Study the distribution of amphibian species in farmland environments of Zhoushan Archipelago using eDNA techonology

Study the distribution of amphibian species in farmland environments of Zhoushan Archipelago using eDNA techonology | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 10 March 2026 V1 Latest version Share on Study the distribution of amphibian species in farmland environments of Zhoushan Archipelago using eDNA techonology Authors : Yaxin Guo , Kaixin Wang 0009-0000-1207-6254 , Qianjin Lin , Junru Guo , Shuangyue Shi , Haojie Tong 0000-0002-7094-2832 , and Yuan-Ting Jin 0000-0002-1001-2158 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177312999.99439932/v1 156 views 94 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Environmental DNA (eDNA) technology has become a powerful tool for biodiversity monitoring due to its high sensitivity and cost-effectiveness in species detection. This study pioneered the application of eDNA metabarcoding at a regional island scale to systematically investigate the distribution patterns of amphibian diversity and their nestedness patterns across 42 sites (encompassing paddy fields, vegetable plots, and other typical habitats) on 19 islands in the Zhoushan Archipelago, and to examine its relationships with environmental factors. Using aquatic environmental samples amplified with two newly designed amphibian-specific 16S rRNA primer pairs followed by PE300 sequencing, we successfully obtained 2,067,594 effective amphibian sequences, identifying ten amphibian species. Sequencing validation confirmed a minimum coverage depth of 10x for identical markers across species. The number of amphibian species per islands ranged from 2 to 9, with the main Zhoushan Island having the largest number of species. Among them, Fejervarya zhoushanensis dominated, accounting for approximately 82.46 % of total sequences. Island area exhibited extremely significant positive correlations with Shannon-Wiener Index, Simpson Index, Pielou Index and Species Number. Additionally, the distance to the nearest island and distance to the nearest large island were also positively correlated with all four diversity indices, whereas the island shape index showed a negative correlation. The stepwise regression identified Island area as the key predictor for all diversity indices. Nestedness analysis also revealed that island area was the primary factor influencing the nested distribution pattern. This research validates the efficacy and potential of eDNA technology in island biogeography and offers new empirical insights into the distribution patterns and drivers of amphibian diversity and nestedness in archipelagic ecosystems. Study the distribution of amphibian species in farmland environments of Zhoushan Archipelago using eDNA techonology Yaxin Guo # , Kaixin Wang # , Qianjin Lin, Junru Guo, Shuangyue Shi, Haojie Tong, Yuanting Jin * College of Life Sciences, China Jiliang University, Hangzhou, China. #equally to this work. *Corresponding author: Yuanting Jin; Email: [email protected] Abstract: Environmental DNA (eDNA) technology has become a powerful tool for biodiversity monitoring due to its high sensitivity and cost-effectiveness in species detection. This study pioneered the application of eDNA metabarcoding at a regional island scale to systematically investigate the distribution patterns of amphibian diversity and their nestedness patterns across 42 sites (encompassing paddy fields, vegetable plots, and other typical habitats) on 19 islands in the Zhoushan Archipelago, and to examine its relationships with environmental factors. Using aquatic environmental samples amplified with two newly designed amphibian-specific 16S rRNA primer pairs followed by PE300 sequencing, we successfully obtained 2,067,594 effective amphibian sequences, identifying ten amphibian species. Sequencing validation confirmed a minimum coverage depth of 10x for identical markers across species. The number of amphibian species per islands ranged from 2 to 9, with the main Zhoushan Island having the largest number of species. Among them, Fejervarya zhoushanensis dominated, accounting for approximately 82.46 % of total sequences. Island area exhibited extremely significant positive correlations with Shannon-Wiener Index, Simpson Index, Pielou Index and Species Number. Additionally, the distance to the nearest island and distance to the nearest large island were also positively correlated with all four diversity indices, whereas the island shape index showed a negative correlation. The stepwise regression identified Island area as the key predictor for all diversity indices. Nestedness analysis also revealed that island area was the primary factor influencing the nested distribution pattern. This research validates the efficacy and potential of eDNA technology in island biogeography and offers new empirical insights into the distribution patterns and drivers of amphibian diversity and nestedness in archipelagic ecosystems. Keywords: biogeography, environmental DNA, mitochodnrial DNA, amphibian, Zhoushan Archipelago, species diversity, nestedness, biogeography 1 Introduction Environmeatal DNA (eDNA) refers to genetic material that can be extracted from non-invasive environmental samples including soil, water, fecal pellets or air (Deiner et al., 2017; Taberlet et al., 2018; Lodge et al., 2012). In recent years, eDNA-based approaches have been extensively applied across multiple research domains, including species detection and biomass estimation (Taberlet et al., 2012; Ficetola et al., 2019; Thomsen & Willerslev, 2015), biodiversity and community structure analysis (Li et al., 2024), intraspecific genetic diversity monitoring (Zanovello et al., 2023), wildlife diseases surveillance (Sun et al., 2024), and the study of biological interactions (Riaz et al., 2023). Compared to conventional field surveys, which are often time-consuming (Thomsen et al., 2012; Yamamoto et al., 2016) and susceptible to observer bias (Fujii et al., 2019), eDNA enables efficient detection of rare, elusive, or morphologically cryptic species (Lopes et al., 2017; Spear et al., 2015; Fujii et al., 2019). By analyzing the DNA traces recovered from environmental samples such as water (Taberlet et al., 2018), this approach has demonstrated superior sensitivity and cost-effectiveness (Biggs et al., 2015; Li et al., 2024). As such, when integrated with molecular tools, eDNA represents a highly promising and innovative approach for biogeographical studies. Indeed, numerous studies utilizing eDNA have yielded significant outcomes, particularly in amphibian research. Li et al. (2021) employed an eDNA-based approach to assess amphibian diversity across 288 sites in 18 regions of Hainan Island, successfully detecting 15 species. In an earlier study, Pilliod et al. (2014) used eDNA to estimate the relative abundance of two amphibian species, Ascaphus montanus and Dicamptodon aterrimus in streams, USA. In addition, eDNA has proven highly effective in determining the presence of invasive species, such as Lithobates catesbeianus (Ficetola et al., 2008; Dejean et al., 2012), as well as rare and endangered species including Urspelerpes brucei (Pierson et al., 2016), Ascaphus montanus and Dicamptodon aterrimus (Goldberg et al., 2011). Environmental DNA (eDNA) is increasingly recognized as a reliable and effective tool for detecting rare and elusive species. Island formation is typically accompanied by habitat fragmentation and environmental restructuring, leading to distinct biodiversity patterns across different islands (Suárez et al., 2014; Tamar et al., 2019). A foundational concept in island biogeography is nestedness (Whittaker & FernandezPalacios, 2007), which describes a distribution pattern in which species assemblages on species-poor islands constitute subsets of those on species-rich islands (Patterson & Atmar, 1986), often driven by selective extinction (Simberloff & Levin, 1985). Previous research in the Zhoushan Archipelago have identified significant nested structure in butterfly communities, with island area and minimum area requirements emerging as key drivers (Zhang et al., 2016). More recently, Chen et al. (2022) extended this analysis to amphibians by integrating taxonomic, functional, and phylogenetic dimensions. Their results showed that while all three dimensions exhibited nestedness, island isolation uniquely influenced functional and phylogenetic diversity, an signal masked in traditional taxonomic analyses. These findings underscore the necessity of employing multi-faceted approaches to elucidate community assembly mechanisms in archipelagic systems. To data, eDNA approaches have demonstrated considerable effectiveness in monitoring amphibian diversity (Deiner et al., 2017; Thomsen et al., 2015; Yates et al., 2019). The Zhoushan Archipelago, the largest island group in China, comprises 4,696 relatively isolated islands and reefs (Wang et al., 2014) and has experienced six historical separation events from the mainland due to sea-level fluctuations (Xu and Lin, 1993). This complex geological history has profoundly influenced the structure of regional biodiversity. Although a previous study have determined species-area relationship by surveying amphibian assemblages across 21 islands of the Zhoushan Archipelago and three adjacent mainland sites (Li et al., 2024), a comprehensive understanding of amphibian distribution and community composition across the entire Zhoushan Archipelago remains lacking. In particular, the environmental drivers shaping amphibian biodiversity patterns in this region have yet to be systematically evaluated. Furthermore, no study has yet applied eDNA metabarcoding at a regional island scale to investigate their nestedness. Therefore, in this study, we used eDNA methods to conduct a comprehensive assessment of amphibian distribution across the Zhoushan Archipelago, and to analyze the correlation between environmental factors and amphibian distribution. The aim of this work were to (1) characterize the detailed distribution patterns of amphibian diversity and its nested structure throughout the Zhoushan Archipelago; (2) reveal the key environmental factors influencing amphibian species distribution patterns in this island system. 0pt 2.5ex plus 1ex minus .2ex 1.5ex plus .2ex 0pt 2ex plus .5ex minus .2ex 1ex plus .2ex 0pt 1.5ex plus .5ex minus .2ex 0.8ex plus .2ex 2 Materials and methods 2.1 Sample collection For this study, 19 islands of varying sizes and degrees of isolation within the Zhoushan Archipelago were selected. A total of 42 sampling sites were established within potential amphibian habitats across these islands (Fig. 1, Table S1). At each site, one to three representative transects, each approximately 50-60 m in length, were deployed, with the specific number determined by the site area (sites larger than 800 m² were allocated three transects, while those smaller than 800 m² were allocated one or two). The transects were spaced 200 to 400 m apart, depending on the size of the sampling area, to ensure coverage of both core habitats and transitional zones. Water sampling was conducted between 21:00 and 24:00 at night. Along the shoreline of each transect, five equidistant sampling points were established. At each point, 100 mL of surface water (depth ≤ 5 cm) was collected using a sterile syringe, and the five subsamples were combined into a single 500 mL sterile bottle. The composite water sample was immediately filtered through a 1.6 μm glass fiber filter. The filter membrane was then transferred to a centrifuge tube containing 1 mL of DNA/RNA Shield™ preservation solution (Jianshi Biotechnology Co., Ltd., Beijing). To prevent cross-contamination, nitrile gloves were worn throughout the sampling process and changed between handling each sample. Furthermore, all syringes and filtration equipment were thoroughly rinsed three times with ultrapure water after each use. Water samples collected from different transects at the same site were combined. All water samples were stored at 4°C until eDNA extraction. 2.2 eDNA extraction and quality control eDNA was extracted from water samples using the Environmental DNA Mini Extraction Kit (Guangzhou Ark Biotechnology Co., Ltd.) according to the manufacturer’s protocol. Subsequently, 2 μL extracted eDNA was subjected to 1% agarose gel electrophoresis, following the detailed procedure below: (1) Preparation of 1× TAE buffer: 20 mL of 50× TAE buffer was measured and diluted with ultrapure water to a final volume of 1000 mL. Then, 0.4 g of agarose was dissolved in 40 mL of 1× TAE buffer to prepare a 1% agarose gel solution and heated in a microwave oven until transparent. (2) 4 μL nucleic acid dye was added to the heated agarose gel solution, mixed thoroughly, and reheated for 10 seconds. (3) The mixture was poured into an electrophoresis tray, solidified for approximately 30 minutes and placed in an electrophoresis tank prefilled with 1× TAE buffer to submerge the gel completely. (4) 1 μL 6× DNA loading buffer was mixed with 5 μL DNA sample (the DNA sample was added first) and loaded into wells. A 2 kb DNA marker (6 μL) was loaded into flanking wells. (5) After electrophoresis, the gel was visualized using a gel imaging system to confirm the presence of a single band. Samples that passed quality control were stored at -80°C for subsequent analysis. 0pt 2.5ex plus 1ex minus .2ex 1.5ex plus .2ex 0pt 2ex plus .5ex minus .2ex 1ex plus .2ex 0pt 1.5ex plus .5ex minus .2ex 0.8ex plus .2ex 2.3 Primer design and high-throughput sequencing Two universal primer pairs targeting the mitochondrial 16S rRNA region were designed based on reference sequences from 12 amphibian species in the Zhoushan region (Chac and Thinh, 2023; Wang et al., 2023). Primer sequences were determined through multiple sequence alignment using Cluster W in MEGA11 and subsequent conservative region analysis. The primer pair F1 consists of forward primer 5′-GCCTGTTTACCAAAAACATCGC-3′ and reverse primer 5′-CTCCATRGGGTCTTCTCGTCTT-3′; primer pair F2 consists of forward primer 5′-AAGACGAGAAGACCCYATGGAGCTT-3′ and reverse primer 5′-CGGTCTGAACTCAGATCACGTAGG-3′. Target fragments from eDNA samples were amplified via PCR using the following reaction system: 25 μL PrimeSTAR HS (Premix), 1 μL each of forward and reverse primer, 2 μL eDNA template, and ddH₂O added to a final volume of 50 μL. The PCR protocol was as follows: initial denaturation at 94 °C for 5 min; 35 cycles of 94 °C for 30 s, 58 °C for 30 s, and 72 °C for 30 s; followed by a final extension at 72 °C for 10 min. Amplified products were purified using VAHTS DNA Clean Beads (Vazyme Biotech Co., Ltd., Nanjing). The purified PCR products were sent to Shanghai Sangon Biotech for library preparation, which was performed with the Hieff NGS® MaxUpⅡ DNA Library Prep Kit for Illumina® (12200ES96, YEASEN, China) according to the manufacturer’s instructions. The constructed libraries were purified and recovered using Hieff NGS™ DNA Selection Beads, quantified by Qubit® 4.0 Fluorometer, and finally sequenced on the Nextseq 2000 platform (PE300, Illumina, USA). 2.4 Mitochondrial genome reference database Based on literature and data review (Chac and Thinh, 2023; Wang et al., 2023), 12 amphibian species are known to be distributed in the Zhoushan Archipelago: Fejervarya limnocharis (the Zhoushan population is considered a distinct new species, tentatively named Fejervarya zhoushanensis (Tong et al., 2025)), Pelophylax nigromaculatus , Rana plancyi , Bufo gargarizans , Hylarana latouchii , Polypedates braueri , Hyla chinensis , Boulenophrys boettgeri , Rana zhenhaiensis , Microhyla ornata , Quasipaa spinosa , and Hynobius yiwuensis . A comprehensive and accurate reference database comprising 22 mitochondrial genome sequences was assembled. It included the previously sequenced mitochondrial genome of Fejervarya zhoushanensis , along with mitochondrial genomic data from the NCBI database (https://www.ncbi.nlm.nih.gov/) for two other members of Fejervarya limnocharis species complex, Fejervarya kawamurai and Fejervarya multistriata , as well as other 11 species Pelophylax nigromaculatus , Rana plancyi , Bufo gargarizans , Hylarana latouchii , Polypedates braueri , Hyla chinensis , Boulenophrys boettgeri , Rana zhenhaiensis , Microhyla ornata , Quasipaa spinosa , and Hynobius yiwuensis . Detailed information on the mitochondrial genomes sequences is provided in Table S2. 0pt 2.5ex plus 1ex minus .2ex 1.5ex plus .2ex 0pt 2ex plus .5ex minus .2ex 1ex plus .2ex 0pt 1.5ex plus .5ex minus .2ex 0.8ex plus .2ex 2.5 Bioinformatics analysis Raw sequencing data were first processed using fastp (v0.23.2) with parameters ”-q 20 -l 50 –detect_adapter_for_pe” to eliminate low-quality reads and sequences shorter than 50 bp, while generating quality control reports in both HTML and JSON formats. Subsequently, primer sequences were trimmed using cutadapt (v4.4) with an error rate threshold of 10%, a minimum overlap length of 20 bp, and removal of reads lacking primer matches. Paired-end reads were merged via vsearch (v2.22.1) with a minimum overlap of 20 bp and a maximum of 5 mismatches allowed. The merged sequences were then filtered with vsearch –fastq_filter, discarding those with an expected error greater than 1. After complete dereplication (retaining sequences with ≥ 2 copies) and chimera detection against the reference database (Table S2), taxonomic assignment was performed by comparison to a custom mitochondrial database at a 97% similarity, with results output in BLAST6 format. 0pt 2.5ex plus 1ex minus .2ex 1.5ex plus .2ex 0pt 2ex plus .5ex minus .2ex 1ex plus .2ex 0pt 1.5ex plus .5ex minus .2ex 0.8ex plus .2ex 2.6 Environmental factor setting To investigate the effects of environmental factors on amphibian species diversity across islands in the Zhoushan Archipelago by analyzing three categories of environmental variables: island attributes, ecosystem service value, and degree of human interference. Island attributes included island area, the shortest distance to the mainland, the distance to the nearest large island (defined as an island with an area greater than 100% of the focal island’s area), the distance to the nearest island (defined as an island with an area > 0.05 km²), and island shape index. To quantify these geographic attributes, the Zhoushan map was extracted from the Global Administrative Areas (GADM) database (https://gadm.org/). The island area and the shortest distance to the mainland for the 19 surveyed islands were calculated using the ”Generate Near Table” tool in ArcGIS. Each of the 19 surveyed islands was treated as a focal island. After sequentially removing all islands with an area less than 100% of the focal island’s area and all islands with an area < 0.05 km² from the map, the shortest distances from the focal island to the nearest large island and to the nearest island were computed, respectively. The island perimeters was calculated using the ”Data Management Tools” in ArcGIS and the island shape index (SI) was then determined using the following formula: SI = C / [2 × (π × A)^0.5], where C represents the island perimeter and A represents the island area (Wang et al., 2023). The human disturbance dataset was sourced from Figshare (figshare.com) and the ecosystem service value dataset, with a resolution of 1 km and units of 10,000 CNY/km², was downloaded from the Resource and Environment Science and Data Center of the Chinese Academy of Sciences (www.resdc.cn). 2.7 Nestedness quantification and influencing factors analysis A species-site matrix was constructed, with rows representing species and columns representing the study islands. The matrix was populated with the relative abundance of each species on each island. To quantify the degree of nestedness, we employed the WNODF (weighted nestedness metric based on overlap and decreasing fill) (Almeida-Neto & Ulrich, 2011). All calculations were performed using NODF 2.0 (Simaiakis & Martínez-Morales, 2010; Almeida-Neto & Ulrich, 2011). The species and site rankings resulting from the rearrangement of the species-site matrix based on the WNODF analysis are referred to as the nested ranking. To test whether various factors significantly influence the formation of the nested pattern, we performed Spearman’s rank correlation analysis between the nested ranking and each environmental parameter (Schouten et al, 2007; Wang et al, 2010). 2.8 Statistical analyses The species abundance table generated from the read alignment results was imported and transposed into a ”sample × species” matrix. The relative abundance of each species at each sampling point was calculated by dividing each row by its row sum. A stacked bar plot was subsequently generated with species names displayed in italics. To account for variations in island size and potential habitat area, sequence counts from multiple sampling points within the same island were summed to represent the total sequence count per species per island. Four α diversity indices, including the Shannon-Wiener Index, Simpson Index, Pielou Index, and Species Number, were calculated for each island. A clustered heatmap based on these indices was constructed using Ward’s clustering method and the ’Temperature’ color palette. Islands ranking in the top third across all four α diversity indices were classified as having relatively high amphibian diversity. All environmental variables were first tested for normality and then, correlation was employed to assess relationships between α diversity indices and environmental variables (island attributes, ecosystem service value, and human disturbance intensity). Prior to multiple regression analysis, all variables were standardized. Subsequently, multiple linear stepwise regression was performed to identify the key environmental factors influencing amphibian diversity across Zhoushan Archipelago. Statistical significance is indicated with p -values as follows: * p < 0.05, ** p < 0.01 and *** p < 0.001. All analysis and visualizations were conducted using ArcMap 10.8.1, R software (v4.5.1), SPSS 27, and Origin 2025b. 0pt 2.5ex plus 1ex minus .2ex 1.5ex plus .2ex 0pt 2ex plus .5ex minus .2ex 1ex plus .2ex 0pt 1.5ex plus .5ex minus .2ex 0.8ex plus .2ex 3 Results 3.1 Identification of advantageous species in the Zhoushan Archipelago A total of 2,067,594 valid amphibian sequences were obtained following alignment with the reference database, enabling the identification of ten amphibian species: Fejervarya zhoushanensis (1,705,102 reads), Fejervarya kawamurai (236,917 reads), Microhyla ornata (86,559 reads), Bufo gargarizans (3,780 reads), Rana plancyi (12,136 reads), Pelophylax nigromaculatus (7,085 reads), Hynobius yiwuensis (47 reads), Polypedates braueri (72 reads), Hyla chinensis (15,184 reads), and Rana zhenhaiensis (712 reads) (Table S3). Among this, Fejervarya zhoushanensis and Fejervarya kawamurai belong to Fejervarya limnocharis species complex. Sequencing validation confirmed a minimum coverage depth of 10x for homologous markers across species. The number of amphibian species across different islands ranged from 2 to 9, with Zhoushan Island supporting the highest species. Among these, Fejervarya zhoushanensis was identified as the absolutely dominant amphibian species in the Zhoushan Archipelago (Fig. 2), comprising approximately 82.46% of the total sequenced reads. 3.2 Amphibian diversity across the Zhoushan Archipelago islands The α diversity indices of amphibian communities across the Zhoushan Archipelago islands are summarized in Table 1. Among the 19 surveyed islands, Zhoushan Island, Qushan Island, Jintang Island, Liuheng Island, and Xiaoyangshan Island exhibited relatively higher α diversity indices, indicating greater amphibian species richness and more complex community structures (Fig. 3, Table 1). 0pt 2.5ex plus 1ex minus .2ex 1.5ex plus .2ex 0pt 2ex plus .5ex minus .2ex 1ex plus .2ex 0pt 1.5ex plus .5ex minus .2ex 0.8ex plus .2ex 3.3 Key environmental factors influencing species diversity distribution Since all environmental variables deviated from a normal distribution, Spearman’s rank correlation coefficient was employed for all correlation analyses. Among all environmental factors (Table S4) examined, island area demonstrated the strongest and most consistent positive correlations with all four α diversity indices: Shannon-Wiener Index (r = 0.628, p = 0.004), Simpson Index (r = 0.618, p = 0.005), Pielou Index (r = 0.628, p = 0.004), and Species Number (r = 0.848, p < 0.001). Distance to the nearest large island and distance to the nearest island also showed significant positive correlations with Shannon-Wiener Index (r = 0.528, p = 0.020; r = 0.491, p = 0.033), Simpson Index (r = 0.528, p = 0.020; r = 0.493, p = 0.032), Pielou Index (r = 0.528, p = 0.020; r = 0.491, p = 0.033), and Species Number (r = 0.580, p = 0.009; r = 0.540, p = 0.017), respectively (Table 2). Conversely, island shape index exhibited significant negative correlations with Shannon-Wiener Index (r = -0.653, p = 0.002), Simpson Index (r = -0.644, p = 0.003), Pielou Index (r = -0.653, p = 0.002), and Species Number (r = -0.501, p = 0.029). The remaining environmental variables showed no statistically significant correlations with any α diversity indices (Table 2). Stepwise multiple linear regression analysis identified island area as the sole significant predictor for three α diversity indices: Shannon-Wiener Index (R² = 0.354, p = 0.007), Simpson Index (R² = 0.363, p = 0.006), and Pielou Index (R² = 0.354, p = 0.007). Other variables including the shortest distance to the mainland, the distance to the nearest large island, the distance to the nearest island, island shape index, degree of human interference, and ecosystem service value, were excluded from the final models for these indices. For Species Number, both island area and island shape index were retained as significant predictors in the final model (R² = 0.699, p = 0.048), while the shortest distance to the mainland, the distance to the nearest large island, the distance to the nearest island, degree of human interference, and ecosystem service value were excluded by the stepwise procedure. 3.4 Analysis of nestedness patterns in the Zhoushan Archipelago The amphibian communities in the Zhoushan Archipelago exhibited a significantly nested distribution pattern (WNODF = 70.0509, Z = 7.481, p =0.001). Island area (r = -0.891, p < 0.001, n = 19) had a highly significant effect on the formation of this pattern, while the distance to the nearest large island (r = -0.566, p = 0.012, n = 19) and the distance to the nearest island (r = -0.536, p = 0.018, n = 19) also showed significant effects (Table 3, S4-S5). 4 Discussion The fragmented distribution of islands in the Zhoushan Archipelago, along with the complex topography and restricted habitats on certain islands, has historically constrained the effectiveness of traditional survey methods (Fujii et al., 2019). In this study, environmental DNA (eDNA) analysis of water samples effectively overcame these limitations, enabling the detection of ten amphibian species that collectively represent the majority of the known regional diversity (Fig. 2, Table S1). Notably, on several previously under-surveyed small islands such as Bixia Island and Gouqi Island, we successfully identified multiple target species, including rare or elusive taxa such as Hynobius yiwuensis . Furthermore, Fejervarya kawamurai was detected on several islands in close proximity to the Chinese mainland, including Jintang Island, Liuheng Island, and Putuo Island, which may be due to the dense human traffic caused by proximity to the mainland or tourist areas. In addition, three species ( Boulenophrys boettgeri , Quasipaa spinosa , and Hylarana latouchii ) were not detected, likely because their typical habitats (e.g., mountain streams, canals, and ponds) differ from the agricultural environments sampled in this study, such as paddy fields and vegetable plots (AmphibiaChina, 2024; Long et al., 2020; Wang et al., 2009). These findings highlight the potential of eDNA in enhancing species detection rates and reaffirm its distinct advantages for assessing biodiversity distribution across island systems (Deiner et al., 2017; Thomsen et al., 2015). Our findings clearly reveal substantial variation in amphibian diversity across islands within the Zhoushan Archipelago (Table 2). Among the environmental factors examined, island area emerged as the most predominant driver of this observed pattern, showing significant positive correlations with all four α diversity indices and serving as the primary predictor in linear regression analyses. This finding aligns with preliminary observations reported by Li et al. (2024) in the Zhoushan Archipelago and provides robust validation for the classic Species-Area Relationship (MacArthur & Wilson, 1967), confirming that larger islands generally support higher species richness through greater habitat heterogeneity and resource diversity. Nestedness analysis further reinforced the primacy of area in shaping community structure, with island area identified as the key factor influencing the nested distribution pattern of amphibians (Table 3). This result is consistent with previous studies in the same archipelago, which demonstrated that butterfly communities exhibited significant nestedness driven primarily by island area and minimum area requirements (Zhang et al., 2016), supporting the selective extinction hypothesis. Interestingly, amphibian diversity also showed significant positive correlations with both the distance to the nearest large island and the distance to the nearest island (Table 2), a result that appears to contradict the traditional concept of ”isolation effect” in island biogeography (MacArthur & Wilson, 1967). We hypothesize that this pattern may be attributed to the ”target effect” (Lomolino, 1982; Johnson, 1980), whereby immigration rates are influenced by island size (Carter et al., 2020; Hauffe et al., 2020). Larger islands, generally offering more favorable habitat conditions and greater resource availability, may attract higher immigration from surrounding smaller islands (Wardle et al., 2003; Henneron et al., 2019), thereby reducing diversity on smaller islands located in closer proximity. This ecological mechanism would have been difficult to systematically evaluate without eDNA technology, which enables comprehensive and unbiased biodiversity sampling across entire archipelago. The significant negative correlation between island shape index and diversity indices represents another noteworthy finding (Table 2), contrasting with patterns commonly observed in bird and insect studies (Yu et al., 2012). Complex-shaped islands typically feature extended coastlines, where interior freshwater habitats may be vulnerable to saltwater intrusion and anthropogenic development (McKinney, 2002, 2006). This conditions create suboptimal environments for amphibians, which require stable aquatic systems for reproduction and survival. This finding further illustrates the potential of eDNA approaches in detecting taxon-specific responses to habitat quality. In summary, we innovatively applied environmental DNA (eDNA) technology to systematically characterize the distribution patterns of amphibians across the entire Zhoushan Archipelago at a regional island scale for the first time. Our findings not only validate the effectiveness and applicability of eDNA in biogeographical studies, but also furnish novel empirical evidence for understanding the mechanisms underlying amphibian diversity distribution in heterogeneous island systems. It is important to note that eDNA methodology has certain limitations, including potential influences of water flow, DNA degradation rates, and environmental contamination (Fonseca et al., 2018; Ficetola et al., 2021). Consequently, our results indicate that eDNA technology is a valid and effective tool for detecting potential amphibian distributions in varied environments that are difficult to survey using traditional transect methods. 5 References Deiner, K. et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Molecular Ecology , 2017, 6, 5872–5895. Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. Environmental DNA: For biodiversity research and monitoring (Oxford University Press, 2018). Lodge, D. M., Turner, C. R., Jerde, C. L., Barnes, M. A., Chadderton, L., Egan, S. P., Feder, J. L., Mahon, A. R., & Pfrender, M. E. Conservation in a cup of water: Estimating biodiversity and population abundance from environmental DNA. 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Molecular Ecology , 2021, 30, 3189–3202. 6 Acknowledgements Thanks to Leijie Wang, Haoye Pan, Lishan Dai, Chenfei Zhou, Chenghao Zheng, Shenghao Zhang, Yuhao Zhou, and Jun Xu for their assistance in samples collection. This work was supported by the Zhoushan Municipal Ecology and Environment Bureau, Zhejiang Province. 7 Conflict of interest The authors declare no conflicts of interests. 8 Funding This work was supported by the Natural Science Foundation of Zhejiang Province, China (LY21C040002) and (ZCLMS26C0401), National Natural Science Foundation of China (32370441). We wish to thank Zhoushan ecological Bureau with their supports on research foundation (03106/243079) and field work. 9 Supplementary Material Supplementary Table 1. Geographic Coordinates of Water Sample Collection Sites in the Zhoushan Archipelago Supplementary Table 2. GenBank accession numbers of mitochondrial genome sequences used in this study. Supplementary Table 3. Sequence read counts of amphibian species per sampling site in the Zhoushan Archipelago. Supplementary Table 4. Values of environmental factors and nested ranking for each island in the Zhoushan Archipelago. Supplementary Table 5. Amphibian species-site abundance matrix for the Zhoushan Archipelago. 10 Data Availability The data that support the findings of this study can be found in Supplementary Material. Table Table 1 α diversity indices of amphibian communities across the Zhoushan Archipelago islands Area Shannon-Wiener Index Simpson Index Pielou Index Species Number Zhoushan Archipelago 1.698382129 0.965889069 0.511263965 9 Daishan Island 0.549308306 0.335567004 0.165358277 5 Qushan Island 0.897849084 0.45754922 0.270279506 5 Shengsi Island 0.754695275 0.470354454 0.227185915 4 Changtu Island 0.073472617 0.027520898 0.022117462 2 Wangjiadun Village 0.000502089 9.13339E-05 0.000151144 2 Houmen Island 0.000359688 6.33212E-05 0.000108277 2 Xiaogan Island 1.139525335 0.640927246 0.343031307 4 Putuo Island 0.513164302 0.253492803 0.154477847 5 Dengbu Island 0.593380929 0.320342287 0.178625458 4 Taohua Island 0.336088885 0.122285306 0.101172835 6 Xiazhi Island 0.192568205 0.089646492 0.057968806 3 Liuheng Island 1.175193929 0.681642926 0.353768623 6 Cezi Island 0.773015522 0.514182369 0.232700859 3 Jintang Island 0.753432009 0.353145275 0.226805634 5 Xiaoyangshan Island 1.538679558 0.788761703 0.463188701 6 Putuoshan Town 0.277172379 0.141998432 0.0834372 3 Bixia Island 0.189624415 0.087123443 0.057082637 3 Gouqi Island 0.405198174 0.240781517 0.121976805 3 0pt 2.5ex plus 1ex minus .2ex 1.5ex plus .2ex 0pt 2ex plus .5ex minus .2ex 1ex plus .2ex 0pt 1.5ex plus .5ex minus .2ex 0.8ex plus .2ex Table 2 Correlations between α diversity indices and island attributes in the Zhoushan Archipelago Shannon-Wiener Index Simpson Index Pielou Index Species Number r p n r p n r p n r p n Island Area 0.628 ** 0.004 19 0.618 ** 0.005 19 0.628 ** 0.004 19 0.848 *** <0.001 19 The shortest distance to the mainland -0.184 0.450 19 -0.177 0.468 19 -0.184 0.450 19 -0.246 0.310 19 The distance to the nearest large island 0.528 * 0.020 19 0.528 * 0.020 19 0.528 * 0.020 19 0.580 ** 0.009 19 The distance to the nearest island 0.491 * 0.033 19 0.493 * 0.032 19 0.491 * 0.033 19 0.540 * 0.017 19 Island Shape Index -0.653 ** 0.002 19 -0.644 ** 0.003 19 -0.653 ** 0.002 19 -0.501 * 0.029 19 Ecosystem service value 0.254 0.293 19 0.224 0.356 19 0.254 0.293 19 0.364 0.125 19 Degree of human interference -0.056 0.821 19 -0.014 0.956 19 -0.056 0.821 19 -0.109 0.657 19 * p < 0.05, ** p < 0.01 and *** p < 0.001 Table 3 Correlations between nested ranking and island attributes in the Zhoushan Archipelago Island Area (km 2 ) The shortest distance to the mainland (km) The distance to the nearest large island (km) The distance to the nearest island (km) Island Shape Index Ecosystem service value Degree of human interference Nested ranking r -0.891 *** 0.219 -0.566 * -0.536 * 0.454 -0.329 0.061 p <0.001 0.367 0.012 0.018 0.051 0.170 0.803 n 19 19 19 19 19 19 19 Figure legends Figure 1: Water sample collection sites in the Zhoushan Archipelago. Figure 2: Relative abundance of detected species in the Zhoushan Archipelago. The different horizontal lines along the x-axis represent the islands or regions to which each sampling site belongs. Figure 3: Heatmap of diversity indices of islands in the Zhoushan Archipelago. Figure 1 Figure 2 Figure 3 Information & Authors Information Version history V1 Version 1 10 March 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords community ecology comparative ecosystem freshwater molecular genetics sequencing statistical terrestrial vertebrate Authors Affiliations Yaxin Guo China Jiliang University View all articles by this author Kaixin Wang 0009-0000-1207-6254 China Jiliang University View all articles by this author Qianjin Lin China Jiliang University View all articles by this author Junru Guo China Jiliang University View all articles by this author Shuangyue Shi China Jiliang University View all articles by this author Haojie Tong 0000-0002-7094-2832 China Jiliang University View all articles by this author Yuan-Ting Jin 0000-0002-1001-2158 [email protected] China Jiliang University View all articles by this author Metrics & Citations Metrics Article Usage 156 views 94 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yaxin Guo, Kaixin Wang, Qianjin Lin, et al. Study the distribution of amphibian species in farmland environments of Zhoushan Archipelago using eDNA techonology. Authorea . 10 March 2026. DOI: https://doi.org/10.22541/au.177312999.99439932/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 . 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