Detection of terrestrial species using environmental DNA during heavy rainfall events and associated influencing factors

Detection of terrestrial species using environmental DNA during heavy rainfall events and associated influencing factors | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 28 March 2025 V1 Latest version Share on Detection of terrestrial species using environmental DNA during heavy rainfall events and associated influencing factors Authors : CHEN XU 0009-0004-2487-1625 and KEI NUKAZAWA 0000-0001-5356-2064 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174312906.60655689/v1 460 views 207 downloads Contents Abstract 4. Discussion Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Recent developments in environmental DNA (eDNA) analysis have facilitated the monitoring of terrestrial vertebrates. However, because DNA is rarely released into aquatic environments, an efficient sampling design for eDNA from terrestrial vertebrates has not yet been established. In this study, we targeted eDNA transported from land to rivers through surface runoff during rainfall in three rivers and one irrigation channel within the Kiyotake River system, Japan. We quantified the eDNA concentration of a specific terrestrial vertebrate using digital PCR and examined the efficiency of using filter papers with different pore sizes (0.7 µm and 2.7 µm). We also assessed the influence of various environmental factors (e.g., rainfall characteristics described by the parameters of Gaussian distribution, water turbidity) on eDNA detection across different rainfall events. During the surveys, target DNA was detected in 42 out of 47 samples, suggesting the feasibility of stable detection of terrestrial mammals from stormwater. Overall, compared with the glass fiber filter with larger pores (GF/D), the smaller pore size filters (GF/F) captured more eDNA. The generalized linear mixed model revealed that prolonged rainfall duration, turbidity, and pH had a significant positive effect on eDNA concentration, whereas the distance from the assumed point of entry into the river to the sampling point had a significant negative effect. These results suggest that the runoff and transport of eDNA from terrestrial areas to rivers are enhanced under prolonged rainfall conditions, although eDNA degrades while transported along a longer watercourse by biochemical decomposition and sedimentation. 1. Introduction Biological surveillance using environmental DNA (eDNA) has rapidly developed in the last two decades (Ficetola et al., 2008; Minamoto et al., 2012; Pawlowski et al., 2021; Takahashi et al., 2023). Environmental DNA refers to the DNA present in environmental samples (e.g., sea, and soil) (Taberlet et al., 2012a). Based on the detectability of the target DNA, the presence or absence of species can be determined. The advantages of eDNA analysis include low cost, rapidity, and non-invasiveness (Dyson et al., 2024; Fediajevaite et al., 2021; Suren et al., 2024). Environmental DNA analysis is widely used in surveys of aquatic species (Reinhardt et al., 2019; Valentini et al., 2016) from rivers, lakes, or sea (Goldberg et al., 2011; Mächler et al., 2014; Minamoto et al., 2017; Takahara et al., 2015). Besides water, eDNA from flowers, soil, and air is also used to detect different communities (Calvignac-Spencer et al., 2013; Clare et al., 2021; Lynggaard et al., 2024; Taberlet et al., 2012b; Thomsen and Sigsgaard, 2019). Terrestrial mammals also release their DNA into water while bathing or defecating in water sources (Rodgers and Mock, 2015). This DNA becomes detectable as eDNA which was verified in the water ponds at a zoo (Ushio et al., 2017). Another study tested the applicability of multispecies eDNA detection from the Amazon River (Coutant et al., 2021). In addition to terrestrial mammals, birds were also detected (Ritter et al., 2022). These studies suggested that aquatic eDNA can be used to monitor the biodiversity of terrestrial watersheds, and it is capable of assessing biodiversity at larger spatial scales (Macher et al., 2021; Mariani et al., 2021). However, for species with low river utilization rate and low abundance, eDNA released into rivers cannot be detected even if the target terrestrial mammals are present living around the sampling location (Harper et al., 2019). In addition, the accelerated degradation of eDNA in water, depending on environmental factors such as pH, water temperature, and flow distance, makes detection even more difficult (Mauvisseau et al., 2022; Nukazawa et al., 2018; Seymour et al., 2018; Strickler et al., 2015). Previous studies speculated that surface runoff during a rainfall event may transport terrestrial eDNA to rivers and other water bodies (Coutant et al., 2021; Yang et al., 2021), although the idea has not yet been verified through a systematic study design. In the study exploring the effect of rainfall on eDNA in vegetation areas found that rainfall rapidly removed eDNA from vegetation regardless of whether the surface of the vegetation is smooth or rough (Valentin et al., 2021). These results imply that, through a physical process, terrestrial eDNA is transported to rivers through surface runoff associated with rainfall. Therefore, eDNA surveys of rivers during rainfall events may allow for the rapid detection of terrestrial vertebrates that would normally have little contact with water bodies. However, it must be considered that surface runoff also carries fine to coarse sediments and transports them into the water bodies, resulting in high water turbidity. Therefore, a specific method is needed to efficiently recover eDNA from highly turbid water samples. Most eDNA samplings have been conducted under normal water conditions, with a limited number of cases in which chronically turbid waters were targeted (Sanches and Schreier, 2020; Williams et al., 2017a). The use of a filter with a smaller pore size is generally recommended to improve the rate of eDNA recovery (Kumar et al., 2022; Minamoto et al., 2016); however, it hampers the sampling of eDNA in turbid water environments due to the clogging of pores. To prevent clogging, some studies have attempted to decrease the filtration volume, but this has resulted in a lower detectability of the target species (Deiner et al., 2015; Li et al., 2018). Although eDNA can be detected from turbid water by concentration through centrifugation (Williams et al., 2017b), as the authors sampled bathing water with a high concentration of eDNA belonging to the target species, the proposed method seems unsuitable for natural environments, which generally have low eDNA concentrations. Hence, further studies are required to better understand the impact of environmental and experimental conditions on the detectability of species in turbid water samples. Understanding the necessary conditions for detecting eDNA from turbid water during rainfall events and establishing reliable methods could pave the way for the simultaneous monitoring of aquatic and terrestrial mammals in watershed environments. Rainwater collected after rainfall events from crown of trees and eDNA metabarcoding have been used to detect terrestrial invertebrate species (Macher et al., 2023). However, the effect of varying rainfall intensities on the observed species richness was not explored, and the results were barely statistically discussed. Similarly, post-rainfall eDNA surveys have also been conducted in places such as rivers (Staley et al., 2018). The results show more diverse eDNA profiles compared with those during their drier sampling dates, with relatively more sequences from mammalian and bird species, which were absent during dry sampling dates. It should be noted that the study focused on the detection of fecal sources in river water; thus, the source of the detected terrestrial mammalian DNA and the environmental factors influencing the detectability remain unclear. The factors that determine eDNA detectability from stormwater should be further investigated to better understand the conditions where such a method can be applied. In this study, we used digital PCR (dPCR) approach to analyze eDNA of turbid water sampled in the Kiyotake River catchment, southwest Japan, during rainfall events of different magnitudes and investigated the detectability of a terrestrial mammal popularly farmed in the area. We used dPCR instead of eDNA metabarcoding because of its high specificity. A third-generation PCR technology, dPCR, allows for the absolute quantification of target DNA and is superior to the relative quantification provided by real-time PCR (qPCR) (Doi et al., 2015; Kuypers and Jerome, 2017). Results of eDNA quantification via dPCR were less variable than those via qPCR (Nathan et al., 2014). In addition, we explored the factors influencing the detectability of terrestrial species in turbid stormwater (e.g., water quality, rainfall patterns). The study area is characterized by high annual precipitation, which allowed us to evaluate eDNA detection across various rainfall patterns. 2. Materials and Methods 2.1. Study area and target species The study area included three rivers within the Kiyotake River catchment in Miyazaki Prefecture, southwestern Japan, i.e., the Mizunashi, Oka, and Tagami Rivers as well as the irrigation canal connected to the latter (Figure 1-a). The Kiyotake River is 28.8 km long and its catchment area covers 166.4 km 2 . The climate in the catchment is characterized by an annual average temperature of approximately 18.0°C and annual precipitation of approximately 3000 mm. A total of 12 land use categories (e.g., Urban, Field, Deciduous Broadleaf Trees, Deciduous Needleleaf Trees, Figure 1-b) were obtained from the Advanced Land Observing Satellite (10-m accuracy, data available: https://earth.jaxa.jp/ja/data) and further classified into four categories: forest (e.g., Deciduous Broadleaf Trees, Deciduous Needleleaf Trees), farmland (Paddy Field, and Field), urban areas (Urban), and others (e.g., Water Area, Grassland). The proportion of each land use was derived by dividing the number of meshes corresponding to the land-use type (e.g., forested area) by the total number of meshes in the watershed of the studied sites The major watershed land used in the studied rivers are forest in the Mizunashi River (71.57%), forest and farmland in the Oka River (39.25% and 31.25%, respectively), and farmland and urban areas in the Tagami River (43.24% and 21.38%, respectively). Our study focused on livestock cattle species, Bos taurus , which is widely distributed at fixed locations (i.e., it is an eDNA release source) in the basin (Figure 1-a). This is a model species with low abundance. Because Bos taurus is a domestic animal, they are confined within enclosures and designated areas. Each cattle shed is situated at a certain distance and elevation from the corresponding river, making it physically impossible for the cattle to enter these natural water bodies. Therefore, the direct use of rivers by target species is negligible. Local regulations prohibit wastewater from being directly discharged into rivers, thereby limiting eDNA from wastewater sources. The sewer system is a combined system that treats wastewater at water treatment facilities before it is discharged. This species has a low risk of false positive results due to the absence of individuals from the same species in the wild. At least one cattle barn raising individuals outdoors is present in each river catchment (Fig. 1). The specific geographic coordinates of the sampling locations were: the Mizunashi River (31.829925°N, 131.362669°E), Oka River (31.85162°N, 131.386959°E), Tagami River (31.838143°N, 131.417732°E), and irrigation canal (31.839362°N, 131.414762°E) and geographic coordinates of the cattle barn were: the Mizunashi River (31.828235°N, 131.359762°E), Oka River (31.852260°N, 131.385643°E) and Tagami River irrigation canal (31.839116°N, 131.413398°E). 2.2 Sampling A scheme of the field investigations and experiments is presented in Fig. 2. Prior to water sampling, 1- and 0.25-L plastic bottles, tethered buckets, cooler boxes, and a scoop were sterilized with 10% bleach for at least 30 min to prevent contamination by DNA and microorganisms other than the target species. We collected samples multiple times after and during moderate to heavy rainfall events from 2021 to 2023. Specifically, we collected samples twice a day (Sampling 1 and 2) on September 14, 2021, November 22, 2021, and June 2, 2023, to understand the temporal pattern of eDNA detection. The time interval between sampling was approximately 2 h. In addition to these dates, samples were collected once a day, with the sampling on November 22, 2021, performed after rainfall. Each sampling involved collecting 1 L of river water (4 replicates) for filtration and 0.25 L for the measurement of basic environmental parameters, i.e., pH, electric conductivity (EC), and turbidity. Water temperature was measured at sampling sites using a bar-shaped mercury thermometer, and the other parameters were measured from 250-mL samples transported to the laboratory. EC and pH were measured using a portable multiwater quality meter (HORIBA TOA DKK), and turbidity was measured using a turbidity meter (PT-200_TROAM, Asahi Science) in the laboratory. Turbidity, EC, pH, and temperature were not measured in September 2021; temperature was not measured in November 2021; and EC was not measured in June 2023 due to a technical problem. During the surveys, tethered buckets were used to collect water samples in cases where entering the river was physically impossible. All the sampling events were completed within 2 h. The collected samples were immediately transferred to the laboratory in a cooler box with ice packs, and filtration was completed within 5 h. To prevent sample-to-sample contamination and contamination from external sources during the survey and experiments, a 1L of sterile distilled water prepared in the laboratory was placed in a cooler box and transported to the sampling site. After being opened and closed at the sampling site, it was returned to the laboratory with river water samples. We obtained rainfall data for the 24-h period before each sampling event under rainfall conditions from the nearest meteorological station (Akae Observatory, data available: https://www.data.jma.go.jp/obd/stats/etrn/index), and the data are shown in Appendix 1. As a negative control, 1 L of surface water was collected following a period without rainfall at the same survey sites on December 27, 2021, and November 28, 2022. In addition to 1 L of surface water, riverbed sediment was collected on August 8 and December 13, 2024. 2.3. Filtration and extraction Figure 2 shows the experimental process. The water samples collected during the rainfall events were filtered using a glass fiber filter with a pore size of 0.7 μm (GF/F; GE Healthcare Japan, Tokyo), which optimizes the time and cost efficiency (Takahashi et al., 2020). To avoid filter paper clogging when filtering turbid water, we used a filter membrane with a pore size of 2.7 μm (GF/D; GE Healthcare Japan, Tokyo), which is the largest pore size among the glass fiber filters. We compared the filtration efficiency and eDNA recovery rate from each filter with two replicates per filter. The samples collected without rainfall were filtered using only GF/F (1 replicate). The funnels and bases (ADVANTEC) used for filtration were sterilized in advance via autoclaving (LSX-700, TOMY). Before processing the samples, the funnels, bases, clamps, and tweezers were sterilized again using 10% bleach. We shook the bottle before filtering, to prevent eDNA from settling at the bottom due to prolonged filtration time. When the filtration rate dropped below 20 drops/10 s, the filter paper was considered clogged. An additional filtration with a new filter paper was then conducted, with a maximum of two filter papers used. This limitation was set to avoid the large labor cost involved in the filtration and extraction processes, which decreases the applicability of eDNA analysis to turbid water (Hinlo et al., 2017). The water volume and filtration time until clogging were recorded for each filter paper. In cases where two filter papers were used for a sample, the total volume and time were used as the volume and time respectively. After processing the samples, all filter papers were frozen at −20℃ until DNA extraction. DNA extraction was performed using the DNeasy PowerSoil Kit (Qiagen, Hilden, Germany), which is known to ensure low DNA variability when extracting eDNA (Eichmiller et al., 2016). This kit is also able to capture extracellular DNA resulted from unfreezing processes (Gielings et al., 2021; Hermans et al., 2018). Firstly, using sterilized scissors and tweezers, the samples were cut to pieces of approximately 1 × 3 mm on a clean bench. Subsequently, DNA was extracted from the filter fragments following the kit manufacturer’s protocol and eluted finally in 100-µL volumes. After extraction, the DNA template solutions were stored at −80°C for subsequent PCR amplification. We adopted two methods for eDNA extraction from sediment samples. In the first approach, approximately 28 g of sediment sample was mixed with 100 mL of sterile phosphate buffer solution (PBS, pH = 7) for 2 min in a plastic bottle pre-sterilized with bleach for 30 min. Subsequently, 100 mL of the mixture was poured into a sterile funnel, taking care not to introduce sand particles, and filtered through a GF/F glass fiber filter paper (Nevers et al., 2020). In the second approach, 20 g of sediment sample was mixed with 20 mL of phosphate buffer for 15 min. Two milliliters of the mixture was centrifuged (10 min at 10,000 g), and 600 µL of the resulting supernatant was used for subsequent extraction (Rota et al., 2020; Taberlet et al., 2012c). All samples were extracted following the DNeasy PowerSoil Pro Kit manufacturer’s protocol and eluted in 100-µL volumes (Since the DNeasy PowerSoil Kit has been discontinued). 2.4. eDNA quantification and PCR conditions The species-specific cattle DNA concentration was quantified using primers and probes targeting the mitochondrial cytochrome b (Cytb) region (Dooley et al., 2004). The mixture was dispensed into the independent wells of a QuantStudio 3D dPCR 20-K chip using a QuantStudio 3D dPCR Chip Loader (Applied Biosystems, CA). The end-point PCR was performed using a thermal cycler (ProFlex, Applied Biosystems, CA). To determine the optimal PCR conditions, preliminary experiments were performed using a dilution series of DNA extracted from beef meat (10 -2 –10 -3 fold). DNA was extracted from the beef samples using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions. The results obtained for the optimal PCR conditions are shown in Appendix 2. When using forward and reverse primers at 450 nM and the probe at 125 nM, an annealing temperature of 60°C and 50 cycles yielded an adequate separation of fluorescence between positive and negative wells in the dPCR system for both 100-fold and 1000-fold dilutions. Therefore, all samples were adopted under these conditions for the subsequent PCR amplification. PCR was performed in a final volume of 14.5 µL consisting of 7.25 µL 1 × Quant Studio 3D Digital PCR Master Mix, 2 µL of DNA template solution, 450 nM each primer (the forward and reverse primer) and 125 nM Taqman probe. The fluorescence intensity thresholds defined in the default settings of the Quant Studio 3D Analysis Suite (Applied Biosystems, CA) were used to distinguish between positive and negative wells. However, in cases where the fluorescence intensity of positive wells was low and not clearly separated from that of negative wells in the plots, those wells were considered negative (Nukazawa et al., 2020). Because the QuantStudio 3D dPCR 20-K chip has been discontinued, non-rainfall samples collected in August and December 2024 were quantified using the QIAcuity one digital system (Qiagen, Hilden, Germany). The same PCR conditions as those mentioned above were used. PCR was performed in a final volume of 43 µL consisting of 11.25 µL Probe PCR Master Mix, 2 µL of DNA template solution, 450 nM each primer (the forward and reverse primer) and 125 nM Taqman probe. The mixture was dispensed into a Nanoplate 26K 24-well. Finally, the results were confirmed using the QIAcuity Software Suite. Because the Quant Studio 3D Digital PCR Master Mix and Probe PCR Master Mix may contain bovine serum components, we included two samples without DNA in each type of dPCR to check for reagent contamination. 2.5. Environmental factors The detectability of terrestrial mammals in river water during rainfall is likely to depend on the process of terrestrial eDNA transport to water and, specifically, on parameters related to degradation and sedimentation during transport and in the receiving water body. Therefore, in this study, specific environmental variables influencing the detectability of eDNA were selected, and their relationship with eDNA concentration was assessed. In the terrestrial environment, the percentages of forest, farmland, and urban areas were taken into consideration, given their relevance to the occurrence of surface runoff (Guzha et al., 2018). Furthermore, two specific distances were considered: the distance from eDNA release sources to the study river (straight-line, referred to as land distance) and the path that terrestrial eDNA travels to reach the sampling site after being transported into the water body (nonstraight line, referred to as the waterways distance). The land and waterways distances were approximately 125 and 360 m for the Mizunashi River, 36 and 180 m for the Oka River, approximately 311 and 187 m for the Tagami River, and 17 and 134 m for its irrigation channel, respectively (Google Maps). In the aquatic environment, we considered water temperature, pH, EC, and turbidity. The magnitude and duration of rainfall are potentially key factors regulating terrestrial eDNA runoff. To determine their influence, we fitted the rainfall time series (i.e., hyetograph) for each sampling campaign using a normally distributed probability distribution to describe the characterization of each rain event. The hourly precipitation recorded over a 24-h period at the Akae rainfall station was used as an observation value, and a function describing a calculated precipitation was developed; αf(x), where f(x) and α are the normal distribution function and a constant, respectively. Here, the time when the water sampling started was set to time 24, and 24 hours before sampling started was set to time 1 to initialize the time axis; thereby, the mean (µ) of the normal distribution varied from 1 to 24. The search range for the standard deviation (σ) of the normal distribution and the constant α were set from 0 to 100 and from 0 to 1000, respectively, because these parameters varied greatly in the preliminary attempts. Iterative parameter searches were performed using R4.3.2 (R core team, 2023) to determine the values of μ, σ, and α that minimized the root mean square error of observation and calculated value. A larger μ suggested that the peak rainfall occurred closer to the sampling time, whereas a larger σ indicated that similar magnitude rainfall events lasted longer (Figure 3, Appendix 1). 2.6. Regression analysis Generalized linear mixed models (GLMM) with the Gaussian family were used to evaluate the relationship between eDNA concentrations and environmental factors (μ, σ, turbidity, land distance, waterways distance, percentage of each land use, pH, EC, water temperature, and type of filter), with each of the four rivers as a random effect. In the models, the interaction between turbidity and the type of filter paper was considered based on a previous report that examined the effect of suspended solids on eDNA particle distribution size (Barnes et al., 2021). Prior to modeling, the collinearity between environmental factors was determined using generalized variance inflation factors (GVIF) using the R package “car” (Fox and Weisberg, 2019). The factor with the highest adjustment value of GVIF was repeatedly omitted until all fell below 2 (Fox and Monette, 1992). Through this procedure, the percentages of the forest, farmland, and urban areas and water temperature were excluded from the model. The glmmTMB package (Brooks et al., 2017) was used to build the GLMMs using log-transformed eDNA concentration as the dependent variable to identify factors affecting eDNA content. One was added to eDNA concentration prior to log-transformation to accommodate values of 0 (Everts et al., 2024). All statistical analyses were conducted in R ver. 4.3.2 (R Core Team, 2020). Additionally, two GLMMs were used to evaluate the relationships between filtration time and turbidity and filter paper type; and the relationships between filtration volume, turbidity, and filter paper type. To determine whether the concentration of eDNA differed between GF/F and GF/D, we used the paired Wilcoxon rank-sum test. 3. Results 3.1. Measurements of basic water quality parameters Table 1 shows the measurements of the basic water quality parameters in each river. from the ranges of water temperature at each river are as follows: Mizunashi River , 14.9°C–18°C (average, 17.30 ± 0.60); Oka, 18.4°C–21.6°C (average, 20.32 ± 0.57); Tagami, 19°C– 24 .2°C (average, 21.30 ± 1.08). The water temperature of the irrigation canal was recorded only once in November 2022, and it was 19°C. The pH values at each river are; the Mizunashi River , 6.61–7.96 (average, 7.54 ± 0.48); Oka, 6.09–7.70 (average, 7.18 ± 0.63); Tagami, 5.98-7.27 (average, 6.95 ± 0.49); irrigation canal, 7.08–7.33 (average, 7.20 ± 0.12). Based on observations performed twice daily, the pH values of the samples in the same site tended to decrease over time. The EC values at each river are as follows: the Mizunashi River, 53.60–119.30 μS/cm (average, 83.40 ± 29.69); Oka, 55.70–76.70 μS/cm (average, 67.70 ± 8.91); Tagami, 39.80–126.60 μS/cm (average, 85.96 ± 36.52); irrigation canal, 25.80- 166. 20 μS/cm (average, 118.90 ± 80.63). The fluctuations in EC in the Tagami River and irrigation canal were larger than those in the Oka and Mizunashi Rivers. The turbidity at each river are as follows: the Mizunashi River, 5.78–285.82 ppm (average, 78.13 ± 97.69); Oka, 46.73– 314.34 ppm (average, 142.23 ± 108.30); Tagami, 4.07–138.54 ppm (average, 31.04 ± 52.86); irrigation canal, 0.45–189.94 ppm (average, 63.78 ± 109.26). Turbidity fluctuated greatly in all sites . 3.2. Filtration volume and time The filtration volume and time for each DNA concentration method are shown in Appendix 3. A higher filtration volume and a shorter filtration time were observed for GF/D than for GF/F. For the samples collected from November 2021 to June 2023, two filter papers were used in 87.71% of the GF/F-based filtrations. Among them, the minimum turbidity detected was 4.07 ppm. Additionally, for samples with turbidity equal to or greater than 25.38 ppm, even two filter papers did not allow full filtration (61.1% of the samples). The sample collected during the survey conducted in November 2022 had the longest filtration time (water volume: 690 mL, filtration time: 2610 s). For GF/D-based filtration, two filter papers were used for 42.86% of the samples. For samples with turbidity equal to or greater than 110 ppm, a water volume of 1 L could not be filtered even using two filter papers (72.22% of the samples). Overall, GF/D papers were more efficient in filtering high-turbidity samples than GF/F papers. Table 2 shows the relationships between filtration volume, filtration time, filter type, and turbidity. The GLMMs suggested significant positive effect of turbidity on filtration time. And significant positive effect of FilterGF/D, significant negative effect of turbidity on filtration volume. 3.3. eDNA quantification The target DNA was not detected in the autoclaved distilled water sample transported to the site on each survey date and the PCR control, confirming the absence of contamination between samples during the surveys and reagent contamination. Figure 4 shows the results of the eDNA survey conducted in the rainfall and non-rainfall events. In non-rainfall events, the target eDNA was not detected in most samples, and in the few where it was detected, the concentration was extremely low. In contrast, considerably more target DNA was observed during the rainfall period (November 2022 samples), suggesting that even though target eDNA may be present in the rivers, the amount was negligible, as external eDNA contributed to the results obtained during the rainfall period (Figure 4). Figure 4 shows the results of eDNA quantification during the rainfall period. Environmental DNA was detected in 42 out of 47 samples. In terms of survey periods, the eDNA concentrations were in the range of 855–8,267 copies/L for September 2021, 0–1,350 copies/L for November 2021, 250–3,801 copies/L for May 2022, 0–2,730 copies/L for September 2022, 4,807–399,185 copies/L for November 2022, and 0–1,040 copies/L for June 2023. In addition, during the heavy rainfall event of November 29, 2022, high concentrations of cattle DNA were detected, whereas in November 2021, which was a less rainy month, many samples showed low eDNA concentrations or even none. Among the five samples with no eDNA traces, collected throughout the entire investigation period three were from the Mizunashi River. During the survey period on the same date, higher eDNA concentrations tended to be detected in the irrigation channel than in the Tagami River. 3.4. Relationships between environmental factors and eDNA concentration The GLMMs suggested significant positive effects of σ, turbidity, pH, and FilterGF/F and significant negative effects of waterways distance on eDNA concentration. However, no significant effects of μ, land distance, EC, or the interaction between turbidity and filter type were observed (Table 3). 4. Discussion In this study, we investigated the feasibility of detecting low abundance terrestrial mammals that cannot enter the water by analyzing river water during rainfall using glass filter papers with two pore sizes. In addition, we examined environmental factors that may affect the detection of terrestrial mammals. The DNA of the target terrestrial mammals was detected in 42 out of the 47 samples collected during different rainfall events. Thus, we conclude that the terrestrial target species can be consistently detected and quantified from river water samples during rainfall events throughout our surveys. While the target DNA was indeed detected in partial samples during non-rainfall events, its concentration was extremely low. Although some eDNA concentrations detected during the rainfall period were as low as those recorded during the no rainfall period, such intensive rainfall increases streamflow that dilutes eDNA, resulting in extremely low (or no detection) eDNA detection. Thus, the detection of high concentrations in rainfall samples suggests that the source of the target terrestrial eDNA is terrestrial and could be consistently detected in river water during rainfall events (Figure 4). Previous research indicates that larger pore sizes enhance eDNA capture rates by filter papers used in turbid waters (Barnes et al., 2021). However, in our experiment, the smaller pore size filter (GF/F) tended to recover more eDNA (p < 0.05), except for four samples with high turbidity, where the larger pore sized filter (GF/D) recovered more eDNA (Table 1, Figure 4). Moreover, filter clogging remains an issue when filtering turbid water. Despite the filter GF/D having a larger pore size, excessive turbidity could still prevent it from completely filtering the 1-L water sample in a short time. Indeed, turbidity can be influenced by many factors, such as organic matter, suspended particles, or sediment in the water. Different materials each have an impact on the adherence of eDNA. Compared with organic matter, eDNA was strongly and instantaneously adsorbed on clay, and microorganisms colonized by organic matter assimilate eDNA and change eDNA particle size (Brandão-Dias et al., 2023). Therefore, this may why, in the irrigation canal and Tagami River, where organic matter input from surrounding farmland was the primary source of increased turbidity, the particle size of eDNA did not effectively increase. GF/F filters recovered much more eDNA than GF/D filters even under high turbidity conditions. Among all the samples, the highest number of non-detections occurred in the Mizunashi River, involving both GF/F and GF/D filter papers, specifically during the November 2021 sampling period. This lack of detection could be attributed to the predominance of forest land in the Mizunashi River area, which may reduce the intensity of surface runoff. As a result, during November 2021, which was characterized by relatively weak rainfall events with short duration, the target DNA could not be detected in the river. However, because the strong multicollinearity among land use variables decreased the model accuracy, these variables were not included. At the same time though, different land uses affect the intensity of surface runoff (Guzha et al., 2018). Thus, future research needs to encompass different land use in the survey catchment. According to the GLMMs results, rainfall duration had a significant positive effect on eDNA concentration, whereas the timing of rainfall peaks did not. Therefore, the amount of rainfall is more important than its intensity (Valentin et al., 2021). In the presence of sufficient rainfall, terrestrial eDNA can be continuously transported into rivers to compensate for the reduction caused by dilution. The observed tendency toward higher eDNA concentrations under high-turbidity conditions appeared to be caused by the correlation between turbidity and rainfall, rainfall potentially being the true driving factor behind the increase in eDNA concentration. Terrestrial materials such as soil, organic matter, and terrestrial eDNA are washed into rivers by surface runoff during rainfall, increasing river turbidity (Chen et al., 2019) Since eDNA may be sticky (Barnes et al., 2021), it tends to attach to these materials, increasing its particle size and making it more easily captured by filter paper. Similarly, pH was also shown to have a significantly positive effect on the eDNA concentration. Therefore, high pH favored the detection of eDNA because low pH tends to degrade eDNA (Jo et al., 2022). Environmental DNA was consistently detected in the irrigation channel, with no cases of nondetection. This can be explained by the proximity of the irrigation channel to the source point, i.e., at a land distance of only about 17 m. Nonetheless, our GLMMs did not detect any significant association between the land distance and the eDNA concentration. This was likely due to the multiplicity and complexity of the pathways of eDNA transport from land to rivers, which makes it a particularly challenging research topic. Although the land distance of the Oka River was only 36 m, the presence of an embankment likely affected the transport of eDNA from land to river. Whether during rainfall or non-rainfall periods, the concentration of eDNA in the irrigation channel was higher than that in the Tagami River. One reason for this is the longer distance of waterways in the Tagami River, along which eDNA undergoes microbial decomposition and is subjected to various physical processes (e.g., sedimentation). In addition, the confluence of tributaries in the Tagami River can contribute to the dilution of eDNA. Although many challenges remain, the fact that rainfall can significantly increase the probability of detection of terrestrial mammals highlights the usefulness of our approach. However, our study only simulated the detectability of a specific taxonomic group from natural water bodies during rainfall, and the impact of rainfall on other biological groups remains unknown. While the simultaneous detection of aquatic, semi-aquatic, and terrestrial vertebrates or aquatic invertebrates from aquatic eDNA has become common, the attempts to detect terrestrial species are severely limited. In terrestrial ecosystems, the sources of eDNA are more diverse than in river ecosystems. These sources include soil, spider webs, air, pollen, scavenging insects, and material with trace amounts of eDNA. Investigating substrate sources of terrestrial vertebrate eDNA can be time-consuming and costly(Gogarten et al., 2020; Seeber and Epp, 2022). Previous research has revealed that vertebrate families are associated with fecal samples, whereas Formicidae and Termites are linked to pitfall trapping (van der Heyde et al., 2020). Analysis of eDNA from wildflowers is more suitable for detecting arthropods and birds than mammals (Thomsen and Sigsgaard, 2019). Environmental DNA from spider webs is generally used for monitoring invertebrates (Gregorič et al., 2022), but its capability for detecting vertebrate biodiversity in natural environments remains insufficient (Newton et al., 2024). Aerial eDNA can also be used to detect vertebrates. More mammalian species than birds were detected in zoos (Lynggaard et al., 2022), whereas the opposite results were observed in the wild (Lynggaard et al., 2024). Because of the diverse sources of terrestrial eDNA, its transport and degradation processes in the environment are complex. Currently, utilizing eDNA from a single environmental source to monitor organisms across different domains remains challenging. In this context, our approach to sampling stream water during rainfall events addresses the aforementioned issue by enhancing the interaction of various media between the river and terrestrial ecosystems. As the surveys described in this study were conducted during rainfall events, the increase in water flow and velocity also had an impact on eDNA concentration and transport (Curtis et al., 2021; Nukazawa et al., 2018). Thus, future research based on modeling should focus on more community, different watersheds and encompass various hydrological factors. Our rainfall data comprised the precipitation amount of the 24 h preceding the sampling. However, within the catchment area, the transport time of terrestrial eDNA may vary, resulting in variable sampling times among the sites. This problem can be addressed by hydrologic analysis because it can calculate the eDNA transport time from the source to the river. Environmental DNA was detected in some Sampling after rainfall events, suggesting that terrestrial eDNA is transported to rivers continuously. Nevertheless, greater flow rates are associated with high turbidity, which increases the risk of safe surveillance. Thus, future studies should combine our approach with unmanned sampling technology (e.g., drone) that can collect samples during various rainfall-related events (O’Mahony et al., 2024; Preston et al., 2024). Acknowledgements This study was supported by the River Fund of The River Foundation, Japan, Japan Society for the Promotion of Science (Award Number: 24KK0086 and 24H00329) and the Kurita Water and Environment Foundation. We would like to thank Mr. Inoue, Mr. Higuchi, and Mr. Nanri (University of Miyazaki) for their help in experiments and field observations. References Barnes, M.A., Chadderton, W.L., Jerde, C.L., Mahon, A.R., Turner, C.R., Lodge, D.M., 2021. Environmental conditions influence eDNA particle size distribution in aquatic systems. Environmental DNA 3, 643–653. https://doi.org/10.1002/edn3.160 Brandão-Dias, P.F.P., Tank, J.L., Snyder, E.D., Mahl, U.H., Peters, B., Bolster, D., Shogren, A.J., Lamberti, G.A., Bibby, K., Egan, S.P., 2023. Suspended Materials Affect Particle Size Distribution and Removal of Environmental DNA in Flowing Waters. Environ Sci Technol 57, 13161–13171. https://doi.org/10.1021/acs.est.3c02638 Brooks, M.E., Kristensen, K., Van Benthem, K.J., Magnusson, A., Berg, C.W., Nielsen, A., Skaug, H.J., Mächler, M., Bolker, B.M., 2017. glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling. Calvignac-Spencer, S., Merkel, K., Kutzner, N., Kühl, H., Boesch, C., Kappeler, P.M., Metzger, S., Schubert, G., Leendertz, F.H., 2013. Carrion fly-derived DNA as a tool for comprehensive and cost-effective assessment of mammalian biodiversity. Mol Ecol 22, 915–924. https://doi.org/10.1111/mec.12183 Clare, E.L., Economou, C.K., Faulkes, C.G., Gilbert, J.D., Bennett, F., Drinkwater, R., Littlefair, J.E., 2021. eDNAir: Proof of concept that animal DNA can be collected from air sampling. PeerJ 9. https://doi.org/10.7717/peerj.11030 Coutant, O., Richard-Hansen, C., de Thoisy, B., Decotte, J.B., Valentini, A., Dejean, T., Vigouroux, R., Murienne, J., Brosse, S., 2021. Amazonian mammal monitoring using aquatic environmental DNA. Mol Ecol Resour 21, 1875–1888. https://doi.org/10.1111/1755-0998.13393 Curtis, A.N., Tiemann, J.S., Douglass, S.A., Davis, M.A., Larson, E.R., 2021. High stream flows dilute environmental DNA (eDNA) concentrations and reduce detectability. Divers Distrib 27, 1918–1931. https://doi.org/10.1111/ddi.13196 Deiner, K., Walser, J.C., Mächler, E., Altermatt, F., 2015. Choice of capture and extraction methods affect detection of freshwater biodiversity from environmental DNA. Biol Conserv 183, 53–63. https://doi.org/10.1016/j.biocon.2014.11.018 Doi, H., Takahara, T., Minamoto, T., Matsuhashi, S., Uchii, K., Yamanaka, H., 2015. Droplet digital polymerase chain reaction (PCR) outperforms real-time PCR in the detection of environmental DNA from an invasive fish species. Environ Sci Technol 49, 5601–5608. https://doi.org/10.1021/acs.est.5b00253 Dooley, J.J., Paine, K.E., Garrett, S.D., Brown, H.M., 2004. Detection of meat species using TaqMan real-time PCR assays. Meat Sci 68, 431–438. https://doi.org/10.1016/j.meatsci.2004.04.010 Dyson, K., Nicolau, A.P., Tenneson, K., Francesconi, W., Daniels, A., Andrich, G., Caldas, B., Castaño, S., de Campos, N., Dilger, J., Guidotti, V., Jaques, I., McCullough, I.M., McDevitt, A.D., Molina, L., Nekorchuk, D.M., Newberry, T., Pereira, C.L., Perez, J., Richards-Dimitrie, T., Rivera, O., Rodriguez, B., Sales, N., Tello, J., Wespestad, C., Zutta, B., Saah, D., 2024. Coupling remote sensing and eDNA to monitor environmental impact: A pilot to quantify the environmental benefits of sustainable agriculture in the Brazilian Amazon. PLoS One 19. https://doi.org/10.1371/journal.pone.0289437 Eichmiller, J.J., Miller, L.M., Sorensen, P.W., 2016. Optimizing techniques to capture and extract environmental DNA for detection and quantification of fish. Mol Ecol Resour 16, 56–68. https://doi.org/10.1111/1755-0998.12421 Everts, T., Van Driessche, C., Neyrinck, S., Haegeman, A., Ruttink, T., Jacquemyn, H., Brys, R., 2024. Phenological mismatches mitigate the ecological impact of a biological invader on amphibian communities. Ecological Applications 34. https://doi.org/10.1002/eap.3017 Fediajevaite, J., Priestley, V., Arnold, R., Savolainen, V., 2021. Meta-analysis shows that environmental DNA outperforms traditional surveys, but warrants better reporting standards. Ecol Evol 11, 4803–4815. https://doi.org/10.1002/ece3.7382 Ficetola, G.F., Miaud, C., Pompanon, F., Taberlet, P., 2008. Species detection using environmental DNA from water samples. Biol Lett 4, 423–425. https://doi.org/10.1098/rsbl.2008.0118 Fox, J., Monette, G., 1992. Generalized Collinearity Diagnostics. J Am Stat Assoc 87, 178–183. https://doi.org/10.1080/01621459.1992.10475190 Fox, J., Weisberg, S., 2019. An {R} Companion to Applied Regression. Sage publications. Gielings, R., Fais, M., Fontaneto, D., Creer, S., Costa, F.O., Renema, W., Macher, J.N., 2021. DNA Metabarcoding Methods for the Study of Marine Benthic Meiofauna: A Review. Front Mar Sci. https://doi.org/10.3389/fmars.2021.730063 Gogarten, J.F., Hoffmann, C., Arandjelovic, M., Sachse, A., Merkel, K., Dieguez, P., Agbor, A., Angedakin, S., Brazzola, G., Jones, S., Langergraber, K.E., Lee, K., Marrocoli, S., Murai, M., Sommer, V., Kühl, H., Leendertz, F.H., Calvignac-Spencer, S., 2020. Fly-derived DNA and camera traps are complementary tools for assessing mammalian biodiversity. Environmental DNA 2, 63–76. https://doi.org/10.1002/edn3.46 Goldberg, C.S., Pilliod, D.S., Arkle, R.S., Waits, L.P., 2011. Molecular detection of vertebrates in stream water: A demonstration using rocky mountain tailed frogs and Idaho giant salamanders. PLoS One 6. https://doi.org/10.1371/journal.pone.0022746 Gregorič, M., Kutnjak, D., Bačnik, K., Gostinčar, C., Pecman, A., Ravnikar, M., Kuntner, M., 2022. Spider webs as eDNA samplers: Biodiversity assessment across the tree of life. Mol Ecol Resour 22, 2534–2545. https://doi.org/10.1111/1755-0998.13629 Guzha, A.C., Rufino, M.C., Okoth, S., Jacobs, S., Nóbrega, R.L.B., 2018. Impacts of land use and land cover change on surface runoff, discharge and low flows: Evidence from East Africa. J Hydrol Reg Stud. https://doi.org/10.1016/j.ejrh.2017.11.005 Harper, L.R., Lawson Handley, L., Carpenter, A.I., Ghazali, M., Di Muri, C., Macgregor, C.J., Logan, T.W., Law, A., Breithaupt, T., Read, D.S., McDevitt, A.D., Hänfling, B., 2019. Environmental DNA (eDNA) metabarcoding of pond water as a tool to survey conservation and management priority mammals. Biol Conserv 238. https://doi.org/10.1016/j.biocon.2019.108225 Hermans, S.M., Buckley, H.L., Lear, G., 2018. Optimal extraction methods for the simultaneous analysis of DNA from diverse organisms and sample types. Mol Ecol Resour 18, 557–569. https://doi.org/10.1111/1755-0998.12762 Hinlo, R., Gleeson, D., Lintermans, M., Furlan, E., 2017. Methods to maximise recovery of environmental DNA from water samples. PLoS One 12. https://doi.org/10.1371/journal.pone.0179251 Jo, T., Tsuri, K., Hirohara, T., Yamanaka, H., 2022. Warm temperature and alkaline conditions accelerate environmental RNA degradation. Environmental DNA. https://doi.org/10.1002/edn3.334 Kumar, G., Farrell, E., Reaume, A.M., Eble, J.A., Gaither, M.R., 2022. One size does not fit all: Tuning eDNA protocols for high- and low-turbidity water sampling. Environmental DNA 4, 167–180. https://doi.org/10.1002/edn3.235 Kuypers, J., Jerome, K.R., 2017. Applications of digital PCR for clinical microbiology. J Clin Microbiol. https://doi.org/10.1128/JCM.00211-17 Li, J., Lawson Handley, L.J., Read, D.S., Hänfling, B., 2018. The effect of filtration method on the efficiency of environmental DNA capture and quantification via metabarcoding. Mol Ecol Resour 18, 1102–1114. https://doi.org/10.1111/1755-0998.12899 Lynggaard, C., Bertelsen, M.F., Jensen, C. V., Johnson, M.S., Frøslev, T.G., Olsen, M.T., Bohmann, K., 2022. Airborne environmental DNA for terrestrial vertebrate community monitoring. Current Biology 32, 701-707.e5. https://doi.org/10.1016/j.cub.2021.12.014 Lynggaard, C., Frøslev, T.G., Johnson, M.S., Olsen, M.T., Bohmann, K., 2024. Airborne environmental DNA captures terrestrial vertebrate diversity in nature. Mol Ecol Resour 24. https://doi.org/10.1111/1755-0998.13840 Macher, T.H., Schütz, R., Arle, J., Beermann, A.J., Koschorreck, J., Leese, F., 2021. Beyond fish edna metabarcoding: Field replicates disproportionately improve the detection of stream associated vertebrate species. Metabarcoding Metagenom 5, 59–71. https://doi.org/10.3897/mbmg.5.66557 Macher, T.H., Schütz, R., Hörren, T., Beermann, A.J., Leese, F., 2023. It’s raining species: Rainwash eDNA metabarcoding as a minimally invasive method to assess tree canopy invertebrate diversity. Environmental DNA 5, 3–11. https://doi.org/10.1002/edn3.372 Mächler, E., Deiner, K., Steinmann, P., Altermatt, F., 2014. Utility of Environmental DNA for Monitoring Rare and Indicator Macroinvertebrate Species. Freshwater Science 33, 1174–1183. https://doi.org/10.1086/678128 Mariani, S., Harper, L.R., Collins, R.A., Baillie, C., Wangensteen, O.S., McDevitt, A.D., Heddell-Cowie, M., Genner, M.J., 2021. Estuarine molecular bycatch as a landscape-wide biomonitoring tool. Biol Conserv 261. https://doi.org/10.1016/j.biocon.2021.109287 Mauvisseau, Q., Harper, L.R., Sander, M., Hanner, R.H., Kleyer, H., Deiner, K., 2022. The Multiple States of Environmental DNA and What Is Known about Their Persistence in Aquatic Environments. Environ Sci Technol. https://doi.org/10.1021/acs.est.1c07638 Minamoto, T., Fukuda, M., Katsuhara, K.R., Fujiwara, A., Hidaka, S., Yamamoto, S., Takahashi, K., Masuda, R., 2017. Environmental DNA reflects spatial and temporal jellyfish distribution. PLoS One 12. https://doi.org/10.1371/journal.pone.0173073 Minamoto, T., Naka, T., Moji, K., Maruyama, A., 2016. Techniques for the practical collection of environmental DNA: filter selection, preservation, and extraction. Limnology (Tokyo) 17, 23–32. https://doi.org/10.1007/s10201-015-0457-4 Minamoto, T., Yamanaka, H., Takahara, T., Honjo, M.N., Kawabata, Z., 2012. Surveillance of fish species composition using environmental DNA. Limnology (Tokyo). https://doi.org/10.1007/s10201-011-0362-4 Nathan, L.M., Simmons, M., Wegleitner, B.J., Jerde, C.L., Mahon, A.R., 2014. Quantifying environmental DNA signals for aquatic invasive species across multiple detection platforms. Environ Sci Technol 48, 12800–12806. https://doi.org/10.1021/es5034052 Nevers, M.B., Przybyla-Kelly, K., Shively, D., Morris, C.C., Dickey, J., Byappanahalli, M.N., 2020. Influence of sediment and stream transport on detecting a source of environmental DNA. PLoS One 15. https://doi.org/10.1371/journal.pone.0244086 Newton, J.P., Nevill, P., Bateman, P.W., Campbell, M.A., Allentoft, M.E., 2024. Spider webs capture environmental DNA from terrestrial vertebrates. iScience 27. https://doi.org/10.1016/j.isci.2024.108904 Nukazawa, K., Akahoshi, K., Suzuki, Y., 2020. Are bacteria potential sources of fish environmental DNA? PLoS One 15. https://doi.org/10.1371/journal.pone.0230174 Nukazawa, K., Hamasuna, Y., Suzuki, Y., 2018. Simulating the Advection and Degradation of the Environmental DNA of Common Carp along a River. Environ Sci Technol 52, 10562–10570. https://doi.org/10.1021/acs.est.8b02293 O’Mahony, É.N., Sremba, A.L., Keen, E.M., Robinson, N., Dundas, A., Steel, D., Wray, J., Baker, C.S., Gaggiotti, O.E., 2024. Collecting baleen whale blow samples by drone: A minimally intrusive tool for conservation genetics. Mol Ecol Resour. https://doi.org/10.1111/1755-0998.13957 Pawlowski, J., Bonin, A., Boyer, F., Cordier, T., Taberlet, P., 2021. Environmental DNA for biomonitoring, in: Molecular Ecology. John Wiley and Sons Inc, pp. 2931–2936. https://doi.org/10.1111/mec.16023 Preston, C., Yamahara, K., Pargett, D., Weinstock, C., Birch, J., Roman, B., Jensen, S., Connon, B., Jenkins, R., Ryan, J., Scholin, C., 2024. Autonomous eDNA collection using an uncrewed surface vessel over a 4200-km transect of the eastern Pacific Ocean. Environmental DNA 6. https://doi.org/10.1002/edn3.468 R Development Core Team. (2020). R: A language and environment for statistical computing . R Foundation for Statistical Computing. Reinhardt, T., van Schingen, M., Windisch, H.S., Nguyen, T.Q., Ziegler, T., Fink, P., 2019. Monitoring a loss: Detection of the semi-aquatic crocodile lizard (Shinisaurus crocodilurus) in inaccessible habitats via environmental DNA. Aquat Conserv 29, 353–360. https://doi.org/10.1002/aqc.3038 Ritter, C.D., Dal Pont, G., Stika, P.V., Horodesky, A., Cozer, N., Netto, O.S.M., Henn, C., Ostrensky, A., Pie, M.R., 2022. Wanted not, wasted not: Searching for non-target taxa in environmental DNA metabarcoding by-catch. Environmental Advances 7. https://doi.org/10.1016/j.envadv.2022.100169 Rodgers, T.W., Mock, K.E., 2015. Drinking water as a source of environmental DNA for the detection of terrestrial wildlife species. Conserv Genet Resour 7, 693–696. https://doi.org/10.1007/s12686-015-0478-7 Rota, N., Canedoli, C., Ferrè, C., Ficetola, G.F., Guerrieri, A., Padoa-Schioppa, E., 2020. Evaluation of soil biodiversity in alpine habitats through eDNA metabarcoding and relationships with environmental features. Forests 11. https://doi.org/10.3390/F11070738 Sanches, T.M., Schreier, A.D., 2020. Optimizing an eDNA protocol for estuarine environments: Balancing sensitivity, cost and time. PLoS One 15. https://doi.org/10.1371/journal.pone.0233522 Seeber, P.A., Epp, L.S., 2022. Environmental DNA and metagenomics of terrestrial mammals as keystone taxa of recent and past ecosystems. Mamm Rev. https://doi.org/10.1111/mam.12302 Seymour, M., Durance, I., Cosby, B.J., Ransom-Jones, E., Deiner, K., Ormerod, S.J., Colbourne, J.K., Wilgar, G., Carvalho, G.R., de Bruyn, M., Edwards, F., Emmett, B.A., Bik, H.M., Creer, S., 2018. Acidity promotes degradation of multi-species environmental DNA in lotic mesocosms. Commun Biol 1. https://doi.org/10.1038/s42003-017-0005-3 Staley, Z.R., Chuong, J.D., Hill, S.J., Grabuski, J., Shokralla, S., Hajibabaei, M., Edge, T.A., 2018. Fecal source tracking and eDNA profiling in an urban creek following an extreme rain event. Sci Rep 8. https://doi.org/10.1038/s41598-018-32680-z Strickler, K.M., Fremier, A.K., Goldberg, C.S., 2015. Quantifying effects of UV-B, temperature, and pH on eDNA degradation in aquatic microcosms. Biol Conserv 183, 85–92. https://doi.org/10.1016/j.biocon.2014.11.038 Suren, A.M., Burdon, F.J., Wilkinson, S.P., 2024. eDNA Is a Useful Environmental Monitoring Tool for Assessing Stream Ecological Health. Environmental DNA 6. https://doi.org/10.1002/edn3.596 Taberlet, P., Coissac, E., Hajibabaei, M., Rieseberg, L.H., 2012a. Environmental DNA. Mol Ecol. https://doi.org/10.1111/j.1365-294X.2012.05542.x Taberlet, P., Prud’Homme, S.M., Campione, E., Roy, J., Miquel, C., Shehzad, W., Gielly, L., Rioux, D., Choler, P., Clément, J.C., Melodelima, C., Pompanon, F., Coissac, E., 2012b. Soil sampling and isolation of extracellular DNA from large amount of starting material suitable for metabarcoding studies. Mol Ecol 21, 1816–1820. https://doi.org/10.1111/j.1365-294X.2011.05317.x Taberlet, P., Prud’Homme, S.M., Campione, E., Roy, J., Miquel, C., Shehzad, W., Gielly, L., Rioux, D., Choler, P., Clément, J.C., Melodelima, C., Pompanon, F., Coissac, E., 2012c. Soil sampling and isolation of extracellular DNA from large amount of starting material suitable for metabarcoding studies. Mol Ecol 21, 1816–1820. https://doi.org/10.1111/j.1365-294X.2011.05317.x Takahara, T., Minamoto, T., Doi, H., 2015. Effects of sample processing on the detection rate of environmental DNA from the Common Carp (Cyprinus carpio). Biol Conserv 183, 64–69. https://doi.org/10.1016/j.biocon.2014.11.014 Takahashi, M., Saccò, M., Kestel, J.H., Nester, G., Campbell, M.A., van der Heyde, M., Heydenrych, M.J., Juszkiewicz, D.J., Nevill, P., Dawkins, K.L., Bessey, C., Fernandes, K., Miller, H., Power, M., Mousavi-Derazmahalleh, M., Newton, J.P., White, N.E., Richards, Z.T., Allentoft, M.E., 2023. Aquatic environmental DNA: A review of the macro-organismal biomonitoring revolution. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2023.162322 Takahashi, S., Sakata, M.K., Minamoto, T., Masuda, R., 2020. Comparing the efficiency of open and enclosed filtration systems in environmental DNA quantification for fish and jellyfish. PLoS One 15. https://doi.org/10.1371/journal.pone.0231718 Thomsen, P.F., Sigsgaard, E.E., 2019. Environmental DNA metabarcoding of wild flowers reveals diverse communities of terrestrial arthropods. Ecol Evol 9, 1665–1679. https://doi.org/10.1002/ece3.4809 Ushio, M., Fukuda, H., Inoue, T., Makoto, K., Kishida, O., Sato, K., Murata, K., Nikaido, M., Sado, T., Sato, Y., Takeshita, M., Iwasaki, W., Yamanaka, H., Kondoh, M., Miya, M., 2017. Environmental DNA enables detection of terrestrial mammals from forest pond water. Mol Ecol Resour 17, e63–e75. https://doi.org/10.1111/1755-0998.12690 Valentin, R.E., Kyle, K.E., Allen, M.C., Welbourne, D.J., Lockwood, J.L., 2021. The state, transport, and fate of aboveground terrestrial arthropod eDNA. Environmental DNA 3, 1081–1092. https://doi.org/10.1002/edn3.229 Valentini, A., Taberlet, P., Miaud, C., Civade, R., Herder, J., Thomsen, P.F., Bellemain, E., Besnard, A., Coissac, E., Boyer, F., Gaboriaud, C., Jean, P., Poulet, N., Roset, N., Copp, G.H., Geniez, P., Pont, D., Argillier, C., Baudoin, J.M., Peroux, T., Crivelli, A.J., Olivier, A., Acqueberge, M., Le Brun, M., Møller, P.R., Willerslev, E., Dejean, T., 2016. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol Ecol 25, 929–942. https://doi.org/10.1111/mec.13428 van der Heyde, M., Bunce, M., Wardell-Johnson, G., Fernandes, K., White, N.E., Nevill, P., 2020. Testing multiple substrates for terrestrial biodiversity monitoring using environmental DNA metabarcoding. Mol Ecol Resour 20, 732–745. https://doi.org/10.1111/1755-0998.13148 Williams, K.E., Huyvaert, K.P., Piaggio, A.J., 2017a. Clearing muddied waters: Capture of environmental DNA from turbid waters. PLoS One 12. https://doi.org/10.1371/journal.pone.0179282 Williams, K.E., Huyvaert, K.P., Piaggio, A.J., 2017b. Clearing muddied waters: Capture of environmental DNA from turbid waters. PLoS One 12. https://doi.org/10.1371/journal.pone.0179282 Yang, H., Du, H., Qi, H., Yu, L., Hou, X., Zhang, H., Li, J., Wu, J., Wang, C., Zhou, Q., Wei, Q., 2021. Effectiveness assessment of using riverine water eDNA to simultaneously monitor the riverine and riparian biodiversity information. Sci Rep 11. https://doi.org/10.1038/s41598-021-03733-7 Data Accessibility and Benefit-Sharing Data Accessibility All relevant data are included in the paper or its Supplementary Information. PCR data available after published: https://doi.org/10.5061/dryad.2fqz61314 Benefit Sharing This study did not involve any direct contact with animals in the survey area, particularly wild or endangered species. We are committed to investigating and detecting biodiversity without direct interaction with the natural fauna. Author Contributions CX performed the experiment, analysed data, and led the writing of manuscript. KN conceived the ideas, supervised the research and acquired the funds. KN and CX performed the sampling and edited and approved the manuscript. Conflict of Interest The authors declared no conflict of interest for this article. Tables Table 1 . Results of water quality analysis. The ”Sampling1” and ”Sampling2” indicate the first and second sampling of the day, respectively. “-” indicates not recorded. 2021_9_Tagami_Sampling1 - - - - 15:00 2021_9_Tagami_Sampling2 - - - - 17:00 2021_9_Irrigation_Sampling1 - - - - 15:15 2021_9_Irrigation_Sampling2 - - - - 17:15 2021_11_Mizunashi_Sampling1 55.2 111.6 7.37 - 10:00 2021_11_Mizunashi_Sampling2 5.78 119.3 7.37 - 12:00 2021_11_Tagami_Sampling1 6.72 119.4 7.25 - 11:40 2021_11_Tagami_Sampling2 4.07 126.6 7.27 - 13:40 2021_11_Irrigation_Sampling1 0.96 164.7 7.18 - 11:15 2021_11_Irrigation_Sampling2 0.45 166.2 7.08 - 13:15 2022_5_Mizunashi_Sampling1 19.3 53.6 7.96 14.9 13:23 2022_5_Mizunashi_Sampling2 25.38 61.1 7.91 17.7 15:28 2022_5_Oka_Sampling1 174.52 76.7 7.44 19.7 13:47 2022_5_Oka_Sampling2 46.73 71.3 7.18 18.4 15:46 2022_9_Oka_Sampling1 63.25 55.7 7.7 21.6 7:36 2022_9_Tagami_Sampling1 16.72 67.3 7.11 24.2 8:20 2022_11_Mizunashi_Sampling1 45.12 98.2 7.87 18 7:30 2022_11_Tagami_Sampling1 138.54 39.8 7.18 19 8:40 2022_11_Irrigation_Sampling1 189.94 25.8 7.33 19 8:21 2023_6_Mizunashi_Sampling1 285.82 - 7.72 17.9 7:29 2023_6_Mizunashi_Sampling2 110.31 56.6 6.61 18 9:54 2023_6_Oka_Sampling1 314.34 - 7.47 20.9 9:54 2023_6_Oka_Sampling2 112.33 67.1 6.09 21 10:14 2023_6_Tagami_Sampling1 12.77 - 6.9 21 8:42 2023_6_Tagami_Sampling2 7.43 76.7 5.98 21 11:01 Table 2 . Effects of filter type and turbidity on filtration time (a) and filtration volume (b) based on the generalized linear models. Factors Estimate Std. Error z value p (Intercept) 661.658 264.985 2.497 0.0125 FilterGF/D -483.941 273.395 -1.77 0.0767 FilterGF/F 56.584 273.395 0.207 0.836 Turbidity 1.824 0.446 4.09 4.31E-05 (b) Factors Estimate Std. Error z value p (Intercept) 754.824 99.238 7.606 2.82E-14 FilterGF/D 290.555 102.387 2.838 0.00454 FilterGF/F 194.222 102.387 1.897 0.05784 Turbidity -2.485 0.167 -14.877 < 2e-16 Table 3 . Effects of environmental factors on eDNA concentration based on generalized linear mixed models. Here Turbidity:FilterGF/F indicates their interaction term. (Intercept) -9.051 3.241 -2.792 0.005 μ 0.073 0.066 1.106 0.269 σ 0.533 0.192 2.771 0.006 Turbidity 0.011 0.005 2.345 0.019 Land distance 0.003 0.003 1.076 0.282 Waterways distance -0.001 0.005 -0.140 0.028 Electrical conductivity 1.392 0.425 3.273 0.888 pH 1.392 0.425 3.273 0.001 FilterGF/F 0.988 0.347 2.842 0.004 Turbidity:FilterGF/F -0.005 0.004 -1.262 0.207 Figure 1 The sampling sites, the meteorological station, and the target cattle sheds (as eDNA point sources) in the Kiyotake River catchment, southwestern Japan (a) and land use distribution map (b). Figure 2 Flow diagram of the sampling process and experiment. The water quality parameters examined were: turbidity, electrical conductivity, pH, and water temperature. During rainfall events, 1 L river water was collected (4 replicates), and filtered using GF/F and GF/D filters (2 replicates each). During non-rainfall events, 1 L river water and riverbed sediment were collected. For the sediment samples, two eDNA extraction methods were employed. Fi gure 3 Partial results of rainfall time-series fit with a normal distribution (red curve; full results are available in the appendix-1). The ”Sampling_1” and ”Sampling_2” indicate the first and second sampling of the day, respectively. “σ” and “μ” represent the variance and mean of normal distribution, here indicating duration of rainfall and intense rainfall timing, respectively. The time ’24’ represents the sampling time for each sampling event. Figure 4 eDNA concentrations during different rainfall events (a) and non-rainfall events (b). ”Sampling_1” and ”Sampling_2” indicate the first and second sampling of the day, respectively. “M”, “O”, “T”, and “I” represent the Mizunashi River, Oka River, Tagami River, and irrigation canal. “F” indicates results of a glass fiber filter with a pore size of 0.7 μm while “D” indicates a glass fiber filter with a pore size of 2.7 μm. ”N.D.” indicates that the target eDNA was not detected. ”W” indicate results of water samples, ”S-N” represents the sediment extraction results based on the method referenced from Nevers et al. ”S-T” represents the sediment extraction results based on the method referenced from Taberlet et al. Information & Authors Information Version history V1 Version 1 28 March 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords digital pcr environmental dna generalized linear mixed model pore size terrestrial mammals turbid water Authors Affiliations CHEN XU 0009-0004-2487-1625 University of Miyazki - Kibana Campus View all articles by this author KEI NUKAZAWA 0000-0001-5356-2064 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 460 views 207 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation CHEN XU, KEI NUKAZAWA. Detection of terrestrial species using environmental DNA during heavy rainfall events and associated influencing factors. Authorea . 28 March 2025. DOI: https://doi.org/10.22541/au.174312906.60655689/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 . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); Cited by Manuela Mauro, Rosi De Luca, Mario Lo Valvo, Slobodanka Radovic, Aiti Vizzini, Grazia Orecchio, Francesco Longo, Vinicius Queiroz, Rosario Badalamenti, Claudio Gargano, Mirella Vazzana, Environmental DNA: A Preliminary Characterization of Invertebrate Biodiversity in a Sicilian River, Environments, 12 , 12, (465), (2025). https://doi.org/10.3390/environments12120465 Crossref Loading... View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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