decreases with time since glacier
retreat (Giachello et al. 2024). Therefore, despite the lower power
to detect relationships within a specific taxon, generalist markers
are valuable for disentangling complex impacts on distinct com-
ponents of biodiversity. They can also guide the selection of taxa
to be targeted by more detailed studies, for instance because they
appear to be heavily affected by stressors.

5 | Conclusion

Are generalist markers adapted for the monitoring of hyperdi-
verse groups, such as arthropods? Even in species-poor envi-
ronments, such as glacier-related habitats, generalist markers
only detect a fraction of the present taxa, and provide a poorer
analytical power compared to the specific ones. Nevertheless,
the decision of the number and the identity of markers to exam-
ine heavily depends on study aims and available resources. An
appropriately selected generalist marker, such as Arth02, may
be able to identify the main biodiversity components and eco-
logical gradients; still, if more resources are available, replacing
a generalist marker with multiple specific markers can boost
resolution and analytical power. Finally, a combination of three
markers (e.g., Coll01, Inse02, and Euka02) is expected to provide
a rather complete assessment of soil biodiversity, with sufficient
taxonomic details for key taxa, the addition of some insects and
springtails not detected by the specific markers, as well as the de-
tection of other important soil animals (Arachnida, Nematoda,
etc.). Adding a further generalist marker such as Arth02 is not
expected to greatly improve diversity estimation in this case, as
Arth02 detected very few taxa that were not detected by any of
the other three markers (Figure 4). Still, when study conclusions
depend on the ability of markers to detect the largest propor-
tion of present species, it may be important to adopt even more
resource-intensive approaches. For instance, the markers used
in this study targeted the 18S and 16S genes, because they include
highly conserved regions that can facilitate the amplification
of degraded eDNA (Ficetola et al. 2021; Marquina et al. 2019;
Taberlet et al. 2018). Nevertheless, the integration of additional
markers amplifying distinct genes (e.g., also including COI) and

with different amplicon sizes can greatly enhance taxonomic
resolution and taxonomic coverage (Brunetti et al. 2024; Kirse
et al. 2021; Martins et al. 2021; Meyer et al. 2021; Van Nynatten
et al. 2023).

DNA metabarcoding now allows us to produce biodiversity
assessments with unprecedented taxonomic and geographical
scopes, covering both remote areas that were extremely challeng-
ing to reach until a few years ago and human-dominated eco-
systems. This potentially revolutionizes the understanding and
monitoring of life on Earth (Thomsen et al. 2024). Nevertheless,
key methodological choices, including the selection of the most
appropriate primers, affect the conclusions of studies. A careful
a priori planning, balancing the available resources with study
targets remains fundamental to maximize the quality of results
and their usefulness, for instance in the context of environmen-
tal monitoring and management.

Author Contributions

Design of the study: G.F.F., P.T., A.C., A.B., A.G.; Acquisition, analysis and interpretation of the data: G.F.F., A.C., S.M., A.B., I.C., A.G.; writing of the manuscript: all the authors.

Acknowledgments

This study was funded by the European Research Council under the
European Community Horizon 2020 Program, Grant Agreement
no. 772284 (IceCommunities). This research was also supported by
Biodiversa+, the European Biodiversity Partnership under the 2021-
2022 BiodivProtect joint call for research proposals, co-funded by the
European Commission (grant agreement no. 101052342 "PrioritIce-
Vanishing habitats: conservation priorities for glacier-related biodi-
versity threatened by climate change"; funding organization: MUR).
G.F.F. was also funded by the European Union—NextGenerationEU,
Italian National Recovery and Resilience Plan (Mission 4, Component
2, Investment 1.5 "Innovation Ecosystems"), project MUSA. Open ac-
cess publishing facilitated by Universita degli Studi di Milano, as part of
the Wiley - CRUI-CARE agreement.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

Raw sequence data and filtered sequence data are deposited in https://
doi.org/10.5281/zenodo.6620359, https://zenodo.org/doi/10.5281/ze-
nodo.10423968 and 10.6084/m9 figshare.28678418.

References

Alberdi, A., and M. T. P. Gilbert. 2019. "A Guide to the Application of
Hill Numbers to DNA-Based Diversity Analyses." *Molecular Ecology
Resources* 19, no. 4: 804–817. https://doi.org/10.1111/1755-0998.13014.

Andújar, C., P. Arribas, D. W. Yu, A. P. Vogler, and B. C. Emerson. 2018. "Why the COI Barcode Should Be the Community DNA Metabarcode for the Metazoa." *Molecular Ecology* 27, no. 20: 3968–3975. https://doi.org/10.1111/mec.14844.

Baamrane, M. A. A., W. Shehzad, A. Ouhammou, et al. 2012. "Assessment of the Food Habits of the Moroccan Dorcas Gazelle in M'Sabih Talaa, West Central Morocco, Using the trnL Approach." *PLoS One* 7, no. 4: e35643. https://doi.org/10.1371/journal.pone.0035643.
Bardgett, R. D., and W. H. van der Putten. 2014. "Belowground Biodiversity and Ecosystem Functioning." *Nature* 515, no. 7528: 505–511. https://doi.org/10.1038/nature13855.

Bartoš, O., M. Chmel, and I. Swierczková. 2024. "The Overlooked Evolutionary Dynamics of 16S rRNA Revises Its Role as the 'Gold Standard' for Bacterial Species Identification." *Scientific Reports* 14, no. 1: 9067. https://doi.org/10.1038/s41598-024-59667-3.

Bonin, A., A. Guerrieri, and G. F. Ficetola. 2023. "Optimal Sequence Similarity Thresholds for Clustering of Molecular Operational Taxonomic Units in DNA Metabarcoding Studies." *Molecular Ecology Resources* 23: 368–381. https://doi.org/10.1111/1755-0998.13709.

Boyer, F., C. Mercier, A. Bonin, Y. Le Bras, P. Taberlet, and E. Coissac. 2016. "OBITools: A Unix-Inspired Software Package for DNA Metabarcoding." *Molecular Ecology Resources* 16: 176–182. https://doi.org/10.1111/1755-0998.12428.

Breheney, P., and W. Burchett. 2017. "Visualization of Regression Models Using Visreg." *R Journal* 9: 56–71. https://doi.org/10.32614/RJ-2017-046.

Brun, L., J. Schneider, E. M. Carrió, et al. 2022. "Focal vs. Fecal: Seasonal Variation in the Diet of Wild Vervet Monkeys From Observational and DNA Metabarcoding Data." *Ecology and Evolution* 12, no. 10: e9358. https://doi.org/10.1002/ece3.9358.

Brunetti, M., G. Magoga, A. Cussigh, et al. 2024. "Soil Invertebrate Biodiversity and Functionality Within the Intensively Farmed Areas of the Po Valley." *Applied Soil Ecology* 197: 105326. https://doi.org/10.1016/j.apsoil.2024.105326.

Bukin, Y. S., I. S. Mikhailov, D. P. Petrova, Y. P. Galachyants, Y. R. Zakharova, and Y. V. Likhoshway. 2023. "The Effect of Metabarcoding 18S rRNA Region Choice on Diversity of Microeukaryotes Including Phytoplankton." *World Journal of Microbiology and Biotechnology* 39, no. 9: 229. https://doi.org/10.1007/s11274-023-03678-1.

Calderón-Sanou, I., T. Münkemüller, F. Boyer, L. Zinger, and W. Thuiller. 2020. "From Environmental DNA Sequences to Ecological Conclusions: How Strong Is the Influence of Methodological Choices?" *Journal of Biogeography* 47: 193–206. https://doi.org/10.1111/jbi.13681.

Calderón-Sanou, I., M. Ohlmann, T. Münkemüller, et al. 2024. "Mountain Soil Multitrophic Networks Shaped by the Interplay Between Habitat and Pedoclimatic Conditions." *Soil Biology and Biochemistry* 190: 109282. https://doi.org/10.1016/j.soilbio.2023.109282.

Cantera, I., A. Carteron, A. Guerrieri, et al. 2024. "The Importance of Species Addition Versus Replacement Varies Over Succession in Plant Communities After Glacial Retreat." *Nature Plants* 10: 256–267. https://doi.org/10.1038/s41477-023-01609-4.

Capra, E., R. Giannico, M. Montagna, et al. 2016. "A New Primer Set for DNA Metabarcoding of Soil Metazoa." *European Journal of Soil Biology* 77: 53–59. https://doi.org/10.1016/j.ejsobi.2016.10.005.

Chang, J. J. M., Y. C. A. Ip, W. L. Neo, M. A. D. Mowe, Z. Jaafar, and D. Huang. 2024. "Primed and Ready: Nanopore Metabarcoding Can Now Recover Highly Accurate Consensus Barcodes That Are Generally Indel-Free." *BMC Genomics* 25, no. 1: 842. https://doi.org/10.1186/s12864-024-10767-4.

Clarke, L. J., J. Soubrier, L. S. Weyrich, and A. Cooper. 2014. "Environmental Metabarcodes for Insects: In Silico PCR Reveals Potential for Taxonomic Bias." *Molecular Ecology Resources* 14: 1160–1170. https://doi.org/10.1111/1755-0998.12265.

Coissac, E., and C. Gonindard-Melodelima. 2019. "Assessing the Shared Variation Among High-Dimensional Data Matrices: A Modified Version of the Procrustean Correlation Coefficient." *bioRxiv*. https://doi.org/10.1101/842070.

Collins, R. A., J. Bakker, O. S. Wangensteen, et al. 2019. "Non-Specific Amplification Compromises Environmental DNA Metabarcoding With

COI." *Methods in Ecology and Evolution* 10, no. 11: 1985–2001. https://doi.org/10.1111/2041-210x.13276.

De Barba, M., C. Miquel, F. Boyer, et al. 2014. "DNA Metabarcoding Multiplexing and Validation of Data Accuracy for Diet Assessment: Application to Omnivorous Diet." *Molecular Ecology Resources* 14: 306–323. https://doi.org/10.1111/1755-0998.12188.

De Santiago, A., T. J. Pereira, T. J. Ferrero, et al. 2025. "Persistent Gaps and Errors in Reference Databases Impede Ecologically Meaningful Taxonomy Assignments in 18S rRNA Studies: A Case Study of Terrestrial and Marine Nematodes." *Environmental DNA* 7, no. 2: e70080. https://doi.org/10.1002/edn3.70080.

Devetter, M., L. Hanel, N. Raschmanova, M. Bryndova, and J. Schlaghamersky. 2021. "Terrestrial Invertebrates Along a Gradient of Deglaciation in Svalbard: Long-Term Development of Soil Fauna Communities." *Geoderma* 383: 114720. https://doi.org/10.1016/j.geode rma.2020.114720.

Elbrecht, V., P. Taberlet, T. Dejean, et al. 2016. "Testing the Potential of a Ribosomal 16S Marker for DNA Metabarcoding of Insects." *PeerJ* 4: 12. https://doi.org/10.7717/peerj.1966.

Epp, L. S., S. Boessenkool, E. P. Bellemain, et al. 2012. "New Environmental Metabarcodes for Analysing Soil DNA: Potential for Studying Past and Present Ecosystems." *Molecular Ecology* 21: 1821–1833. https://doi.org/10.1111/j.1365-294X.2012.05537.x.

Fahner, N. A., S. Shokralla, D. J. Baird, and M. Hajibabaei. 2016. "Large-Scale Monitoring of Plants Through Environmental DNA Metabarcoding of Soil: Recovery, Resolution, and Annotation of Four DNA Markers." *PLoS One* 11: e0157505. https://doi.org/10.1371/journal.pone.0157505.

Ficetola, G. F., A. Bonin, F. Boyer, et al. 2021. "Comparison of Markers for the Monitoring of Freshwater Benthic Biodiversity Through DNA Metabarcoding." *Molecular Ecology* 30: 3189–3202. https://doi.org/10.1111/mec.15632.

Ficetola, G. F., E. Coissac, S. Zundel, et al. 2010. "An In Silico Approach for the Evaluation of DNA Barcodes." *BMC Genomics* 11: 434. https://doi.org/10.1186/1471-2164-11-434.

Ficetola, G. F., A. Guerrieri, I. Cantera, and A. Bonin. 2024. "In Silico Assessment of 18S rDNA Metabarcoding Markers for the Characterization of Nematode Communities." *PLoS One* 19: e0298905. https://doi.org/10.1371/journal.pone.0298905.

Ficetola, G. F., S. Marta, A. Guerrieri, et al. 2024. "The Development of Terrestrial Ecosystems Emerging After Glacier Retreat." *Nature* 332: 336–342. https://doi.org/10.1038/s41586-024-07778-2.

Ficetola, G. F., and P. Taberlet. 2023. "Towards Exhaustive Community Ecology via DNA Metabarcoding." *Molecular Ecology* 32: 6320–6329. https://doi.org/10.1111/mec.16881.

Garrido-Sanz, L., M. À. Senar, and J. Piñol. 2022. "Relative Species Abundance Estimation in Artificial Mixtures of Insects Using Mitogenomics and a Correction Factor for the Mitochondrial DNA Copy Number." *Molecular Ecology Resources* 22, no. 1: 153–167. https://doi.org/10.1111/1755-0998.13464.

Giachello, S., I. Cantera, A. Carteron, et al. 2024. "Functional Changes of Protist Communities in Soil After Glacier Retreat." *Science of the Total Environment* 956: 177265. https://doi.org/10.1016/j.scitotenv.2024.177265.

Guardiola, M., M. J. Uriz, P. Taberlet, E. Coissac, O. S. Wangensteen, and X. Turon. 2015. "Deep-Sea, Deep-Sequencing: Metabarcoding Extracellular DNA From Sediments of Marine Canyons." *PLoS One* 10, no. 10: e0139633. https://doi.org/10.1371/journal.pone.0139633.

Guerrieri, A., A. Bonin, T. Münkemüller, L. Gielly, W. Thuiller, and G. F. Ficetola. 2021. "Effects of Soil Preservation for Biodiversity Monitoring Using Environmental DNA." *Molecular Ecology* 30: 3313–3325. https://doi.org/10.1111/mec.15674.
Guerrieri, A., I. Cantera, S. Marta, et al. 2024. "Local Climate Modulates the Development of Soil Nematode Communities After Glacier Retreat." *Global Change Biology* 30: e17057. https://doi.org/10.1111/gcb.17057.

Guerrieri, A., A. Carteron, A. Bonin, et al. 2023. "Metabarcoding Data Reveal Vertical Multi-Taxa Variation in Topsoil Communities During the Colonization of Deglaciated Forelands." *Molecular Ecology* 32: 6304–6319. https://doi.org/10.1111/mec.16669.

Hågvar, S. 2010. "Primary Succession of Springtails (Collembola) in a Norwegian Glacier Foreland." *Arctic Antarctic and Alpine Research* 42, no. 4: 422–429. https://doi.org/10.1657/1938-4246-42.4.422.

Heeger, F., E. C. Bourne, C. Baschien, et al. 2018. "Long-Read DNA Metabarcoding of Ribosomal RNA in the Analysis of Fungi From Aquatic Environments." *Molecular Ecology Resources* 18: 1500–1514. https://doi.org/10.1111/1755-0998.12937.

Hintikka, S., J. E. L. Carlsson, and J. Carlsson. 2022. "The Bacterial Hitchhiker's Guide to COI: Universal Primer-Based COI Capture Probes Fail to Exclude Bacterial DNA, but 16S Capture Leaves Metazoa Behind." *Metabarcoding and Metagenomics* 6: e80416. https://doi.org/10.3897/mbmg.6.80416.

Holman, L. E., M. de Bruyn, S. Creer, G. Carvalho, J. Robidart, and M. Rius. 2021. "Animals, Protists and Bacteria Share Marine Biogeographic Patterns." *Nature Ecology & Evolution* 5, no. 6: 738–746. https://doi.org/10.1038/s41559-021-01439-7.

Jackson, D. R. 1993. "Stopping Rules in Principal Component Analysis: A Comparison of Heuristical and Statistical Approaches." *Ecology* 74: 2204–2214. https://doi.org/10.2307/1939574.

Jamy, M., C. Biwer, D. Vaulot, et al. 2022. "Global Patterns and Rates of Habitat Transitions Across the Eukaryotic Tree of Life." *Nature Ecology & Evolution* 6: 1458–1470. https://doi.org/10.1038/s41559-022-01838-4.

Janssen, P., S. Bec, M. Fuhr, P. Taberlet, J.-J. Brun, and C. Bouget. 2018. "Present Conditions May Mediate the Legacy Effect of Past Land-Use Changes on Species Richness and Composition of Above- and Below-Ground Assemblages." *Journal of Ecology* 106, no. 1: 306–318. https://doi.org/10.1111/1365-2745.12808.

Jo, T., H. Murakami, R. Masuda, M. K. Sakata, S. Yamamoto, and T. Minamoto. 2017. "Rapid Degradation of Longer DNA Fragments Enables the Improved Estimation of Distribution and Biomass Using Environmental DNA." *Molecular Ecology Resources* 17, no. 6: e25–e33. https://doi.org/10.1111/1755-0998.12685.

Jurburg, S. D., P. Keil, B. K. Singh, and J. M. Chase. 2021. "All Together Now: Limitations and Recommendations for the Simultaneous Analysis of all Eukaryotic Soil Sequences." *Molecular Ecology Resources* 21, no. 6: 1759–1771. https://doi.org/10.1111/1755-0998.13401.

Kirse, A., S. J. Bourlat, K. Langen, and V. G. Fonseca. 2021. "Unearthing the Potential of Soil eDNA Metabarcoding—Towards Best Practice Advice for Invertebrate Biodiversity Assessment." *Frontiers in Ecology and Evolution* 9. https://doi.org/10.3389/fevo.2021.630560.

Kopecký, M., M. Macek, and J. Wild. 2021. "Topographic Wetness Index Calculation Guidelines Based on Measured Soil Moisture and Plant Species Composition." *Science of the Total Environment* 757: 143785. https://doi.org/10.1016/j.scitotenv.2020.143785.

Krabberød, A. K., E. Stokke, E. Thoen, I. Skrede, and H. Kauserud. 2024. "The Ribosomal Operon Database: A Full-Length rDNA Operon Database Derived From Genome Assemblies." *Molecular Ecology Resources* 25, no. 1: e14031. https://doi.org/10.1111/1755-0998.14031.

Kuznetsova, A., B. Brockhoff, and H. B. Christensen. 2017. "lmerTest Package: Tests in Linear Mixed Effects Models." *Journal of Statistical Software* 82, no. 13: 1–26. https://doi.org/10.18637/jss.v082.i13.

Laursen, L. 2013. "Why the Tropics Are an Evolutionary Hotbed." *Nature*. https://doi.org/10.1038/nature.2013.12846.

Le Cadre, J., F. L. Klemp, M. Bálint, S. Scheu, and I. Schaefer. 2024. "Applicability and Perspectives for DNA Barcoding of Soil Invertebrates." *PeerJ* 12: e17709. https://doi.org/10.7717/peerj.17709.

Leandro, C., P. Jay-Robert, and J. Pétillon. 2024. "eDNA for Monitoring and Conserving Terrestrial Arthropods: Insights From a Systematic Map and Barcode Repositories Assessments." *Insect Conservation and Diversity* 17: 565–578. https://doi.org/10.1111/icad.12726.

Leese, F., M. Sander, D. Buchner, V. Elbrecht, P. Haase, and V. M. A. Zizka. 2021. "Improved Freshwater Macroinvertebrate Detection From Environmental DNA Through Minimized Nontarget Amplification." *Environmental DNA* 3: 261–276. https://doi.org/10.1002/edn3.177.

Legendre, P., and D. Borcard. 2018. "Box–Cox-Chord Transformations for Community Composition Data Prior to Beta Diversity Analysis." *Ecography* 41: 1820–1824. https://doi.org/10.1111/ecog.03498.

Legendre, P., and L. Legendre. 2012. *Numerical Ecology*. Elsevier.

Li, D. J. 2018. "hillR: Taxonomic, Functional, and Phylogenetic Diversity and Similarity Through Hill Numbers." *Journal of Open Source Software* 3, no. 31: 1041. https://doi.org/10.21105/joss.01041.

Li, F., S. Qin, Z. Wang, Y. Zhang, and Z. Yang. 2023. "Environmental DNA Metabarcoding Reveals the Impact of Different Land Use on Multitrophic Biodiversity in Riverine Systems." *Science of the Total Environment* 855: 158958. https://doi.org/10.1016/j.scitotenv.2022.158958.

Lunghi, E., B. Valle, A. Guerrieri, et al. 2022. "Environmental DNA of Insects and Springtails From Caves Reveals Complex Processes of eDNA Transfer in Soils." *Science of the Total Environment* 826: 154022. https://doi.org/10.1016/j.scitotenv.2022.154022.

Mächler, E., J.-C. Walser, and F. Altermatt. 2021. "Decision-Making and Best Practices for Taxonomy-Free Environmental DNA Metabarcoding in Biomonitoring Using Hill Numbers." *Molecular Ecology* 30: 3326–3339. https://doi.org/10.1111/mec.15725.

Marquina, D., A. F. Andersson, and F. Ronquist. 2019. "New Mitochondrial Primers for Metabarcoding of Insects, Designed and Evaluated Using In Silico Methods." *Molecular Ecology Resources* 19, no. 1: 90–104. https://doi.org/10.1111/1755-0998.12942.

Marta, S., R. S. Azzoni, D. Fugazza, et al. 2021. "The Retreat of Mountain Glaciers Since the Little Ice Age: A Spatially Explicit Database." *Data* 6, no. 10: 107. https://doi.org/10.3390/data6100107.

Marta, S., A. Zimmer, M. Caccianiga, et al. 2023. "Heterogeneous Changes of Soil Microclimate in High Mountains and Glacier Forelands." *Nature Communications* 14: 5306. https://doi.org/10.1038/s41467-023-41063-6.

Martinez-Almoyna, C., W. Thuiller, L. Chalmandrier, et al. 2019. "Multitrophic β-Diversity Mediates the Effect of Environmental Gradients on the Turnover of Multiple Ecosystem Functions." *Functional Ecology* 33, no. 10: 2053–2064. https://doi.org/10.1111/1365-2435.13393.

Martins, F. M. S., M. Porto, M. J. Feio, et al. 2021. "Modelling Technical and Biological Biases in Macroinvertebrate Community Assessment From Bulk Preservative Using Multiple Metabarcoding Markers." *Molecular Ecology* 30, no. 13: 3221–3238. https://doi.org/10.1111/mec.15620.

McMartin, L., E. Saso, S. A. Vohsen, et al. 2024. "Nuclear eDNA Metabarcoding Primers for Anthozoan Coral Biodiversity Assessment." *PeerJ* 12: e18607. https://doi.org/10.7717/peerj.18607.

Meyer, A., F. Boyer, A. Valentini, et al. 2021. "Morphological vs. DNA Metabarcoding Approaches for the Evaluation of Stream Ecological Status With Benthic Invertebrates: Testing Different Combinations of Markers and Strategies of Data Filtering." *Molecular Ecology* 30: 3203–3220. https://doi.org/10.1111/mec.15723.

Nakagawa, S., P. C. D. Johnson, and H. Schielzeth. 2017. "The Coefficient of Determination R² and Intra-Class Correlation Coefficient From Generalized Linear Mixed-Effects Models Revisited and Expanded."
*Journal of the Royal Society Interface* 14, no. 134: 20170213. https://doi.org/10.1098/rsif.2017.0213.

Ohlmann, M., F. Mazel, L. Chalmandrier, et al. 2018. "Mapping the Imprint of Biotic Interactions on β-Diversity." *Ecology Letters* 21, no. 11: 1660-1669. https://doi.org/10.1111/ele.13143.

Oksanen, J., G. Simpson, F. Blanchet, et al. 2022. *vegan: Community Ecology Package Version 2.6-4*. www.r-project.org.

Pedro, P. M., L. Cullen Jr., F. Prado, et al. 2023. "Ecological Monitoring Using Collembola Metabarcoding With Extremely Low Bycatch Amplification." *Ecological Indicators* 157: 111202. https://doi.org/10.1016/j.ecolind.2023.111202.

Potapov, A., B. Bellini, S. Chown, et al. 2020. "Towards a Global Synthesis of Collembola Knowledge—Challenges and Potential Solutions." *Soil Organisms* 92, no. 3: 161–188. https://doi.org/10.25674/s092iss3pp161.

Romahn, J., D. Baranski, A. Schmidt, et al. 2024. "Glimpse of Past Dynamics: A New Set of Phytoplankton Primers for sedaDNA." *Environmental DNA* 6, no. 3: 16. https://doi.org/10.1002/edn3.577.

Rosenthal, R. 1994. "Parametric Measures of Effect Size." In *The Handbook of Research Synthesis*, edited by H. Cooper and L. V. Hedges, 231–244. Russel Sage Foundation.

Rota, N., C. Canedoli, C. Ferrè, G. F. Ficetola, A. Guerrieri, and E. Padoa-Schioppa. 2020. "Evaluation of Soil Biodiversity in Alpine Habitats Through eDNA Metabarcoding and Relationships With Environmental Features." *Forests* 11: 738. https://doi.org/10.3390/f11070738.

Taberlet, P., A. Bonin, L. Zinger, and E. Coissac. 2018. *Environmental DNA for Biodiversity Research and Monitoring*. Oxford University Press.

Takenaka, M., Y. Hasebe, K. Yano, et al. 2024. "Environmental DNA Metabarcoding on Aquatic Insects: Comparing the Primer Sets of MtInsects-16S Based on the mtDNA 16S and General Marker Based on the mtDNA COI Region." *Environmental DNA* 6, no. 4: e588. https://doi.org/10.1002/edn3.588.

Thioulouse, J., S. Dray, A. B. Dufour, A. Siberchicot, T. Jombart, and S. Pavoine. 2018. *Multivariate Analysis of Ecological Data With ade4*. Springer.

Thomsen, P. F., M. R. Jensen, and E. E. Sigsgaard. 2024. "A Vision for Global eDNA-Based Monitoring in a Changing World." *Cell* 187: 4444–4448. https://doi.org/10.1016/j.cell.2024.04.019.

Van Nynatten, A., K. S. Gallage, N. K. Lujan, N. E. Mandrak, and N. R. Lovejoy. 2023. "Ichthyoplankton Metabarcoding: An Efficient Tool for Early Detection of Invasive Species Establishment." *Molecular Ecology Resources* 23, no. 6: 1319–1333. https://doi.org/10.1111/1755-0998.13803.

Yao, M., S. Zhang, Q. Lu, et al. 2022. "Fishing for Fish Environmental DNA: Ecological Applications, Methodological Considerations, Surveying Designs, and Ways Forward." *Molecular Ecology* 31, no. 20: 5132–5164. https://doi.org/10.1111/mec.16659.

Yeh, Y.-C., J. McNichol, D. M. Needham, E. B. Fichot, L. Berdjeb, and J. A. Fuhrman. 2021. "Comprehensive Single-PCR 16S and 18S rRNA Community Analysis Validated With Mock Communities, and Estimation of Sequencing Bias Against 18S." *Environmental Microbiology* 23, no. 6: 3240–3250. https://doi.org/10.1111/1462-2920.15553.

Zinger, L., A. Bonin, I. Alsos, et al. 2019. "DNA Metabarcoding—Need for Robust Experimental Designs to Draw Sound Ecological Conclusions." *Molecular Ecology* 28: 1857–1862. https://doi.org/10.1111/mec.15060.

## Supporting Information

Additional supporting information can be found online in the Supporting Information section. **TABLE S1:** Pearson's correlations between

variables describing environmental features of plots, and between these variables and the two components extracted by principal component analysis (PCA). NDVI, normalized difference vegetation index (a proxy of primary production); TWI, topographic wetness index. For each environmental variable, the principal component with which it shows the strongest correlation is highlighted in bold. N=1251 for all correlations.
**TABLE S2:** Results of Wilcoxon signed-rank tests comparing the richness of insects and springtails, estimated using specific markers (Inse01 and Coll01, respectively), and the diversity of insects and springtails estimated using generalist markers (Euka02 and Arth02). N=1486 for all the comparisons. Positive values of V indicate that the specific marker returns a higher species richness. **TABLE S3:** Pearson's correlations between the taxonomic richness (calculated using Hill's number =1) of insects and springtails, estimated using specific markers Inse01 and Coll01, respectively, and the taxonomic richness of insects and springtails estimated using generalist markers (Euka02 and Arth02). N=1486 for all the correlations. **TABLE S4:** Results of generalized linear mixed models showing relationships between the MOTU richness of insects and springtails, estimated using both specific (Inse01 and Coll01) and generalist markers (Euka01 and Arth02), and environmental variables of glacier forelands, summarized by two principal components. High values of PC1 indicate early successional plots (low time since glacier retreat, low NDVI, low temperature, poor plant communities); high values of PC2 mostly indicate plots with low topographic wetness. **TABLE S5:** Taxonomic resolution of the different markers tested in silico on (a) insects and (b) springtails. To calculate taxonomic resolution, two sets of *in silico* amplicon sequences were prepared as follows for each marker. First, an *in silico* PCR was first run on GenBank version 249 with the program ecoPCR (Ficetola et al. 2010), using the marker primers as entry parameters. Three mismatches at most were allowed per primer (-e option), and amplicon size was set between 10 and 1000bp (-l and -L options). The resulting sequences were first filtered to retain only those displaying taxonomic information at least at the family level, and the remaining sequences were dereplicated using the *obiuniq* command from the OBITools (Boyer et al. 2016). Another filtering step was performed to keep sequences associated either to insects or springtails, resulting in two sets of sequences (one for insects and one for springtails). The command *ecotax specificity* from the OBITools was then run on each set to assess taxonomic resolution of each marker at different taxonomic levels (order, family, genus, species) within insects (Table a) or springtails (Table b). Note that differences in the number of taxa amplified in silico also occur because the primers amplify different genomic regions, and a different number of sequences is available in the reference database. **FIGURE S1:** Biplot of the principal component analysis on environmental variables. The inset shows the eigenvalues of the PCA components; the two selected components are in black. **FIGURE S2:** Frequency of samples in which specific markers (Coll01 and Inse01) detected more (blue), less (orange) or a similar number of taxa (gray), compared to generalist markers (Arth02 and Euka02). The detected richness is considered to be similar if the differences in richness were <2.