Optimising eDNA analysis for urban otter monitoring: seasonal patterns, detection strategies and prey availability

Significant advancements in environmental DNA (eDNA) analysis technology have led to its widespread adoption for species monitoring. However, the low DNA concentration in eDNA often raises concerns about PCR bias, which is a primary issue in enhancing the reliability of eDNA survey results. This study aims to identify the suitable eDNA anal - ysis method and detection strategy for obtaining the presence data of Eurasian otters in an urban stream and to investigate the seasonal distribution patterns of Eurasian otters and their potential prey species. The eDNA survey revealed the presence of six out of the 13 mammal species recorded in fieldwork and literature reviews, including Canis lupus , Felis silvestris and Mustela sibirica . Notably, Eurasian otters were not de - tected in the eDNA metabarcoding conducted in April and October, despite traditional surveys confirming their presence. In contrast, qPCR assays successfully amplified Eur - asian otters from the same samples that were analysed using metabarcoding. When evaluating detection criteria, based on the number of positive samples in repetitions, Eurasian otters were detected at 42.9% to 85.7% of the sampling sites in April and at all sampling sites in October. This suggests a higher detection probability of Eurasian ot - ters in October, indicating a potential expansion of their home range during that season compared to April. Metabarcoding results revealed similar findings regarding fish spe - cies as traditional surveys, with Cyprinidae accounting for the largest proportion of fish species at the family level (April, 54.57%; October, 43.58%), followed by Gobiidae (April, 16.90%; October, 22.88%). At the species level, P. parva was the dominant fish species in Saetgang, constituting 5.68% and 6.35% of relative abundance in April and October, respectively. This implies that Eurasian otters as opportunistic predators, may increas - ingly take advantage of the availability of species within the family Cyprinidae, notably Pseudorasbora parv a, as a food source within the study area during the months of April and October. This study highlights that qPCR is the more effective approach in urban areas to offer insights into otters’ distribution patterns, while metabarcoding is useful to provide the properties of the biological environment. Furthermore, this study indicated that it is necessary to determine the suitable eDNA analysis methods depending on the research purpose to obtain detection results effectively.


Introduction
Environmental DNA (eDNA) analysis technology has advanced considerably in recent years, leading to the widespread use of eDNA surveys for species monitoring.This conservative investigation method involves detecting genetic material released by organisms (such as faeces, skin, hair and eggs) and is characterised by a brief analysis process and high sensitivity (Bohmann et al. 2014).Next-generation sequencing enables the detection of multiple species and determination of species composition in environmental samples (Valentini et al. 2016).Alternatively, targeted PCR using conventional or quantitative PCR (qPCR) with a set of species-specific primers can effectively identify a single species (Davy et al. 2015;Hernandez et al. 2020).Previous studies have shown that eDNA results can provide not only presence-absence data, but also quantitative data, given the positive proportion correlation between eDNA amplification and the biomass/population size of species (Takahara et al. 2012;Di Muri et al. 2020;Breton et al. 2022).This approach has been utilised to describe distribution patterns and population sequential changes in several studies, especially for rare and low-density species, such as endangered and invasive species (Thomsen et al. 2012;Buxton et al. 2018;Zhang et al. 2019).Several studies have suggested that eDNA investigations can be used to understand the ecological characteristics of species, such as distribution patterns, potential habitat estimation and food resource analysis.The discovery of species through eDNA is not limited to a specific taxonomic group, but has also been used to detect various species, such as mammals, birds, reptiles and amphibians (Miya et al. 2015;Ishige et al. 2017;Ushio et al. 2018).For example, Buxton et al. (2018) evaluated the seasonal changes in the eDNA concentration of Triturus cristatus and discovered that the concentration of eDNA reflected the breeding season and larval abundance.Yamamoto et al. (2017) used eDNA metabarcoding to define fish community structures in species-rich coastal areas.Furthermore, eDNA detection results have been applied as input data to model settings, such as species distribution models (SDMs) and occupancy models for evaluation and prediction (Muha et al. 2017;Da Silva Neto et al. 2020;Riaz et al. 2020).
On the one hand, eDNA has a low concentration of DNA, so improving accuracy and reproducibility has consistently been required.PCR bias is usually mentioned as the main issue that needs to be controlled to increase the reliability of eDNA survey results (Smith and Peay 2014;Krehenwinkel et al. 2017;Mauvisseau et al. 2019;Shirazi et al. 2021).For instance, when using eDNA metabarcoding to detect terrestrial animals in a water body, the 'species masking effect' can occur, whereby high-abundance species like fish prevent the detection of low-abundance species like terrestrial mammals (Lintermans 2016;Yu et al. 2022).Therefore, targeted PCR for single species detection could be more effective in enhancing the detection probability of fewer individuals or lower DNA concentration.However, targeted PCR with low-concentration samples can produce different presence-absence assessments of target species depending on the number of PCR repetitions in the laboratory or sampling repetitions at study sites.Consequently, it is important to determine the differences in detection results, especially for low-abundance species, by experimental conditions and target species and to select the appropriate methodology according to the purpose of the survey.
The Eurasian otter (Lutra lutra) is an endangered species distributed widely from Europe to Asia (Loy et al. 2022).It is a carnivorous animal that feeds on fish, birds and other aquatic organisms and plays a crucial role in evaluating the stability of ecosystems as an umbrella species (Erlinge 1967).With the increase in food sources due to urban stream restoration projects and water quality management, Eurasian otters have expanded their habitat to urban areas in Korea.As a semi-aquatic mammal that inhabits water bodies and has low density in certain areas, it has been selected as a model species to compare the detection probability between metabarcoding and qPCR using water samples (Harper et al. 2019a;Macher et al. 2021;Mariani et al. 2021;Ritter et al. 2022).Previous investigations have employed metabarcoding and qPCR methodologies to explore the distribution and dietary habits of Eurasian otters (Harper et al. 2020;Jang-Liaw 2021;Jamwal et al. 2023).Additionally, species diversity has been assessed through the analysis of spraints metabarcoding (Dou et al. 2023).The research question of this study is to determine the appropriate eDNA analysis method and detection strategy to obtain the presence data of Eurasian otters.Subsequently, based on the eDNA metabarcoding and qPCR results, we aim to investigate the distribution pattern of Eurasian otters and potential food species by season.

Study area and sampling design
eDNA surveys and trace-tracking fieldwork were conducted in Yeouido Saetgang Ecological Park, which is in Seoul, South Korea.Saetgang is a small river that branches off from the Han River and flows around an alluvial island called 'Yeouido'.The Yeouido Saetgang Ecological Park is a green space that emphasises ecological elements, such as emergent plant communities, eco-friendly ponds and bioswales, with a total length of 4.6 km and a total area of 758,000 m 2 .To identify the typical distribution of the Eurasian otter in Saetgang, both surveys were conducted from the river entrance to the confluence points.Along the river, six sampling sites were located at intervals of approximately 500 m (YS1, YS2, YS4, YS5, YS6 and YS7).Additionally, one sampling site was in a pond connected to the river inside the Ecological Park (YS3) (Fig. 1).To assess the physical environment at each study site, we examined stream width, actual water width and substrate composition.The substrate was assessed in five classes, based on particle size: B (Boulder, > 256 mm), C (Cobble, 64-256 mm), P (Pebble, 4-64 mm), G (Gravel, 2-4 mm) and S (Silt, < 2 mm) (Suppl.material 3).

Sampling: eDNA and traditional field survey
The eDNA sampling at seven sites in study area was conducted on 27 April (spring) and 11 October 2022 (autumn).To compile an adequate species list for the study area, we used Sterivex cartridge filters with a 0.45 µm pore size (Millipore), assuming replication and filtered 330 ml through each filter for a total of 990 ml of water (Sato et al. 2017;Dickie et al. 2018).In addition, we filtered the water in the vivarium of Eurasian otters at Korean otter Research Center as the positive eDNA sample.Contamination occurring during the sampling process was monitored using negative controls.Independent sampling materials including cartridge filter and 30 ml syringe were used to prevent contamination.After filtration, cartridge filters were stored in an ice box to prevent degradation and later moved to the laboratory and kept at -20 °C in a refrigerator before extraction.DNA was extracted using the DNeasy Blood and Tissue Kit (Qiagen, Germany).Extracted DNA was stored at -20 °C and quantified using a Nanodrop 2000c (Thermo Fisher Scientific, Waltham, MA, USA) before sequencing.
To enhance the reliability of the environmental DNA survey findings, simultaneous observational surveys for fish and mammals were conducted.The traditional field survey, an indirect survey to identify mammalian habitats and traces of movement, was carried out by professionals in pairs on 21 July (summer) and 18 October (autumn) 2022.Summer and autumn were chosen as the survey periods to accurately ascertain the presence of otters in the target area, given that these seasons correspond to their low and high activity phases in South Korea, respectively (Kim 2018;Jo and Won 2019;Lee et al. 2020).Traces such as tunnels, footprints, dead bodies, food foraging, scat, ground nest and hair were investigated along the Saetgang.During the same period, fish were surveyed using a kick net, collecting fish for 40 minutes at each survey point and a casting net was employed 10 times.At the collection site, fish were identified to the species level, based on their morphological traits using Korean reference books (Kim 1997;Kim and Park 2002;Kim et al. 2005) and then released.This survey method is routinely utilised in the national natural environment survey in Korea (National Institute of Environmental Research 2016) In addition, a literature review preceded the fieldwork to determine the past presence of the Eurasian otter and other mammals in Yeouido between 2002 and 2019 (Gang et al. 2015;Seoul Metropolitan Government 2017;Yeongdeungpo Cultural Foundation 2020).

Metabarcoding of vertebrate species
To determine vertebrate species and their taxon, DNA amplification was used together with three universal primers called MiFish (Miya et al. 2015), MiBird (Ushio et al. 2018) and MiMammal (Ushio et al. 2017).In order to create the Next Generation Sequencing (NGS) library, two phases of PCR were carried out.The first PCR was conducted with a total reaction volume of 12 μl, comprising 2 μl of DNA template, 6 μl of 2× KAPA HiFi ready mix (KAPA Biosystems, Inc., Wilmington, MA, USA), 0.36 μl of forward universal primer and 0.36 μl of reverse primer, with the remaining volume of 3.28 μl filled with distilled water.The three universal primers (MiFish, MiMammal, MiBird) were individually utilised in PCR reaction at a concentration of 10 µM.The primary PCR conditions were set to 35 cycles of 98 °C for 20 seconds of denaturation, 65 °C for 15 seconds of annealing, 72 °C for 15 seconds of extension and 72 °C for 5 minutes.PCR products were visualised on a 1% agarose gel to evaluate amplification success.Prior to the second PCR, the PCR product was purified using 20 μl of Ampure XP Beads.After purification, the concentration of the product was measured using the Infinite F200 PRO (Tecan) and normalised to 20 ng/μl.The reaction mixture for the second PCR was prepared with a total volume of 12 μl, consisting of 6 μl of 2× KAPA HiFi Ready Mix, 3 μl of distilled water, 1 μl of each unique dual index ID (P5 [Nextera, S5, 1 μM] and P7 [Nextera, N7XX, 1 μM], totalling 2 μl) and 1 μl of the normalised DNA template.The cycling conditions were identical to those of the first PCR, except for reducing the cycle number to 12. Following the second PCR, the concentration was measured again using the Infinite F200 PRO and adjusted to 100 ng/μl for sequencing.The Illumina libraries were sequenced using a MiSeq Reagent Kit v.3 (600-cycles) on an Illumina MiSeq 300PE platform (Illumina, San Diego, CA, USA).To address potential contamination, any amplification observed in negative controls may warrant exclusion of the detected organisms or the establishment of a threshold to filter out low-level noise (Forstchen 2020).Additionally, increased sequencing depth enhances the detection of rare species, with sequencing depths between 50,000 and 200,000 reads generally considered appropriate for metagenomic analyses (Bruce et al. 2021).Therefore, in this study, MiSeq sequencing results yielding fewer than 50,000 reads were considered negative, indicating a lack of amplification (Suppl.material 4).
The MiFish pipeline's biogenetic information DB and the FASTQ file it analysed were compared (http://mitofish.aori.u-tokyo.ac.jp/mifish) to create a species list (Sato et al. 2018).For the bioinformatic processing of FASTQ files generated using MiBird and MiMammal, the PMiFish bioinformatics pipeline (PMiFish v. 2.4; the latest version is available from https://github.com/rogotoh/PMiFish.git;Miya et al. 2020) was utilised.Data preprocessing and analysis of MiSeq raw reads are performed using USEARCH v.10.0.240 (Edgar and Bateman 2010).The process begins with the assembly of quality-filtered forward (R1) and reverse (R2) reads.Subsequently, a length filter is applied to remove primer sequences and retain only those sequences that are not shorter than 140 bp.This is followed by quality filtering to further refine sequence integrity.After quality filtering, sequences undergo dereplication, where a depth filter is applied to ensure that only sequences with at least four replicates are retained.The next steps include denoising, to reduce error rates and taxonomic assignment of the cleaned and verified sequences.Results are rejected for sequences that are ambiguous or chimeric, with a BLASTn similarity of less than 97% to the clustered sequence.
Species list derived from metabarcoding results were refined in consideration of the study area (Gang et al. 2015;Seoul Metropolitan Government 2017;Yeongdeungpo Cultural Foundation 2020).The saltwater fish, alien species undocumented in South Korea and species extinct within native natural ecosystems were omitted from the detected species list (National Institute of Biological Resources 2021).Besides, considering the environment of the target site, some mammal detection results that do not display subspecies, such as wild boars (Sus scrofa) and wolves (Canis lupus), were recorded as domestic pigs (Sus scrofa domesticus) and dogs (Canis lupus familiaris), including subspecies.To ensure the accurate estimation of potential food species, we conducted a comparison between the species abundance, quantified by the number of individuals, obtained from traditional surveys and the species abundance measured in terms of the number of reads through eDNA metabarcoding.Before the analysis, the number of eDNA reads underwent a natural logarithmic transformation to standardise the data, consistent with the methodology employed in prior studies (Yates et al. 2019(Yates et al. , 2021;;Rourke et al. 2022).

Primer validation
To amplify eDNA of L. lutra, otter-specific primers reported by (Park et al. 2011) were used.A set of primers, LutcytF, 'CCACAATCCTCAACAACTCGC' and LutcytR, 'CTCCGTTTGGGTGTATGTATCG', were designed to amplify a 227 bp long amplicon of the mitochondrial cytochrome b.These reference primers can amplify eDNA of L. lutra by conventional PCR using otter tissue and positive eDNA samples collected in a vivarium of L. lutra and at study sites in this study.The amplification conditions for validation included an initial denaturation at 94 °C for 5 minutes, followed by 35 cycles of 94 °C for 30 seconds, 50 °C for 30 seconds and 72 °C for 30 seconds, with a final extension at 72 °C for 7 minutes.As the reaction mix, i-StarTaq™ DNA Polymerase (iNtRON Biotechnology, South Korea) was used for conventional PCR.The reaction mix used i-StarTaq™ DNA Polymerase (iNtRON Biotechnology, South Korea) for conventional PCR.The composition of the reaction mix is as follows: per sample, 0.5 μl of forward primer and 0.5 μl of reverse primer (10 pM), 1 μl of 10× buffer, 1 μl of dNTPs, 0.1 μl of i-StarTaq™ DNA polymerase and 5.9 μl of distilled water (Suppl.material 1).

Detection thresholds and quantification
Quantitative real time PCR (qPCR) assays were conducted with an Quantstudio 3 (ThermoFisher) instrument.qPCR conditions consisted in an initial denaturation at 95 °C for 10 minutes, followed by 35 cycles of 94 °C for 30 seconds, 50 °C for 30 seconds and 72 °C for 30 seconds and melt curve stage for standard curve.For eDNA sample assay, the cycle was increased to 40 under the same PCR conditions.TOPreal™ SYBR Green qPCR PreMIX (Enzynomix) was used for reaction mix.In accordance with the total capacity of 10 μl, it was composed of TOPreal SYBR Green qPCR PreMIX 5 µl, DNA 2 μl, LutcytF 0.5 μl and LutcytR 0.5 μl per sample.Template DNA was 1 μl for generating the standard curve and 2 μl for the eDNA assay.For the negative control samples, an additional 2 μl of ultrapure water was added in place of the DNA template to monitor for contamination.To generate a standard curve, a 10-fold serial dilution was performed from 90 ng/µl of genomic DNA of otter and the assay was repeated in triplicate.The appropriateness of the primer efficiency and standard curve was assessed by ensuring that the efficiency percentage fell between 90 and 110% (Raymaekers et al. 2009).The Limit of Detection (LoD) was calculated using the following equation: where σ represents the standard deviation intercept and s denotes the absolute value of the slope: A positive sample was determined, based on the LoD and the eDNA concentration was calculated through the standard curve function.Positive eDNA samples were also assessed by threshold criteria.'Without Threshold (WT)' refers to a case where a positive is confirmed in one or more of sampling and qPCR repetition.'Repetition Thresholds (RT)' means determining a positive sample when detecting more than three times out of 10 repetitions of qPCR.'Sample Thresholds (ST)' are evaluated as positive samples when detected more than twice out of three repeated samples.In the absence of any threshold criterion, the lowest reliability is assumed and, in the case of applying the threshold criterion at the sample and repetition level, the highest reliability is assumed.Ct = Slope * log 10 (Quantity) + Y intercept Quantity = 10 (Ct -Yintercept)/Slope

Field survey results based on observation and literature review
A total of eight families and 13 species of mammals were recorded in the field survey and literature review (Table 1).From 2008 to 2019, there were no records of Eurasian otters in the study area.However, footprints of the Eurasian otter were observed twice during fieldwork for this study in October 2022.Given that the known home ranges of female and male otters are 7 km and 15 km, respectively (Néill et al. 2009), it is suggested that the otter can be detected across the study area, which spans 4.5 km.Additionally, mammals commonly found adjacent to urban areas, such as raccoon dogs, feral cats and rodents which were detected as the study sites, are within an urban area.Our observations and literature review did not reveal any presence of wild boars in the study area.
In our field study for fish, we identified a total of four families and 16 different fish species (Table 2).Fish abundance was notably higher at specific locations, particularly at the stream entrance (YS 1) and the trough (YS3), compared to other survey sites.There was a discrepancy in the number of species collected during April and October, but the overall species count remained consistent.Moreover, the autumn season exhibited a higher abundance of fish, with 381 individuals observed compared to 285 in the spring.Amongst the species observed, the number of Hemiculter leucisculus individuals increased from 72 in the spring to 148 in autumn.

Comparing the detectability Eurasian Otters using eDNA: Metabarcoding vs. qPCR
The mammal list derived from metabarcoding results showed a total of six families and 10 species (Table 3).Negative samples, which were amplified to under 50,000 reads after MiSeq sequencing, indicated that there was no cross-contamination between samples.The eDNA survey revealed 6 of 13 species recorded in fieldworks and literature reviews, but there was no detection of Eurasian otters in both eDNA results (Fig. 2).Throughout surveys in April and October, Sus scrofa accounted for the highest percentage of reads (88.2%, 548,880 reads), followed by Bos taurus (6.7%, 41,745 reads), Ovis aries (2.9%, 18,078 reads) and Mus musculus (1.4%, 8,541 reads).It is estimated that genetic materials from domestic animals commonly consumed by people in the city were detected by the eDNA survey.In addition, the common urban species like the Muridae family were consistently identified by metabarcoding across all sampling sites and seasons.
In contrast, the qPCR assay amplified Eurasian otters from the same samples analysed by metabarcoding.The LoD of the assay confirmed by the standard curve (Slope = -3.108,Y-intercept = 24.623,R 2 = 0.949, Eff% = 109.773),was determined to be 0.1 ng/µl DNA (Suppl.material 2).Positive samples were evaluated by the LoD calculated from the standard curve.Of the 10 PCR repetitions with 21 samples, five samples were positive for all 10 times, while nine samples were all negative.The trend line between the detection rate and Ct value showed was inversely proportional (Slope = -2.843Y-intercept = 29.154,R 2 = 0.74) (Fig. 3), suggesting that the number of qPCR repetitions could be determined according to the concentration of samples and their detection rate.For instance, to achieve 100% of detection, the concentration of   eDNA sample should exceed a cycle threshold of 26.3.With a high confidence level and considering the criteria, Eurasian otters were detected at three of seven sites (42.9%) in April (Fig. 4a).When thresholds were not considered, the number of detections increased to six of seven sites (85.7%).The repetition threshold with a high repetition number is more influential than the sample threshold in determining the number of detections.However, in October, despite differences in detection times amongst sample repetitions, eDNA detection confirmed Eurasian otters at all sampling sites (100.0%),regardless of the confidence level (Fig. 4b).

Distribution patterns of L. lutra by seasons
As the detection probability of Eurasian otters is high in October, it is estimated that Eurasian otters expanded their home range during this month compared to April (Fig. 5).In April, without applying thresholds, otters were detected at six sites, except for the YS4 site located in the middle of Saetgang.The average detection ratio at these sites was 37.8 ± 30.2% (mean ± SD).When thresholds were applied to determine positive samples, YS6 and YS7 were classified as absent sites, while the average detection ratio for positive samples increased to 95.0 ± 11.2% (mean ± SD).In October, all sites indicated the presence of Eurasian otters, showing a high average detection ratio of 91.0 ± 14.3% (mean ± SD) without a threshold and 100.0%with a threshold.

The estimation of potential food species for L. lutra in Saetgang
As Eurasian otters are known to prefer fish, making up over 70% of their diet (Suppl.material 5), only fish species identified in the eDNA metabarcoding results were considered potential food species in this study (Krawczyk et al. 2016;Narváez et al. 2020).A total of 3,898,508 and 1,089,451 reads, identified as fish species, were recorded in April and October, respectively.After excluding alien species not recorded in Korea and those unsuitable for freshwater, we retained 3,039,297 (Apr) and 748,001 (Oct) reads, constituting 78.0% and 68.7% of the raw data, respectively (Suppl.material 6).A total of 15 families and 52 species were detected.In both seasons, Cyprinidae accounted for the largest proportion of fish species at the family level (Apr, 54.57%; Oct, 43.58%), followed by Gobiidae (Apr, 16.90%; Oct, 22.88%) (Fig. 6).At the species level, Pseudorasbora parva was the dominant species in Saetgang, representing 5.68% and 6.35% of the natural log-transformed total number of reads (Suppl.material 7).Additionally, Tridentiger obscurus (5.16%), Acheilognathus macropterus (5.16%), Acheilognathus chankaensis (4.65%) and Cyprinus carpio (4.22%) were prominent in April.In October, A.chankaensis (5.05%) and T. obscurus (4.97%) were prevalent and Hemibarbus sp.(4.82%) and Zacco platypus (4.39%) also appeared.Similarly, in the conventional collection-based survey, Cyprinidae (Apr, 81.75%, Oct, 86.61%) and Gobiidae (Apr, 14.04%, Oct, 7.87%) were the dominant families (Table 2).At the species level, there was little difference in species with high relative abundance including H. leucisculus, but it displayed proportions similar to several other species, such as A. macropterus, C. carpio, P. parva and Z. platypus.

Discussion
The ecological characteristics of Eurasian otters in the Yeouido Ecological Park Trace tracking survey and qPCR results confirmed the presence of Eurasian otters in Yeouido Ecological Park.Furthermore, the increased detection probability and the discovery of footprints in October suggest that Eurasian otters expand their home range during autumn.Eurasian otters are known to breed at times of the year when conditions are favourable, typically in early spring between February and March in Korea (Heggberget and Christensen 1994;National Institute of Biological Resources 2018).Since gestation lasts 60 to 70 days and weaning occurs at 3 months, it is estimated that the home range was reduced in April for nurturing purposes, coinciding with a decrease in detection probability (Han and Han 2022).Moreover, the concentration of DNA has been used in several studies to estimate biomass or population sizes (Takahara et al. 2012;Di Muri et al. 2020;Rourke et al. 2022).Therefore, the distribution patterns of Eurasian otters can be determined by the eDNA concentration further from the appearance location (Figs 5, 7).The eDNA survey conducted in October revealed that L. lutra was identified at all study sites.The eDNA concentration indicated that the density of L. lutra was higher at sites YS1 and YS2 compared to other sites, especially the lentic site YS3.As opportunistic predators, otters are likely to exploit locally abundant food sources, adapting their diet to seasonal variations and differences in habitat type (Ruiz-Olmo et al. 2001;Kloskowski et al. 2013).An analysis of prey preferences in Eurasian otters, based on spraint analysis from freshwater and estuarine environments in Korea, revealed that families Cyprinidae, Channidae and Centrarchidae exhibited the highest detection rates (Lee 2012;Kumari et al. 2019).In contrast, species from the orders Mugiliformes, Gadiformes and Pleuronectiformes were more prevalent in marine environments (Choi and Yoon 2012).Notably, the dominance of Cyprinidae, particularly P. parva, in April and October at this study area suggests a high availability of these species as a food source for Eurasian otters.Although environmental DNA surveys offer valuable insights into the habitat and ecological characteristics of species, it is essential to acknowledge and address potential biases that may influence these surveys.

How to improve the detection probability and reliability of rare species
In addition to implementing repeated site sampling and qPCR analysis in the lab to enhance the stability and reliability of PCR results, it is crucial to address challenges, such as the presence of inhibitors and primer efficiency in eDNA surveys (Macher et al. 2021).eDNA surveys enable highly sensitive detection of species that are low in abundance.However, the sensitivity of eDNA detection, especially with low sample concentrations, introduces uncertainties in presence-absence evaluations (Harper et al. 2018;Yu et al. 2022).
This study conducted three independent samplings at each site and ten independent qPCR analyses per sample.Findings showed that, in the 10 repetitions, 71.4% of the samples detected less than three times were considered positive, while those detected more than seven times accounted for the remaining 28.6%.This indicates the necessity of a minimum of three repeats to enhance detection probability.The decision-making process for establishing presence-absence criteria in qPCR assays can, therefore, be tailored, based on these results, with a high threshold for confident presence data or a lower threshold when determining broader habitat occupancy.
Although this study achieved high consistency in qPCR results without proceeding with inhibitor removal, eliminating or reducing inhibitors is vital for achieving high-quality detection results.Field methods, such as collecting small sampling volumes, using large pore-sized filters and increasing sampling intensity, help mitigate inhibitors (U.S. Fish and Wildlife Service 2022).However, these methods might reduce the concentration of target eDNA, necessitating careful application (Harper et al. 2019b).In the lab, commercial master mixes that include inhibitor removal columns and PCR enhancers provide additional tools to counteract inhibitors (Hunter et al. 2019;Sanches and Schreier 2020).
Additionally, understanding the characteristics of the study area, such as Yeouido Saetgang Ecological Park -an urban park vulnerable to genetic pollution from wastewater and artificial factors -is crucial before deciding on the analysis strategy (Lee et al. 2021).This study also found that otters were not detected and industrial and companion animals were frequently identified through eDNA metabarcoding.The study's limitations due to cost and sample amount prevented repetition in metabarcoding, thus restricting comparisons between metabarcoding and qPCR detection rates.Moreover, the efficiency of the MiMammal universal primer was not evaluated against the Lutcyt species-specific primer for Eurasian otters.Although prior research has demonstrated that MiMammal primers can detect Eurasian otters (Ushio et al. 2017;Sales et al. 2020;Kim et al. 2021), further assessments focusing on comparative performance, environmental and sample type suitability and quantitative capabilities are required to improve result robustness (Bustin and Huggett 2017;Kumar et al. 2022).

Limitations and potentials for habitat evaluation and distribution patterns of semi-aquatic mammals with eDNA survey
Compared to other terrestrial mammals, semi-aquatic mammals spend most of their time in the aquatic environment engaging in activities, such as foraging and reproduction.This makes it easier to analyse environmental DNA (eDNA) through water samples.In other words, estimating the distribution and density of these species can be straightforward when obtaining eDNA samples with a certain concentration or higher.However, it is essential to consider the characteristics of the eDNA survey, including degradation, suspension and sedimentation, which can affect eDNA detection and concentration.eDNA particles share similar transport dynamics with fine particulate organic matter (Harrison et al. 2019).Lentic systems, such as lakes and ponds, are primarily influenced by gravitational settling and can disperse eDNA about 100 m (Nevers et al. 2020).Additionally, eDNA concentration in aquatic environments is commonly influenced or degraded by physiochemical factors, such as water turbidity, stratification caused by water temperature and benthic substrate (Deiner and Altermatt 2014;Shogren et al. 2017).Biological factors also play a crucial role.The production of eDNA varies not only with the biomass of the target species or community size, but also with species interactions like predation or competition (Stewart 2019).Therefore, the consideration of abiotic and biotic factors affecting eDNA detection should be reflected in the eDNA sampling and analysis strategy.Moreover, since the most reliable evaluation method involves assessing presence-absence through trace investigation or camera trapping and identifying the source of food through analysis of spraints and stomach contents, it is necessary to complement the eDNA investigation with these traditional methods.

Conclusions
Our study aimed to compare environmental DNA (eDNA) analysis methods for detecting Eurasian otters in urban areas.We utilised both eDNA metabarcoding and qPCR techniques to gain insights into the ecological characteristics of Eurasian otters, including their seasonal distribution patterns and potential prey availability.Our research highlights the effectiveness of qPCR in elucidating otter distribution patterns in urban environments and the utility of metabarcoding in characterising the biological environment.In contrast to metabarcoding, which failed to detect otters, qPCR successfully identified otters at a minimum of three sites within the study area, demonstrating its superior efficacy in assessing otter distribution.qPCR analysis revealed that otters expanded their home range more significantly in October than in April.Additionally, metabarcoding analysis indicated that Cyprinidae, particularly Pseudorasbora parva, exhibited the highest prey availability.However, our study also underscores the importance of meticulously selecting eDNA analysis techniques, especially in environments dominated by certain species.We discovered that pigs (Sus scrofa) comprised over 88.2% of the mammals detected through metabarcoding, highlighting the risk of species-masking effects.This emphasises the need for employing customised eDNA analysis methods suited to the characteristics of the study area.Our study contributes not only to understanding the ecological characteristics of Eurasian otters, but also to enhancing the accuracy and reproducibility of eDNA surveys, thereby enriching our understanding of ecological dynamics in aquatic ecosystems.

Figure 1 .
Figure 1.The location of seven sampling sites in Yeouido Saetgang Ecological Park.

Figure 2 .
Figure 2. The comparison of identified mammal species between traditional survey and eDNA metabarcoding.

Figure 3 .
Figure 3.The regression plot between the detection rate and a cycle of threshold (Ct).

Figure 4 .
Figure 4.The number of detections according to criterion in April (a) and October (b).The reliability level was determined, based on the criterion that reliability increases when the threshold value is applied.

Figure 5 .
Figure 5.The distribution pattern of Eurasian otters according to seasons, based on eDNA detection with qPCR.

Figure 6 .
Figure 6.The relative abundance (%) of detected fish species at the family level and species level in April and October a family level in April b family level in October c species level in April d species level in October.

Figure 7 .
Figure 7. Estimates of L.lutra density at the sampling site based on eDNA concentration in October.

Table 1 .
The mammal species list identified from field survey and literature review in the study area.

Table 2 .
The fish species list identified from field survey in the study area (four families and 16 species).

Table 3 .
The detected mammal list by eDNA metabarcoding in April and October.
*, The recorded species in fieldwork and literature reviews.