Evaluating amphibian community composition in the southeastern U.S. using eDNA metabarcoding

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Strasko, Jarrett R. Johnson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7190508/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Metabarcoding utilizes environmental DNA (eDNA) shed by living organisms to allow for the detection of many related species at once from an environmental substrate such as soil or water. We explored the tools required for amphibian metabarcoding within the southeast US and implemented a comparative study that examined the utility of metabarcoding in lieu of traditional visual encounter surveys and the ability of the metabarcoding strategy to detect temporal variation in the composition of pond-breeding amphibian assemblages. We tested a previously developed mitochondrial ribosomal RNA gene assay (12S rRNA) for the ability to detect and discriminate among southeastern amphibian species. We successfully detected 11 amphibian taxa either to the genus or species level at our study sites with the 12S assay, indicating the potential to adequately describe amphibian assemblages across the southeastern U.S. However, a lack of a comprehensive 12S gene sequence reference database for all southeastern amphibians and the inability of the 12S assay to distinguish between certain taxa at the species level indicates caution should be used in implementing this strategy for important conservation and management purposes. In our study, the combination of both visual encounter surveys and metabarcoding yielded the highest levels of detection. Our results suggest that the development of a reliable amphibian metabarcoding strategy will improve our ability to efficiently and rapidly assess amphibian assemblages at ephemeral pools compared to traditional methods. environmental DNA (eDNA) amphibians metabarcoding Kentucky ephemeral pond biomonitoring Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Globally, amphibian populations experience elevated risk of declines or extinction, therefore efficient and accurate surveying methods are of great importance to informing conservation efforts (Collins 2010, Henle et al. 2025, McCallum 2007). With 40.7% of amphibian species considered threatened, region-specific monitoring is needed to reduce local extinction probability (Luedtke et al. 2023). The southeastern U.S. is a particularly species-rich yet understudied region for amphibians that is often considered a “biodiversity hotspot” (Graham et al. 2010; Walls 2014). With such an abundance of species diversity, the need for efficient population monitoring increases, as does the complexity of the task. This study will compare the effectiveness of a traditional monitoring strategy with one based on environmental DNA (eDNA) at the Western Kentucky University (WKU) Green River Preserve (GRP), located in Hart County, Kentucky. Our goal is to support amphibian population monitoring efforts globally through the demonstration and optimization of metabarcoding protocols for the species found in the southeastern U.S. A major challenge in amphibian population monitoring efforts is the temporal variation in breeding phenology and the concentration of adults at breeding sites (Bergstrom 2010; Johnson et al. 2016; Greenberg et al. 2017). Over the course of a year, amphibian assemblages at breeding sites will vary (Griffiths 1997), requiring repeated and persistent monitoring of sites to gain a complete picture of site occupancy. Variation in life history strategies and the unpredictability of the timing of activities such as migration, breeding and metamorphosis create challenges for cost-effective traditional monitoring protocols. Ephemeral pools boast species-rich assemblages when conditions are favorable and are the preferred habitat of many pond-breeding amphibian species (Drayer and Richter 2016). Temporary pools offer protection to species otherwise predated by fish due to short pond hydroperiods and frequent, often annual, drying events (Baber and Babbitt 2003; Kloskowski et al. 2020). Drying periods reduce or eliminate the occurrence of fish and therefore support greater amphibian species diversity, provided hydroperiod length is sufficient for completion of the aquatic larval period of pond-breeding frogs and salamanders. High levels of amphibian biodiversity within the southeastern US makes it difficult to develop simple, efficient monitoring strategies that are appropriate for all species. The advent of environmental DNA (eDNA) metabarcoding allows for the simultaneous detection of many related species, relying on the discrimination of species using reference databases and phylogenetic inference (Hoffmann et al. 2016; Ficetola et al. 2019; Sawaya et al. 2019). The 12S ‘Batr01’ assay (Valentini et al. (2016) was originally developed for the metabarcoding of all Batrachia (a clade of amphibians that includes frogs and salamanders, but not caecilians nor related extinct lineages), and we chose this assay after in vitro testing revealed that it reliably amplifies 30 species of Southeastern US amphibians. Batr01 has been utilized to detect amphibians from a variety of countries such as Norway (Osman et al. 2022), Brazil (Sasso et al. 2017), and China (Li et al. 2022). A recent study in the southeastern US also used this assay to characterize the diet of Sus scrofa (wild pigs), which revealed 12 species of amphibians (Canright et al. 2023). The objectives of this study are to 1) use visual encounter surveys and metabarcoding to investigate the amphibian assemblage within Western Kentucky University’s Green River Preserve (GRP), 2) determine if metabarcoding techniques using a preexisting 12S primer set are useful for the detection of southeastern U.S. amphibians, and 3) compare assemblage composition between metabarcoding and traditional surveying methods and across a temporal sampling strategy. We predicted that more amphibian species will be identified using metabarcoding than with visual encounter surveys, but the sensitivity to temporal shifts in assemblages will be reduced for the metabarcoding strategy compared to the visual encounter surveys. Materials and Methods Field Observations The GRP comprises nearly 1600 acres of land ranging from rivers, uplands and bottomlands, barrens, caves, and more(Meier 2019) and is owned and managed by the Western Kentucky University. In this study, eight study sites spanned two areas of the GRP (Fig. 1 ). Longitudinal data sampling at these sites may reveal differences in amphibian species diversity over time. Visual encounter surveys were performed for eggs, larvae, and adults, with dip-netting and seining at five time periods spaced evenly throughout the year. Adult amphibians were identified and recorded in the field, along with frog vocalizations for a positive presence at that sampling location and time period. When identification in the field was not possible for eggs or larvae, representative specimens were collected and brought back to the laboratory for further investigation and identification (IACUC 22 − 09, KY educational collecting permit SC2311101). Environmental Sampling Each site was sampled every three months from May 2022 through May 2023. All field equipment was washed with 10% bleach three times and then rinsed with deionized water prior to use. Negative controls were taken alongside all field samples for collection, transportation, storage, filtration, extraction, amplification, cleaning, pooling, and sequencing (“Field Negatives”). At each site, 2 L of pond water were collected and transported back to the lab to be filtered. The filtration procedure collects cells and free-floating DNA from all organisms present at the site for DNA purification. Samples were stored at 4°C and filtered within a week (Pilliod et al. 2014). Pond water was vacuum-filtered through a 1.0 µm Millipore® glass-fiber filters with a 4.7 cm diameter (Sigma-Aldrich, Burlington, MA, USA) in a PCR-free room. One liter of water was passed through filters for each site, with some sites requiring the use of multiple filters due to clogging with suspended debris. Each filter was then cut into 30–40 pieces, where half was stored in 95% ethanol as a backup and the other half was immediately placed into Qiagen buffer ATL and Proteinase K to begin the DNA purification procedure, following the Qiagen DNEasy Blood & Tissue Kit (Qiagen, Germantown, MD, USA) protocol with one exception. Due to continued absorption of reagents by the filters, we quadrupled the amount of buffer ATL and Proteinase K, maintaining manufacturer ratios but ensuring there was enough liquid to extract in the proceeding steps. DNA concentration was measured using a Qubit fluorometer (Thermo Fisher Scientific, Waltham, MA). Filters with the highest DNA concentration were chosen for the sites that required more than one filter per time period/pond combination. DNA Library Preparation Laboratory equipment was sterilized using either a 10% bleach solution or RNAse Away (Thermo Scientific™ 7002) prior to use. DNA samples were purified to remove possible inhibitors using a Zymo inhibition removal kit (Zymo Research, Irvine, CA) prior to amplification. DNA concentrations were measured post-inhibition removal using a Qubit fluorometer (Table S1 ). Genomic DNA extracted from the filters was used in polymerase chain reaction (PCR) to enrich the sample for amphibian DNA. We targeted a portion of the 12S gene using Batr01 PCR primers that anneal to amphibian DNA as well as DNA from other vertebrates (Table S2; Valentini et al. 2016). A two-step PCR process was followed to combine all necessary components for amplification, barcoding, and sequencing (Glenn et al. 2019). Reaction conditions are available in Table S3 and components in Table S4. DNA was also isolated directly from tissue samples of 30 species of interest using the same methodology as utilized in the eDNA library for use as positive controls and for building a reference database to later compare eDNA sequences for taxonomic identification. From the positive control DNA samples, an equimolar mock community of six taxa (three salamanders, three frogs) was generated alongside the eDNA products to evaluate detection probabilities and as a positive control. Five mock community samples were created using serial dilutions, described in Table S5. The DNA dilution samples of the mock community library were prepared and standardized using the same approach as the eDNA library, as outlined in tables S3 and S4. Post-PCR 1, each sample was purified using Sera-Mag SpeedBeads (Cytiva Life Sciences, Marlborough, MA) to eliminate residual primer and reaction artifacts. Both “right” and “left-hand” clean-ups were performed to remove large and small library components, respectively, starting with a 0.5x bead concentration (relative to sample) to remove larger fragments, followed by a 1.2x concentration clean-up to remove smaller fragments. Following bead clean-up, PCR 2 anneals sequencing primers followed by sample-specific indices (“barcodes”) and flow cell handles within a short-cycle PCR ligation (description of adapters previously outlined in Table S2). PCR 2 reaction conditions are described in Table S6 and components given in Table S7. Six identical reactions were run, the final products were checked via gel electrophoresis on a 1.5% agarose gel and then pooled and purified again with Sera-Mag SpeedBeads. DNA was quantified using a Qubit assay and visually checked via gel electrophoresis on a 1.5% agarose gel. Sequencing and Bioinformatics Next-generation sequencing of DNA libraries was completed using Illumina NovaSeq X Plus 10B PE150 (300 Cycles) flow cell at the Oklahoma Medical Research Foundation (OMRF) Clinical Genomics Center. Sequencing utilized one lane and approximately 10% of the reads were allotted to this library. A 20% PhiX spike was added to increase clustering propensity. Upon receiving the sequence data back from OMRF Clinical Genomics Center, samples were demultiplexed using ‘Cutadapt’ version 4.9 (Martin 2010). Illumina adapters and locus-specific primers were also trimmed using ‘Cutadapt’. The ‘dada2’ pipeline (Callahan et al. 2016) was utilized for filtering and quality control, denoising and removing chimeras from sequences, and ultimately generating amplicon sequence variants (ASVs). Taxonomic information was assigned to the ASVs by downloading a local version of the NCBI nucleotide database (Sayers et al. 2022) and using ‘blastn’ within the ‘blast+’ suite (Camacho et al. 2009). ASVs found in negative controls were programmatically removed from the entirety of the resulting dataset to ensure no contamination. Relative abundance was calculated in the R package phyloseq (McMurdie and Holmes 2013) on the cleaned dataset. A Kruskal-Wallis (KW) test was run on the resulting ASV read counts by date to determine if read abundance varied significantly over time. Calculating diversity : The data were henceforth split into two categories: metabarcoding data (“MB”), and visual encounter surveys (“VES”). Metadata and ASVs were merged in R, which was also used throughout the rest of the analysis (R Core Team 2021). Relative abundance values were calculated and then compared by genus across the whole dataset and by relative abundance of species by site and collection date. To ensure comparability across samples with varying sequencing depth, we converted raw counts to relative abundances using the transform_sample_counts() function in phyloseq. After removing contaminants (by subtracting the read counts observed in negative controls) and combining any technical replicates, we repeated this normalization step on the resulting positive samples. Alpha diversity metrics including the Shannon and Simpson diversity indices were calculated using the R package phyloseq (function ‘estimate_richness’, measures = “shannon” and “simpson”; McMurdie and Holmes 2013). Each dataset was evaluated for differences in richness and species composition across sampling location ‘Site’ (spatial factor) and sampling period ‘Time’ (temporal factor) and compared by data set ‘Survey Type’ (method factor). ‘Site’ is defined as the eight sampling locations: Cabin Pond (CA), Grove Pond (GR), Knob Pond (KN), Long Pond (LO), Luccio’s Pond (LU), Twin Pond (TW), Vinegar Ridge (VR), and Yellow Flower Pond (YE). ‘Time’ includes the five sampling periods separated as independent factors. ‘Survey Type’ indicates whether the dataset falls under the MB or VES sampling method category within the comparative analyses. A KW test was run on the resulting diversity data to test for significant differences between sites, dates, and methods (MB vs. VES). An additional Analysis of Variance (ANOVA) test for parametric data was run in parallel on all diversity data for direct comparison. Bray-Curtis distances were calculated using the vegan package in R, version 2.6-8 (function vegdist, method = "bray"; Oksanen et al. 2024). On binary data, Bray–Curtis effectively acts as the Sorensen–Dice coefficient, allowing an approach for presence/absence. We conducted Permutational Multivariate Analysis of Variance ( PERMANOVA ; “adonis2” in vegan ) to test how amphibian community composition varied by ‘Site’, ‘Date’, and ‘Survey Type’. The matrix and metadata were subset to VES and MB samples, respectively, and generated Bray–Curtis distances for each. We then ran PERMANOVA models testing (a) ‘Site’, (b) ‘Date’, and (c) ‘Site + Date’. We also used the full presence/absence matrix to assess whether Survey Type (VES vs. MB), Site, and Date jointly explained community variation. We specified a model using “adonis2” to test whether Bray–Curtis dissimilarity in community composition (response variable) could be explained by Survey_Type + Site + Date and used by = "margin" to get marginal effects (R², F , and p -values) for each factor. We performed Principal Coordinates Analysis (PCoA) (cmdscale, stats package, base R) and Non-metric Multidimensional Scaling (NMDS; metaMDS, vegan package) on the Bray–Curtis distance matrices to visualize patterns in community composition. Mock Community The mock community was utilized as a positive control, and evaluation included direct comparisons across the six included species via A) a visual plot of amplification by species via relative abundance of sequences amplified, and B) a line graph of raw counts examining log-scaled dilution factors compared to the raw counts to determine variation in sequencing reads as the dilution factor decreased. Results Visual Encounter Survey Data Throughout the sampling period, 14 species of amphibians were encountered via visual encounter surveys, including eight species of frogs and six species of salamanders (Table 1 ). Differences in assemblages were observed across seasons in accordance with breeding phenology for species richness (Fig. S1 ) and presence/absence of sequences (Table 2 ). No differences were observed in ponds with short hydroperiods that dried during the study versus ponds with longer hydroperiods that did not dry during our sampling period. Table 1 List of ponds and their associated characteristics, including general Green River Preserve location, ephemeral versus perennial, number of hydroperiods observed, and total number of species found across all five sampling periods. Note that “permanence” is relative to time, rainfall, and other climactic factors – any of these ponds could be ephemeral on a given year. Factors such as visual depth, hydroperiods observed, fish presence, etc. were utilized to determine baseline differences in our examination of permanence of ponds. Pond Name Location Coordinates Ephemeral or perennial Hydroperiods observed # of species found Long Pond Lawler Bend 37.244722, -85.934444 Perennial 5 4 Grove Pond Lawler Bend 37.24750, -85.935278 Ephemeral 5 9 Twin Pond Lawler Bend 37.250833, -85.936667 Ephemeral 3 8 Cabin Pond Lawler Bend 37.244167, -85.938333 Perennial 5 5 Yellow Flower Pond Lawler Bend 37.24750, -85.943611 Ephemeral 2 7 Knob Pond GRP Main Property 37.240833, -85.985000 Perennial 5 6 Vinegar Ridge GRP Main Property 37.233889, -86.003611 Ephemeral 4 6 Luccio’s Pond GRP Main Property 37.234722, -86.002222 Perennial 4 6 Table 2 Visual encounter survey results (“X”) compared to metabarcoding results (pink squares) encompassing total presence/absence data per site over the course of the year sampling period. Note: Metabarcoding samples were only able to decipher Ambystoma and Anaxyrus to genus level, therefore the “AmOp” column was utilized as the presence of some sort of Ambystoma species for metabarcoding, while “AnFo” is a generalized stand-in for Anaxyrus. AcCr AmJe AmMa AmOp AnFo DrCr EuCi HeSc NoVi LiCa LiCl LiSp LiSy PlDo PlGl PsCr Long X X X X Grove X X X X X X X X X Twin X X X X X X X Cabin X X X X X Yellow X X X X X X X Knob X X X X X X Luccio X X X X X X Vinegar X X X X X X Metabarcoding Data : Next-generation sequencing resulted in 170,312,183 paired-end raw reads. All reads > 15% error rate threshold in cutadapt were discarded. Post-processing in cutadapt, 107,878 unique paired reads (0.03%) were retained by dada2. The dada2 algorithm combines similar reads, meaning that while only a small percentage of total reads were retained within “unique” reads, this does not necessarily reflect the “tossing out” of a large portion of sequences but simply the merging of repeat reads. The output of the dada2 pipeline revealed 785 unique ASV groupings, of which 32 (4.1%) were retained as relevant to our study (Table S8). Total read counts did not differ significantly by date (KW: χ² = 5.6, df = 4, p = 0.23). The blastn search primarily identified non-target taxa such as algae and bacteria. Additional viruses, plants, invertebrates, and other vertebrate species were also identified. For the purposes of this study, only relevant amphibian ASVs were utilized downstream. ASV and visual encounter survey data were paired with appropriate metadata for the proceeding analyses (Table S9). Relative abundance values (Table S10) were compared by genus across the whole dataset (Fig. S2) and relative abundance of species by site and collection date (Fig. 2 ). Notably, the taxonomic classification using 12S sequence data was unable to distinguish certain taxa to species level, including members of Ambystoma and Anaxyrus . Ambystoma species did not have a robust reference database available for the 12S locus, and the sequences generated were not sufficient for species-level classification. Anaxyrus fowleri and Anaxyrus americanus are identical at the 12S locus, deeming it impossible to distinguish between the two species using only this locus. Twenty-nine amphibian species and one fish were successfully amplified for reference purposes. Resulting sequences were aligned and trimmed in Geneious v. R11.1.5 (Table S11). Diversity Analyses Overall, more taxa were found in the visual encounter dataset (14) than the metabarcoding dataset (11). This difference is likely influenced by lack of assignment to species-level for some taxa (Fig. 3 ). The full dataset did not meet normality assumptions (Shapiro-Wilk test, p < 0.05 for both Shannon and Simpson indices), therefore we ran nonparametric KW tests on the entire dataset to assess site-level alpha diversity. For the sake of comparison, we also examined each site as a factor in an ANOVA framework. Under the ANOVA factor-based model, the within‐site residuals passed normality and homogeneity tests in most instances but with some exceptions; therefore, we ran parametric ANOVAs as well. No significant differences in Shannon or Simpson diversity were found between survey methods (VES vs. MB), by either non-parametric (KW) or parametric (ANOVA) tests (all p > 0.05, Table S12, Fig. S3). A similar non-significant result was obtained with a two-factor model (‘Survey Method’ + ‘Site’), in which neither factor was significant (all p > 0.05). When examining diversity across sites, the KW tests also showed no significant differences in either Shannon or Simpson diversity (Fig. S4; p > 0.05). In the MB dataset, KW indicated no differences in alpha diversity by site or date ( p > 0.05). The one-way ANOVAs (Shannon or Simpson) likewise found no significant effects of site or date ( p = > 0.05). Both methods indicate that amphibian diversity detected by metabarcoding did not vary significantly across sites or sampling periods. The KW tests demonstrated a significant difference in diversity across sampling dates for the VES data but not among sites. The ANOVA results corroborate the significant temporal differences for Shannon (F 4,30 =14.0, p < 0.001) and for Simpson (F 4,30 =14.03, p 0.9). Thus, both parametric and non‐parametric approaches indicate that amphibian diversity via visual encounter surveys is strongly influenced by sampling period (likely reflecting seasonal changes in breeding activity) but does not differ greatly among ponds at our study site. We tested for differences across sites alone amongst the entire MB and VES dataset using KW and one-way ANOVA, and again no significant variation was found ( p > 0.05), confirming that alpha diversity does not vary substantially among these sampling locations under either method. Although the normality assumption was violated in several of these ANOVA models, the non-significant outcomes align with the nonparametric results, suggesting that both approaches, VES and MB, produce comparable alpha diversity estimates, and no ‘Site’ effect is detectable. A ’betadisper’ test confirmed reasonable homogeneity in centroids that allowed for further analysis (Fig. S5). PERMANOVA indicated that for VES data, ‘Time’ and ‘Site + Time’ contributed significant variation ( p = 0.001 and 0.003 respectively; Table S13), whereas for MB data no factors were significant (all p > 0.39; Table 3 ). While ‘Site’ does not significantly contribute to variation, ‘Time’ and ‘Site + Time’ do significantly contribute to variance, indicating the potential for time and site + time to impact community composition within the VES dataset (Fig. S6). The results indicate no significant differences observed in community composition over ‘Site,’ ‘Time,’ or ‘Site + Time’ within the MB dataset (Fig. 4 ). Table 3 Bray-Curtis based PERMANOVA analysis of time using adonis2 in R, analyzing differences in community composition amongst the amphibian metabarcoding presence/absence data over time with 999 permutations. Site explained approximately 17.4% of the variation ( pseudo-F = 0.693, R 2 = 0.174, p = 0.79). Time explained approximately 14.7% of the variation ( pseudo-F = 1.19, R² = 0.147, p = 0.39). Site + Time explained approximately 33.7% of the variation ( pseudo-F = 0.88, R² = 0.337, p = 0.63). The results herein suggest no significant differences observed in community composition over site, time, or site + time within the presence/absence MB dataset. Factor Df Sum of Squares R 2 F p-value Site Analysis Site 7 0.867 0.174 0.693 0.787 Residual 23 4.112 0.826 – – Total 30 4.980 1.000 – – Time Analysis Date 4 0.731 0.147 1.118 0.386 Residual 26 4.249 0.853 – – Total 30 4.980 1.000 – – Site + Time Analysis Site + Date 11 1.680 0.337 0.879 0.634 Residual 19 3.300 0.663 – – Total 30 4.980 1.000 – – The model-based marginal-effects PERMANOVA used three predictors: ‘Survey Type’ (to distinguish methodology), ‘Site’ (spatial factor), and ‘Date’ (temporal factor) tested the marginal effects of each factor, effectively treating each as if it were the last entered in the model (Table 4 ). This approach reports partial R² (the proportion of variance explained) and permutation-based p -values for each factor independently. A total of 999 permutations were used. ‘Survey Type’ had the largest effect on community composition, explaining 27.1% of the variance ( p = 0.001). ‘Date’ also showed a statistically significant though smaller effect (10.4% explained; p = 0.002). ‘Site’ accounted for 9.5% of the total variation but was not significant at the 5% level ( p = 0.129). The model’s residual (= 51.4%) is attributed to other unmeasured factors or natural variability. The results indicate that of the variance present, ‘Survey Type’ had the largest influence, followed by ‘Date’ and then ‘Site.’ The residual values indicate a large amount of unexplained variance. Table 4 Marginal effects of each factor on amphibian community composition (Bray–Curtis), from PERMANOVA (adonis2 in R) with 999 permutations. Survey_Type (Visual vs. Metabarcoding) explains about 27% of the variance (p = 0.001), Date about 10% (p = 0.002), while Site was not significant (p = 0.129). Factor Df Sum of Squares R² F-value Pr(> F) Survey_Type 1 4.9354 0.27065 27.3851 0.001 Site 7 1.7300 0.09487 1.3713 0.129 Date 4 1.8973 0.10404 2.6319 0.002 Residual 52 9.3715 0.51391 — — Total 64 18.2355 1.00000 — — Mock Community The mock community revealed differences in amplification rates across the six species (Fig. 5 ). Dryophytes , Lithobates , and Ambystoma revealed the highest proportions of amplification, despite all taxa starting in equal concentrations. Anaxyrus was fourth proportionally, while Hemidactylium was phased out of amplification by the fifth dilution factor (0.001). Notophthalmus amplified successfully in all replicates, but at a much lower proportion of the whole. Proportional amplifications were relatively consistent by taxa across dilution factors. Fig. S7 provides a log-scaled line graph of reductions in raw read count by dilution factors, where I noted that while overall read counts decrease with decreasing dilution factors, there was an unusual spike at 0.100 (MC3). Discussion We evaluated variation in species richness over time within and between amphibian breeding ponds using two different survey strategies. We expected that the species richness estimates would vary based on patterns of breeding phenology of local species. When examining the differences in species richness between ponds over time, a record of habitat usage and preferences can be inferred and applied to conservation strategies. Our data will inform conservation biologists and land managers as to the utility of metabarcoding as a population monitoring strategy. Longitudinal sampling of the sites will provide improved understanding of how local amphibians utilize ephemeral pond habitat as part of their natural history. Additionally, we investigated the utility of eDNA/metabarcoding as a survey strategy for southeastern US amphibians. This study answers questions regarding the validity and importance of longitudinal sampling for accurately revealing community composition at a site, which will help emphasize the wider implications of metabarcoding for amphibian populations. We found no significant differences in richness or overall diversity by sampling period within either the MB or VES datasets. However, the longitudinal data does provide accurate assessments of presence associated with our understanding of breeding phenology. For example, in the late winter/spring months (February and May), Ambystoma spp. samples are abundant in all datasets, and absent in most summer and fall samples (August and November). Other eDNA studies have emphasized variation in sequence counts as a signal of temporal variation. For example, Johnson et al. (2025) noted strong variation in read abundance – samples taken during the spring breeding season had higher read abundance, indicating seasonal influence on metabarcoding data. Contrarily, we did not have a higher read abundance in any of our sampling periods. One factor that may influence read abundance is wetland type. Lentic and lotic systems may retain eDNA at differing rates. Shogren et al. (2018) indicated that biofilm-rich, high-velocity systems degrade DNA at a higher rate than nonflowing mesocosms. This study also found that short DNA fragments remained detectable for prolonged periods. Our study targeted a short 12S fragment (< 60 bp) in nonflowing lentic ponds; therefore, prolonged DNA retention past the point of species-specific seasonality makes sense in the context of our study. The results from the alpha diversity analysis suggest that there are no significant differences in diversity between the methods or among the sites. Consistent with expectations, sampling date (season) affected species diversity in visual surveys, reflecting breeding phenology, whereas metabarcoding data showed less temporal variation, indicating that DNA sequences may persist longer than the actual dates of occupation by adults or larvae. Increasing sampling periods and extending this type of research for longer periods may allow for finer-scale details to emerge about DNA persistence relative to pond-breeding amphibians. The lack of species-level resolution for Ambystoma and Anaxyrus contributed to lower species richness estimates for the metabarcoding data. Our efforts to sequence a single representative of each species proved insufficient to assign species with confidence, due to high levels of intraspecific genetic diversity at the 12S locus for Ambystoma . Future studies with more robust reference sequence data may not encounter this issue. Contrastingly, we also demonstrated that some species, such as A. fowleri and A. americanus , are not able to be distinguished using the 12S region flanked by the Batr01 primers due to amplification of an invariant sequence. The time of sampling alone was not statistically significant in any dataset. Within the MB dataset, there is very little variance partitioning by ‘Time’ ( p > 0.05), explaining only 14.7% of the total variation ( R 2 = 0.147). A model incorporating both ‘Site and Time’ explained 33.7% of the variation but was also non-significant ( p > 0.05), suggesting weak combined spatial and temporal structuring. An NMDS of Bray–Curtis distances similarly showed partial clustering by site, though date-based grouping was less pronounced The metabarcoding strategy was unable to effectively predict change in assemblage by time, which may indicate that sequences are retained in the water column for longer than the source organisms’ occupancy. However, our analyses investigated changes in alpha and beta diversity over time, meaning that the ponds utilized in this study may just host a wide range of diversity year-round. Studies examining the retention of specific species’ DNA sequences over time may better elucidate the nature of occupancy and sequence retention from a seasonal perspective. The results of the multivariate factorial model suggest that the choice of survey method (Visual vs. Metabarcoding) explains the most variance of the detected amphibian community composition. Within this framework, ‘Date’ (sampling month/year) does include a significant amount of partitioned variance, aligning with the expectation that amphibian communities may vary seasonally. In contrast, ‘Site’ did not emerge as a significant factor in this dataset, implying that across the sites sampled, variability in amphibian composition is better explained by methodology and temporal changes rather than purely spatial differences among sites. The near proximity of all sites makes it likely that species composition would be relatively similar throughout our ponds, therefore this is not a surprising result. At only eight sampling points, this structure of partitioning may indicate that there could be different results in a larger system or with more sampling effort. Overall, these data highlight the importance of survey methods in shaping detected amphibian assemblages and a time (Date) factor, whereas site-level effects appear less pronounced here. With this in mind, we support the notion that visual surveillance is still of high importance in amphibian conservation work. We also emphasize that the choice of metabarcoding primers and genetic locus is massively influential in survey findings; had more robust Ambystoma references been available, our results may have been dramatically different. The small number of species present (less than 20 total species across all sites) should be noted, and differences in alpha and beta diversity measures are likely heavily impacted by even a few missing species. The results of this study should be interpreted as a cautious tale of only utilizing one survey method for amphibian species composition estimation. While the physical surveys did result in more findings, species were found by the metabarcoding data that were not found by visual or auditory encounters. Using a combination of both methods, we generated a more complete picture of the amphibian assemblage present at the Green River Preserve. Amphibian eDNA biomonitoring may require a more intricate approach. Single species eDNA assays have proven useful in a variety of applications for amphibian detection, including within Ambystoma species (Brammell et al. 2023, Strasko et al. 2023). Harper et al. (2018) evaluated the efficacy of single-species assays versus metabarcoding in the detection of the great crested newt ( Triturus cristatus) and found that under certain thresholds, targeted qPCR performed better at detecting T. cristatus . The authors recommended considering metabarcoding for community biodiversity assessments and targeted assays for single-species detection. In our study sites, less common species such as H. scutatum and E. cirrigera may have better detection using a single-species assay. A meaningful takeaway from this study is the importance of primer and locus choice. Other metabarcoding studies reported greater success and confidence using multiple loci (Richardson et al. 2015, Weitemeyer et al. 2021, Wizenberg et al. 2023). The Batr01 assay was designed to target amphibian species in European countries originally, meaning that its use in this study was experimental. Only 4.1% of the ASVs generated were target amphibian taxa, meaning that the vast majority of reads generated by Batr01 were off-target taxa. The co-amplification of an abundance of nontarget species may have reduced our ability to effectively target for amphibians. To our knowledge, only one other study has used this assay in the southeastern U.S. (Canright et al. 2023). Canright et al. (2023) used Batr01 to examine the diet composition of Sus scrofa , an invasive wild pig species in the United States. Like our study, Canright et al. (2023) also reported a low total amount of vertebrate reads (8,763 reads) using the Batr01 primers, but it is not clear whether they may have also amplified off-target taxa. It is possible the low number of amphibian sequence reads in the Canright et al. (2023) data reflects the low abundance of amphibians within the diet of Sus scrofa , but based on our data, it is also possible that significant non-target sequences amplified by the Batr01 primers depressed the abundance of other taxa, including amphibians. Other studies in the U.S. have used this assay in different regions, including Texas (Collins 2022) and Michigan (Ruppert et al. 2025). Notably, the Michigan study also reported a reduced ability to identify to species level using the 12S marker. The Michigan study had similar findings in regard to visual versus metabarcoding surveys yielding similar results in number of species found, further indicating that eDNA metabarcoding may be more suitable as a complement rather than a substitution. The Texas study also amplified a number of non-target taxa, specifically referencing fish and amphibian taxa. The mock community demonstrated amplification bias by species, further casting doubt on the utility of this marker. While the mock community did amplify 5/6 taxa at a dilution scale of 0.001 (MC5), this was also under a scenario with little to no competition with non-target taxa. The potential issue of amplification bias is demonstrated by N. viridescens , which was by far the most encountered species in most of our ponds in visual encounter surveys but is under-represented in the metabarcoding results. This was likely influenced by swamping of target DNA by off-target taxa. The large volume of unusable data generated using this primer set, primarily from algae and bacteria, eliminated a large portion of our sequencing data, decreasing the efficiency of the metabarcoding strategy. Primers that are more targeted to a specific taxonomic group or utilize more conserved genetic regions may be the way forward in utilizing a metabarcoding approach for amphibian biomonitoring. Inadequate amphibian biomonitoring in hotspots like the Southeast U.S. leave us with a lack of historical records and comparative data to utilize in conservation efforts. Walls (2014) indicated a severe lack of anuran and caudate monitoring in the Southeast, with only 73.8 and 33.3%, respectively, of species known to occur in the Southeast receiving some form of continuous monitoring. Walls also emphasizes the need to take into account temporal and spatial scales in amphibian monitoring. Improving the scope of amphibian conservation requires rigorous testing of monitoring tools and techniques such as eDNA metabarcoding. Campbell Grant et al. (2019) emphasizes the need for targeted research approaches in applied conservation plans. We have presented a thorough discussion on the promises and pitfalls of eDNA metabarcoding of ephemeral pond amphibians as it stands today, which will be useful to managers and researchers looking for ways to implement this form of biomonitoring. As threats towards amphibian populations increase and funding availability for applied research and conservation decreases, we emphasize the importance of presenting specific details of both what works and what does not work in amphibian biomonitoring. Conclusion We strongly recommend future studies involving metabarcoding surveys with amphibians A) use multiple loci/multiplexing of primers for more taxonomic coverage, and B) accompany metabarcoding efforts with physical surveys. The 12S locus can still be a valuable genetic region in conjunction with other genetic loci that better account for the taxa the 12S primers are biased against or unable to distinguish to species level. Surveys that prioritize a multi-locus approach and a multi-survey-type approach will be better at detecting community composition. Future studies should focus on reducing off-target amplification, primer multiplexing, and more targeted, taxon-specific approaches. Amphibian biomonitoring efforts are of crucial importance as we enter the next mass extinction event and continue to see a decline in global amphibian populations (Luedtke et al. 2023). Providing researchers and managers with ready-made surveying tools and protocols will accelerate conservation initiatives. Declarations Acknowledgements We would like to sincerely thank Madison Layer, Matthew Simmons, Adam Miles, Anna Favalon, Hank Hardin, Jack Mayo, Josie Griffith, Naiya Sims, Andrew Jackson, Jerica Eaton, and Marly Askren for helping with field collections and lab work. This study was funded by the WKU Graduate School, The Kentucky Society of Natural History, and Sigma Xi Grants in Aid of Research. Funding: This study was funded by the Western Kentucky University Graduate School, The Kentucky Society of Natural History, and Sigma Xi Grants in Aid of Research (Grant ID: G20230315-5245). Competing Interests: The authors declare no conflict of interest. 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14:18:44","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":119195,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7190508/v1/ac25df6aa46a1d0eecaf1c93.html"},{"id":94433000,"identity":"0102e078-99fc-419d-994e-0bd10b2495f8","added_by":"auto","created_at":"2025-10-27 14:18:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":427137,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the Green River Preserve sites, including the main property (left) and Lawler Bend (right). Located in Hart County, Kentucky\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7190508/v1/415f71357d8991ca2f1219a3.png"},{"id":94433050,"identity":"18f2d53c-e7f5-44df-89fd-29e56c06dae3","added_by":"auto","created_at":"2025-10-27 14:18:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":475881,"visible":true,"origin":"","legend":"\u003cp\u003eASV relative sequence abundance by location and collection date. Note that Ambystoma and Anaxyrus are genus-level identifications only, meaning that there is likely higher variation within those categories and higher diversity overall\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7190508/v1/3fbf369015d1da7d14c2981a.png"},{"id":94433480,"identity":"44e87944-b53a-4141-99c3-0c8c5bf5bea8","added_by":"auto","created_at":"2025-10-27 14:18:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":461760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCirclize\u003c/em\u003e plot of community composition across visual encounter survey data (VES) in blue vs. metabarcoding data (eDNA) in pink, with overlapping species (eDNA \u0026amp; VES) in purple. \u003cem\u003eAnaxyrus \u003c/em\u003eand \u003cem\u003eAmbystoma \u003c/em\u003ecould not be identified to species level in the metabarcoding dataset and were therefore constrained to genus level. \u003cem\u003eCirclize\u003c/em\u003e was developed by Gu et al. (2014)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7190508/v1/c86076e8602289324c95009f.png"},{"id":94433763,"identity":"13b7cd06-2d86-428f-9a88-d5adf436f6b1","added_by":"auto","created_at":"2025-10-27 14:19:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":518725,"visible":true,"origin":"","legend":"\u003cp\u003eBeta‐diversity ordinations of amphibian MB communities using Bray–Curtis distances. (A) PCoA by Site, with polygons representing the convex hull of points for each site. Axis labels indicate the percentage of total variance explained by each axis. (B) NMDS by Site, plotted similarly with polygons enclosing each site’s samples. (C) PCoA by Date, with polygons for date groups. (D) NMDS by Date. The spread or overlap of polygons highlights how sites or time points differ (or overlap) in their amphibian community composition based on the ASV data\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7190508/v1/c0d66c551042f1643645252e.png"},{"id":94432927,"identity":"a261f09b-3826-4fd2-be3b-0bf785e6a839","added_by":"auto","created_at":"2025-10-27 14:18:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":82084,"visible":true,"origin":"","legend":"\u003cp\u003eRelative sequence abundance within the five mock communities, each of which was diluted in a stepwise fashion. Bars split within a genus represent two or more ASVs per genus, which represent within-species variation\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7190508/v1/bc4274f53848d62a7316b82e.png"},{"id":97136119,"identity":"b41b79ba-b268-4b4d-b05c-ca8a476a1e63","added_by":"auto","created_at":"2025-12-01 09:55:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2725925,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7190508/v1/b49506e9-5003-4007-b60a-3a4607c88b86.pdf"},{"id":94433331,"identity":"816817ac-6729-4898-a334-c81339805ba7","added_by":"auto","created_at":"2025-10-27 14:18:43","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1000821,"visible":true,"origin":"","legend":"","description":"","filename":"StraskoJohnsonGRPMetabarcodingSuppMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7190508/v1/6399ca6fda8947491ec661d2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating amphibian community composition in the southeastern U.S. using eDNA metabarcoding","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, amphibian populations experience elevated risk of declines or extinction, therefore efficient and accurate surveying methods are of great importance to informing conservation efforts (Collins 2010, Henle et al. 2025, McCallum 2007). With 40.7% of amphibian species considered threatened, region-specific monitoring is needed to reduce local extinction probability (Luedtke et al. 2023). The southeastern U.S. is a particularly species-rich yet understudied region for amphibians that is often considered a \u0026ldquo;biodiversity hotspot\u0026rdquo; (Graham et al. 2010; Walls 2014). With such an abundance of species diversity, the need for efficient population monitoring increases, as does the complexity of the task. This study will compare the effectiveness of a traditional monitoring strategy with one based on environmental DNA (eDNA) at the Western Kentucky University (WKU) Green River Preserve (GRP), located in Hart County, Kentucky. Our goal is to support amphibian population monitoring efforts globally through the demonstration and optimization of metabarcoding protocols for the species found in the southeastern U.S.\u003c/p\u003e\u003cp\u003eA major challenge in amphibian population monitoring efforts is the temporal variation in breeding phenology and the concentration of adults at breeding sites (Bergstrom 2010; Johnson et al. 2016; Greenberg et al. 2017). Over the course of a year, amphibian assemblages at breeding sites will vary (Griffiths 1997), requiring repeated and persistent monitoring of sites to gain a complete picture of site occupancy. Variation in life history strategies and the unpredictability of the timing of activities such as migration, breeding and metamorphosis create challenges for cost-effective traditional monitoring protocols.\u003c/p\u003e\u003cp\u003eEphemeral pools boast species-rich assemblages when conditions are favorable and are the preferred habitat of many pond-breeding amphibian species (Drayer and Richter 2016). Temporary pools offer protection to species otherwise predated by fish due to short pond hydroperiods and frequent, often annual, drying events (Baber and Babbitt 2003; Kloskowski et al. 2020). Drying periods reduce or eliminate the occurrence of fish and therefore support greater amphibian species diversity, provided hydroperiod length is sufficient for completion of the aquatic larval period of pond-breeding frogs and salamanders.\u003c/p\u003e\u003cp\u003eHigh levels of amphibian biodiversity within the southeastern US makes it difficult to develop simple, efficient monitoring strategies that are appropriate for all species. The advent of environmental DNA (eDNA) metabarcoding allows for the simultaneous detection of many related species, relying on the discrimination of species using reference databases and phylogenetic inference (Hoffmann et al. 2016; Ficetola et al. 2019; Sawaya et al. 2019). The 12S \u0026lsquo;Batr01\u0026rsquo; assay (Valentini et al. (2016) was originally developed for the metabarcoding of all Batrachia (a clade of amphibians that includes frogs and salamanders, but not caecilians nor related extinct lineages), and we chose this assay after \u003cem\u003ein vitro\u003c/em\u003e testing revealed that it reliably amplifies 30 species of Southeastern US amphibians. Batr01 has been utilized to detect amphibians from a variety of countries such as Norway (Osman et al. 2022), Brazil (Sasso et al. 2017), and China (Li et al. 2022). A recent study in the southeastern US also used this assay to characterize the diet of \u003cem\u003eSus scrofa\u003c/em\u003e (wild pigs), which revealed 12 species of amphibians (Canright et al. 2023).\u003c/p\u003e\u003cp\u003eThe objectives of this study are to 1) use visual encounter surveys and metabarcoding to investigate the amphibian assemblage within Western Kentucky University\u0026rsquo;s Green River Preserve (GRP), 2) determine if metabarcoding techniques using a preexisting 12S primer set are useful for the detection of southeastern U.S. amphibians, and 3) compare assemblage composition between metabarcoding and traditional surveying methods and across a temporal sampling strategy. We predicted that more amphibian species will be identified using metabarcoding than with visual encounter surveys, but the sensitivity to temporal shifts in assemblages will be reduced for the metabarcoding strategy compared to the visual encounter surveys.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eField Observations\u003c/strong\u003e\u003cp\u003eThe GRP comprises nearly 1600 acres of land ranging from rivers, uplands and bottomlands, barrens, caves, and more(Meier 2019) and is owned and managed by the Western Kentucky University. In this study, eight study sites spanned two areas of the GRP (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Longitudinal data sampling at these sites may reveal differences in amphibian species diversity over time.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eVisual encounter surveys were performed for eggs, larvae, and adults, with dip-netting and seining at five time periods spaced evenly throughout the year. Adult amphibians were identified and recorded in the field, along with frog vocalizations for a positive presence at that sampling location and time period. When identification in the field was not possible for eggs or larvae, representative specimens were collected and brought back to the laboratory for further investigation and identification (IACUC 22\u0026thinsp;\u0026minus;\u0026thinsp;09, KY educational collecting permit SC2311101).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEnvironmental Sampling\u003c/strong\u003e\u003cp\u003eEach site was sampled every three months from May 2022 through May 2023. All field equipment was washed with 10% bleach three times and then rinsed with deionized water prior to use. Negative controls were taken alongside all field samples for collection, transportation, storage, filtration, extraction, amplification, cleaning, pooling, and sequencing (\u0026ldquo;Field Negatives\u0026rdquo;). At each site, 2 L of pond water were collected and transported back to the lab to be filtered. The filtration procedure collects cells and free-floating DNA from all organisms present at the site for DNA purification. Samples were stored at 4\u0026deg;C and filtered within a week (Pilliod et al. 2014). Pond water was vacuum-filtered through a 1.0 \u0026micro;m Millipore\u0026reg; glass-fiber filters with a 4.7 cm diameter (Sigma-Aldrich, Burlington, MA, USA) in a PCR-free room. One liter of water was passed through filters for each site, with some sites requiring the use of multiple filters due to clogging with suspended debris. Each filter was then cut into 30\u0026ndash;40 pieces, where half was stored in 95% ethanol as a backup and the other half was immediately placed into Qiagen buffer ATL and Proteinase K to begin the DNA purification procedure, following the Qiagen DNEasy Blood \u0026amp; Tissue Kit (Qiagen, Germantown, MD, USA) protocol with one exception. Due to continued absorption of reagents by the filters, we quadrupled the amount of buffer ATL and Proteinase K, maintaining manufacturer ratios but ensuring there was enough liquid to extract in the proceeding steps. DNA concentration was measured using a Qubit fluorometer (Thermo Fisher Scientific, Waltham, MA). Filters with the highest DNA concentration were chosen for the sites that required more than one filter per time period/pond combination.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDNA Library Preparation\u003c/strong\u003e\u003cp\u003eLaboratory equipment was sterilized using either a 10% bleach solution or RNAse Away (Thermo Scientific\u0026trade; 7002) prior to use. DNA samples were purified to remove possible inhibitors using a Zymo inhibition removal kit (Zymo Research, Irvine, CA) prior to amplification. DNA concentrations were measured post-inhibition removal using a Qubit fluorometer (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Genomic DNA extracted from the filters was used in polymerase chain reaction (PCR) to enrich the sample for amphibian DNA. We targeted a portion of the 12S gene using Batr01 PCR primers that anneal to amphibian DNA as well as DNA from other vertebrates (Table S2; Valentini et al. 2016). A two-step PCR process was followed to combine all necessary components for amplification, barcoding, and sequencing (Glenn et al. 2019). Reaction conditions are available in Table S3 and components in Table S4.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eDNA was also isolated directly from tissue samples of 30 species of interest using the same methodology as utilized in the eDNA library for use as positive controls and for building a reference database to later compare eDNA sequences for taxonomic identification. From the positive control DNA samples, an equimolar mock community of six taxa (three salamanders, three frogs) was generated alongside the eDNA products to evaluate detection probabilities and as a positive control. Five mock community samples were created using serial dilutions, described in Table S5. The DNA dilution samples of the mock community library were prepared and standardized using the same approach as the eDNA library, as outlined in tables S3 and S4.\u003c/p\u003e\u003cp\u003ePost-PCR 1, each sample was purified using Sera-Mag SpeedBeads (Cytiva Life Sciences, Marlborough, MA) to eliminate residual primer and reaction artifacts. Both \u0026ldquo;right\u0026rdquo; and \u0026ldquo;left-hand\u0026rdquo; clean-ups were performed to remove large and small library components, respectively, starting with a 0.5x bead concentration (relative to sample) to remove larger fragments, followed by a 1.2x concentration clean-up to remove smaller fragments. Following bead clean-up, PCR 2 anneals sequencing primers followed by sample-specific indices (\u0026ldquo;barcodes\u0026rdquo;) and flow cell handles within a short-cycle PCR ligation (description of adapters previously outlined in Table S2). PCR 2 reaction conditions are described in Table S6 and components given in Table S7. Six identical reactions were run, the final products were checked via gel electrophoresis on a 1.5% agarose gel and then pooled and purified again with Sera-Mag SpeedBeads. DNA was quantified using a Qubit assay and visually checked via gel electrophoresis on a 1.5% agarose gel.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSequencing and Bioinformatics\u003c/strong\u003e\u003cp\u003eNext-generation sequencing of DNA libraries was completed using Illumina NovaSeq X Plus 10B PE150 (300 Cycles) flow cell at the Oklahoma Medical Research Foundation (OMRF) Clinical Genomics Center. Sequencing utilized one lane and approximately 10% of the reads were allotted to this library. A 20% PhiX spike was added to increase clustering propensity.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eUpon receiving the sequence data back from OMRF Clinical Genomics Center, samples were demultiplexed using \u0026lsquo;Cutadapt\u0026rsquo; version 4.9 (Martin 2010). Illumina adapters and locus-specific primers were also trimmed using \u0026lsquo;Cutadapt\u0026rsquo;. The \u0026lsquo;dada2\u0026rsquo; pipeline (Callahan et al. 2016) was utilized for filtering and quality control, denoising and removing chimeras from sequences, and ultimately generating amplicon sequence variants (ASVs). Taxonomic information was assigned to the ASVs by downloading a local version of the NCBI nucleotide database (Sayers et al. 2022) and using \u0026lsquo;blastn\u0026rsquo; within the \u0026lsquo;blast+\u0026rsquo; suite (Camacho et al. 2009). ASVs found in negative controls were programmatically removed from the entirety of the resulting dataset to ensure no contamination. Relative abundance was calculated in the R package \u003cem\u003ephyloseq\u003c/em\u003e (McMurdie and Holmes 2013) on the cleaned dataset. A Kruskal-Wallis (KW) test was run on the resulting ASV read counts by date to determine if read abundance varied significantly over time.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCalculating diversity\u003c/em\u003e: The data were henceforth split into two categories: metabarcoding data (\u0026ldquo;MB\u0026rdquo;), and visual encounter surveys (\u0026ldquo;VES\u0026rdquo;). Metadata and ASVs were merged in R, which was also used throughout the rest of the analysis (R Core Team 2021). Relative abundance values were calculated and then compared by genus across the whole dataset and by relative abundance of species by site and collection date. To ensure comparability across samples with varying sequencing depth, we converted raw counts to relative abundances using the transform_sample_counts() function in \u003cem\u003ephyloseq.\u003c/em\u003e\u0026nbsp;After removing contaminants (by subtracting the read counts observed in negative controls) and combining any technical replicates, we repeated this normalization step on the resulting positive samples.\u003c/p\u003e\u003cp\u003eAlpha diversity metrics including the Shannon and Simpson diversity indices were calculated using the R package \u003cem\u003ephyloseq\u003c/em\u003e (function \u0026lsquo;estimate_richness\u0026rsquo;, measures = \u0026ldquo;shannon\u0026rdquo; and \u0026ldquo;simpson\u0026rdquo;; McMurdie and Holmes 2013). Each dataset was evaluated for differences in richness and species composition across sampling location \u0026lsquo;Site\u0026rsquo; (spatial factor) and sampling period \u0026lsquo;Time\u0026rsquo; (temporal factor) and compared by data set \u0026lsquo;Survey Type\u0026rsquo; (method factor). \u0026lsquo;Site\u0026rsquo; is defined as the eight sampling locations: Cabin Pond (CA), Grove Pond (GR), Knob Pond (KN), Long Pond (LO), Luccio\u0026rsquo;s Pond (LU), Twin Pond (TW), Vinegar Ridge (VR), and Yellow Flower Pond (YE). \u0026lsquo;Time\u0026rsquo; includes the five sampling periods separated as independent factors. \u0026lsquo;Survey Type\u0026rsquo; indicates whether the dataset falls under the MB or VES sampling method category within the comparative analyses. A KW test was run on the resulting diversity data to test for significant differences between sites, dates, and methods (MB vs. VES). An additional Analysis of Variance (ANOVA) test for parametric data was run in parallel on all diversity data for direct comparison.\u003c/p\u003e\u003cp\u003eBray-Curtis distances were calculated using the \u003cem\u003evegan\u003c/em\u003e package in R, version 2.6-8 (function vegdist, method = \"bray\"; Oksanen et al. 2024). On binary data, Bray\u0026ndash;Curtis effectively acts as the Sorensen\u0026ndash;Dice coefficient, allowing an approach for presence/absence. We conducted Permutational Multivariate Analysis of Variance (\u003cem\u003ePERMANOVA\u003c/em\u003e; \u0026ldquo;adonis2\u0026rdquo; in \u003cem\u003evegan\u003c/em\u003e) to test how amphibian community composition varied by \u0026lsquo;Site\u0026rsquo;, \u0026lsquo;Date\u0026rsquo;, and \u0026lsquo;Survey Type\u0026rsquo;.\u003c/p\u003e\u003cp\u003eThe matrix and metadata were subset to VES and MB samples, respectively, and generated Bray\u0026ndash;Curtis distances for each. We then ran PERMANOVA models testing (a) \u0026lsquo;Site\u0026rsquo;, (b) \u0026lsquo;Date\u0026rsquo;, and (c) \u0026lsquo;Site\u0026thinsp;+\u0026thinsp;Date\u0026rsquo;. We also used the full presence/absence matrix to assess whether Survey Type (VES vs. MB), Site, and Date jointly explained community variation. We specified a model using \u0026ldquo;adonis2\u0026rdquo; to test whether Bray\u0026ndash;Curtis dissimilarity in community composition (response variable) could be explained by Survey_Type\u0026thinsp;+\u0026thinsp;Site\u0026thinsp;+\u0026thinsp;Date and used by = \"margin\" to get marginal effects (R\u0026sup2;, \u003cem\u003eF\u003c/em\u003e, and \u003cem\u003ep\u003c/em\u003e-values) for each factor. We performed Principal Coordinates Analysis (PCoA) (cmdscale, \u003cem\u003estats\u003c/em\u003e package, base R) and Non-metric Multidimensional Scaling (NMDS; metaMDS, \u003cem\u003evegan\u003c/em\u003e package) on the Bray\u0026ndash;Curtis distance matrices to visualize patterns in community composition.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMock Community\u003c/strong\u003e\u003cp\u003eThe mock community was utilized as a positive control, and evaluation included direct comparisons across the six included species via A) a visual plot of amplification by species via relative abundance of sequences amplified, and B) a line graph of raw counts examining log-scaled dilution factors compared to the raw counts to determine variation in sequencing reads as the dilution factor decreased.\u003c/p\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eVisual Encounter Survey Data\u003c/strong\u003e\u003cp\u003eThroughout the sampling period, 14 species of amphibians were encountered via visual encounter surveys, including eight species of frogs and six species of salamanders (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Differences in assemblages were observed across seasons in accordance with breeding phenology for species richness (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) and presence/absence of sequences (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). No differences were observed in ponds with short hydroperiods that dried during the study versus ponds with longer hydroperiods that did not dry during our sampling period.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList of ponds and their associated characteristics, including general Green River Preserve location, ephemeral versus perennial, number of hydroperiods observed, and total number of species found across all five sampling periods. Note that \u0026ldquo;permanence\u0026rdquo; is relative to time, rainfall, and other climactic factors \u0026ndash; any of these ponds could be ephemeral on a given year. Factors such as visual depth, hydroperiods observed, fish presence, etc. were utilized to determine baseline differences in our examination of permanence of ponds.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePond Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoordinates\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEphemeral or perennial\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHydroperiods observed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e# of species found\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLong Pond\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLawler Bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.244722,\u003c/p\u003e\u003cp\u003e-85.934444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePerennial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrove Pond\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLawler Bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.24750,\u003c/p\u003e\u003cp\u003e-85.935278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEphemeral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTwin Pond\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLawler Bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.250833,\u003c/p\u003e\u003cp\u003e-85.936667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEphemeral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCabin Pond\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLawler Bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.244167,\u003c/p\u003e\u003cp\u003e-85.938333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePerennial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYellow Flower Pond\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLawler Bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.24750,\u003c/p\u003e\u003cp\u003e-85.943611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEphemeral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnob Pond\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGRP Main Property\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.240833,\u003c/p\u003e\u003cp\u003e-85.985000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePerennial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVinegar Ridge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGRP Main Property\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.233889,\u003c/p\u003e\u003cp\u003e-86.003611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEphemeral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLuccio\u0026rsquo;s Pond\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGRP Main Property\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.234722,\u003c/p\u003e\u003cp\u003e-86.002222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePerennial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVisual encounter survey results (\u0026ldquo;X\u0026rdquo;) compared to metabarcoding results (pink squares) encompassing total presence/absence data per site over the course of the year sampling period. Note: Metabarcoding samples were only able to decipher Ambystoma and Anaxyrus to genus level, therefore the \u0026ldquo;AmOp\u0026rdquo; column was utilized as the presence of some sort of \u003cem\u003eAmbystoma\u003c/em\u003e species for metabarcoding, while \u0026ldquo;AnFo\u0026rdquo; is a generalized stand-in for \u003cem\u003eAnaxyrus.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"17\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAcCr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAmJe\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAmMa\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAmOp\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAnFo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDrCr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eEuCi\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHeSc\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNoVi\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eLiCa\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eLiCl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eLiSp\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003eLiSy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003ePlDo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003ePlGl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c17\"\u003e\u003cp\u003ePsCr\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrove\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTwin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCabin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c1\"\u003e\u003cp\u003eYellow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnob\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLuccio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVinegar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eMetabarcoding Data\u003c/em\u003e: Next-generation sequencing resulted in 170,312,183 paired-end raw reads. All reads\u0026thinsp;\u0026gt;\u0026thinsp;15% error rate threshold in cutadapt were discarded. Post-processing in cutadapt, 107,878 unique paired reads (0.03%) were retained by dada2. The dada2 algorithm combines similar reads, meaning that while only a small percentage of total reads were retained within \u0026ldquo;unique\u0026rdquo; reads, this does not necessarily reflect the \u0026ldquo;tossing out\u0026rdquo; of a large portion of sequences but simply the merging of repeat reads. The output of the dada2 pipeline revealed 785 unique ASV groupings, of which 32 (4.1%) were retained as relevant to our study (Table S8). Total read counts did not differ significantly by date (KW: χ\u0026sup2; = 5.6, df\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;=\u0026thinsp;0.23). The blastn search primarily identified non-target taxa such as algae and bacteria. Additional viruses, plants, invertebrates, and other vertebrate species were also identified. For the purposes of this study, only relevant amphibian ASVs were utilized downstream. ASV and visual encounter survey data were paired with appropriate metadata for the proceeding analyses (Table S9). Relative abundance values (Table S10) were compared by genus across the whole dataset (Fig. S2) and relative abundance of species by site and collection date (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNotably, the taxonomic classification using 12S sequence data was unable to distinguish certain taxa to species level, including members of \u003cem\u003eAmbystoma\u003c/em\u003e and \u003cem\u003eAnaxyrus\u003c/em\u003e. \u003cem\u003eAmbystoma\u003c/em\u003e species did not have a robust reference database available for the 12S locus, and the sequences generated were not sufficient for species-level classification. \u003cem\u003eAnaxyrus fowleri\u003c/em\u003e and \u003cem\u003eAnaxyrus americanus\u003c/em\u003e are identical at the 12S locus, deeming it impossible to distinguish between the two species using only this locus.\u003c/p\u003e\u003cp\u003eTwenty-nine amphibian species and one fish were successfully amplified for reference purposes. Resulting sequences were aligned and trimmed in Geneious v. R11.1.5 (Table S11).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDiversity Analyses\u003c/strong\u003e\u003cp\u003eOverall, more taxa were found in the visual encounter dataset (14) than the metabarcoding dataset (11). This difference is likely influenced by lack of assignment to species-level for some taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe full dataset did not meet normality assumptions (Shapiro-Wilk test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for both Shannon and Simpson indices), therefore we ran nonparametric KW tests on the entire dataset to assess site-level alpha diversity. For the sake of comparison, we also examined each site as a factor in an ANOVA framework. Under the ANOVA factor-based model, the within‐site residuals passed normality and homogeneity tests in most instances but with some exceptions; therefore, we ran parametric ANOVAs as well.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNo significant differences in Shannon or Simpson diversity were found between survey methods (VES vs. MB), by either non-parametric (KW) or parametric (ANOVA) tests (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Table S12, Fig. S3). A similar non-significant result was obtained with a two-factor model (\u0026lsquo;Survey Method\u0026rsquo; + \u0026lsquo;Site\u0026rsquo;), in which neither factor was significant (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eWhen examining diversity across sites, the KW tests also showed no significant differences in either Shannon or Simpson diversity (Fig. S4; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In the MB dataset, KW indicated no differences in alpha diversity by site or date (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The one-way ANOVAs (Shannon or Simpson) likewise found no significant effects of site or date (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Both methods indicate that amphibian diversity detected by metabarcoding did not vary significantly across sites or sampling periods.\u003c/p\u003e\u003cp\u003eThe KW tests demonstrated a significant difference in diversity across sampling dates for the VES data but not among sites. The ANOVA results corroborate the significant temporal differences for Shannon (F\u003csub\u003e4,30\u003c/sub\u003e=14.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and for Simpson (F\u003csub\u003e4,30\u003c/sub\u003e=14.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) diversity indices, whereas site was non-significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.9). Thus, both parametric and non‐parametric approaches indicate that amphibian diversity via visual encounter surveys is strongly influenced by sampling period (likely reflecting seasonal changes in breeding activity) but does not differ greatly among ponds at our study site.\u003c/p\u003e\u003cp\u003eWe tested for differences across sites alone amongst the entire MB and VES dataset using KW and one-way ANOVA, and again no significant variation was found (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), confirming that alpha diversity does not vary substantially among these sampling locations under either method. Although the normality assumption was violated in several of these ANOVA models, the non-significant outcomes align with the nonparametric results, suggesting that both approaches, VES and MB, produce comparable alpha diversity estimates, and no \u0026lsquo;Site\u0026rsquo; effect is detectable.\u003c/p\u003e\u003cp\u003eA \u0026rsquo;betadisper\u0026rsquo; test confirmed reasonable homogeneity in centroids that allowed for further analysis (Fig. S5). PERMANOVA indicated that for VES data, \u0026lsquo;Time\u0026rsquo; and \u0026lsquo;Site\u0026thinsp;+\u0026thinsp;Time\u0026rsquo; contributed significant variation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 and 0.003 respectively; Table S13), whereas for MB data no factors were significant (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.39; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). While \u0026lsquo;Site\u0026rsquo; does not significantly contribute to variation, \u0026lsquo;Time\u0026rsquo; and \u0026lsquo;Site\u0026thinsp;+\u0026thinsp;Time\u0026rsquo; do significantly contribute to variance, indicating the potential for time and site\u0026thinsp;+\u0026thinsp;time to impact community composition within the VES dataset (Fig. S6). The results indicate no significant differences observed in community composition over \u0026lsquo;Site,\u0026rsquo; \u0026lsquo;Time,\u0026rsquo; or \u0026lsquo;Site\u0026thinsp;+\u0026thinsp;Time\u0026rsquo; within the MB dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBray-Curtis based PERMANOVA analysis of time using adonis2 in R, analyzing differences in community composition amongst the amphibian metabarcoding presence/absence data over time with 999 permutations. Site explained approximately 17.4% of the variation (\u003cem\u003epseudo-F\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.693, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.174, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.79). Time explained approximately 14.7% of the variation (\u003cem\u003epseudo-F\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.19, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.147, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.39). Site\u0026thinsp;+\u0026thinsp;Time explained approximately 33.7% of the variation (\u003cem\u003epseudo-F\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.88, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.337, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.63). The results herein suggest no significant differences observed in community composition over site, time, or site\u0026thinsp;+\u0026thinsp;time within the presence/absence MB dataset.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSum of Squares\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSite Analysis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.787\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTime Analysis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.386\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSite\u0026thinsp;+\u0026thinsp;Time Analysis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSite\u0026thinsp;+\u0026thinsp;Date\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.634\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe model-based marginal-effects PERMANOVA used three predictors: \u0026lsquo;Survey Type\u0026rsquo; (to distinguish methodology), \u0026lsquo;Site\u0026rsquo; (spatial factor), and \u0026lsquo;Date\u0026rsquo; (temporal factor) tested the marginal effects of each factor, effectively treating each as if it were the last entered in the model (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This approach reports partial R\u0026sup2; (the proportion of variance explained) and permutation-based \u003cem\u003ep\u003c/em\u003e-values for each factor independently. A total of 999 permutations were used. \u0026lsquo;Survey Type\u0026rsquo; had the largest effect on community composition, explaining 27.1% of the variance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). \u0026lsquo;Date\u0026rsquo; also showed a statistically significant though smaller effect (10.4% explained; p\u0026thinsp;=\u0026thinsp;0.002). \u0026lsquo;Site\u0026rsquo; accounted for 9.5% of the total variation but was not significant at the 5% level (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.129). The model\u0026rsquo;s residual (=\u0026thinsp;51.4%) is attributed to other unmeasured factors or natural variability. The results indicate that of the variance present, \u0026lsquo;Survey Type\u0026rsquo; had the largest influence, followed by \u0026lsquo;Date\u0026rsquo; and then \u0026lsquo;Site.\u0026rsquo; The residual values indicate a large amount of unexplained variance.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMarginal effects of each factor on amphibian community composition (Bray\u0026ndash;Curtis), from PERMANOVA (adonis2 in R) with 999 permutations. Survey_Type (Visual vs. Metabarcoding) explains about 27% of the variance (p\u0026thinsp;=\u0026thinsp;0.001), Date about 10% (p\u0026thinsp;=\u0026thinsp;0.002), while Site was not significant (p\u0026thinsp;=\u0026thinsp;0.129).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSum of Squares\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePr(\u0026gt;\u0026thinsp;F)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurvey_Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.9354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.27065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.3851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.7300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.09487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.8973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.10404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.6319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.3715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.51391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.2355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMock Community\u003c/strong\u003e\u003cp\u003eThe mock community revealed differences in amplification rates across the six species (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). \u003cem\u003eDryophytes\u003c/em\u003e, \u003cem\u003eLithobates\u003c/em\u003e, and \u003cem\u003eAmbystoma\u003c/em\u003e revealed the highest proportions of amplification, despite all taxa starting in equal concentrations. \u003cem\u003eAnaxyrus\u003c/em\u003e was fourth proportionally, while \u003cem\u003eHemidactylium\u003c/em\u003e was phased out of amplification by the fifth dilution factor (0.001). \u003cem\u003eNotophthalmus\u003c/em\u003e amplified successfully in all replicates, but at a much lower proportion of the whole. Proportional amplifications were relatively consistent by taxa across dilution factors. Fig. S7 provides a log-scaled line graph of reductions in raw read count by dilution factors, where I noted that while overall read counts decrease with decreasing dilution factors, there was an unusual spike at 0.100 (MC3).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe evaluated variation in species richness over time within and between amphibian breeding ponds using two different survey strategies. We expected that the species richness estimates would vary based on patterns of breeding phenology of local species. When examining the differences in species richness between ponds over time, a record of habitat usage and preferences can be inferred and applied to conservation strategies. Our data will inform conservation biologists and land managers as to the utility of metabarcoding as a population monitoring strategy. Longitudinal sampling of the sites will provide improved understanding of how local amphibians utilize ephemeral pond habitat as part of their natural history.\u003c/p\u003e\u003cp\u003eAdditionally, we investigated the utility of eDNA/metabarcoding as a survey strategy for southeastern US amphibians. This study answers questions regarding the validity and importance of longitudinal sampling for accurately revealing community composition at a site, which will help emphasize the wider implications of metabarcoding for amphibian populations. We found no significant differences in richness or overall diversity by sampling period within either the MB or VES datasets. However, the longitudinal data does provide accurate assessments of presence associated with our understanding of breeding phenology. For example, in the late winter/spring months (February and May), \u003cem\u003eAmbystoma spp.\u003c/em\u003e samples are abundant in all datasets, and absent in most summer and fall samples (August and November). Other eDNA studies have emphasized variation in sequence counts as a signal of temporal variation. For example, Johnson et al. (2025) noted strong variation in read abundance \u0026ndash; samples taken during the spring breeding season had higher read abundance, indicating seasonal influence on metabarcoding data. Contrarily, we did not have a higher read abundance in any of our sampling periods. One factor that may influence read abundance is wetland type. Lentic and lotic systems may retain eDNA at differing rates. Shogren et al. (2018) indicated that biofilm-rich, high-velocity systems degrade DNA at a higher rate than nonflowing mesocosms. This study also found that short DNA fragments remained detectable for prolonged periods. Our study targeted a short 12S fragment (\u0026lt;\u0026thinsp;60 bp) in nonflowing lentic ponds; therefore, prolonged DNA retention past the point of species-specific seasonality makes sense in the context of our study.\u003c/p\u003e\u003cp\u003eThe results from the alpha diversity analysis suggest that there are no significant differences in diversity between the methods or among the sites. Consistent with expectations, sampling date (season) affected species diversity in visual surveys, reflecting breeding phenology, whereas metabarcoding data showed less temporal variation, indicating that DNA sequences may persist longer than the actual dates of occupation by adults or larvae. Increasing sampling periods and extending this type of research for longer periods may allow for finer-scale details to emerge about DNA persistence relative to pond-breeding amphibians.\u003c/p\u003e\u003cp\u003eThe lack of species-level resolution for \u003cem\u003eAmbystoma\u003c/em\u003e and \u003cem\u003eAnaxyrus\u003c/em\u003e contributed to lower species richness estimates for the metabarcoding data. Our efforts to sequence a single representative of each species proved insufficient to assign species with confidence, due to high levels of intraspecific genetic diversity at the 12S locus for \u003cem\u003eAmbystoma\u003c/em\u003e. Future studies with more robust reference sequence data may not encounter this issue. Contrastingly, we also demonstrated that some species, such as \u003cem\u003eA. fowleri\u003c/em\u003e and \u003cem\u003eA. americanus\u003c/em\u003e, are not able to be distinguished using the 12S region flanked by the Batr01 primers due to amplification of an invariant sequence.\u003c/p\u003e\u003cp\u003eThe time of sampling alone was not statistically significant in any dataset. Within the MB dataset, there is very little variance partitioning by \u0026lsquo;Time\u0026rsquo; (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), explaining only 14.7% of the total variation (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.147). A model incorporating both \u0026lsquo;Site and Time\u0026rsquo; explained 33.7% of the variation but was also non-significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting weak combined spatial and temporal structuring. An NMDS of Bray\u0026ndash;Curtis distances similarly showed partial clustering by site, though date-based grouping was less pronounced The metabarcoding strategy was unable to effectively predict change in assemblage by time, which may indicate that sequences are retained in the water column for longer than the source organisms\u0026rsquo; occupancy. However, our analyses investigated changes in alpha and beta diversity over time, meaning that the ponds utilized in this study may just host a wide range of diversity year-round. Studies examining the retention of specific species\u0026rsquo; DNA sequences over time may better elucidate the nature of occupancy and sequence retention from a seasonal perspective.\u003c/p\u003e\u003cp\u003eThe results of the multivariate factorial model suggest that the choice of survey method (Visual vs. Metabarcoding) explains the most variance of the detected amphibian community composition. Within this framework, \u0026lsquo;Date\u0026rsquo; (sampling month/year) does include a significant amount of partitioned variance, aligning with the expectation that amphibian communities may vary seasonally. In contrast, \u0026lsquo;Site\u0026rsquo; did not emerge as a significant factor in this dataset, implying that across the sites sampled, variability in amphibian composition is better explained by methodology and temporal changes rather than purely spatial differences among sites. The near proximity of all sites makes it likely that species composition would be relatively similar throughout our ponds, therefore this is not a surprising result. At only eight sampling points, this structure of partitioning may indicate that there could be different results in a larger system or with more sampling effort.\u003c/p\u003e\u003cp\u003eOverall, these data highlight the importance of survey methods in shaping detected amphibian assemblages and a time (Date) factor, whereas site-level effects appear less pronounced here. With this in mind, we support the notion that visual surveillance is still of high importance in amphibian conservation work. We also emphasize that the choice of metabarcoding primers and genetic locus is massively influential in survey findings; had more robust \u003cem\u003eAmbystoma\u003c/em\u003e references been available, our results may have been dramatically different. The small number of species present (less than 20 total species across all sites) should be noted, and differences in alpha and beta diversity measures are likely heavily impacted by even a few missing species. The results of this study should be interpreted as a cautious tale of only utilizing one survey method for amphibian species composition estimation. While the physical surveys did result in more findings, species were found by the metabarcoding data that were not found by visual or auditory encounters. Using a combination of both methods, we generated a more complete picture of the amphibian assemblage present at the Green River Preserve.\u003c/p\u003e\u003cp\u003eAmphibian eDNA biomonitoring may require a more intricate approach. Single species eDNA assays have proven useful in a variety of applications for amphibian detection, including within \u003cem\u003eAmbystoma\u003c/em\u003e species (Brammell et al. 2023, Strasko et al. 2023). Harper et al. (2018) evaluated the efficacy of single-species assays versus metabarcoding in the detection of the great crested newt (\u003cem\u003eTriturus cristatus)\u003c/em\u003e and found that under certain thresholds, targeted qPCR performed better at detecting \u003cem\u003eT. cristatus\u003c/em\u003e. The authors recommended considering metabarcoding for community biodiversity assessments and targeted assays for single-species detection. In our study sites, less common species such as \u003cem\u003eH. scutatum\u003c/em\u003e and \u003cem\u003eE. cirrigera\u003c/em\u003e may have better detection using a single-species assay.\u003c/p\u003e\u003cp\u003eA meaningful takeaway from this study is the importance of primer and locus choice. Other metabarcoding studies reported greater success and confidence using multiple loci (Richardson et al. 2015, Weitemeyer et al. 2021, Wizenberg et al. 2023). The Batr01 assay was designed to target amphibian species in European countries originally, meaning that its use in this study was experimental. Only 4.1% of the ASVs generated were target amphibian taxa, meaning that the vast majority of reads generated by Batr01 were off-target taxa. The co-amplification of an abundance of nontarget species may have reduced our ability to effectively target for amphibians. To our knowledge, only one other study has used this assay in the southeastern U.S. (Canright et al. 2023). Canright et al. (2023) used Batr01 to examine the diet composition of \u003cem\u003eSus scrofa\u003c/em\u003e, an invasive wild pig species in the United States. Like our study, Canright et al. (2023) also reported a low total amount of vertebrate reads (8,763 reads) using the Batr01 primers, but it is not clear whether they may have also amplified off-target taxa. It is possible the low number of amphibian sequence reads in the Canright et al. (2023) data reflects the low abundance of amphibians within the diet of \u003cem\u003eSus scrofa\u003c/em\u003e, but based on our data, it is also possible that significant non-target sequences amplified by the Batr01 primers depressed the abundance of other taxa, including amphibians. Other studies in the U.S. have used this assay in different regions, including Texas (Collins 2022) and Michigan (Ruppert et al. 2025). Notably, the Michigan study also reported a reduced ability to identify to species level using the 12S marker. The Michigan study had similar findings in regard to visual versus metabarcoding surveys yielding similar results in number of species found, further indicating that eDNA metabarcoding may be more suitable as a complement rather than a substitution. The Texas study also amplified a number of non-target taxa, specifically referencing fish and amphibian taxa.\u003c/p\u003e\u003cp\u003eThe mock community demonstrated amplification bias by species, further casting doubt on the utility of this marker. While the mock community did amplify 5/6 taxa at a dilution scale of 0.001 (MC5), this was also under a scenario with little to no competition with non-target taxa. The potential issue of amplification bias is demonstrated by \u003cem\u003eN. viridescens\u003c/em\u003e, which was by far the most encountered species in most of our ponds in visual encounter surveys but is under-represented in the metabarcoding results. This was likely influenced by swamping of target DNA by off-target taxa. The large volume of unusable data generated using this primer set, primarily from algae and bacteria, eliminated a large portion of our sequencing data, decreasing the efficiency of the metabarcoding strategy. Primers that are more targeted to a specific taxonomic group or utilize more conserved genetic regions may be the way forward in utilizing a metabarcoding approach for amphibian biomonitoring.\u003c/p\u003e\u003cp\u003eInadequate amphibian biomonitoring in hotspots like the Southeast U.S. leave us with a lack of historical records and comparative data to utilize in conservation efforts. Walls (2014) indicated a severe lack of anuran and caudate monitoring in the Southeast, with only 73.8 and 33.3%, respectively, of species known to occur in the Southeast receiving some form of continuous monitoring. Walls also emphasizes the need to take into account temporal and spatial scales in amphibian monitoring. Improving the scope of amphibian conservation requires rigorous testing of monitoring tools and techniques such as eDNA metabarcoding. Campbell Grant et al. (2019) emphasizes the need for targeted research approaches in applied conservation plans. We have presented a thorough discussion on the promises and pitfalls of eDNA metabarcoding of ephemeral pond amphibians as it stands today, which will be useful to managers and researchers looking for ways to implement this form of biomonitoring. As threats towards amphibian populations increase and funding availability for applied research and conservation decreases, we emphasize the importance of presenting specific details of both what works and what does not work in amphibian biomonitoring.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe strongly recommend future studies involving metabarcoding surveys with amphibians A) use multiple loci/multiplexing of primers for more taxonomic coverage, and B) accompany metabarcoding efforts with physical surveys. The 12S locus can still be a valuable genetic region in conjunction with other genetic loci that better account for the taxa the 12S primers are biased against or unable to distinguish to species level. Surveys that prioritize a multi-locus approach and a multi-survey-type approach will be better at detecting community composition. Future studies should focus on reducing off-target amplification, primer multiplexing, and more targeted, taxon-specific approaches. Amphibian biomonitoring efforts are of crucial importance as we enter the next mass extinction event and continue to see a decline in global amphibian populations (Luedtke et al. 2023). Providing researchers and managers with ready-made surveying tools and protocols will accelerate conservation initiatives.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to sincerely thank Madison Layer, Matthew Simmons, Adam Miles, Anna Favalon, Hank Hardin, Jack Mayo, Josie Griffith, Naiya Sims, Andrew Jackson, Jerica Eaton, and Marly Askren for helping with field collections and lab work. This study was funded by the WKU Graduate School, The Kentucky Society of Natural History, and Sigma Xi Grants in Aid of Research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding:\u003c/em\u003e This study was funded by the Western Kentucky University Graduate School, The Kentucky Society of Natural History, and Sigma Xi Grants in Aid of Research (Grant ID: G20230315-5245).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting Interests:\u003c/em\u003e The authors declare no conflict of interest. The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor Contributions:\u003c/em\u003e E.K.S. and J.R.J. contributed to the study conception and design. Material preparation, data collection and analysis were performed by E.K.S. and J.R.J. The first draft of the manuscript was written by E.K.S. and both E.K.S. and J.R.J. commented on previous versions of the manuscript. E.K.S. and J.R.J. read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData Availability:\u003c/em\u003e Raw sequence reads are available via the NCBI Short Read Archive (SRA): PRJNA1277257.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics Approval:\u003c/em\u003e All research was conducted in accordance with proper permitting: IACUC 22-09, KY permit SC2311101.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBaber MJ, Babbitt KJ (2003) The relative impacts of native and introduced predatory fish on a temporary wetland tadpole assemblage. \u003cem\u003eOecologia\u003c/em\u003e 136:289\u0026ndash;295. https://doi.org/10.1007/s00442-003-1251-2\u003cbr\u003eBergstrom TM (2010) A natural history study of \u003cem\u003eBufo a. americanus\u003c/em\u003e, the eastern American toad, and the phenology of spring breeders in southwest West Virginia. 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We explored the tools required for amphibian metabarcoding within the southeast US and implemented a comparative study that examined the utility of metabarcoding in lieu of traditional visual encounter surveys and the ability of the metabarcoding strategy to detect temporal variation in the composition of pond-breeding amphibian assemblages. We tested a previously developed mitochondrial ribosomal RNA gene assay (12S rRNA) for the ability to detect and discriminate among southeastern amphibian species. We successfully detected 11 amphibian taxa either to the genus or species level at our study sites with the 12S assay, indicating the potential to adequately describe amphibian assemblages across the southeastern U.S. However, a lack of a comprehensive 12S gene sequence reference database for all southeastern amphibians and the inability of the 12S assay to distinguish between certain taxa at the species level indicates caution should be used in implementing this strategy for important conservation and management purposes. In our study, the combination of both visual encounter surveys and metabarcoding yielded the highest levels of detection. Our results suggest that the development of a reliable amphibian metabarcoding strategy will improve our ability to efficiently and rapidly assess amphibian assemblages at ephemeral pools compared to traditional methods.\u003c/p\u003e","manuscriptTitle":"Evaluating amphibian community composition in the southeastern U.S. using eDNA metabarcoding","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 08:53:21","doi":"10.21203/rs.3.rs-7190508/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a1b7b126-8abf-482c-980b-416a1c820591","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-26T01:53:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 08:53:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7190508","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7190508","identity":"rs-7190508","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}