Influence of short-term water sample storage on environmental RNA metabarcoding-based biodiversity assessment

Environmental RNA (eRNA) metabarcoding has emerged as a promising tool in various fields. During field sampling and sample processing, collected water samples often require short-term storage prior to analysis. However, the effects of different storage conditions on eRNA metabarcoding-based biodiversity recovery remain largely unexplored. In this study, we evaluated the impacts of various storage temperatures (4°C, 10°C, 20°C, and ambient temperature) and durations (1-72 h) on fish biodiversity recovery from eRNA samples collected from coastal ecosystems. Our findings revealed that taxon richness declined significantly with increasing storage time and temperature, with storage time having a notably greater impact than temperature. While high-abundance taxa were generally more resilient, they still exhibited significant losses in detectability over time. Low-abundance taxa experienced a faster and more pronounced decline, with many detected only transiently. Both storage time and temperature, as well as taxon abundance, significantly affected detection rates, with taxon abundance having the strongest effect, followed by storage time and storage temperature. In addition to affecting taxon detection, short-term storage significantly impacted community structure and reduced the reproducibility of replicates. These results provide crucial empirical evidence for developing standardized handling procedures in aquatic eRNA research and contribute to the broader methodological framework for reliable biodiversity monitoring using eRNA. Given the limited effectiveness of conventional temperature control, the immediate addition of preservation agents into collected water or the use of passive sampling techniques, such as RNA-absorbent materials, may help mitigate eRNA degradation to ensure accurate eRNA-based biodiversity assessment. Influence of short-term water sample storage on environmental RNA metabarcoding-based biodiversity assessment Fuwen Wang 1,2, Wei Xiong 1,2*, Xuena Huang 1, Aibin Zhan 1,2 * 1 Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing 100085, China 2 University of Chinese Academy of Sciences, Chinese Academy of Sciences, 19A Yuquan Road, Shijingshan District, Beijing 100049, China *Corresponding authors: Prof. Aibin Zhan; Email: [email protected] or [email protected]; Phone & Fax: (+86)-10-6284-9882. Or Associate Prof. Wei Xiong; Email: [email protected]; Phone (+86)-10-6284-1639; Fax: (+86)-10-6284-9882. Running title: Impact of water storage on eRNA biodiversity

Environmental RNA (eRNA) metabarcoding has emerged as a promising tool in various fields. During field sampling and sample processing, collected water samples often require short-term storage prior to analysis. However, the effects of different storage conditions on eRNA metabarcoding-based biodiversity recovery remain largely unexplored. In this study, we evaluated the impacts of various storage temperatures (4°C, 10°C, 20°C, and ambient temperature) and durations (1-72 h) on fish biodiversity recovery from eRNA samples collected from coastal ecosystems. Our findings revealed that taxon richness declined significantly with increasing storage time and temperature, with storage time having a notably greater impact than temperature. While high-abundance taxa were generally more resilient, they still exhibited significant losses in detectability over time. Low-abundance taxa experienced a faster and more pronounced decline, with many detected only transiently. Both storage time and temperature, as well as taxon abundance, significantly affected detection rates, with taxon abundance having the strongest effect, followed by storage time and storage temperature. In addition to affecting taxon detection, short-term storage significantly impacted community structure and reduced the reproducibility of replicates. These results provide crucial empirical evidence for developing standardized handling procedures in aquatic eRNA research and contribute to the broader methodological framework for reliable biodiversity monitoring using eRNA. Given the limited effectiveness of conventional temperature control, the immediate addition of preservation agents into collected water or the use of passive sampling techniques, such as RNA-absorbent materials, may help mitigate eRNA degradation to ensure accurate eRNA-based biodiversity assessment.

biodiversity, degradation, eRNA, metabarcoding, storage duration, storage temperature. 1. Introduction The use of environmental nucleic acids (eNAs), including environmental DNA (eDNA) and environmental RNA (eRNA), has emerged as a powerful tool for biodiversity assessment, offering comprehensive insights into both the theoretical understanding of ecosystem dynamics and practical applications in environmental management ( Beng et al., 2020; Carraro et al., 2020; Xiong et al., 2020 ). Since eDNA can persist in the environment for a relatively long time, it reflects both the current and past presence of organisms ( Angeles et al., 2023; Sakata et al., 2020 ). In contrast, eRNA degrades relatively rapidly, providing a more recent snapshot of biological activities in ecosystems ( Jo et al., 2023; Scriver et al., 2023; Wang et al., 2025b). For example, a quantitative analysis demonstrates that eRNA generally degrades much faster than eDNA, with eRNA remaining detectable for ~57 hours after the removal of organisms, whereas eDNA can persist for ~143 days (Kagzi et al., 2022). The rapid degradation of eRNA minimizes the detection of legacy genetic materials from organisms no longer present (i.e., natural eDNA contamination due to extended persistence), reducing the likelihood of false positives in biodiversity assessments (Littlefair et al., 2022; Wang et al., 2025b). Meanwhile, this property makes eRNA particularly effective in distinguishing real signals of living organisms from eDNA contamination resulting from human activities, such as the discharge of treated effluent from wastewater treatment plants in urban and coastal areas (i.e., artificial eDNA contamination due to human activities, Xiong et al., 2024; Wang et al., 2025b; Zhan, 2025). The relatively rapid degradation of eRNA enhances its capacity to capture real-time biological changes, making it a valuable tool for monitoring the immediate responses of ecosystems to environmental stressors. However, the rapid degradation of eRNA poses substantial technical challenges for its collection, storage, and transportation, increasing the risk of false-negative results (Wang et al., 2025b; Zhang et al., 2024). To preserve eRNA integrity, samples are typically stored at low temperatures, even at ultra temperatures such as in liquid nitrogen or dry ice (Jo, 2023; Zhan, 2025). Yet, transporting such storage solutions to remote or inaccessible field sites, especially those unreachable by vehicles, remains a major logistical obstacle. To minimize degradation, immediate filtration in the field is often recommended to rapidly capture eRNA. However, this approach becomes impractical in certain environments, such as oceans or fast-flowing rivers, where wave turbulence and strong currents hinder onsite filtration ( Benedicenti et al., 2024). Furthermore, during extensive field expeditions covering broad geographic areas, the large number of samples collected causes additional strain on the feasibility of immediate processing. Meanwhile, water filtration can be time-consuming, especially for samples collected from high turbidity environments (Goldberg et al., 2016). This extended processing time may further accelerate eRNA degradation. Therefore, if eRNA in water samples can remain stable for a short period before transportation, it would greatly alleviate fieldwork constraints and improve processing efficiency (Jo et al., 2023). Moreover, understanding water preservation time can serve as a valuable reference for filtration timing in laboratory settings, especially when handling large batches of samples. This is particularly crucial in turbid waters, where filtration delays are common and the risk of eRNA degradation is inherently higher (Goldberg et al., 2016). Studies on eRNA persistence durations offer testable time windows to determine whether short-term preservation is feasible for both sampling and sample processing such as filtering. Despite the general consensus on the rapid degradation of eRNA, studies have reported varying persistence durations. For example, eRNA remained detectable for approximately 57 hours (Kagzi et al., 2022), while in marine ecosystems, it persisted for up to 13 hours after organism removal (Wood et al., 2020). These varying persistence durations are likely influenced by a range of environmental factors, including water quality, temperature, salinity, and microbial activity (Scriver et al., 2023). The interplay between these factors can significantly affect the rate of eRNA degradation and, consequently, its detectability over time (Wang et al., 2025b). In addition to the need for further investigation into the factors influencing eRNA persistence in different environments, these results suggest that short-term storage of collected water samples may be feasible. This would facilitate sample transport and allow for extended filtration times during fieldwork, enabling more flexible sampling schedules without the constraint of immediate preservation. Consequently, researchers can obtain reliable eRNA data while minimizing the risk of sample degradation. Using eRNA samples collected from coastal ecosystems as a case study, here we aim to evaluate the effects of various short-term storage conditions on eRNA-based fish biodiversity recovery. Specifically, we examined the impact of different storage times and temperatures on the diversity and composition of both abundant and rare fish communities, as detected through eRNA metabarcoding. The results will provide valuable insights into best practices for eRNA sampling and storage, enhancing the reliability of eRNA-based biodiversity assessments under challenging field conditions and sample processing. By understanding how storage conditions affect fish community integrity revealed by eRNA metabarcoding, the findings in this study will contribute to the standardization and optimization of eRNA methods for environmental monitoring and conservation. 2. Materials and Methods 2.1 Field Sample collection and treatment design Sampling was conducted in the Pearl River Estuary (113.602943 °E, 22.174221 °N), a region routinely monitored by a national surveillance program due to intense anthropogenic disturbances from nearby megacities such as Guangzhou and Shenzhen. Seawater was collected using sterile bottles and divided into 102 replicates of 1 L each. Six of these 1 L samples were immediately filtered in the field (0 h) to serve as control samples for subsequent storage time and temperature experiments. To minimize potential degradation of eRNA during filtration, the filtration of control samples was finished within 5 mins. Immediately afterward, the obtained membranes were transferred to liquid nitrogen for preservation. The remaining 96 samples were evenly divided into four temperature treatment groups: 4 °C, 10 °C, 20 °C, and ambient air temperature (airT, ~28 °C). For each temperature condition, samples were taken at four time points: 1 h, 8 h, 24 h, and 72 h. At each time point, six of these 1 L replicates were randomly selected and filtered, resulting in 24 eRNA samples per temperature treatment group. The desired storage temperatures were maintained using onboard refrigerators with adjustable temperature settings. All samples were filtered through mixed cellulose ester (MCE) membranes (0.45 μm pore size, Millipore, USA) using field-based equipment, then transferred into 2 mL sterile, enzyme-free cryovials (BioShark, China) and stored in liquid nitrogen until eRNA extraction. For negative controls, 1 L of sterile distilled water was processed in parallel as a blank sample using the same procedures in the field (van der Loos et al., 2021). 2.2 eRNA extraction, DNA digestion and reverse transcription Total eRNA was extracted from each filtered membrane using the TRIzol Kit (Thermo Fisher Scientific, USA). To remove residual DNA, the extracted eRNA was treated with the TURBO DNA-free Kit (Thermo Fisher Scientific, USA). RNA concentration and purity were assessed using a NanoDrop UV spectrophotometer (Thermo Scientific Inc., USA). All eRNA samples, including the negative control, were reverse transcribed into cDNA using the PrimeScript II 1 st Strand cDNA Synthesis Kit (TaKaRa Bio Inc., Shiga, Japan). Following the protocol of Wang et al. (2025a), the best-performing random primer N 5 was used to ensure high and consistent reverse transcription efficiency. Each 10 μL reaction mix contained 1 μL of 50 μM random primer N 5, 1 μL of 10 mM dNTPs, and 300 ng of eRNA. The mixture was incubated at 65 °C for 5 minutes and then rapidly chilled on ice. Subsequently, 4 μL of 5 × PrimeScript II Buffer, 0.5 μL of RNase inhibitor (40 U/μL), 1 μL of PrimeScript II RTase (200 U/μL), and 4.5 μL of RNase-free water were added to bring the final volume to 20 μL. Reverse transcription was carried out under the following conditions: 30 °C for 10 minutes, 42 °C for 45 minutes, and 95 °C for 5 minutes for enzyme inactivation. To reduce stochastic variation, especially in the detection of rare taxa, eight technical replicates were performed for each reverse transcription reaction (Stahlberg et al., 2004; Zhan et al., 2013, 2014b). The eRNA concentration was measured prior to cDNA synthesis to ensure equal input across all samples. 2.3 PCR amplification, library preparation, and sequencing For each sample replicate, cDNA was used for PCR amplification targeting a fragment of the fish 12S rRNA gene using the MiFish-U primer pair (Miya et al., 2015). Each 25 μL PCR reaction contained 2.5 μL of 10× PCR Buffer (Takara, Japan), 2 μL of dNTPs (2.5 mM each), 1.5 μL each of forward and reverse primers (10 μM), 2 U of Ex Taq DNA polymerase (Takara), 1 μL of cDNA template, and 16.5 μL of ddH₂O. To reduce stochastic amplification effects, particularly those influencing rare taxa detection, eight parallel PCR reactions were performed per replicate sample (Zhan et al., 2013, 2014b). The thermal cycling conditions were as follows: initial denaturation at 95 °C for 5 minutes; 35 cycles of denaturation at 95 °C for 30 seconds, annealing at 58 °C for 30 seconds, and extension at 72 °C for 30 seconds; followed by a final extension at 72 °C for 10 minutes. The eight PCR products from each sample were pooled and purified using the SanPrep Column PCR Product Purification Kit (Sangon Biotech, China). Purified amplicons were then subjected to high-throughput sequencing on the Illumina NovaSeq 6000 platform with paired-end 150 bp reads (2 × 150 bp). 2.4 Bioinformatics analysis Bioinformatics analysis was performed following the methods of Xiong and Zhan (2018) with minor modifications. In general, raw sequence data were processed using a Galaxy-based pipeline platform (https://dmap.denglab.org.cn/) as described by Feng et al. (2017). Primer sequences were removed using the “trim primer” function, allowing up to 1.5 mismatches and a maximum starting position of 1. The resulting primer-trimmed reads were merged using the “flash” function, with a minimum required overlap of 10 bp. Subsequently, the “btrim” function was used to filter out low-quality reads, discarding sequences with an average quality score below 20 or those containing ambiguous bases (i.e., ‘N’). High-quality sequences were then clustered into zero-radius operational taxonomic units (ZOTUs) using the “Unoise for FASTA to generate ZOTUs” function, with a minimum abundance threshold of 8. This process produced a taxa distribution table and representative sequences for each ZOTU. Taxonomic assignment of each ZOTU was performed by aligning the representative sequences against the MitoFish database (version 3.95; https://mitofish.aori.u-tokyo.ac.jp/) using Seed version 2.1.2 (Větrovský et al., 2018; Zhu et al., 2023). The resulting taxonomic annotations were manually reviewed and validated following the procedures described by Zhang et al. (2022) and Wang et al. (2025b). To eliminate potential false-positive eRNA signals from freshwater species (Wang et al., 2005a, b), habitat information for all detected fish species was obtained from the FishBase database (https://www.fishbase.se/search.php). Species identified as freshwater were excluded from subsequent analyses. 2.5 Evaluation of eRNA degradation in different storage conditions To evaluate the effects of storage conditions on eRNA-based fish biodiversity recovery, we compared the number of observed fish taxa across four temperature treatments (4 °C, 10 °C, 20 °C, and ambient air temperature [airT, ~28 °C]) at each time point (1 h, 8 h, 24 h, and 72 h). Using the control group (immediate filtering at 0h), temporal changes in community composition under each temperature condition were visualized, and sample reproducibility was assessed over storage duration. Given that stochastic effects (e.g., random sampling error) may influence the detection of rare taxa (Zhan et al., 2013, 2014b), fish taxa were categorized into high- and low-abundance groups to compare the impacts of storage conditions on these two subcommunities. Following the criteria of Wang et al. (2025b), taxa with a relative abundance ≥ 0.1% in the control samples were defined as high-abundance taxa, while the remaining taxa, including those detected in both control and treated samples, were classified as low-abundance taxa. Samples in which all replicates yielded fewer than 1,000 sequence reads were excluded from further analysis, as such low read counts were considered indicative of PCR failure, resulting from high levels of eRNA degradation under the tested storage conditions. For the entire fish community, changes in the number of observed taxa under different storage conditions were visualized using box plots and statistically evaluated with Mann–Whitney U tests. A logarithmic regression was fitted to the change in taxa number over storage time for each temperature treatment group using the geom_smooth function in R (Wickham, 2016). The relative influence of storage time and temperature on taxa richness was further assessed using a two-way ANOVA (Stats package, R Core Team, 2023). To evaluate changes in community composition between control and treated samples, non-metric multidimensional scaling (NMDS) and permutational multivariate analysis of variance (PERMANOVA) were performed based on the Bray-Curtis dissimilarity (vegan package, Oksanen et al., 2025). The Bray-Curtis dissimilarity between control and treated samples was also plotted to visualize the degree of compositional shifts under each storage condition. Biodiversity reproducibility under each storage condition was assessed by calculating the average dissimilarity among replicates. To quantify the contribution of individual taxa to overall community variation, a similarity percentage (SIMPER) analysis was performed (vegan package, Oksanen et al., 2025). Taxa with higher contribution scores were considered more sensitive to storage-induced degradation. For both high-abundance and low-abundance taxa, observed richness and community composition changes across storage conditions were analyzed using the same methods applied to the whole community. Additionally, Sankey diagrams were used to visualize changes in detection rates of these taxa under different storage conditions. Venn diagrams were also constructed to illustrate the presence or absence of each taxon in control and time-treated samples for each temperature group. To further explore how taxon abundance influences detection across different storage conditions, high-abundance taxa were subdivided into two groups with relative abundance ≥ 10% and 0.1% ≤ abundance < 10%, respectively. Similarly, low-abundance taxa were classified into two groups with relative abundance of 0.01% ≤ abundance < 0.1% and abundance < 0.01%, respectively. For each temperature treatment group, detection rates of taxa within these four abundance categories were plotted over storage time to illustrate patterns of degradation across abundance levels. Finally, to identify the major factors influencing taxon detection rates within the community, a three-way ANOVA was conducted, incorporating storage time, storage temperature, and taxon abundance class as explanatory variables. The analysis was performed using the anova function from the stats package in R (R Core Team, 2023). All visualizations were generated using the ggplot2 package (Wickham, 2016) in R. To account for multiple comparisons and control the false discovery rate, the Benjamini-Hochberg correction was applied to all p -values from multiple tests. 3. Results 3.1 Fish biodiversity A total of 30,636,886 raw reads were obtained from one control and 16 treated samples subjected to different storage conditions. After quality filtering, 28,624,804 high-quality reads were retained and clustered into 525 ZOTUs. Following the removal of low-quality or unsuccessful samples due to high levels of eRNA degradation, eight treated samples and one control sample were retained for further analysis. From these retained samples, a total of 75 fish taxa were identified, including two at the family level, 23 at the genus level, and 50 at the species level (Table S1). The negative control using pure water did not yield any detectable fish sequences. Among the retained taxa, seven were classified as high-abundance taxa, while the remaining 68 were classified as low-abundance. Notably, three Sardinella species were the most frequently detected and collectively accounted for 90.28% of the total reads. The genus Ambassis, comprising three species, accounted for 2.32% of the total reads, followed by the genus Planiliza, which contributed 1.21% of the reads (Table S1). 3.2 Biodiversity variation across different storage conditions As expected, the highest number of fish taxa was recovered from the control sample (Fig. 1, Table S2). In the temperature-treated groups, the number of observed fish taxa declined significantly with increasing storage time, following a logarithmic decay pattern (Fig. 1A, p < 0.01). Among the treatments, samples stored at ambient air temperature (airT) exhibited the most rapid decline in detected taxa (decay coefficient = -11.28), while those stored at 4 °C showed the slowest reduction (coefficient = –5.78). This finding clearly illustrates that even short-term storage at any temperatures can accelerate eRNA degradation and substantially affect biodiversity detection. When comparing storage temperatures at each specific time point, no significant differences in the number of observed taxa were found among the 4 °C, 10 °C, and 20 °C treatments after 1 hour of storage. Only the air temperature group showed a significant reduction in taxon recovery at this time point (Table S3). At 8h, the differences between the 4 °C and 10 °C treatments, as well as between 10 °C and 20 °C, were not statistically significant; however, a significant difference was detected between the 4 °C and 20 °C treatments (Table S2). A two-way ANOVA further confirmed that both storage temperature and storage time had significant effects on the number of fish taxa recovered. However, storage time showed a considerably stronger influence, as reflected by a higher F -value ( F = 258.94 for time vs. F = 14.69 for temperature; Fig. 1A, Table S3). Moreover, a significant interaction between storage temperature and time was observed ( F = 3.52, p < 0.001), indicating that the combined effects of these two factors play a critical role in eRNA degradation and the efficiency of biodiversity recovery. When considering community composition, the control sample exhibited a significant difference compared to all treated samples ( p 0.05, Fig. S1 & Table S4). Over time, the variation in community composition between control and treated samples significantly increased, reaching a high level of dissimilarity (Fig. 1B, Table S5, dissimilarity > 80%). When comparing the samples stored for 1 h, the variation in community composition between the control and treated samples increased with higher storage temperatures (Fig. 1B, Table S5). After 8 hours of storage, the variation in community composition between control and treated samples remained consistently high (with average dissimilarity > 60%), and no significant differences were observed between samples stored at different temperatures. Compared to the control sample, reproducibility in community composition significantly decreased in all treated groups, as indicated by the elevated dissimilarity levels among replicates in treated samples (Fig. 1B, Table S6). Specifically, in the 4 °C group, reproducibility significantly declined after 8 h, whereas in the 10 °C, 20 °C, and air temperature groups, the decline was evident as early as 1 h (Fig. 1B, Table S6). By 8 h of storage, reproducibility remained consistently low (with average dissimilarity > 60%), and no significant differences were observed between samples stored at different temperatures (Fig. 1B, Table S6). The SIMPER analysis revealed that the most abundant species, Sardinella sp. 1, was the primary taxon affected by storage temperature and time (Fig. 1C). Specifically, the relative abundance of this species significantly decreased with increasing storage temperature and time, contributing to substantial variation between control and treated samples (43.49%, Figs. 1C & S1, Table S4). Ambassis gymnocephalus, a high-abundance species, also showed a significant decrease in abundance with higher storage temperatures and longer durations, contributing an average of 14.29% to the community variation between control and treated samples. In contrast, Ariidae sp., a low-abundance species, contributed an average of 10.31% to the community variation. Interestingly, high storage temperatures and prolonged storage times did not affect the detection of certain low-abundance species, such as Ariidae sp. (Fig. S1). 3.3 Biodiversity variation across preservation conditions for high-abundance taxa Unexpectedly, the number of high-abundance taxa also followed a significantly declining trend, fitting a logarithmic function in all four temperature-treated groups (Fig. 2A). Additionally, Mann-Whitney U tests revealed that the number of high-abundance taxa recovered from each treated sample was significantly lower than that in the control sample, except for samples stored at 4 °C for 1 h (Table S7). Moreover, the variation in composition between control and treated groups was significant (Fig. 2B, Table S8) and increased over time (Figs. 2B & S2, Table S9), while reproducibility significantly decreased (Fig. S2, Table S10). Moreover, the detection rate of high-abundance taxa decreased over time in all four temperature-treated groups. For samples stored at 4 °C, the detection rate was 90.5% after 1 h, 61.9% after 8 h, and 47.6% after 24 h. In samples stored for 8 h at 10 °C and 20 °C, the detection rates dropped to 61.9% and 42.9%, respectively (Fig. 2C). Correspondingly, the detection rate of high-abundance taxa across all treated samples declined as their relative abundance decreased (Fig. 2C). While Sardinella sp.1, with a relative abundance of 87.4%, was detected in 100% of replicates across all samples, Planiliza haematocheilus, with a relative abundance of 1.2%, showed a lower detection rate of only 25.9% (Fig. 2C). 3.4 Biodiversity variation across preservation conditions for low-abundance taxa Similar to high-abundance taxa, the number of low-abundance taxa significantly declined compared to the control sample, following a logarithmic decay over time in all four temperature-treated groups. However, the decline rate was faster for low-abundance taxa, as the absolute coefficient values increased under the same temperature conditions (Figs. 2A & S3A, Table S11). Additionally, the composition of low-abundance taxa in treated samples significantly varied from the control samples (Fig. S3B, Table S12), with dissimilarity levels increasing over time (Fig. S3C, Table S13). Notably, reproducibility in both control and treated samples remained low and showed no significant variation between them (Fig. S3C, Table S14). The Venn networks for low-abundance taxa at each treated temperature showed that the control samples detected 48 low-abundance taxa. Among these, only five taxa were consistently recovered across all time points (1 h to 24 h) when samples were stored at 4 °C. The majority of taxa (34.5% at 4 °C, 51.9% at 10 °C, 51.8% at 20 °C, and 69.0% at airT) were transient, detected only once. Interestingly, even at 24 h of storage, there was still a possibility of recovering unique taxa that were not detected at other time points. For example, Collichthys lucidus was detected after samples were stored for 24 h at 4 °C, when the number of detected taxa was very low (Fig. 3A). Similar patterns were observed for samples stored at 10 °C and 20 °C, though only samples stored for 1 h and 8 h were good enough to be included in the analysis (Figs. 3B & 3C). 3.5 Variation in sensitivity to storage conditions across taxa with differing abundance We observed that the sub-group with a relative abundance ≥10% maintained a 100% detection frequency across all storage conditions. In contrast, the detection frequencies of the three lower-abundance groups declined significantly over time, following distinct trajectories depending on the storage temperature (Fig. 4). For example, under the 4 °C treatment, the detection frequency of taxa with relative abundance between 0.1% and 10% decreased from 100% in the control samples to 88.9% at 1 h, 55.6% at 8 h, and 38.9% at 24 h (Fig. 4A). A similar downward trend was observed for the two lower-abundance sub-groups, with detection frequencies declining progressively over time (Fig. 4A). These patterns were consistently observed across the other storage conditions (10 °C, 20 °C, and ambient temperature; Fig. 4B-D). Notably, the group with relative abundance <0.01% exhibited the most pronounced decline. In the 20 °C treatment group, its detection frequency dropped from 30.63% in the control to 3.15% after 8 h, representing an 89.72% reduction. In comparison, the detection frequencies of the ≥10%, 0.1-10%, and 0.01-0.1% groups decreased by 0%, 66.67%, and 66.68%, respectively (Fig. 4C). Similarly, under ambient temperature conditions, detection frequencies decreased by 0%, 41.67%, 53.33%, and 72.06% for the four abundance sub-groups in descending order, after just 1 h of storage (Fig. 4D). 3.6 Interaction effects of storage condition × taxon abundance ANOVA results showed that storage time, storage temperature, and taxon abundance all had significant effects on taxon detection rates ( p < 0.001; Table 1). Among these factors, taxon abundance had the strongest effect ( F = 203.46, p < 0.001), followed by storage time ( F = 101.67, p < 0.001), while storage temperature had the weakest effect ( F = 14.48, p < 0.001; Table 1). In addition, significant two-way interaction effects were observed between abundance and temperature ( F = 3.90, p < 0.001), abundance and time ( F = 19.03, p < 0.001), and temperature and time ( F = 5.76, p < 0.001). A significant three-way interaction among abundance, time, and temperature was also detected ( F = 1.65, p < 0.001; Table 1). 4. Discussion Dissimilar to eDNA which is relatively stable, the venerable nature of eRNA poses considerable challenges for field sampling and sample processing (Yates et al., 2021). In this study, we systematically evaluated the impact of short-term storage conditions for sampled water on biodiversity recovery using eRNA-based metabarcoding for fish communities in a coastal ecosystem. Our results demonstrate that eRNA degrades rapidly under all tested storage conditions, significantly reducing fish biodiversity detection (Fig. 1A, Table S3). Taxon richness for both high- and low-abundance taxa declined logarithmically over time, particularly at higher temperatures (Fig. 2A & S3A, Table S7 & S11). Community composition changed and the reproducibility among replicates declined with increasing storage time at all tested temperatures, even for high-abundance taxa (Fig. 2B & S2, Table S8 - S10). Among the factors tested, storage time had a greater effect than temperature on biodiversity recovery (Fig. 1A, Table S2). When further subdividing both high- and low-abundance taxa into abundance classes, we observed increased vulnerability of lower-abundance taxa to degradation across different storage conditions (Fig. 4). Interestingly, certain low-abundance species, such as Ariidae sp., remained detectable despite high storage temperatures and prolonged storage times (Fig. S1C). ANOVA results revealed that taxon detection rates were significantly influenced by taxon abundance, storage time, and storage temperature, with taxon abundance having the strongest effect, followed by storage time, and temperature showing the weakest impact. These findings offer essential empirical support for establishing standardized protocols in eRNA-based biodiversity studies. 4.1 Degradation of eRNA and its influence on biodiversity detection Our study underscores the high sensitivity of eRNA to short-term storage conditions, as evidenced by a pronounced, logarithmic decline in the number of fish taxa detected over time across all storage treatments. Degradation occurred more rapidly than anticipated, with significant losses observed as early as 1h post-collection, even when samples were stored at 4°C. Despite studies having shown that the persistence of eRNA can exceed >10 hours (Wood et al., 2020; Kagzi et al., 2022), these findings were based on single-species experiments under controlled conditions. In contrast, our community-level analysis in a natural coastal ecosystem highlights the greater variability and vulnerability of eRNA signatures in complex environmental samples, where differential degradation rates among taxa and interactions with environmental matrices likely accelerate signal loss (Du et al., 2024). This underscores the need for stringent and rapid sample processing protocols when using eRNA for biodiversity monitoring. Although previous studies have suggested that flash-freezing collected water samples may help prevent eRNA degradation (Jo, 2023), we did not test such methods (e.g., liquid nitrogen or dry ice), due to the prolonged thawing time required at 4°C for downstream filtering, which can contribute to substantial eRNA degradation. Although eRNA also degrades rapidly in the wild, its shedding and decay provide a unique advantage by reflecting recent biological activities (Yates et al., 2021). In contrast, the degradation that occurs during water storage post-collection, however, does not mirror the natural shedding and decay processes, and the rapid degradation can lead to large-scale false negatives as demonstrated in this study. Another often overlooked aspect is the degradation of eRNA during water filtration processes. Filtering water for eRNA collection typically takes time, often ~1 hour/sample from sample preparation to the final preservation of the filter. In many cases, the filering process takes longer and is not conducted at low temperatures (e.g., 4°C). As shown in this study, such durations can lead to significant eRNA degradation, resulting in biodiversity loss, changes in community structure, and decreased reproducibility among replicates (Fig. 1). Given the limited effectiveness of temperature control and the practical constraints on extending storage times, it may be more effective to add preservation solutions directly into collected water samples in the field. This approach could help to stabilize eRNA and prevent degradation before samples are processed in the laboratory (Jo, 2023). The selection of appropriate preservative types and their optimal concentrations remains an important area for further research, as different preservatives may have varying impacts on the integrity of eRNA and downstream analyses. Alternatively, passive sampling strategies, such as the use of RNA-absorbent materials, may help prevent degradation during water filtration, despite that further studies are needed to optimize both the RNA-absorbent materials and the associated collection parameters (Chen et al., 2024). 4.2. Differential sensitivity of high- vs. low-abundance taxa The degradation of eRNA not only reduced the overall number of detectable taxa but also disproportionately impacted taxa based on their relative abundance, with low-abundance taxa being the most vulnerable. In our study, rare taxa often became undetectable within just a few hours of degradation, while high-abundance taxa showed a more gradual decline in detectability. This differential sensitivity aligns with findings from eDNA research, where the detectability of low-abundance species is frequently compromised by factors such as dilution, degradation, and stochasticity during amplification (Bista et al., 2017; Holman et al., 2022; Zhan et al., 2014a). The phenomenon is largely attributed to the lower starting concentration of nucleic acids from rare taxa, which renders them more susceptible to loss during degradation or sample processing (Barnes & Turner, 2016). Our results contribute new evidence by showing that even taxa initially detected at high abundance are not immune to the effects of degradation (Fig. 2C). Over longer storage durations, signals from these dominant taxa weakened, which in turn altered the overall community composition and reduced similarity among replicate samples (Fig. 2A & S2, Table S10). This decay not only affects reproducibility but also introduces uncertainty into biodiversity assessments, particularly when comparing samples collected or processed under varying field conditions. Such variability can confound ecological interpretations and hinder efforts to monitor species richness, detect invasive taxa, or assess community shifts due to environmental stressors. These findings highlight the urgent need for standardized sampling protocols and rapid preservation strategies for eRNA. Approaches such as on-site filtration or use of RNA-preserving buffers could help mitigate degradation and improve the reliability of eRNA-based biomonitoring (Jo, 2023). Ultimately, careful attention to preservation and sample handling is essential to fully realize the potential of eRNA as a high-resolution, sensitive tool for environmental assessment, especially in ecosystems where low-abundance or cryptic species are of ecological or conservation importance. Interestingly, high storage temperatures and prolonged storage durations did not affect the detection of certain low-abundance species, such as Ariidae sp. This pattern suggests that their persistence may be driven by factors beyond eRNA integrity alone. One possible key factor is the primer-template affinity, which can significantly influence amplification efficiency (Hilário et al., 2023). When primers bind with high specificity and efficiency to target sequences, they can generate robust signals even from minimal or degraded RNA templates (Piñol et al., 2015; Elbrecht & Leese, 2015). Such strong primer binding may effectively lower the functional detection threshold for these taxa, allowing them to be consistently identified even when RNA degradation would typically reduce detectability. 4.3. Effects of storage on community composition and replicate reproducibility Beyond mere species richness, our results reveal that storage conditions significantly altered community composition. Bray-Curtis dissimilarities between control and treated samples increased markedly with storage duration, exceeding 80% in some cases. This pattern suggests not only loss of taxa but also skewed relative abundances, which could misrepresent ecological patterns and community structure. Notably, even when the total number of taxa remained relatively stable, the internal community structure often shifted, likely due to the faster degradation of low-abundance taxa. These findings align with previous studies demonstrating that storage conditions can significantly impact microbial community composition. For instance, Lauber et al. (2010) observed that soil samples stored at room temperature exhibited significantly lower alpha diversity and altered beta diversity compared to those stored at -20°C, indicating that higher temperatures can lead to changes in community structure. Similarly, Sales et al. (2019) demonstrated that storage methods significantly influenced eDNA-based community assessments in freshwater systems, with notable differences in taxonomic profiles depending on the preservation treatment used. These observations underscore that inappropriate or inconsistent storage conditions may obscure real biological signals and confound community-level interpretations. As such, comparisons across studies, or even across samples within a study, should be made with caution if sample handling or preservation conditions differ. In particular, shifts in community composition may not solely reflect ecological variability but could be an artifact of differential molecular decay, preservation efficacy, or amplification bias. We therefore emphasize the importance of employing standardized and well-validated preservation protocols, especially when community metrics such as beta diversity or compositional turnover are central to the research question. The effects of storage on replicate reproducibility are heavily influenced by stochastic processes, especially as degradation progresses. This randomness is particularly evident in the detection of low-abundance taxa, whose quantities are low and are therefore more susceptible to stochastic dropout (Shirazi et al., 2021; Zhan et al., 2014b). Consequently, replicate samples may differ significantly in which rare taxa are detected. This mirrors findings from eDNA research, where stochastic amplification effects cause inconsistent detection of rare taxa across PCR replicates (Ficetola et al., 2015; Xiong et al., 2016; Zhan & MacIsaac, 2015). Notably, as degradation advances, the remaining community becomes increasingly dominated by fragments from rare or partially degraded eRNAs, which enhances stochastic effects further. Thus, high levels of degradation not only reduce the absolute detectability of eRNA but also increase the proportion of rare and unstable sequences, amplifying random detection patterns and decreasing the overlap among replicates. 4.4. Interplay between taxon abundance and storage conditions on detection rates Our study clearly demonstrates that taxon detection rates are significantly influenced by a combination of taxon abundance, storage time, and storage temperature (Table 1). Among these, taxon abundance emerged as the most influential factor, followed by storage time, while storage temperature had the weakest, albeit still significant, impact. This pattern is consistent with the broader literature highlighting that detection rates in environmental nucleic acid (eNA) studies are closely tied to initial template concentration and taxon-specific shedding characteristics, which govern the production and persistence of eNAs in aquatic environments (Scriver et al., 2023). High-abundance taxa, particularly those with relative abundance ≥10%, maintained a 100% detection rate across all tested conditions, illustrating their robustness to eRNA degradation. This observation supports previous findings that abundant taxa are more readily detected due to higher starting concentrations, which provide resilience against enzymatic degradation and environmental loss (Scriver et al., 2023). In contrast, low-abundance taxa experienced a steep and rapid decline in detection rates over time and at higher storage temperatures (Fig. S3A). These results underscore the critical vulnerability of rare taxa to degradation-related signal loss, a phenomenon also emphasized by Scriver et al. (2023), who noted that RNA is particularly unstable due to its single-stranded nature and susceptibility to hydrolysis. The effect of storage time was pronounced, with a consistent logarithmic decay in taxon richness observed across all temperature treatments. This pattern reflects classical first-order degradation kinetics commonly applied to model eDNA/eRNA decay (Scriver et al., 2023) and highlights the importance of minimizing storage duration prior to analysis. Notably, even at the low storage temperature (such as 4 °C), storage resulted in a significant loss of biodiversity signals, especially for low-abundance taxa. This aligns with experimental observations summarized by Scriver et al. (2023), in which eRNA decay was found to accelerate under field-relevant thermal and microbial conditions (e.g., Jo et al., 2023; Wood et al., 2020). Although the temperature had a significant effect, its impact was relatively modest compared to abundance and time. This may be explained by the inherently rapid decay of eRNA, which reduces the relative influence of storage temperature beyond a certain threshold. Indeed, Scriver et al. (2023) note that while temperature is one of the most consistent abiotic predictors of eRNA degradation, primarily through increased enzymatic activity, it may play a secondary role when decay is already rapid due to molecular instability or microbial action. 4. Conclusions Our study systematically assessed the impact of short-term storage conditions for collected water samples on eRNA-based fish biodiversity detection, revealing clear patterns of degradation that significantly influenced both taxonomic richness and community composition. Our findings demonstrate that even short-term storage can substantially compromise biodiversity recovery from eRNA samples, with pronounced effects on both high- and low-abundance taxa. This study provides essential empirical evidence for establishing standardized handling procedures in aquatic eRNA studies and contributes to the broader methodological framework for reliable biodiversity monitoring using eRNA. Given the limited efficacy of conventional temperature control and the logistical challenges of minimizing storage times in field settings, an effective alternative may involve the immediate addition of preservation agents to water samples at the time of collection. This approach could help stabilize eRNA and mitigate degradation prior to laboratory processing. However, the choice of preservative and its optimal concentration deserved more investigations. Meanwhile, passive sampling techniques, such as the deployment of RNA-absorbent materials, offer another promising strategy to reduce degradation during in situ sample collection. Further work is required to optimize the design and deployment of such materials to enhance eRNA retention and consistency. Advancing these methodological strategies is essential for improving the sensitivity, reproducibility, and robustness of eRNA-based biodiversity monitoring and assessment. Author Contributions Conceptualization, A.Z.; methodology, F.W., W.X., X.H., A.Z.; formal analyses, F.W., W.X., X.H.; investigation, W.X., X.H., A.Z.; resources, W.X., X.H., A.Z.; writing – original draft, F.W., W.X., A.Z.; writing – review & editing, F.W., W.X., X.H., A.Z.; project administration, A.Z.; funding acquisition, W.X., X.H., A.Z.

This work was supported by the National Natural Science Foundation of China [grant number: 32471608] and Guiding Funds of Central Government for Supporting the Development of Local Science and Technology [grant number: 2024ZY0128]. Conflict of interest The authors declare no conflicts of interest. Data Availability All sequencing data in this paper will be permanently archived in NCBI GenBank after the paper is accepted for publication. ORCID Fuwen Wang: https://orcid.org/0000-0002-5811-7389 Wei Xiong: https://orcid.org/0000-0002-5645-1631 Xuena Huang: https://orcid.org/0000-0002-1517-7496 Aibin Zhan: https://orcid.org/0000-0003-1416-1238 Figure Captions Fig. 1 Performance of different eRNA storage conditions in a metabarcoding-based fish community study. A) Taxonomic richness significantly declined with prolonged storage durations (1 h, 8 h, 24 h, and 72 h) across different temperatures (4 °C, 10 °C, 20 °C, and ambient air temperature). B) Bray-Curtis dissimilarity between stored samples (blue violins) and the control group (0 h, immediate filtering) increased significantly with storage time, while reproducibility among replicates (red violins) significantly decreased. C) Impacts of storage conditions on individual taxa assessed using SIMPER (Similarity Percentage Analysis), based on each taxon’s contribution to community variation. Fig. 2 Performance of different eRNA storage conditions in detecting high-abundance taxa in assessing eRNA metabarcoding-based fish community. A) A significant decline in the number of detected taxa was observed with increasing storage duration (1 h, 8 h, 24 h, and 72 h) across temperatures (4 °C, 10 °C, 20 °C, and ambient air temperature). B) Non-metric multidimensional scaling (NMDS) analysis revealed significant compositional differences between the control and stored samples. C) Variation in detection rates of high-abundance taxa across replicates; taxa were ordered by their average relative abundance in the control sample. **: p < 0.01; ***: p < 0.001. Fig. 3 Variation in the detection of low-abundance fish taxa across different temperatures over storage time, illustrated using Venn networks. A-D) Results for each storage condition: 4 °C (A), 10 °C (B), 20 °C (C), and ambient air temperature (D). The diameter of each sample node is proportional to the number of detected fish taxa. Fig. 4 Changes in detection rates of fish taxa across different abundance classes over storage time under varying storage temperatures. A-D) Results for each temperature condition: 4 °C (A), 10 °C (B), 20 °C (C), and ambient air temperature (D).

Adams, C. I. M., Hepburn, C., Jeunen, G., Cross, H., Taylor, H. R., Gemmell, N. J., Bunce, M. and Knapp, M., 2023. Environmental DNA reflects common haplotypic variation. Environ. DNA 5 (5), 906–919. https://doi.org/10.1002/edn3.352 Angeles, I. B., Romero-Martínez, M. L., Cavaliere, M., Varrella, S., Francescangeli, F., Piredda, R., Mazzocchi, M. G., Montresor, M., Schirone, A., Delbono, I., Margiotta, F., Corinaldesi, C., Chiavarini, S., Montereali, M. R., Rimauro, J., Parrella, L., Musco, L., Dell’Anno, A., Tangherlini, M., Pawlowski, J. and Frontalini, F., 2023. Encapsulated in sediments: eDNA deciphers the ecosystem history of one of the most polluted European marine sites. Environ. Int. 172, 107738. https://doi.org/10.1016/j.envint.2023.107738 Benedicenti, O., Amundsen, M. M., Mohammad, S. N., Vrålstad, T., Strand, D. A., Weli, S. C., Patel, S. and Sindre, H., 2024. A refinement to eRNA and eDNA-based detection methods for reliable and cost-efficient screening of pathogens in Atlantic salmon aquaculture. PLoS One 19 (10), e0312337. https://doi.org/10.1371/journal.pone.0312337 Beng, K.C. and Corlett, R.T., 2020. Applications of environmental DNA (eDNA) in ecology and conservation: opportunities, challenges and prospects. Biodivers. Conserv. 29 (7), 2089-2121. https://doi.org/10.1007/s10531-020-01980-0 Bista, I., Carvalho, G.R., Walsh, K., Seymour, M., Hajibabaei, M., Lallias, D., Christmas, M. and Creer, S., 2017. Annual time-series analysis of aqueous eDNA reveals ecologically relevant dynamics of lake ecosystem biodiversity. Nat. Commun. 8 (1), 14087. https://doi.org/10.1038/ncomms14087 Carraro, L., Mächler, E., Wüthrich, R. and Altermatt, F., 2020. Environmental DNA allows upscaling spatial patterns of biodiversity in freshwater ecosystems. Nat. Commun. 11 (1). https://doi.org/10.1038/s41467-020-17337-8 Chen, X., Li, S., Zhao, J. and Yao, M., 2024. Passive eDNA sampling facilitates biodiversity monitoring and rare species detection. Environ. Int. 187, 108706. https://doi.org/10.1016/j.envint.2024.108706 Du, X., Xiong, W., Li, S. and Zhan, A., 2024. Conventional net tow versus environmental DNA for metabarcoding-based analysis of plankton-environment interactions in polluted aquatic ecosystems. Ecol. Indic. 158, 111356. https://doi.org/10.1016/j.ecolind.2023.111356 Elbrecht, V., and Leese, F., 2015. Can DNA-Based Ecosystem Assessments Quantify Species Abundance? Testing Primer Bias and Biomass—Sequence Relationships with an Innovative Metabarcoding Protocol. PLoS One 10 (7), e0130324. https://doi.org/10.1371/journal.pone.0130324 Feng, K., Zhang, Z., Cai, W., Liu, W., Xu, M., Yin, H., Wang, A., He, Z. and Deng, Y., 2017. Biodiversity and species competition regulate the resilience of microbial biofilm community. Mol. Ecol. 26 (21), 6170–6182. https://doi.org/10.1111/mec.14356 Ficetola, G.F., Pansu, J., Bonin, A., Coissac, E., Giguet‐Covex, C., De Barba, M., Gielly, L., Lopes, C.M., Boyer, F., Pompanon, F. and Rayé, G., 2015. Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data. Mol. Ecol. Resour. 15 (3), 543–556. https://doi.org/10.1111/1755-0998.12338 Goldberg, C.S., Turner, C., Deiner, K., Klymus, K.E., Thomsen, P.F., Murphy, M.A., Spear, S.F., McKee, A.M., Oyler‐McCance, S.J., Cornman, R.S., Laramie, M.B., Mahon, A.R., Lance, R.F., Pilliod, D.S., Strickler, K.M., Waits, L.P., Fremier, A.K., Takahara, T., Herder, J. and Taberlet, P., 2016. Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods Ecol. Evol. 7 (11), 1299–1307. https://doi.org/10.1111/2041-210x.12595 Hilário, H.O., Mendes, I.S., Guimarães Sales, N. and Carvalho, D.C., 2023. DNA metabarcoding of mock communities highlights potential biases when assessing Neotropical fish diversity. Environ. DNA 5 (6), 1351–1361. https://doi.org/10.1002/edn3.456 Holman, L. E., Chng, Y. and Rius, M., 2022. How does eDNA decay affect metabarcoding experiments? Environ. DNA 4 (1), 108–116. https://doi.org/10.1002/edn3.201 Jo, T. S., 2023. Methodological considerations for aqueous environmental RNA collection, preservation, and extraction. Anal. Sci. 39 (10), 1711–1718. https://doi.org/10.1007/s44211-023-00382-w Jo, T., Tsuri, K., Hirohara, T. and Yamanaka, H., 2023. Warm temperature and alkaline conditions accelerate environmental RNA degradation. Environ. DNA 5 (5), 836–848. https://doi.org/10.1002/edn3.334 Kagzi, K., Hechler, R. M., Fussmann, G. F. and Cristescu, M. E., 2022. Environmental RNA degrades more rapidly than environmental DNA across a broad range of pH conditions. Mol. Ecol. Resour. 22 (7), 2640–2650. https://doi.org/10.1111/1755-0998.13655 Lauber, C. L., Zhou, N., Gordon, J. I., Knight, R. and Fierer, N., 2010. Effect of storage conditions on the assessment of bacterial community structure in soil and human-associated samples. FEMS Microbiol. Lett. 307 (1), 80–86. https://doi.org/10.1111/j.1574-6968.2010.01965.x Littlefair, J. E., Rennie, M. D. and Cristescu, M. E., 2022. Environmental nucleic acids: A field‐based comparison for monitoring freshwater habitats using eDNA and eRNA. Mol. Ecol. Resour. 22 (8), 2928–2940. https://doi.org/10.1111/1755-0998.13671 Miya, M., Sato, Y., Fukunaga, T., Sadõ, T., Poulsen, J. Y., Sato, K., Minamoto, T., Yamamoto, S., Yamanaka, H., Araki, H., Kondo, M. and Iwasaki, W., 2015. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species. R. Soc. Open Sci. 2 (7), 150088. https://doi.org/10.1098/rsos.150088 Oksanen J., Simpson G., Blanchet F., Kindt R., Legendre P., Minchin P., O’Hara R., Solymos P., Stevens M., Szoecs E., Wagner H., Barbour M., Bedward M., Bolker B., Borcard D., Carvalho G., Chirico M., De Caceres M., Durand S., Evangelista H., FitzJohn R., Friendly M., Furneaux B., Hannigan G., Hill M., Lahti L., McGlinn D., Ouellette M., Ribeiro Cunha E., Smith T., Stier A., Ter Braak C., Weedon J. and Borman T., 2025. vegan: Community Ecology Package. R package version 2.6-10. https://CRAN.R-project.org/package=vegan Piñol, J., Mir, G., Gomez‐Polo, P.and Agustí, N., 2014. Universal and blocking primer mismatches limit the use of high‐throughput DNA sequencing for the quantitative metabarcoding of arthropods. Mol. Ecol. Resour. 15 (4), 819–830. https://doi.org/10.1111/1755-0998.12355 R Core Team., 2023. R: A language and environment for statistical computing. R Foundation for Statistical Computing . http://www.R-project.org/ Sakata, M. K., Yamamoto, S., Gotoh, R. O., Miya, M., Yamanaka, H. and Minamoto, T., 2020. Sedimentary eDNA provides different information on timescale and fish species composition compared with aqueous eDNA. Environ. DNA 2 (4), 505–518. https://doi.org/10.1002/edn3.75 Sales, N. G., Wangensteen, O. S., Carvalho, D. C. and Mariani, S., 2019. Influence of preservation methods, sample medium and sampling time on eDNA recovery in a neotropical river. Environ. DNA 1 (2). https://doi.org/10.1002/edn3.14 Scriver, M., Zaiko, A., Pochon, X. and von Ammon, U., 2023. Harnessing decay rates for coastal marine biosecurity applications: A review of environmental DNA and RNA fate. Environ. DNA 5 (5), 960–972. https://doi.org/10.1002/edn3.405 Shirazi, S., Meyer, R. S. and Shapiro, B., 2021. Revisiting the effect of PCR replication and sequencing depth on biodiversity metrics in environmental DNA metabarcoding. Ecol. Evol. 11 (22), 15766–15779. https://doi.org/10.1002/ece3.8239 Stahlberg, A., Hakansson, J., Xian, X., Semb, H. and Kubista, M., 2004. Properties of the reverse transcription reaction in mRNA quantification. Clin. Chem. 50 (3), 509–515. https://doi.org/10.1373/clinchem.2003.026161 van der Loos, L.M. and Nijland, R., 2021. Biases in bulk: DNA metabarcoding of marine communities and the methodology involved. Mol. Ecol. 30 (13), 3270–3288. https://doi.org/10.1111/mec.15592 Větrovský, T., Baldrián, P. and Morais, D., 2018. SEED 2: a user-friendly platform for amplicon high-throughput sequencing data analyses. Bioinformatics 34 (13), 2292–2294. https://doi.org/10.1093/bioinformatics/bty071 Wang, F., Xiong, W., Huang, X. and Zhan, A., 2025a. Selecting Competent Reverse Transcription Strategies to Maximise Biodiversity Recovery With eRNA Metabarcoding. Mol. Ecol. Resour. e14092 . https://doi.org/10.1111/1755-0998.14092 Wang, F., Xiong, W., Liu, Y., Zhai, X., Zhou, J., Li, H., Huang, X., Chen, Y., Zhou, K., and Zhan, A., 2025b. Exploring technical improvements for environmental nucleic acids-based biodiversity assessment and management in coastal ecosystems. J. Environ. Manage. 377, 124724. https://doi.org/10.1016/j.jenvman.2025.124724 Wickham, H., 2016. ggplot2: Elegant graphics for data analysis. Springer-Verlag . https://ggplot2.tidyverse.org Wood, S. A., Biessy, L., Latchford, J. L., Zaiko, A., Von Ammon, U., Audrezet, F., Cristescu, M. E. and Pochon, X., 2020. Release and degradation of environmental DNA and RNA in a marine system. Sci. Total Environ. 704, 135314. https://doi.org/10.1016/j.scitotenv.2019.135314 Xiong, W., Huang, X., Chen, Y., Fu, R., Du, X., Chen, X. and Zhan, A., 2020. Zooplankton biodiversity monitoring in polluted freshwater ecosystems: A technical review. Environ. Sci. Ecotechnol. 1, 100008. https://doi.org/10.1016/j.ese.2019.100008 Xiong, W., Li, H. and Zhan, A., 2016. Early detection of invasive species in marine ecosystems using high-throughput sequencing: technical challenges and possible solutions. Mar. Biol. 163 (6). https://doi.org/10.1007/s00227-016-2911-1 Xiong, W., MacIsaac, H.J. and Zhan, A., 2024. An overlooked source of false positives in eDNA-based biodiversity assessment and management. J. Environ. Manage. 358, 120949. https://doi.org/10.1016/j.jenvman.2024.120949 Xiong, W. and Zhan, A., 2018. Testing clustering strategies for metabarcoding‐based investigation of community–environment interactions. Mol. Ecol. Resour. 18 (6), 1326–1338. https://doi.org/10.1111/1755-0998.12922 Yates, M.C., Derry, A.M. and Cristescu, M.E., 2021. Environmental RNA: a revolution in ecological resolution? Trends Ecol. Evol. 36 (7), 601–609. https://doi.org/10.1016/j.tree.2021.03.001 Zhan, A., 2025. Overlooked eDNA contamination in human-influenced ecosystems: a call to manage large-scale false positives in biodiversity assessments. WBS. 100374. https://doi.org/10.1016/j.watbs.2025.100374 Zhan, A., He, S., Brown, E., Chain, F.J., Therriault, T.W., Abbott, C.L., Heath, D.D., Cristescu, M.E. and MacIsaac, H.J., 2014a. Reproducibility of pyrosequencing data for biodiversity assessment in complex communities. Methods Ecol. Evol. 5 (9), 881–890. https://doi.org/10.1111/2041-210x.12230 Zhan, A., Hulák, M., Sylvester, F., Huang, X., Adebayo, A.A., Abbott, C.L., Adamowicz, S.J., Heath, D.D., Cristescu, M.E. and MacIsaac, H.J., 2013. High sensitivity of 454 pyrosequencing for detection of rare species in aquatic communities. Methods Ecol. Evol. 4 (6), 558–565. https://doi.org/10.1111/2041-210x.12037 Zhan, A. and MacIsaac, H.J., 2015. Rare biosphere exploration using high-throughput sequencing: research progress and perspectives. Conserv. Genet. 16 (3), 513–522. https://doi.org/10.1007/s10592-014-0678-9 Zhan, A., Xiong, W., He, S. and MacIsaac, H. J., 2014b. Influence of artifact removal on rare species recovery in natural complex communities using High-ThroughPut Sequencing. PLoS One 9 (5), e96928. https://doi.org/10.1371/journal.pone.0096928 Zhang, S., Zheng, Y., Zhan, A., Dong, C., Zhao, J. and Yao, M., 2022. Environmental DNA captures native and non-native fish community variations across the lentic and lotic systems of a megacity. Sci. Adv. 8 (6), eabk0097. https://doi.org/10.1126/sciadv.abk0097 Zhang, Y., Qiu, Y., Liu, K., Zhong, W., Yang, J., Altermatt, F. and Zhang, X., 2024. Evaluating eDNA and eRNA metabarcoding for aquatic biodiversity assessment: from bacteria to vertebrates. Environ. Sci. Ecotechnol. 21, 100441. https://doi.org/10.1016/j.ese.2024.100441 Zhu, Τ., Sato, Y., Sadõ, T., Miya, M. and Iwasaki, W., 2023. MitoFish, MitoAnnotator, and MiFish Pipeline: Updates in 10 years. Mol. Biol. Evol. 40 (3). https://doi.org/10.1093/molbev/msad035 Supplementary Material File (table.docx) - Download - 17.21 KB Information & Authors Information Version history Peer review timeline Published Journal of Environmental Sciences Version of Record1 Nov 2025Published Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 340views 260downloads Citations Download citation Fuwen Wang, Wei Xiong, Xuena Huang, et al. Influence of short-term water sample storage on environmental RNA metabarcoding-based biodiversity assessment. Authorea. 02 May 2025. DOI: https://doi.org/10.22541/au.174619292.28752937/v1 DOI: https://doi.org/10.22541/au.174619292.28752937/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.