Seasonal Diet Partition among Top Predators of a Small Island, Iriomotejima Island in the Ryukyu Archipelago, Japan

Seasonal Diet Partition among Top Predators of a Small Island, Iriomotejima Island in the Ryukyu Archipelago, Japan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Seasonal Diet Partition among Top Predators of a Small Island, Iriomotejima Island in the Ryukyu Archipelago, Japan Alisa Tobe, Yukuto Sato, Nakatada Wachi, Nozomi Nakanishi, Masako Izawa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3907562/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Apr, 2024 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Small islands lack predators because species at higher trophic levels often cannot survive. However, two top predators—the Iriomote cat Prionailurus bengalensis iriomotensis , and the Crested Serpent Eagle Spilornis cheela perplexus —live on small Iriomotejima Island in the Ryukyu Archipelago. To understand how these predators coexist on the island with limited resources, we focused on their seasonal diets which are considered crucial for survival in such an island ecosystem. To compare the diets of them, we used DNA metabarcoding analysis of their fecal samples. In the summer, we identified 16 prey items from Iriomote cat fecal samples, and 15 from Crested Serpent Eagle fecal samples. In the winter, we identified 37 and 14 prey items, respectively. Using a non-metric multidimensional scaling and a permutational multivariate analysis of variance, our study reveals significant differences in the diet composition at the order level between the predators during both seasons. Furthermore, although some prey items at the species-to-order level overlapped between them, the frequency of occurrence of most prey items differed in both seasons. These results suggest that this difference in diets was one of the reasons why the Iriomote cat and the Crested Serpent Eagle coexisted on such a small island. Biological sciences/Ecology/Ecological networks Biological sciences/Ecology/Ecosystem ecology Biological sciences/Ecology/Molecular ecology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Island ecosystems exhibit unique ecological characteristics that are sometimes simpler or more monotonic than those of continental ecosystems. This is mainly because these islands have a limited number of species and individuals, and immigration and emigration events are less likely to occur 1,2 . On such islands, predators at higher trophic levels are often absent, because predator species, such as carnivores, cannot maintain their populations without sufficient prey resources 3 . However, multiple predators have been able to co-exist on Iriomotejima Island, located in the southern part of the Ryukyu Archipelago, which covers an area of approximately 289 square kilometers 4 . Two top predators are known from this island: the Iriomote cat (IRC) Prionailurus bengalensis iriomotensis , and the Crested Serpent Eagle (CSE) Spilornis cheela perplexus . The IRC is endemic to Iriomotejima Island and is a subspecies of the leopard cat P. bengalensis , which is widely distributed in East and Southeast Asia 5 . The CSE is also endemic to Iriomotejima, Ishigakijima, and Yonagunijima Islands in the Ryukyu Archipelago and is a subspecies of the Crested Serpent Eagle S. cheela , which is widely distributed in East and South Asia 6 . The mechanism of coexistence of these predators is important for understanding the ecological features of Iriomotejima Island, which has limited resources. To address this question, we focused on the seasonal diets of the IRC and CSE, as these are thought to be one of the most important factors for their survival in a small island ecosystem. When multiple top predators are sympatric on a small island with restricted food resources, their feeding habits are expected to differ from each other 7 . Although a few studies have compared the diet of sympatric animals that exhibit similar ecological niches 8–10 , few studies have focused on sympatric predators endemic to small islands such as Iriomotejima Island 11 . Previous studies on the diets of IRC and CSE on Iriomotejima Island have shown that both are carnivorous and feed on a variety of prey animals, such as mammals, birds, reptiles, amphibians, fishes, insects, and crustaceans 12,13 . Most of these prey animals overlapped between the two species 12–17 . The diet of the IRC was analyzed based on the morphological observation of fecal and stomach contents of dead individuals 13–17 , although it is often difficult to identify prey animals at the species level from such digested items. Analysis of the CSE fecal content is more challenging, as carnivorous birds often spit out undigested items such as pellets, and only soluble materials are excreted as feces. Therefore, previous studies on the CSE diet have largely been limited to stomach content analysis of dead individuals and direct observation of their feeding behaviors 12,16 . However, in these studies, the sample size was insufficient because of the difficulty in obtaining dead individuals or the limited frequency of direct observations. In this study, we performed DNA barcoding analysis of feces to examine the prey of the IRC and CSE. DNA barcoding generally provides a higher taxonomic resolution than the morphological observation of fecal and stomach samples and has recently become a major method for animal diet analysis 18,19 . This method enabled us to analyze CSE diets using fecal samples with larger sample sizes than stomach content analysis or direct observations. Furthermore, it allows a direct comparison of the prey animals of birds and mammals based on the same method. Some studies in Pakistan and China conducted DNA barcoding to investigate the diets of other subspecies of the leopard cat 20–23 , while no studies have addressed any CSE subspecies. In the present study, we aimed to elucidate the coexistence mechanism of the two top predators, IRC and CSE, living on Iriomotejima Island. Our goal was to examine their competition for limited food resources on a small island. To achieve this, we utilized DNA barcoding to analyze their diets, providing higher resolution (Fig. 1 ). Results Fecal sampling and DNA sequencing results We obtained 38 and 76 fecal samples from the IRC in the summer and winter, respectively. Similarly, we collected 24 and 12 CSE fecal samples in the summer and winter, respectively. The numbers of IRC samples that contained DNA sequences from more than one prey item were 31 (81.6%) and 64 (90.1%) in the summer and winter, respectively. The numbers were 21 (87.5%) and nine (75.0%) among CSE samples in the summer and winter, respectively. These samples were used for subsequent analyses. Using the DNA metabarcoding sequencing results, we obtained 333 sequencing reads using the COI#1; 10,335 sequencing reads using the COI#2; 208,673 sequencing reads using the Ecoprimer; and 74,483 sequencing reads using the MtAnr. These sequences were derived from 55 different prey items, including five species of Mammalia; 17 species, one genus, one order, and one family of Aves; 10 species and one genus of Reptilia; seven species and one family of Amphibia; one species of Osteichthyes; three species of Malacostraca; four species and one order of Insecta; and two families of Chilopoda (Supplementary Table 2). We could identify 83.5% of the MOTUs at the species level. Characteristics of detected prey species by each primer set The number of sequencing reads for the detected prey taxa varied remarkably among primer sets. Invertebrates were detected using COI#1 and COI#2, whereas vertebrates were detected using all primer sets (Fig. 3 ). The COI#1 detected the Brown-eared Bulbul Hypsipetes amaurotis stejnegeri which was not detected by any other primer sets. COI#2 detected a relatively large number of invertebrates, such as the mudflat crab Chiromantes dehaani , the blue land crab Discoplax hirtipes , the vine hawkmoth Hippotion celerio , the bush cricket Mecopoda elongate , the privet hawkmoth Psilogramma menephron , and centipedes Scolopendromorpha sp., that were not detected by any other primer sets. The Ecoprimer detected each class of land vertebrates almost evenly. In particular, Osteichthyes (the barred mudskipper Periophthalmus argentilineatus ) was not detected by any other primer sets, and was only detected in one fecal sample (Fig. 3 ). The sequencing reads derived from Amphibia accounted for 97.2% of the total number of sequencing reads obtained by the MtAnr. The Yaeyama kajika frog Buergeria choui and Utsunomiya's tip-nosed frog Odorrana utsunomiyaorum were detected only using this primer set. The estimated diet composition of IRC and CSE The number of prey items detected from the fecal samples of each predator during each season was as follows—summer samples of IRC: 14 species, one genus, and one order belonging to five classes, nine orders, 12 families, and 15 genera; summer samples of CSE: 12 species, one genus, and two families belonging to five classes, five orders, 11 families, and 12 genera; winter samples of IRC: 30 species, two genera, three families, and two orders belonging to eight classes, 16 orders, 23 families, and 30 genera; winter samples of CSE: 12 species and two families belonging to six classes, seven orders, 12 families, and 12 genera (Fig. 7 , Supplementary Table 2). The prey taxonomic richness for each predator showed no significant difference in both seasons (Supplementary Fig. 1). Based on a comparison of the FOO values of IRC prey items obtained in this study with those obtained in a previous study 13 , except for Aves and Insecta, there were no significant differences in the FOO values of Mammalia, Reptilia, Amphibia, and Osteichthyes (Fig. 4 ). It should be noted that Aves and Insecta were significantly less frequent in this study than in the previous study (Fig. 4 ; p < 0.05 and p < 0.01, respectively). Regarding the detected prey classes, Mammalia was not detected in any CSE samples, while they were frequently detected in IRC samples (Fig. 5 ). The FOO values of the Mammalia were significantly different between the two predators in winter ( p < 0.05). In contrast, Malacostraca and Chilopoda were rarely detected in IRC samples while they were frequently detected in CSE samples (Fig. 5 ). These difference between CSE and IRC was significant in both seasons ( p < 0.01 in Malacostraca in summer and Chilopoda in winter, p < 0.05 in Chilopoda in summer and Malacostraca in winter). Reptilia were significantly more frequent in IRC samples than in CSE samples in summer ( p < 0.05), whereas the difference was not statistically significant in winter. On the other hand, Amphibia were significantly more frequent in CSE samples than in IRC samples in summer ( p < 0.05), whereas this trend was not statistically significant in winter. Within each predator, the FOO of Mammalia and Reptilia detected in IRC feces showed significant differences between seasons ( p < 0.05, p < 0.01 in Mammalia and Reptilia, respectively). In addition, the FOO of Amphibia detected in CSE showed a significant difference between seasons ( p < 0.05). The NMDS and PERMANOVA analyses for comparison of diet composition between IRC and CSE showed that the prey composition at the order level was significantly different between them in both seasons (Fig. 6 ; p < 0.001) and the differences were not caused by different degrees of variation among the samples of IRC and CSE ( p = 0.4899 in summer; p = 0.7077 in winter). When prey species at the species-to-order level were sorted by FOO and wPOO values, there was little difference in the ranking of prey species, with the exception of winter CSE, which had a small sample size (Fig. 7 ). In Mammalia, the black rat Rattus rattus , had the highest FOO and wPOO values in the IRC samples in winter (Fig. 7 ), although FOO was not significantly higher in the IRC samples than in the CSE samples (Supplementary Table 2). In both seasons, black rats were the most frequently detected mammals in the IRC samples (Fig. 7 ), and their FOO was significantly higher in winter than in summer (Table S2; P < 0.05). In Reptilia, the FOO and wPOO values of Japanese skinks Plestiodon sp., were the highest in the detected IRC prey items in summer (Fig. 7 ), and its FOO was significantly higher in IRC samples than in CSE samples (Supplementary Table 2; p < 0.05). Within the IRC samples, the FOO value of Japanese skinks was significantly higher in summer than in winter (Supplementary Table 2; p < 0.01). Similarly, the Sakishima beauty snake Elaphe taeniura schmackeri , showed a significantly higher FOO in summer than in winter in the IRC samples (Supplementary Table 2; p < 0.05). The detection of Sakishima smooth skink Scincella boettgeri was significantly more frequent in the CSE samples than in the IRC samples in winter (Supplementary Table 2; p < 0.01), and was significantly more frequent in winter than in summer within the CSE samples (Supplementary Table 2; p < 0.05). In Amphibia, the Sakishima rice frog Fejervarya sakishimensis , had the highest FOO and wPOO values in the detected CSE prey items in summer (Fig. 7 ). The FOO value of this frog was significantly higher in CSE samples than in IRC samples (Supplementary Table 2; P < 0.01), although this frog also had the second highest FOO and wPOO values in the detected IRC prey items during this season (Fig. 7 , Supplementary Table 2). On the other hand, in winter, this frog was not detected in CSE samples, and was detected in IRC samples at a lower frequency (Fig. 7 ), exhibiting no significant difference between IRC and CSE in the Sakishima rice frog in winter (Supplementary Table 2). Within the CSE, the FOO of this frog was significantly higher in summer than in winter (Supplementary Table 2; P < 0.01). In the IRC samples in winter, the greater tip-nosed frog Odorrana supranarina , Sakishima rice frog F. sakishimensis , Owston’s green tree frog Rhacophorus owstoni , and Yaeyama Narrow-mouthed toad Microhyla kuramotoi were the second- to fifth-ranking prey in terms of the FOO value (Fig. 7 ). Within these Anura species, Owston’s green tree frog and Yaeyama Narrow-mouthed toad were detected also in CSE samples in winter (Fig. 7 , Supplementary Table 2). Among invertebrates, only crabs Decapoda sp., were detected as Malacostraca and only centipedes Scolopendromorpha sp., were detected as Chilopoda in the CSE samples (Supplementary Table 2). The FOO value of the black cicada Cryptotympana facialis was significantly higher in CSE samples than in IRC samples in summer (Supplementary Table 2; p < 0.05). Discussion Because both IRC and CSE are known to feed on a wide range of prey animals, including invertebrates and vertebrates 12–17 , this study aimed to identify prey items from various taxonomic groups using multiple primer sets. By integrating the results of each primer set, we successfully identified various prey species from a wide range of taxa. A previous study that utilized visual analysis of stomach contents, which is thought to provide higher resolution of prey identification than that of fecal contents 24 , identified approximately 61.6% of prey items at the species level 14 . The present fecal DNA metabarcoding analysis identified 85.5% of prey items at the species level (Supplementary Table 2). Consequently, DNA-based methods appear to be well-suited for conducting detailed dietary analyses. A comparison of the FOO of IRC prey items obtained in this study with that obtained in a previous study 13 suggests that for Mammalia, Reptilia, Amphibia, and Osteichthyes, DNA metabarcoding demonstrates the same level of detectability as visual analysis of fecal contents at the class level when identifying IRC prey items (Fig. 4 ). However, Aves and Insecta were detected less frequently in this study for several reasons. According to Watanabe (2012) 13 , winter birds, which visit Iriomotejima Island from autumn to winter, account for 40% of the bird species detected in fecal samples of IRC. In contrast, in this study, only 17.6% of the bird species were identified as winter birds. The number of migratory birds arriving in a particular area can vary significantly from year to year owing to various environmental changes 25,26 . Therefore, the annual variation in the number of winter bird visits between the samples analyzed in this study and those used by Watanabe (2012) 13 may have influenced the results. Insecta may not have been detected adequately using our method. One possible explanation for this is the insufficient accumulation of reference sequencing data in NCBI 27 . In particular, the sequence data of insects on Iriomotejima Island are still poor. Sufficient reference database is important to use the COI region as a barcode region due to the high genetic variation in genes that encode proteins in the COI region 28,29 . Indeed, many cockroaches Rhabdoblatta sp. were identified in a study conducted by Watanabe (2012) 13 , whereas they were not detected in the present study. The absence of sequence information for Rhabdoblatta sp. from Iriomotejima Island leads to the possibility that ambiguity in taxonomic assignment, or the failure to amplify its sequence due to mutations in primer biding region, contributed to the non-detection of Rhabdoblatta sp. Previous study that used a primer set amplifying the same region as the current study also noted the presence of uncertain taxonomic assignment resulting from an insufficient insect database 30 . Collecting sequence information on insects from Iriomotejima Island is a necessary step for the future. We will not discuss details regarding Aves and Insecta below because the reasons for the limited detection of these taxa remain unclear; however, it is noteworthy that a significant comparison of diet between IRC and CSE was achieved in this study using the same methods for both species. Additionally, the ranking of CSE prey frequency in winter was not accurately determined in this study due to the difference in prey species ranking by FOO and wPOO values. The inconsistency in rankings may be the result of the insufficient sample size of CSE feces in winter. However, the comparison of the frequency of each prey species between predators and seasons is meaningful. Based on the results of the NMDS and PERMANOVA analyses at the order level (Fig. 6 ), significant differences were found between IRC and CSE in the composition of prey items in both summer and winter. To consider how prey animals differed between the two predators, we focus on each prey taxon. Mammalian prey was detected in IRC feces in both seasons. Malacostraca (crabs) and Chilopoda (centipedes) were detected with higher FOO in CSE compared to IRC feces (Fig. 5 ). Previous studies have shown that Mammalia and crabs are the main prey animals of IRC and CSE, respectively, with FOO values of 30.9% in the visual analysis of IRC fecal contents and 57.1% in the analysis of CSE stomach contents 13,16 . While predation on centipedes by CSE has been observed visually 12 , it was not detected in their stomach contents presumably due to digestion 16 . Furthermore, although IRC have been shown to prey on Malacostraca and Chilopoda 13 , FOO values were low in the present study with 4.22% and 0.11%, respectively, in the visual analysis of their fecal contents. Previous studies also reported that the black rat could be a target of CSE predation 12 . However, the predation frequency of the black rat by CSE remains undetermined, and it was not detected in feces in the present study. In summary, it appears as though IRC feeds more frequently on Mammalia and that CSE feeds more frequently on crabs and centipedes. As the FOO values of Mammalia were not significantly different between the two predators in the summer season, additional research is required to examine the predation of Mammalia by CSE, especially the black rat. Differences in the prey animals of IRC and CSE may be attributed to their distinct feeding behaviors. Iriomote cat actively hunts animals walking around the ground 15,17 , whereas CSE exhibits a sit-and-wait, or passive, foraging strategy based on perching and searching for prey animals on the ground 12,31 . The latter passive strategy of CSE may make it difficult to capture swiftly moving, larger-sized animals such as mammals which rarely appear in open areas. The frequent detection of crabs and centipedes in CSE feces may be explained by the fact that crabs and centipedes appear more frequently in open areas where CSE tend to hunt and exhibit slower movements. The detection of Reptilia was significantly more frequent in IRC samples than in CSE samples during the summer. In particular, Japanese skink Pleistodon sp. exhibited the highest FOO and wPOO values among the detected IRC prey items, and the FOO was significantly higher than that in the CSE samples (Fig. 7 and Supplementary Table 2). This finding is concordant with that of a previous study that identified skink as a major prey for the IRC in summer 13 . However, in winter, the FOO of Reptilia was not significantly different between the two predators, probably due to the decreased FOO of Sakishima beauty snakes as well as skinks in the IRC samples during this season (Fig. 7 and Supplementary Table 2). In winter, the poikilothermic reptiles exhibit decreased activity owing to lower temperatures and appear only during warm daytime periods. Thus, the nocturnal predator IRC would have fewer opportunities to feed on reptiles 13 . On the other hand, CSE did not significantly affect the FOO of Reptilia between the two seasons. It is plausible that diurnal CSE has a sufficient chance of finding skinks in both seasons. Additionally, the detection of Sakishima smooth skink was significantly more frequent in the CSE in winter than in summer, and was not observed in the IRC (Fig. 7 and Supplementary Table 2). The potential for frequent feeding on Sakishima smooth skinks in CSE is the first finding of this study and requires further investigation. IRC and CSE samples exhibited relatively high FOO and wPOO values for amphibians in both seasons (Fig. 5 ). Sakishima rice frogs were detected more frequently in the summer for both predators (Fig. 7 and Supplementary Table 2). This is possible because the biomass of the Sakishima rice frog is the highest on Iriomotejima Island, and its activity increases during the summer 13,32 . In CSE samples, the FOO and wPOO of this frog were the highest among all prey species in summer (Fig. 7 ), although frogs are generally nocturnal animals, and the FOO was significantly higher than that of the IRC samples. This may be due to the relative increase in daytime activity of the sakishima rice frog during summer. In winter, the FOO of Sakishima rice frogs decreased significantly in the CSE samples, and the difference between the two predators was not significant. This may be related to the decreased activity of Sakishima rice frogs during winter 13 . Other amphibian prey species, the Yaeyama narrow-mouthed toad and the Owston’s green tree frog, were detected for both predators in winter (Fig. 7 and Supplementary Table 2). The supposed competition for the frogs between IRC and CSE in winter requires further discussion, particularly with a larger CSE sample size. In summary, despite previous studies indicating an overlap in prey items between the IRC and CSE 12–17 , we found differences in diets between the IRC and CSE in winter and summer, respectively. We performed fecal DNA barcoding analysis of each predator and calculated the frequency of detection of each prey item as an indicator. This enables a comparison between IRC and CSE diets for the first time. The proposed differential feeding habits of the two predators could be attributed to fundamental ecological differences such as activity patterns, foraging strategies, and seasonal variations in the activity of prey animals. It is noteworthy that prey animals with high biomass were shared by both IRC and CSE; however, we found at least partially significant differences in the FOO of each prey animal between predators. These quantitatively different diets of the IRC and CSE may contribute to distinguishing feeding habits between them. This could potentially allow both predators to survive on a small island with limited resources and challenging environmental conditions. IRC and CSE subspecies in the Eurasian continent, that is, the leopard cat and the Crested Serpent Eagle, predominantly feed on rodents and snakes 10,11,33–37 . Dietary analysis of the subspecies of IRC in the Korean Peninsula, Prionailurus bengalensis euptilurus , revealed that rodents accounted for over 90% of their diet based on morphological observations of fecal samples 35 . Similarly, regarding the subspecies of CSE in India, Spilornis cheela melanotis , snakes constituted over 70% of their diet, based on direct visual observation of their feeding behavior 33 . In contrast, both IRC and CSE on Iriomotejima Island exhibited relatively lower FOO values for rodents (25.3%) and snakes (7.3%) (Supplementary Table 2). These island subspecies tended to feed on more diverse prey species than their continental counterparts (Fig. 8 ). This finding suggests that the unique small island ecosystem of Iriomotejima Island has influenced the ecology of IRC and CSE to adapt to their available prey, along with other external factors, resulting in dietary habits distinct from those of their continental counterparts. Methods Study Area Iriomotejima Island has an area of approximately 284 square kilometers (24° 15–25’ N, 123° 40–55’ E), and is located in the southernmost part of the Ryukyu Archipelago, Japan (Fig. 2 ). The climate is subtropical, and dominated by Castanopsis sieboldii and Quercus miyagii occupy most of the mountainous areas, with the highest elevation at 469 m. Human habitats are limited to lowland areas along the coast, and there are some cultivated areas around villages as well as swampy and mangrove forests along several rivers. The average temperature is 19.2°C in winter (December to February) and 28.4°C in summer (June to August), and the average amounts of rainfall are 483.0 mm and 597.2 mm in winter and summer, respectively (1991 to 2020, Japan Meteorological Agency). Fecal sampling We collected feces from the IRC and CSE once a day in the transects shown in Fig. 2 from November 2018 to February 2019 (winter) and June to September 2019 (summer). The IRC and CSE feces were determined from their shapes. Fresh feces of CSE were collected immediately after excretion was observed from an adequate distance by the author (A.T.). Prey species found in the feces were confirmed through DNA barcoding, as described in a later section. Adherent uric acid mucus was manually removed to prevent PCR inhibition 38–40 . Subsequently, samples were immediately preserved in a 100 mL wide-mouth bottle or 2 mL tube filled with 99% ethanol. We also analyzed IRC feces collected and preserved by the Monitoring Program of Okinawa District Forest Office of Forest Agency and Ministry of the Environment of Japan, and CSE feces collected from individuals injured in traffic accidents and rescued by the Iriomote Wildlife Conservation Center of the Ministry of the Environment of Japan. DNA metabarcoding analysis A total of 100–200 mg of small blocks of feces were evenly sampled from each feces. The samples were homogenized using a Micro Homogenizing System Micro Smash MS-100R (TOMY SEIKO, Tokyo, Japan) with sterilized 3.175 mm stemless beads. Each homogenate was then incubated at 50°C for 120 min with the addition of 5.0 µL of proteinase K solution. Total DNA was extracted from each processed homogenate using a DNeasy PowerSoil Kit (Qiagen, Hilden, Germany), following the manufacturer’s protocol. Finally, 50 µL of total DNA solution was obtained from each sample. First-round PCR was performed for each extracted DNA sample using universal primer sets to amplify partial mitochondrial genes derived from IRC and CSE prey species. Since the prey animals of these species are supposed to be diverse including vertebrates and invertebrates, we used four primer sets targeting different gene regions: Ecoprimer 41 to amplify the vertebrate partial mitochondrial 12S ribosomal RNA (rRNA) gene, COI#1 (mlCOIintF and dgHCO2198) 42,43 for vertebrate and invertebrate partial mitochondrial cytochrome oxidase C subunit 1 (COI) genes, COI#2 (dgLCO1490 and COI-CFMRa) 44,45 for the insect partial COI gene, and MtAnr for the anuran partial mitochondrial 16S rRNA gene (Supplementary Table 1). All PCR reactions were conducted under the same conditions:10 µL mixtures were prepared for each PCR amplification, containing 1.0 µL of 10×Ex Taq Buffer (TaKaRa, Shiga, Japan), 0.80 µL of dNTP Mixture (TaKaRa), 0.10 µL of Ex Taq HS (TaKaRa), 0.60 µL (10 µM) of each primer, 1.0 µL of the extracted DNA solution, and 5.9 µL of nuclease-free water (Thermo Fisher Scientific, Waltham, MA, USA). The PCR conditions were as follows as Hebert et al. (2003) 46 : an initial denaturation at 94°C for 1 min, followed by five cycles of 94°C for 1 min, 45°C for 1 min 30 sec, and 72°C for 1 min 30 sec, followed by 35 cycles of 94°C for 1 min, 50°C for 1 min 30 sec, and 72°C for 1min, and a final incubation at 72°C for 5 min. All PCR reactions included negative controls. Second-round PCR was conducted to add MiSeq (Illumina, San Diego, CA, USA) adaptor sequences and 8 bp index sequences to both ends of the first-round PCR products, as described by Miya et al. (2015) 47 . The PCR mixtures were prepared in a total volume of 10 µL, containing 1.0 µL of 10×Ex Taq Buffer (TaKaRa), 0.80 µL of dNTP Mixture (TaKaRa), 0.050 µL of Ex Taq HS (TaKaRa), 1.5 µL (10 µM) of each index primer, 1.0 µL of diluted 1st-round PCR products (30x) with nuclease-free water (Thermo Fisher Scientific), and 4.15 µL of nuclease-free water (Thermo Fisher Scientific). The PCR conditions were as follows: an initial denaturation at 98°C for 1 min, followed by 12 cycles of 98°C for 30 sec, 65°C for 30 sec, 72°C for 30 sec, and final incubation at 72°C for 5 min. We then pooled 3.0 µL of each 2nd-round PCR product for each respective primer set. The pooled samples were separated using 1.5% agarose gel electrophoresis using L03 Agarose (TaKaRa), and the DNA bands of the expected PCR target size for each primer set were dissected manually from the gel using a sterilized disposable scalpel. DNA was purified using a MinElute Gel Extraction Kit (Qiagen) following the manufacturer’s protocol. After purification using Agencourt AMPure XP (Beckman Coulter, Brea, California,USA) according to the standard protocol, the sample DNA concentration was quantified using the Qubit dsDNA HS assay kit and Qubit 4 Fluorometer (Thermo Fisher Scientific). DNA concentration was adjusted to 6.0 nM based on the expected amplicon size using nuclease-free water (Thermo Fisher Scientific). Finally, the prepared DNA library was subjected to 250 bp pair-end sequencing using MiSeq (Illumina), MiSeq Reagent Kits v2 (Illumina), and Micro flow cell (Illumina). Sequencing data analysis and species identification From the obtained DNA sequence data, low quality 3' ends of the nucleotide sequences (Phred score < 10) were removed using the software DynamicTirm 48 . Paired-end reads were connected using the program FLASH 49 , and merged sequences less than 90 bp were discarded. Singleton sequences were removed using the program Uclust 50 , and the remaining sequences with more than two sequence counts were referred to as molecular operational taxonomic units (MOTUs). MOTUs were further filtered by their sequencing read numbers with a cut-off threshold of 0.01% of the total number of sequences in each sample. Sequence similarity-based Blast analysis 51 was performed using the reference nucleotide database of NCBI (National Center for Biotechnology Information 52 ). The Blast hit record was adapted as a taxon of each MOTU with the following assignment methods: If the sequence similarity was ( 1 ) 99% or higher, the top-hit species was adopted, ( 2 ) 98% or higher but less than 99%, the top-hit genus was adopted, and ( 3 ) 98% or higher but less than 90%, the top-hit family was adopted 53 . If the multiple species were identified as the top-hit, the highest taxonomic rank below ( 1 ) genus, ( 2 ) order, and ( 3 ) family shared by all top-hit species were adopted 53 . Taxa and MOTUs were excluded if they did not inhabit Iriomotejima Island or if they were difficult to consider as prey items for IRC and CSE. These include IRC and CSE (the source of feces), fungi, bacteria, humans, and insects less than 2 cm. The source species of each fecal sample was estimated based on the taxonomic assignment results of the Ecoprimer PCR products. MOTUs assigned to dogs and cattle were also excluded because they were considered environmental contaminants. We also excluded samples that did not contain any prey items from subsequent analyses as described in the results section. Statistical analysis All statistical analyses were performed using R version 4.3.0 ( R Core Team 2023). The quantitative data derived from the sequencing read number of each prey item were used to confirm the detectability of each primer set. The presence/absence data for each prey item were used for other analyses. The prey taxonomic richness was estimated with accumulation and extrapolation curves using ‘iNEXT’ 54–56 . We utilized two metrics, frequency of occurrence (FOO) and weighted percentage of occurrence (wPOO) 57 to conduct multivariate and statistical analyses. The results obtained from this study were examined by comparing the FOO values of each prey item with those obtained from a previous study that utilized morphological observations of fecal content 13 . Fisher’s exact test was used to examine the non-random relationships between the presence/absence ratio of each prey item for each predator to determine whether the differences between them were statistically significant. The weighted POO was not used in the comparative analysis because it was not suitable for interspecific comparisons 58 . To compare the diet composition of the two predators based on the Jaccard Index of the dissimilarity of the presence/absence data for prey items at the order level, we performed a non-metric multidimensional scaling (NMDS) analysis using the ‘vegan’ package 59 . The differentiation of the diet composition between these two species was confirmed by performing a permutational multivariate analysis of variance (PERMANOVA 60 ) using the ‘adonis’ function. The ‘betadisper’ and ‘anova’ functions were also used to examine whether the differences were caused by different degrees of variation among the samples of IRC and CSE or by the different diet composition between these two species. Declarations Acknowledgments We extend our sincere gratitude to Ryosuke Terada, Shun Kobayashi, and the entire staff of the Faculty of Science at the University of the Ryukyus for their support in conducting field surveys and experimental work. We also appreciate the support in the Iriomotejima Island by Hiroyoshi Kohno and Akira Mizutani from the Okinawa Regional Research Center at Tokai University, Kazumasa Ohama and the dedicated staff at Ohama Farm, as well as Miki Sugiyama and Chieko Matsumoto. Additionally, we would like to thank to Mamoru Toda at the Tropical Biosphere Research Center, University of the Ryukyus for supplying us with the experimental information. Miho Murayama-Inoue and Kristin Havercamp from the Wildlife Research Center, Kyoto University reviewed and advised on the manuscript. We thank the Okinawa District Forest Office of the Forest Agency, Ministry of the Environment of Japan, and the Iriomote Wildlife Conservation Center, Ministry of the Environment of Japan, for generously providing us with valuable samples. Each affiliation is current at the time of the study. Author contributions A.T. designed the study, and conceptual the main ideas. A.T. and N.N. conducted the field work on Iriomotejima Island and collected the samples. Y.S. and N.W. designed the experiments. A.T., N.W. and Y.S. performed the laboratory experiments and data analyses. A.T. visualized the outputs from data analyses. M.I., N.N., Y.S. and N.W. aided in interpreting the results. A.T., Y.S. and M.I. worked on the manuscript. M.I. supervised the project. All authors contributed input to the draft and final versions of the manuscript. Funding This work was supported by Pro Natura Foundation Japan’s 29th Pro Natura Fund. The authors have declared that no competing interests exist. Data availability The MiSeq-generated raw sequence reads from this study can be found in the DDBJ Sequence Read Archive (DRA) under the accession numbers DRA017514. The other data and computer code are available on Data Dryad review sharing link ( https://datadryad.org/stash/share/ef8jckjnMMXqvQ9EnHaTJfoFJlKbeE28MNv-ioAnNeg ). The private DOI is only available for review as the manuscript is under review. References MacArthur, R. H., & Wilson, E. O. Equilibrium theory of insular zoogeography. Evolution, 17, 373–387; 10.1111/j.1558-5646.1963.tb03295.x (1963). MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography . vol. 1 (Princeton University Press, 1967). Brown, J. H. & Lomolino, M. V. Biogeography . (Sinuer Associates Publishers, 1998). Okinawa Prefecture. Outline of Yaeyama Region. https://www.pref.okinawa.jp/site/norin/norin-yaeyama-nosui/keikaku/yaeyamanogaiyou.html (2016). Tamada, T. et al. Molecular diversity and phylogeography of the asian leopard cat, felis bengalensis, inferred from mitochondrial and Y-chromosomal DNA sequences. Zool. Sci. 25, 154–163; 10.2108/zsj.25.154 (2008). Del Hoyo, J., Elliot, A. & Sargatal, J. Handbook of the birds of the world, Vol. II. New world vultures to guineafowl. (Lynx Edicions, Barcelona, 1994). Schoener, T. W. Resource partitioning in ecological communities. Science (1979) 185, 27–39 (1974). Crooks, K. R. & Van Vuren, D. Resource utilization by two insular endemic mammalian carnivores, the island fox and island spotted skunk. Oecologia 104, 301–307; 10.1007/BF00328365/METRICS (1995). du Preez, B., Purdon, J., Trethowan, P., Macdonald, D. W. & Loveridge, A. J. Dietary niche differentiation facilitates coexistence of two large carnivores. J. Zool. 302, 149–156; 10.1111/jzo.12443 (2017). Pande, S., Yosef, R., Morelli, F., Pawar, R. & Mone, R. Diet and habitat affinities in six raptor species in India. Avian Res. 9, 1–9; 10.1186/s40657-018-0129-2 (2018). Tatara, M. & Doi, T. Comparative analyses on food habits of Japanese marten, Siberian weasel and leopard cat in the Tsushima islands, Japan. Ecol. Res. 9, 99–107 (1994). Mizutani, A., Nakamoto, J., Himura, S., Tanaka, S. & Kohno, H. Diet of the Crested Serpent Eagle Spilornis cheela perplexus observed in Iriomotejima and Ishigakijima, south Ryukyu Islands, Japan. Study Rev. Iriomote Is. 2016, ORRC, Tokai Univ. 25–43 (2017). Watanabe, S. Ecological flexibility of the top predator in an island ecosystem – food habit of the Iriomote Cat. Diversity of Ecosystems: linking structure and function 465–484 (2012). Nakanishi, N. & Izawa, M. Importance of frogs in the diet of the Iriomote cat based on stomach content analysis. Mamm. Res. 61, 35–44; 10.1007/s13364-015-0246-9 (2016). Sakaguchi, N. & Ono, Y. Seasonal change in the food habits of the Iriomote cat Felis iriomotensis . Ecol. Res. 9, 167–174 (1994). Tokita, H., Yoshino, T., Onuma, M., Kinjo, T. & Asakawa, M. Gastric contents of the Crested Serpent Eagle from Yaeyama Archipelago, Okinawa, Japan. Bird Research 10, 13–18 (2014). Watanabe, S., Nakanishi, N. & Izawa, M. Habitat and prey resource overlap between the Iriomote cat Prionailurus iriomotensis and introduced feral cat Felis catus based on assessment of scat content and distribution. Mammal Study 28, 47–56; 10.3106/mammalstudy.28.47 (2003). Monterroso, P. et al. Feeding ecological knowledge: the underutilised power of faecal DNA approaches for carnivore diet analysis. Mamm. Rev. 49, 97–112; 10.1111/mam.12144 (2019). Ando, H. et al. Methodological trends and perspectives of animal dietary studies by noninvasive fecal DNA metabarcoding. Environmental DNA 2, 391–406; abs/10.1002/edn3.117 (2020). Shao, X. et al. Generalist carnivores can be effective biodiversity samplers of terrestrial vertebrates. Front. Ecol. Environ. 19, 557–563; 10.1002/fee.2407 (2021). Shao, X. et al. Prey partitioning and livestock consumption in the world’s richest large carnivore assemblage. Current Biology 31, 4887–4897.e5; 10.1016/j.cub.2021.08.067 (2021). Xiong, M. et al. Molecular dietary analysis of two sympatric felids in the Mountains of Southwest China biodiversity hotspot and conservation implications. Sci. Rep. 7, 1–12; 10.1038/srep41909 (2017). Xiong, M. et al. Molecular analysis of vertebrates and plants in scats of leopard cats ( Prionailurus bengalensis ) in southwest China. J. Mammal. 97, 1054–1064; 10.1093/jmammal/gyw061 (2016). Cleary, G. P., Corner, L. A. L., O’Keeffe, J. & Marples, N. M. Diet of the European badger ( Meles meles ) in the Republic of Ireland: A comparison of results from an analysis of stomach contents and rectal faeces. Mammalian Biology 76, 470–475; 10.1016/j.mambio.2010.10.012 (2011). Newton, I. Population limitation in migrants. Ibis vol. 146 197–226; 10.1111/j.1474-919X.2004.00293.x (2004). Tanaka, M. & Sato, S. The wintering habitats of the Reed Bunting ( Emberiza schoeniclus ) in Kochi City (Passeriformes: Emberizidae). Bulletin of the Shikoku Institute of Natural History 12, 9–11 (2019). Schoch, C. L. et al. NCBI Taxonomy: a comprehensive update on curation, resources and tools. Database https://doi.org/10.1093/database/baaa062 (2020). Clarke, L. J., Soubrier, J., Weyrich, L. S. & Cooper, A. Environmental metabarcodes for insects: In silico PCR reveals potential for taxonomic bias. Mol. Ecol. Resour. 14, 1160–1170; 10.1111/1755-0998.12265 (2014). Deagle, B. E., Jarman, S. N., Coissac, E., Pompanon, F. & Taberlet, P. DNA metabarcoding and the cytochrome c oxidase subunit I marker: Not a perfect match. Biol. Lett. 10; 10.1098/rsbl.2014.0562 (2014). Bookwalter, J., Niyas, A. M. M., Caballero-López, B., Villari, C. & Claramunt-López, B. Fecal matters: implementing classical Coleoptera species lists with metabarcoding data from passerine bird feces. J. Insect Conserv. 27, 557–569; 10.1007/s10841-023-00479-7 (2023). Walther, B. A., Chou Ta-Ching & Lee, P.-F. Population density, home range, and habitat use of Crested Serpent-Eagles ( Spilornis cheela hoya ) in southern Taiwan: Using distance-based analysis and compositional analysis at different spatial cales. The Journal of Raptor Res. 48, 195–209; 10.3356/JRR-13-36.1 (2014). Watanabe, S., Nakanishi, N. & Izawa, M. Seasonal abundance in the floor-dwelling frog fauna on Iriomote Island of the Ryukyu Archipelago, Japan. J. Trop. Ecol. 21, 85–91; 10.1017/S0266467404002068 (2005). Gokula, V. Breeding ecology of the crested serpent eagle Spilornis cheela (Latham, 1790) (Aves: Accipitriformes: Accipitridae) in Kolli hills, Tamil Nadu, India. TAPROBANICA: The Journal of Asian Biodiversity 4, 77; 10.4038/tapro.v4i2.5059 (2012). Grassman, L. I., Tewes, M. E., Silvy, N. J. & Kreetiyutanont, K. Spatial organization and diet of the leopard cat ( Prionailurus bengalensis ) in north-central Thailand. J. Zool. 266, 45–54; 10.1017/S095283690500659X (2005). Lee, O., Lee, S., Nam, D.-H. & Lee, H. Y. Food Habits of the Leopard Cat ( Prionailurus bengalensis euptilurus ) in Korea. Mammal Study 39, 43–46; 10.3106/041.039.0107 (2014). Rajaratnam, R., Sunquist, M., Rajaratnam, L. & Ambu, L. Diet and habitat selection of the leopard cat ( Prionailurus bengalensis borneoensis ) in an agricultural landscape in Sabah, Malaysian Borneo. J. Trop. Ecol. 23, 209–217; 10.1017/S0266467406003841 (2007). Shehzad, W. et al. Carnivore diet analysis based on next-generation sequencing: Application to the leopard cat ( Prionailurus bengalensis ) in Pakistan. Mol Ecol 21, 1951–1965; 10.1111/j.1365-294X.2011.05424.x (2012). Eriksson, P., Mourkas, E., González-Acuna, D., Olsen, B. & Ellström, P. Evaluation and optimization of microbial DNA extraction from fecal samples of wild Antarctic bird species. Infect. Ecol. Epidemiol. 7, 1386536; 10.1080/20008686.2017.1386536 (2017). Oehm, J., Thalinger, B., Eisenkölbl, S. & Traugott, M. Diet analysis in piscivorous birds: What can the addition of molecular tools offer? Ecol. Evol. 7, 1984–1995; 10.1002/ece3.2790 (2017). Oehm, J., Juen, A., Nagiller, K., Neuhauser, S. & Traugott, M. Molecular scatology: How to improve prey DNA detection success in avian faeces? Mol. Ecol. Resour. 11, 620–628; 10.1111/j.1755-0998.2011.03001.x (2011). Riaz, T. et al. EcoPrimers: Inference of new DNA barcode markers from whole genome sequence analysis. Nucleic Acids Res. 39; 10.1093/nar/gkr732 (2011). Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: Application for characterizing coral reef fish gut contents. Front. Zool. 10, 1–14; 10.1186/1742-9994-10-34 (2013). Meyer, C. P. Molecular Systematics of Cowries (Gastropoda: Cypraeidae) and Diversification Patterns in the Tropics . Biological Journal of the Linnean Society vol. 79 (2003). Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861; 10.1111/1755-0998.12138 (2013). Jusino, M. A. et al. An improved method for utilizing high-throughput amplicon sequencing to determine the diets of insectivorous animals. Mol. Ecol. Resour. 19, 176–190; 10.1111/1755-0998.12951 (2019). Hebert, P. D. N., Cywinska, A., Ball, S. L. & DeWaard, J. R. Biological identifications through DNA barcodes. Proceedings of the Royal Society B: Biological Sciences 270, 313–321; 10.1098/rspb.2002.2218 (2003). Miya, M. et al. MiFish, a set of universal primers for metabarcoding environmental DNA from fishes: detection of > 230 species from aquarium tanks and coral reefs in the subtropical western North Pacific. Genome 58, 257; 10.1126/science.1251156 (2015). Cox, M. P., Peterson, D. A. & Biggs, P. J. SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data. BMC bioinformatics, 11; 10.1186/1471-2105-11-485 (2010). Magoč, T. & Salzberg, S. L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963; 10.1093/bioinformatics/btr507 (2011). Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461; 10.1093/bioinformatics/btq461 (2010). Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinformatics 10; 10.1186/1471-2105-10-421 (2009). NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 46, D8–D13; 10.1093/nar/gkx1095 (2018). Pereira, A., Xavier, R., Perera, A., Salvi, D. & Harris, D. J. DNA metabarcoding to assess diet partitioning and feeding strategies in generalist vertebrate predators: a case study on three syntopic lacertid lizards from Morocco. Biological Journal of the Linnean Society 127, 800–809; 10.1093/biolinnean/blz061 (2019). Chao, A. et al. Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67; 10.1890/13-0133.1 (2014). Hacker, C. E. et al. Use of DNA metabarcoding of bird pellets in understanding raptor diet on the Qinghai-Tibetan Plateau of China. Avian Res. 12; 10.1186/s40657-021-00276-3 (2021). Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456; 10.1111/2041-210X.12613 (2016). Deagle, B. E. et al. Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data? Mol. Ecol. 28, 391–406; 10.1111/mec.14734 (2019). Wright, B. E. Use of chi-square tests to analyze scat-derived diet composition data. Marine Mammal Science 26, 395–401; 10.1111/j.1748-7692.2009.00308.x (2010). Oksanen, J. et al. The vegan package. Community ecology package 10, 719 (2007). Anderson, M. J. Permutational Multivariate Analysis of Variance (PERMANOVA). Wiley StatsRef: Statistics Reference Online 1–15; 10.1002/9781118445112.STAT07841 (2017). Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile.pdf Cite Share Download PDF Status: Published Journal Publication published 02 Apr, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Feb, 2024 Reviews received at journal 22 Feb, 2024 Reviewers agreed at journal 11 Feb, 2024 Reviewers invited by journal 11 Feb, 2024 Editor assigned by journal 11 Feb, 2024 Editor invited by journal 08 Feb, 2024 Submission checks completed at journal 08 Feb, 2024 First submitted to journal 28 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3907562","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":272269526,"identity":"0fda7588-2bab-4ffa-985b-0d68c1877871","order_by":0,"name":"Alisa Tobe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYHACxgMJDAxybMzMB4AcCRmC6nmAGKTFmI+9LQGkhYc4LUCcOI/njAFMAD+wZ2B+cODBHzvGNomcz69u1FjwMLAfProBvy1sBgcS25KZ2SRyt1nnHAM6jCct7QYBhwG1NDCzgbQY57ABtUjwmBHQwv7hQMKfeh42iZxnxjn/iNLCY3Agge2wBBvPGebHuW3EaDnMUwD0y3EDNvY2M+bcPgkeNkJ+YW9v3/jwx5/q+vnNzI8/53yrk+NnP3wMrxYGZgSTTQJM4lWOrvsDKapHwSgYBaNg5AAA9h1CV8ySAoUAAAAASUVORK5CYII=","orcid":"","institution":"University of the Ryukyus, Faculty of Science","correspondingAuthor":true,"prefix":"","firstName":"Alisa","middleName":"","lastName":"Tobe","suffix":""},{"id":272269527,"identity":"4c858896-fc30-4695-be9e-a043dd110fa8","order_by":1,"name":"Yukuto Sato","email":"","orcid":"","institution":"University of the Ryukyus, Research Laboratory Center, Faculty of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yukuto","middleName":"","lastName":"Sato","suffix":""},{"id":272269528,"identity":"95b1cd97-0a51-4b35-8e4b-756e2e947a49","order_by":2,"name":"Nakatada Wachi","email":"","orcid":"","institution":"University of the Ryukyus, Center for Strategic Research Project","correspondingAuthor":false,"prefix":"","firstName":"Nakatada","middleName":"","lastName":"Wachi","suffix":""},{"id":272269529,"identity":"b6930df0-9864-45b0-8bcc-08c8ec51fa5a","order_by":3,"name":"Nozomi Nakanishi","email":"","orcid":"","institution":"University of the Ryukyus, Faculty of Science","correspondingAuthor":false,"prefix":"","firstName":"Nozomi","middleName":"","lastName":"Nakanishi","suffix":""},{"id":272269530,"identity":"deb2a99a-a930-40f1-b05a-d36e9761b691","order_by":4,"name":"Masako Izawa","email":"","orcid":"","institution":"University of the Ryukyus, Faculty of Science","correspondingAuthor":false,"prefix":"","firstName":"Masako","middleName":"","lastName":"Izawa","suffix":""}],"badges":[],"createdAt":"2024-01-29 02:18:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3907562/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3907562/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-58204-6","type":"published","date":"2024-04-02T15:01:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51017722,"identity":"1da9601c-e2ef-458c-abf4-72e3dbf35319","added_by":"auto","created_at":"2024-02-12 19:17:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":211414,"visible":true,"origin":"","legend":"\u003cp\u003eThe Outline of the Present Study. Silhouettes were gained by phylopic (https://beta.phylopic.org/).\u003c/p\u003e","description":"","filename":"OnlineFig.14.png","url":"https://assets-eu.researchsquare.com/files/rs-3907562/v1/54683783e9dd6688c4614ea9.png"},{"id":51017714,"identity":"ecc6ae70-9745-4fdc-96a6-346f152b8337","added_by":"auto","created_at":"2024-02-12 19:16:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":282280,"visible":true,"origin":"","legend":"\u003cp\u003e(a) A map showing the location of Iriomotejima Island. (b) A map of Iriomotejime Island and study area. (c) The eight transect lines (marked in red) used in this study. All maps were made in R ver. 4.3.0 based on information from the Technical Report of the Geospatial Information Authority of Japan https://www.gsi.go.jp/\u003cu\u003e.\u003c/u\u003e\u003c/p\u003e","description":"","filename":"OnlineFig.23.png","url":"https://assets-eu.researchsquare.com/files/rs-3907562/v1/bd95b00c1f7cd10e76668ebe.png"},{"id":51017713,"identity":"d336fb67-9d25-4912-8c4b-2a94e6662b5c","added_by":"auto","created_at":"2024-02-12 19:16:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":142119,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of sequencing reads of detected prey class in each universal primer set. Silhouettes shown in light grey are vertebrates, shown in black are invertebrates. Silhouettes were gained by phylopic (https://beta.phylopic.org/).\u003c/p\u003e","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-3907562/v1/c3592ff27472a9da85d0e027.png"},{"id":51017720,"identity":"46e2eeb8-92e2-495a-89fa-f8bd240fd662","added_by":"auto","created_at":"2024-02-12 19:16:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7000,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of frequency of occurrence (FOO) of each IRC prey class between this study and previous study that used morphological observation of fecal contents (Watanabe 2012).\u003c/p\u003e","description":"","filename":"OnlineFig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-3907562/v1/cc44c9154707d817eedb37c6.png"},{"id":51018874,"identity":"d52bb42d-63dd-439e-aedb-d335e7c16515","added_by":"auto","created_at":"2024-02-12 19:24:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":139939,"visible":true,"origin":"","legend":"\u003cp\u003eFOO of each prey class detected in the IRC and CSE in each season. Silhouettes were gained by phylopic (https://beta.phylopic.org/).\u003c/p\u003e","description":"","filename":"OnlineFig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-3907562/v1/9a437cbc031c2f1937db0a5d.png"},{"id":51017719,"identity":"f4e893c6-1a02-4c79-8a20-4a6d887af46f","added_by":"auto","created_at":"2024-02-12 19:16:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":8786,"visible":true,"origin":"","legend":"\u003cp\u003eDifference in prey order composition between the IRC and CSE samples in each season. We excluded three IRC samples from summer samples because they were outliers. The stress values of NMDS are 0.041 and 0.048 respectively.\u003c/p\u003e","description":"","filename":"OnlineFig..png","url":"https://assets-eu.researchsquare.com/files/rs-3907562/v1/890c66b5e28d83c89377c803.png"},{"id":51017717,"identity":"b2dce54e-ab53-4afe-8e38-75fb6a81c6c9","added_by":"auto","created_at":"2024-02-12 19:16:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2145124,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of occurrence (FOO) and weighted per cent of occurrence (wPOO) values of each prey item detected in the IRC and CSE samples in each season. Silhouettes were gained by phylopic (https://beta.phylopic.org/).\u003c/p\u003e","description":"","filename":"OnlineFig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-3907562/v1/d5ccb08d68c8ec59887cbffe.png"},{"id":51017721,"identity":"99723b97-46ef-40e5-87e4-cdbd206049e9","added_by":"auto","created_at":"2024-02-12 19:16:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":9381,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of frequency of occurrence (FOO) of each prey class between this study and previous studies which analyze the diet of the subspecies of the IRC and CSE, Leopard Cat in Korea (Lee et al., 2014) and Crested Serpent Eagle in India (Gokula, 2012).\u003c/p\u003e","description":"","filename":"OnlineFig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-3907562/v1/75bae6269dd8a009b9abf932.png"},{"id":54304140,"identity":"ba0b2957-94f6-4112-b4f8-b963419285d0","added_by":"auto","created_at":"2024-04-08 15:14:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1689031,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3907562/v1/99bcc316-ec5f-4d08-b75c-7191bc02f935.pdf"},{"id":51017718,"identity":"f4a5a193-a39d-49e0-b7f7-0f7775305133","added_by":"auto","created_at":"2024-02-12 19:16:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":308174,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3907562/v1/699b2d6a1dcf8d757f647863.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seasonal Diet Partition among Top Predators of a Small Island, Iriomotejima Island in the Ryukyu Archipelago, Japan","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIsland ecosystems exhibit unique ecological characteristics that are sometimes simpler or more monotonic than those of continental ecosystems. This is mainly because these islands have a limited number of species and individuals, and immigration and emigration events are less likely to occur\u003csup\u003e1,2\u003c/sup\u003e. On such islands, predators at higher trophic levels are often absent, because predator species, such as carnivores, cannot maintain their populations without sufficient prey resources\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, multiple predators have been able to co-exist on Iriomotejima Island, located in the southern part of the Ryukyu Archipelago, which covers an area of approximately 289 square kilometers\u003csup\u003e4\u003c/sup\u003e. Two top predators are known from this island: the Iriomote cat (IRC) \u003cem\u003ePrionailurus bengalensis iriomotensis\u003c/em\u003e, and the Crested Serpent Eagle (CSE) \u003cem\u003eSpilornis cheela perplexus\u003c/em\u003e. The IRC is endemic to Iriomotejima Island and is a subspecies of the leopard cat \u003cem\u003eP. bengalensis\u003c/em\u003e, which is widely distributed in East and Southeast Asia\u003csup\u003e5\u003c/sup\u003e. The CSE is also endemic to Iriomotejima, Ishigakijima, and Yonagunijima Islands in the Ryukyu Archipelago and is a subspecies of the Crested Serpent Eagle \u003cem\u003eS. cheela\u003c/em\u003e, which is widely distributed in East and South Asia\u003csup\u003e6\u003c/sup\u003e. The mechanism of coexistence of these predators is important for understanding the ecological features of Iriomotejima Island, which has limited resources. To address this question, we focused on the seasonal diets of the IRC and CSE, as these are thought to be one of the most important factors for their survival in a small island ecosystem.\u003c/p\u003e \u003cp\u003eWhen multiple top predators are sympatric on a small island with restricted food resources, their feeding habits are expected to differ from each other\u003csup\u003e7\u003c/sup\u003e. Although a few studies have compared the diet of sympatric animals that exhibit similar ecological niches\u003csup\u003e8\u0026ndash;10\u003c/sup\u003e, few studies have focused on sympatric predators endemic to small islands such as Iriomotejima Island\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies on the diets of IRC and CSE on Iriomotejima Island have shown that both are carnivorous and feed on a variety of prey animals, such as mammals, birds, reptiles, amphibians, fishes, insects, and crustaceans\u003csup\u003e12,13\u003c/sup\u003e. Most of these prey animals overlapped between the two species\u003csup\u003e12\u0026ndash;17\u003c/sup\u003e. The diet of the IRC was analyzed based on the morphological observation of fecal and stomach contents of dead individuals\u003csup\u003e13\u0026ndash;17\u003c/sup\u003e, although it is often difficult to identify prey animals at the species level from such digested items. Analysis of the CSE fecal content is more challenging, as carnivorous birds often spit out undigested items such as pellets, and only soluble materials are excreted as feces. Therefore, previous studies on the CSE diet have largely been limited to stomach content analysis of dead individuals and direct observation of their feeding behaviors\u003csup\u003e12,16\u003c/sup\u003e. However, in these studies, the sample size was insufficient because of the difficulty in obtaining dead individuals or the limited frequency of direct observations.\u003c/p\u003e \u003cp\u003eIn this study, we performed DNA barcoding analysis of feces to examine the prey of the IRC and CSE. DNA barcoding generally provides a higher taxonomic resolution than the morphological observation of fecal and stomach samples and has recently become a major method for animal diet analysis\u003csup\u003e18,19\u003c/sup\u003e. This method enabled us to analyze CSE diets using fecal samples with larger sample sizes than stomach content analysis or direct observations. Furthermore, it allows a direct comparison of the prey animals of birds and mammals based on the same method. Some studies in Pakistan and China conducted DNA barcoding to investigate the diets of other subspecies of the leopard cat\u003csup\u003e20\u0026ndash;23\u003c/sup\u003e, while no studies have addressed any CSE subspecies.\u003c/p\u003e \u003cp\u003eIn the present study, we aimed to elucidate the coexistence mechanism of the two top predators, IRC and CSE, living on Iriomotejima Island. Our goal was to examine their competition for limited food resources on a small island. To achieve this, we utilized DNA barcoding to analyze their diets, providing higher resolution (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eFecal sampling and DNA sequencing results\u003c/h2\u003e \u003cp\u003eWe obtained 38 and 76 fecal samples from the IRC in the summer and winter, respectively. Similarly, we collected 24 and 12 CSE fecal samples in the summer and winter, respectively. The numbers of IRC samples that contained DNA sequences from more than one prey item were 31 (81.6%) and 64 (90.1%) in the summer and winter, respectively. The numbers were 21 (87.5%) and nine (75.0%) among CSE samples in the summer and winter, respectively. These samples were used for subsequent analyses.\u003c/p\u003e \u003cp\u003eUsing the DNA metabarcoding sequencing results, we obtained 333 sequencing reads using the COI#1; 10,335 sequencing reads using the COI#2; 208,673 sequencing reads using the Ecoprimer; and 74,483 sequencing reads using the MtAnr. These sequences were derived from 55 different prey items, including five species of Mammalia; 17 species, one genus, one order, and one family of Aves; 10 species and one genus of Reptilia; seven species and one family of Amphibia; one species of Osteichthyes; three species of Malacostraca; four species and one order of Insecta; and two families of Chilopoda (Supplementary Table\u0026nbsp;2). We could identify 83.5% of the MOTUs at the species level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of detected prey species by each primer set\u003c/h2\u003e \u003cp\u003eThe number of sequencing reads for the detected prey taxa varied remarkably among primer sets. Invertebrates were detected using COI#1 and COI#2, whereas vertebrates were detected using all primer sets (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The COI#1 detected the Brown-eared Bulbul \u003cem\u003eHypsipetes amaurotis stejnegeri\u003c/em\u003e which was not detected by any other primer sets. COI#2 detected a relatively large number of invertebrates, such as the mudflat crab \u003cem\u003eChiromantes dehaani\u003c/em\u003e, the blue land crab \u003cem\u003eDiscoplax hirtipes\u003c/em\u003e, the vine hawkmoth \u003cem\u003eHippotion celerio\u003c/em\u003e, the bush cricket \u003cem\u003eMecopoda elongate\u003c/em\u003e, the privet hawkmoth \u003cem\u003ePsilogramma menephron\u003c/em\u003e, and centipedes \u003cem\u003eScolopendromorpha\u003c/em\u003e sp., that were not detected by any other primer sets. The Ecoprimer detected each class of land vertebrates almost evenly. In particular, Osteichthyes (the barred mudskipper \u003cem\u003ePeriophthalmus argentilineatus\u003c/em\u003e) was not detected by any other primer sets, and was only detected in one fecal sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The sequencing reads derived from Amphibia accounted for 97.2% of the total number of sequencing reads obtained by the MtAnr. The Yaeyama kajika frog \u003cem\u003eBuergeria choui\u003c/em\u003e and Utsunomiya's tip-nosed frog \u003cem\u003eOdorrana utsunomiyaorum\u003c/em\u003e were detected only using this primer set.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eThe estimated diet composition of IRC and CSE\u003c/h2\u003e \u003cp\u003eThe number of prey items detected from the fecal samples of each predator during each season was as follows\u0026mdash;summer samples of IRC: 14 species, one genus, and one order belonging to five classes, nine orders, 12 families, and 15 genera; summer samples of CSE: 12 species, one genus, and two families belonging to five classes, five orders, 11 families, and 12 genera; winter samples of IRC: 30 species, two genera, three families, and two orders belonging to eight classes, 16 orders, 23 families, and 30 genera; winter samples of CSE: 12 species and two families belonging to six classes, seven orders, 12 families, and 12 genera (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Supplementary Table\u0026nbsp;2). The prey taxonomic richness for each predator showed no significant difference in both seasons (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on a comparison of the FOO values of IRC prey items obtained in this study with those obtained in a previous study\u003csup\u003e13\u003c/sup\u003e, except for Aves and Insecta, there were no significant differences in the FOO values of Mammalia, Reptilia, Amphibia, and Osteichthyes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). It should be noted that Aves and Insecta were significantly less frequent in this study than in the previous study (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05 and \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01, respectively).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding the detected prey classes, Mammalia was not detected in any CSE samples, while they were frequently detected in IRC samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The FOO values of the Mammalia were significantly different between the two predators in winter (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). In contrast, Malacostraca and Chilopoda were rarely detected in IRC samples while they were frequently detected in CSE samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These difference between CSE and IRC was significant in both seasons (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01 in Malacostraca in summer and Chilopoda in winter, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05 in Chilopoda in summer and Malacostraca in winter). Reptilia were significantly more frequent in IRC samples than in CSE samples in summer (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05), whereas the difference was not statistically significant in winter. On the other hand, Amphibia were significantly more frequent in CSE samples than in IRC samples in summer (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05), whereas this trend was not statistically significant in winter. Within each predator, the FOO of Mammalia and Reptilia detected in IRC feces showed significant differences between seasons (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01 in Mammalia and Reptilia, respectively). In addition, the FOO of Amphibia detected in CSE showed a significant difference between seasons (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe NMDS and PERMANOVA analyses for comparison of diet composition between IRC and CSE showed that the prey composition at the order level was significantly different between them in both seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) and the differences were not caused by different degrees of variation among the samples of IRC and CSE (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4899 in summer; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.7077 in winter).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen prey species at the species-to-order level were sorted by FOO and wPOO values, there was little difference in the ranking of prey species, with the exception of winter CSE, which had a small sample size (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Mammalia, the black rat \u003cem\u003eRattus rattus\u003c/em\u003e, had the highest FOO and wPOO values in the IRC samples in winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e), although FOO was not significantly higher in the IRC samples than in the CSE samples (Supplementary Table\u0026nbsp;2). In both seasons, black rats were the most frequently detected mammals in the IRC samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e), and their FOO was significantly higher in winter than in summer (Table S2; \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eIn Reptilia, the FOO and wPOO values of Japanese skinks \u003cem\u003ePlestiodon\u003c/em\u003e sp., were the highest in the detected IRC prey items in summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e), and its FOO was significantly higher in IRC samples than in CSE samples (Supplementary Table\u0026nbsp;2; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). Within the IRC samples, the FOO value of Japanese skinks was significantly higher in summer than in winter (Supplementary Table\u0026nbsp;2; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01). Similarly, the Sakishima beauty snake \u003cem\u003eElaphe taeniura schmackeri\u003c/em\u003e, showed a significantly higher FOO in summer than in winter in the IRC samples (Supplementary Table\u0026nbsp;2; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). The detection of Sakishima smooth skink \u003cem\u003eScincella boettgeri\u003c/em\u003e was significantly more frequent in the CSE samples than in the IRC samples in winter (Supplementary Table\u0026nbsp;2; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01), and was significantly more frequent in winter than in summer within the CSE samples (Supplementary Table\u0026nbsp;2; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eIn Amphibia, the Sakishima rice frog \u003cem\u003eFejervarya sakishimensis\u003c/em\u003e, had the highest FOO and wPOO values in the detected CSE prey items in summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The FOO value of this frog was significantly higher in CSE samples than in IRC samples (Supplementary Table\u0026nbsp;2; \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01), although this frog also had the second highest FOO and wPOO values in the detected IRC prey items during this season (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Supplementary Table\u0026nbsp;2). On the other hand, in winter, this frog was not detected in CSE samples, and was detected in IRC samples at a lower frequency (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e), exhibiting no significant difference between IRC and CSE in the Sakishima rice frog in winter (Supplementary Table\u0026nbsp;2). Within the CSE, the FOO of this frog was significantly higher in summer than in winter (Supplementary Table\u0026nbsp;2; \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01). In the IRC samples in winter, the greater tip-nosed frog \u003cem\u003eOdorrana supranarina\u003c/em\u003e, Sakishima rice frog \u003cem\u003eF. sakishimensis\u003c/em\u003e, Owston\u0026rsquo;s green tree frog \u003cem\u003eRhacophorus owstoni\u003c/em\u003e, and Yaeyama Narrow-mouthed toad \u003cem\u003eMicrohyla kuramotoi\u003c/em\u003e were the second- to fifth-ranking prey in terms of the FOO value (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Within these Anura species, Owston\u0026rsquo;s green tree frog and Yaeyama Narrow-mouthed toad were detected also in CSE samples in winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eAmong invertebrates, only crabs Decapoda sp., were detected as Malacostraca and only centipedes Scolopendromorpha sp., were detected as Chilopoda in the CSE samples (Supplementary Table\u0026nbsp;2). The FOO value of the black cicada \u003cem\u003eCryptotympana facialis\u003c/em\u003e was significantly higher in CSE samples than in IRC samples in summer (Supplementary Table\u0026nbsp;2; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBecause both IRC and CSE are known to feed on a wide range of prey animals, including invertebrates and vertebrates\u003csup\u003e12\u0026ndash;17\u003c/sup\u003e, this study aimed to identify prey items from various taxonomic groups using multiple primer sets. By integrating the results of each primer set, we successfully identified various prey species from a wide range of taxa. A previous study that utilized visual analysis of stomach contents, which is thought to provide higher resolution of prey identification than that of fecal contents\u003csup\u003e24\u003c/sup\u003e, identified approximately 61.6% of prey items at the species level\u003csup\u003e14\u003c/sup\u003e. The present fecal DNA metabarcoding analysis identified 85.5% of prey items at the species level (Supplementary Table\u0026nbsp;2). Consequently, DNA-based methods appear to be well-suited for conducting detailed dietary analyses.\u003c/p\u003e \u003cp\u003eA comparison of the FOO of IRC prey items obtained in this study with that obtained in a previous study\u003csup\u003e13\u003c/sup\u003esuggests that for Mammalia, Reptilia, Amphibia, and Osteichthyes, DNA metabarcoding demonstrates the same level of detectability as visual analysis of fecal contents at the class level when identifying IRC prey items (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, Aves and Insecta were detected less frequently in this study for several reasons. According to Watanabe (2012)\u003csup\u003e13\u003c/sup\u003e, winter birds, which visit Iriomotejima Island from autumn to winter, account for 40% of the bird species detected in fecal samples of IRC. In contrast, in this study, only 17.6% of the bird species were identified as winter birds. The number of migratory birds arriving in a particular area can vary significantly from year to year owing to various environmental changes\u003csup\u003e25,26\u003c/sup\u003e. Therefore, the annual variation in the number of winter bird visits between the samples analyzed in this study and those used by Watanabe (2012)\u003csup\u003e13\u003c/sup\u003e may have influenced the results.\u003c/p\u003e \u003cp\u003eInsecta may not have been detected adequately using our method. One possible explanation for this is the insufficient accumulation of reference sequencing data in NCBI\u003csup\u003e27\u003c/sup\u003e. In particular, the sequence data of insects on Iriomotejima Island are still poor. Sufficient reference database is important to use the COI region as a barcode region due to the high genetic variation in genes that encode proteins in the COI region\u003csup\u003e28,29\u003c/sup\u003e. Indeed, many cockroaches \u003cem\u003eRhabdoblatta\u003c/em\u003e sp. were identified in a study conducted by Watanabe (2012)\u003csup\u003e13\u003c/sup\u003e, whereas they were not detected in the present study. The absence of sequence information for \u003cem\u003eRhabdoblatta\u003c/em\u003e sp. from Iriomotejima Island leads to the possibility that ambiguity in taxonomic assignment, or the failure to amplify its sequence due to mutations in primer biding region, contributed to the non-detection of \u003cem\u003eRhabdoblatta\u003c/em\u003e sp. Previous study that used a primer set amplifying the same region as the current study also noted the presence of uncertain taxonomic assignment resulting from an insufficient insect database\u003csup\u003e30\u003c/sup\u003e. Collecting sequence information on insects from Iriomotejima Island is a necessary step for the future.\u003c/p\u003e \u003cp\u003eWe will not discuss details regarding Aves and Insecta below because the reasons for the limited detection of these taxa remain unclear; however, it is noteworthy that a significant comparison of diet between IRC and CSE was achieved in this study using the same methods for both species.\u003c/p\u003e \u003cp\u003eAdditionally, the ranking of CSE prey frequency in winter was not accurately determined in this study due to the difference in prey species ranking by FOO and wPOO values. The inconsistency in rankings may be the result of the insufficient sample size of CSE feces in winter. However, the comparison of the frequency of each prey species between predators and seasons is meaningful.\u003c/p\u003e \u003cp\u003eBased on the results of the NMDS and PERMANOVA analyses at the order level (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), significant differences were found between IRC and CSE in the composition of prey items in both summer and winter. To consider how prey animals differed between the two predators, we focus on each prey taxon.\u003c/p\u003e \u003cp\u003eMammalian prey was detected in IRC feces in both seasons. Malacostraca (crabs) and Chilopoda (centipedes) were detected with higher FOO in CSE compared to IRC feces (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Previous studies have shown that Mammalia and crabs are the main prey animals of IRC and CSE, respectively, with FOO values of 30.9% in the visual analysis of IRC fecal contents and 57.1% in the analysis of CSE stomach contents\u003csup\u003e13,16\u003c/sup\u003e. While predation on centipedes by CSE has been observed visually\u003csup\u003e12\u003c/sup\u003e, it was not detected in their stomach contents presumably due to digestion\u003csup\u003e16\u003c/sup\u003e. Furthermore, although IRC have been shown to prey on Malacostraca and Chilopoda\u003csup\u003e13\u003c/sup\u003e, FOO values were low in the present study with 4.22% and 0.11%, respectively, in the visual analysis of their fecal contents. Previous studies also reported that the black rat could be a target of CSE predation\u003csup\u003e12\u003c/sup\u003e. However, the predation frequency of the black rat by CSE remains undetermined, and it was not detected in feces in the present study. In summary, it appears as though IRC feeds more frequently on Mammalia and that CSE feeds more frequently on crabs and centipedes. As the FOO values of Mammalia were not significantly different between the two predators in the summer season, additional research is required to examine the predation of Mammalia by CSE, especially the black rat.\u003c/p\u003e \u003cp\u003eDifferences in the prey animals of IRC and CSE may be attributed to their distinct feeding behaviors. Iriomote cat actively hunts animals walking around the ground\u003csup\u003e15,17\u003c/sup\u003e, whereas CSE exhibits a sit-and-wait, or passive, foraging strategy based on perching and searching for prey animals on the ground\u003csup\u003e12,31\u003c/sup\u003e. The latter passive strategy of CSE may make it difficult to capture swiftly moving, larger-sized animals such as mammals which rarely appear in open areas. The frequent detection of crabs and centipedes in CSE feces may be explained by the fact that crabs and centipedes appear more frequently in open areas where CSE tend to hunt and exhibit slower movements.\u003c/p\u003e \u003cp\u003eThe detection of Reptilia was significantly more frequent in IRC samples than in CSE samples during the summer. In particular, Japanese skink \u003cem\u003ePleistodon\u003c/em\u003e sp. exhibited the highest FOO and wPOO values among the detected IRC prey items, and the FOO was significantly higher than that in the CSE samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Supplementary Table\u0026nbsp;2). This finding is concordant with that of a previous study that identified skink as a major prey for the IRC in summer\u003csup\u003e13\u003c/sup\u003e. However, in winter, the FOO of Reptilia was not significantly different between the two predators, probably due to the decreased FOO of Sakishima beauty snakes as well as skinks in the IRC samples during this season (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Supplementary Table\u0026nbsp;2). In winter, the poikilothermic reptiles exhibit decreased activity owing to lower temperatures and appear only during warm daytime periods. Thus, the nocturnal predator IRC would have fewer opportunities to feed on reptiles\u003csup\u003e13\u003c/sup\u003e. On the other hand, CSE did not significantly affect the FOO of Reptilia between the two seasons. It is plausible that diurnal CSE has a sufficient chance of finding skinks in both seasons. Additionally, the detection of Sakishima smooth skink was significantly more frequent in the CSE in winter than in summer, and was not observed in the IRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Supplementary Table\u0026nbsp;2). The potential for frequent feeding on Sakishima smooth skinks in CSE is the first finding of this study and requires further investigation.\u003c/p\u003e \u003cp\u003eIRC and CSE samples exhibited relatively high FOO and wPOO values for amphibians in both seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Sakishima rice frogs were detected more frequently in the summer for both predators (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Supplementary Table\u0026nbsp;2). This is possible because the biomass of the Sakishima rice frog is the highest on Iriomotejima Island, and its activity increases during the summer\u003csup\u003e13,32\u003c/sup\u003e. In CSE samples, the FOO and wPOO of this frog were the highest among all prey species in summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e), although frogs are generally nocturnal animals, and the FOO was significantly higher than that of the IRC samples. This may be due to the relative increase in daytime activity of the sakishima rice frog during summer. In winter, the FOO of Sakishima rice frogs decreased significantly in the CSE samples, and the difference between the two predators was not significant. This may be related to the decreased activity of Sakishima rice frogs during winter\u003csup\u003e13\u003c/sup\u003e. Other amphibian prey species, the Yaeyama narrow-mouthed toad and the Owston\u0026rsquo;s green tree frog, were detected for both predators in winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Supplementary Table\u0026nbsp;2). The supposed competition for the frogs between IRC and CSE in winter requires further discussion, particularly with a larger CSE sample size.\u003c/p\u003e \u003cp\u003eIn summary, despite previous studies indicating an overlap in prey items between the IRC and CSE\u003csup\u003e12\u0026ndash;17\u003c/sup\u003e, we found differences in diets between the IRC and CSE in winter and summer, respectively. We performed fecal DNA barcoding analysis of each predator and calculated the frequency of detection of each prey item as an indicator. This enables a comparison between IRC and CSE diets for the first time. The proposed differential feeding habits of the two predators could be attributed to fundamental ecological differences such as activity patterns, foraging strategies, and seasonal variations in the activity of prey animals. It is noteworthy that prey animals with high biomass were shared by both IRC and CSE; however, we found at least partially significant differences in the FOO of each prey animal between predators. These quantitatively different diets of the IRC and CSE may contribute to distinguishing feeding habits between them. This could potentially allow both predators to survive on a small island with limited resources and challenging environmental conditions.\u003c/p\u003e \u003cp\u003eIRC and CSE subspecies in the Eurasian continent, that is, the leopard cat and the Crested Serpent Eagle, predominantly feed on rodents and snakes\u003csup\u003e10,11,33\u0026ndash;37\u003c/sup\u003e. Dietary analysis of the subspecies of IRC in the Korean Peninsula, \u003cem\u003ePrionailurus bengalensis euptilurus\u003c/em\u003e, revealed that rodents accounted for over 90% of their diet based on morphological observations of fecal samples\u003csup\u003e35\u003c/sup\u003e. Similarly, regarding the subspecies of CSE in India, \u003cem\u003eSpilornis cheela melanotis\u003c/em\u003e, snakes constituted over 70% of their diet, based on direct visual observation of their feeding behavior\u003csup\u003e33\u003c/sup\u003e. In contrast, both IRC and CSE on Iriomotejima Island exhibited relatively lower FOO values for rodents (25.3%) and snakes (7.3%) (Supplementary Table\u0026nbsp;2). These island subspecies tended to feed on more diverse prey species than their continental counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This finding suggests that the unique small island ecosystem of Iriomotejima Island has influenced the ecology of IRC and CSE to adapt to their available prey, along with other external factors, resulting in dietary habits distinct from those of their continental counterparts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eIriomotejima Island has an area of approximately 284 square kilometers (24\u0026deg; 15\u0026ndash;25\u0026rsquo; N, 123\u0026deg; 40\u0026ndash;55\u0026rsquo; E), and is located in the southernmost part of the Ryukyu Archipelago, Japan (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The climate is subtropical, and dominated by \u003cem\u003eCastanopsis sieboldii\u003c/em\u003e and \u003cem\u003eQuercus miyagii\u003c/em\u003e occupy most of the mountainous areas, with the highest elevation at 469 m. Human habitats are limited to lowland areas along the coast, and there are some cultivated areas around villages as well as swampy and mangrove forests along several rivers. The average temperature is 19.2\u0026deg;C in winter (December to February) and 28.4\u0026deg;C in summer (June to August), and the average amounts of rainfall are 483.0 mm and 597.2 mm in winter and summer, respectively (1991 to 2020, Japan Meteorological Agency).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eFecal sampling\u003c/h2\u003e \u003cp\u003eWe collected feces from the IRC and CSE once a day in the transects shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003e from November 2018 to February 2019 (winter) and June to September 2019 (summer). The IRC and CSE feces were determined from their shapes. Fresh feces of CSE were collected immediately after excretion was observed from an adequate distance by the author (A.T.). Prey species found in the feces were confirmed through DNA barcoding, as described in a later section. Adherent uric acid mucus was manually removed to prevent PCR inhibition\u003csup\u003e38\u0026ndash;40\u003c/sup\u003e. Subsequently, samples were immediately preserved in a 100 mL wide-mouth bottle or 2 mL tube filled with 99% ethanol. We also analyzed IRC feces collected and preserved by the Monitoring Program of Okinawa District Forest Office of Forest Agency and Ministry of the Environment of Japan, and CSE feces collected from individuals injured in traffic accidents and rescued by the Iriomote Wildlife Conservation Center of the Ministry of the Environment of Japan.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDNA metabarcoding analysis\u003c/h2\u003e \u003cp\u003eA total of 100\u0026ndash;200 mg of small blocks of feces were evenly sampled from each feces. The samples were homogenized using a Micro Homogenizing System Micro Smash MS-100R (TOMY SEIKO, Tokyo, Japan) with sterilized 3.175 mm stemless beads. Each homogenate was then incubated at 50\u0026deg;C for 120 min with the addition of 5.0 \u0026micro;L of proteinase \u003cem\u003eK\u003c/em\u003e solution. Total DNA was extracted from each processed homogenate using a DNeasy PowerSoil Kit (Qiagen, Hilden, Germany), following the manufacturer\u0026rsquo;s protocol. Finally, 50 \u0026micro;L of total DNA solution was obtained from each sample.\u003c/p\u003e \u003cp\u003eFirst-round PCR was performed for each extracted DNA sample using universal primer sets to amplify partial mitochondrial genes derived from IRC and CSE prey species. Since the prey animals of these species are supposed to be diverse including vertebrates and invertebrates, we used four primer sets targeting different gene regions: Ecoprimer\u003csup\u003e41\u003c/sup\u003e to amplify the vertebrate partial mitochondrial 12S ribosomal RNA (rRNA) gene, COI#1 (mlCOIintF and dgHCO2198)\u003csup\u003e42,43\u003c/sup\u003efor vertebrate and invertebrate partial mitochondrial cytochrome oxidase C subunit 1 (COI) genes, COI#2 (dgLCO1490 and COI-CFMRa)\u003csup\u003e44,45\u003c/sup\u003efor the insect partial COI gene, and MtAnr for the anuran partial mitochondrial 16S rRNA gene (Supplementary Table\u0026nbsp;1). All PCR reactions were conducted under the same conditions:10 \u0026micro;L mixtures were prepared for each PCR amplification, containing 1.0 \u0026micro;L of 10\u0026times;Ex Taq Buffer (TaKaRa, Shiga, Japan), 0.80 \u0026micro;L of dNTP Mixture (TaKaRa), 0.10 \u0026micro;L of Ex Taq HS (TaKaRa), 0.60 \u0026micro;L (10 \u0026micro;M) of each primer, 1.0 \u0026micro;L of the extracted DNA solution, and 5.9 \u0026micro;L of nuclease-free water (Thermo Fisher Scientific, Waltham, MA, USA). The PCR conditions were as follows as Hebert et al. (2003)\u003csup\u003e46\u003c/sup\u003e: an initial denaturation at 94\u0026deg;C for 1 min, followed by five cycles of 94\u0026deg;C for 1 min, 45\u0026deg;C for 1 min 30 sec, and 72\u0026deg;C for 1 min 30 sec, followed by 35 cycles of 94\u0026deg;C for 1 min, 50\u0026deg;C for 1 min 30 sec, and 72\u0026deg;C for 1min, and a final incubation at 72\u0026deg;C for 5 min. All PCR reactions included negative controls.\u003c/p\u003e \u003cp\u003eSecond-round PCR was conducted to add MiSeq (Illumina, San Diego, CA, USA) adaptor sequences and 8 bp index sequences to both ends of the first-round PCR products, as described by Miya et al. (2015)\u003csup\u003e47\u003c/sup\u003e. The PCR mixtures were prepared in a total volume of 10 \u0026micro;L, containing 1.0 \u0026micro;L of 10\u0026times;Ex Taq Buffer (TaKaRa), 0.80 \u0026micro;L of dNTP Mixture (TaKaRa), 0.050 \u0026micro;L of Ex Taq HS (TaKaRa), 1.5 \u0026micro;L (10 \u0026micro;M) of each index primer, 1.0 \u0026micro;L of diluted 1st-round PCR products (30x) with nuclease-free water (Thermo Fisher Scientific), and 4.15 \u0026micro;L of nuclease-free water (Thermo Fisher Scientific). The PCR conditions were as follows: an initial denaturation at 98\u0026deg;C for 1 min, followed by 12 cycles of 98\u0026deg;C for 30 sec, 65\u0026deg;C for 30 sec, 72\u0026deg;C for 30 sec, and final incubation at 72\u0026deg;C for 5 min. We then pooled 3.0 \u0026micro;L of each 2nd-round PCR product for each respective primer set. The pooled samples were separated using 1.5% agarose gel electrophoresis using L03 Agarose (TaKaRa), and the DNA bands of the expected PCR target size for each primer set were dissected manually from the gel using a sterilized disposable scalpel. DNA was purified using a MinElute Gel Extraction Kit (Qiagen) following the manufacturer\u0026rsquo;s protocol. After purification using Agencourt AMPure XP (Beckman Coulter, Brea, California,USA) according to the standard protocol, the sample DNA concentration was quantified using the Qubit dsDNA HS assay kit and Qubit 4 Fluorometer (Thermo Fisher Scientific). DNA concentration was adjusted to 6.0 nM based on the expected amplicon size using nuclease-free water (Thermo Fisher Scientific). Finally, the prepared DNA library was subjected to 250 bp pair-end sequencing using MiSeq (Illumina), MiSeq Reagent Kits v2 (Illumina), and Micro flow cell (Illumina).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSequencing data analysis and species identification\u003c/h2\u003e \u003cp\u003eFrom the obtained DNA sequence data, low quality 3' ends of the nucleotide sequences (Phred score\u0026thinsp;\u0026lt;\u0026thinsp;10) were removed using the software DynamicTirm\u003csup\u003e48\u003c/sup\u003e. Paired-end reads were connected using the program FLASH\u003csup\u003e49\u003c/sup\u003e, and merged sequences less than 90 bp were discarded. Singleton sequences were removed using the program Uclust\u003csup\u003e50\u003c/sup\u003e, and the remaining sequences with more than two sequence counts were referred to as molecular operational taxonomic units (MOTUs). MOTUs were further filtered by their sequencing read numbers with a cut-off threshold of 0.01% of the total number of sequences in each sample. Sequence similarity-based Blast analysis\u003csup\u003e51\u003c/sup\u003e was performed using the reference nucleotide database of NCBI (National Center for Biotechnology Information\u003csup\u003e52\u003c/sup\u003e). The Blast hit record was adapted as a taxon of each MOTU with the following assignment methods: If the sequence similarity was (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) 99% or higher, the top-hit species was adopted, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) 98% or higher but less than 99%, the top-hit genus was adopted, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) 98% or higher but less than 90%, the top-hit family was adopted\u003csup\u003e53\u003c/sup\u003e. If the multiple species were identified as the top-hit, the highest taxonomic rank below (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) genus, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) order, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) family shared by all top-hit species were adopted\u003csup\u003e53\u003c/sup\u003e. Taxa and MOTUs were excluded if they did not inhabit Iriomotejima Island or if they were difficult to consider as prey items for IRC and CSE. These include IRC and CSE (the source of feces), fungi, bacteria, humans, and insects less than 2 cm. The source species of each fecal sample was estimated based on the taxonomic assignment results of the Ecoprimer PCR products. MOTUs assigned to dogs and cattle were also excluded because they were considered environmental contaminants. We also excluded samples that did not contain any prey items from subsequent analyses as described in the \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003eresults\u003c/span\u003e section.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using \u003cem\u003eR\u003c/em\u003e version 4.3.0 (\u003cem\u003eR\u003c/em\u003e Core Team 2023). The quantitative data derived from the sequencing read number of each prey item were used to confirm the detectability of each primer set. The presence/absence data for each prey item were used for other analyses. The prey taxonomic richness was estimated with accumulation and extrapolation curves using \u0026lsquo;iNEXT\u0026rsquo;\u003csup\u003e54\u0026ndash;56\u003c/sup\u003e. We utilized two metrics, frequency of occurrence (FOO) and weighted percentage of occurrence (wPOO)\u003csup\u003e57\u003c/sup\u003e to conduct multivariate and statistical analyses. The results obtained from this study were examined by comparing the FOO values of each prey item with those obtained from a previous study that utilized morphological observations of fecal content\u003csup\u003e13\u003c/sup\u003e. Fisher\u0026rsquo;s exact test was used to examine the non-random relationships between the presence/absence ratio of each prey item for each predator to determine whether the differences between them were statistically significant. The weighted POO was not used in the comparative analysis because it was not suitable for interspecific comparisons\u003csup\u003e58\u003c/sup\u003e. To compare the diet composition of the two predators based on the Jaccard Index of the dissimilarity of the presence/absence data for prey items at the order level, we performed a non-metric multidimensional scaling (NMDS) analysis using the \u0026lsquo;vegan\u0026rsquo; package\u003csup\u003e59\u003c/sup\u003e. The differentiation of the diet composition between these two species was confirmed by performing a permutational multivariate analysis of variance (PERMANOVA\u003csup\u003e60\u003c/sup\u003e) using the \u0026lsquo;adonis\u0026rsquo; function. The \u0026lsquo;betadisper\u0026rsquo; and \u0026lsquo;anova\u0026rsquo; functions were also used to examine whether the differences were caused by different degrees of variation among the samples of IRC and CSE or by the different diet composition between these two species.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our sincere gratitude to Ryosuke Terada, Shun Kobayashi, and the entire staff of the Faculty of Science at the University of the Ryukyus for their support in conducting field surveys and experimental work. We also appreciate the support in the Iriomotejima Island by Hiroyoshi Kohno and Akira Mizutani from the Okinawa Regional Research Center at Tokai University, Kazumasa Ohama and the dedicated staff at Ohama Farm, as well as Miki Sugiyama and Chieko Matsumoto. Additionally, we would like to thank to Mamoru Toda at the Tropical Biosphere Research Center, University of the Ryukyus for supplying us with the experimental information. Miho Murayama-Inoue and Kristin Havercamp from the Wildlife Research Center, Kyoto University reviewed and advised on the manuscript. We thank the Okinawa District Forest Office of the Forest Agency, Ministry of the Environment of Japan, and the Iriomote Wildlife Conservation Center, Ministry of the Environment of Japan, for generously providing us with valuable samples. Each affiliation is current at the time of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.T. designed the study, and conceptual the main ideas. A.T. and N.N. conducted the field work on Iriomotejima Island and collected the samples. Y.S. and N.W. designed the experiments. A.T., N.W. and Y.S. performed the laboratory experiments and data analyses. A.T. visualized the outputs from data analyses. \u0026nbsp;M.I., N.N., Y.S. and N.W. aided in interpreting the results. A.T., Y.S. and M.I. worked on the manuscript. M.I. supervised the project. All authors contributed input to the draft and final versions of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Pro Natura Foundation Japan\u0026rsquo;s 29th Pro Natura Fund. The authors have declared that no competing interests exist.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe MiSeq-generated raw sequence reads from this study can be found in the DDBJ Sequence Read Archive (DRA) under the accession numbers DRA017514. The other data and computer code are available on Data Dryad review sharing link (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://datadryad.org/stash/share/ef8jckjnMMXqvQ9EnHaTJfoFJlKbeE28MNv-ioAnNeg\u003c/span\u003e\u003cspan address=\"https://datadryad.org/stash/share/ef8jckjnMMXqvQ9EnHaTJfoFJlKbeE28MNv-ioAnNeg\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The private DOI is only available for review as the manuscript is under review.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMacArthur, R. H., \u0026amp; Wilson, E. O. Equilibrium theory of insular zoogeography. Evolution, 17, 373\u0026ndash;387; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1558-5646.1963.tb03295.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1558-5646.1963.tb03295.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1963).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacArthur, R. H. \u0026amp; Wilson, E. O. \u003cem\u003eThe Theory of Island Biogeography\u003c/em\u003e. vol. 1 (Princeton University Press, 1967).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown, J. H. \u0026amp; Lomolino, M. V. \u003cem\u003eBiogeography\u003c/em\u003e. (Sinuer Associates Publishers, 1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkinawa Prefecture. Outline of Yaeyama Region. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pref.okinawa.jp/site/norin/norin-yaeyama-nosui/keikaku/yaeyamanogaiyou.html\u003c/span\u003e\u003cspan address=\"https://www.pref.okinawa.jp/site/norin/norin-yaeyama-nosui/keikaku/yaeyamanogaiyou.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTamada, T. \u003cem\u003eet al.\u003c/em\u003e Molecular diversity and phylogeography of the asian leopard cat, felis bengalensis, inferred from mitochondrial and Y-chromosomal DNA sequences. Zool. Sci. 25, 154\u0026ndash;163; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2108/zsj.25.154\u003c/span\u003e\u003cspan address=\"10.2108/zsj.25.154\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDel Hoyo, J., Elliot, A. \u0026amp; Sargatal, J. Handbook of the birds of the world, Vol. II. \u003cem\u003eNew world vultures to guineafowl.\u003c/em\u003e (Lynx Edicions, Barcelona, 1994).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchoener, T. W. Resource partitioning in ecological communities. Science (1979) 185, 27\u0026ndash;39 (1974).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrooks, K. R. \u0026amp; Van Vuren, D. Resource utilization by two insular endemic mammalian carnivores, the island fox and island spotted skunk. Oecologia 104, 301\u0026ndash;307; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/BF00328365/METRICS\u003c/span\u003e\u003cspan address=\"10.1007/BF00328365/METRICS\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1995).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003edu Preez, B., Purdon, J., Trethowan, P., Macdonald, D. W. \u0026amp; Loveridge, A. J. Dietary niche differentiation facilitates coexistence of two large carnivores. J. Zool. 302, 149\u0026ndash;156; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jzo.12443\u003c/span\u003e\u003cspan address=\"10.1111/jzo.12443\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePande, S., Yosef, R., Morelli, F., Pawar, R. \u0026amp; Mone, R. Diet and habitat affinities in six raptor species in India. Avian Res. 9, 1\u0026ndash;9; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40657-018-0129-2\u003c/span\u003e\u003cspan address=\"10.1186/s40657-018-0129-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTatara, M. \u0026amp; Doi, T. Comparative analyses on food habits of Japanese marten, Siberian weasel and leopard cat in the Tsushima islands, Japan. Ecol. Res. 9, 99\u0026ndash;107 (1994).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMizutani, A., Nakamoto, J., Himura, S., Tanaka, S. \u0026amp; Kohno, H. Diet of the Crested Serpent Eagle \u003cem\u003eSpilornis cheela perplexus\u003c/em\u003e observed in Iriomotejima and Ishigakijima, south Ryukyu Islands, Japan. \u003cem\u003eStudy Rev. Iriomote Is.\u003c/em\u003e 2016, \u003cem\u003eORRC, Tokai Univ.\u003c/em\u003e 25\u0026ndash;43 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatanabe, S. Ecological flexibility of the top predator in an island ecosystem \u0026ndash; food habit of the Iriomote Cat. Diversity of Ecosystems: linking structure and function 465\u0026ndash;484 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakanishi, N. \u0026amp; Izawa, M. Importance of frogs in the diet of the Iriomote cat based on stomach content analysis. Mamm. Res. 61, 35\u0026ndash;44; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s13364-015-0246-9\u003c/span\u003e\u003cspan address=\"10.1007/s13364-015-0246-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakaguchi, N. \u0026amp; Ono, Y. Seasonal change in the food habits of the Iriomote cat \u003cem\u003eFelis iriomotensis\u003c/em\u003e. Ecol. Res. 9, 167\u0026ndash;174 (1994).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTokita, H., Yoshino, T., Onuma, M., Kinjo, T. \u0026amp; Asakawa, M. Gastric contents of the Crested Serpent Eagle from Yaeyama Archipelago, Okinawa, Japan. Bird Research 10, 13\u0026ndash;18 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatanabe, S., Nakanishi, N. \u0026amp; Izawa, M. Habitat and prey resource overlap between the Iriomote cat \u003cem\u003ePrionailurus iriomotensis\u003c/em\u003e and introduced feral cat \u003cem\u003eFelis catus\u003c/em\u003e based on assessment of scat content and distribution. Mammal Study 28, 47\u0026ndash;56; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3106/mammalstudy.28.47\u003c/span\u003e\u003cspan address=\"10.3106/mammalstudy.28.47\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonterroso, P. \u003cem\u003eet al.\u003c/em\u003e Feeding ecological knowledge: the underutilised power of faecal DNA approaches for carnivore diet analysis. Mamm. Rev. 49, 97\u0026ndash;112; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/mam.12144\u003c/span\u003e\u003cspan address=\"10.1111/mam.12144\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndo, H. \u003cem\u003eet al.\u003c/em\u003e Methodological trends and perspectives of animal dietary studies by noninvasive fecal DNA metabarcoding. Environmental DNA 2, 391\u0026ndash;406; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eabs/10.1002/edn3.117\u003c/span\u003e\u003cspan address=\"abs/10.1002/edn3.117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShao, X. \u003cem\u003eet al.\u003c/em\u003e Generalist carnivores can be effective biodiversity samplers of terrestrial vertebrates. Front. Ecol. Environ. 19, 557\u0026ndash;563; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/fee.2407\u003c/span\u003e\u003cspan address=\"10.1002/fee.2407\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShao, X. \u003cem\u003eet al.\u003c/em\u003e Prey partitioning and livestock consumption in the world\u0026rsquo;s richest large carnivore assemblage. Current Biology 31, 4887\u0026ndash;4897.e5; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cub.2021.08.067\u003c/span\u003e\u003cspan address=\"10.1016/j.cub.2021.08.067\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiong, M. \u003cem\u003eet al.\u003c/em\u003e Molecular dietary analysis of two sympatric felids in the Mountains of Southwest China biodiversity hotspot and conservation implications. Sci. Rep. 7, 1\u0026ndash;12; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/srep41909\u003c/span\u003e\u003cspan address=\"10.1038/srep41909\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiong, M. \u003cem\u003eet al.\u003c/em\u003e Molecular analysis of vertebrates and plants in scats of leopard cats (\u003cem\u003ePrionailurus bengalensis\u003c/em\u003e) in southwest China. J. Mammal. 97, 1054\u0026ndash;1064; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jmammal/gyw061\u003c/span\u003e\u003cspan address=\"10.1093/jmammal/gyw061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCleary, G. P., Corner, L. A. L., O\u0026rsquo;Keeffe, J. \u0026amp; Marples, N. M. Diet of the European badger (\u003cem\u003eMeles meles\u003c/em\u003e) in the Republic of Ireland: A comparison of results from an analysis of stomach contents and rectal faeces. Mammalian Biology 76, 470\u0026ndash;475; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.mambio.2010.10.012\u003c/span\u003e\u003cspan address=\"10.1016/j.mambio.2010.10.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewton, I. Population limitation in migrants. \u003cem\u003eIbis\u003c/em\u003e vol. 146 197\u0026ndash;226; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1474-919X.2004.00293.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1474-919X.2004.00293.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka, M. \u0026amp; Sato, S. The wintering habitats of the Reed Bunting (\u003cem\u003eEmberiza schoeniclus\u003c/em\u003e) in Kochi City (Passeriformes: Emberizidae). Bulletin of the Shikoku Institute of Natural History 12, 9\u0026ndash;11 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchoch, C. L. \u003cem\u003eet al.\u003c/em\u003e NCBI Taxonomy: a comprehensive update on curation, resources and tools. Database \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/database/baaa062\u003c/span\u003e\u003cspan address=\"10.1093/database/baaa062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClarke, L. J., Soubrier, J., Weyrich, L. S. \u0026amp; Cooper, A. Environmental metabarcodes for insects: In silico PCR reveals potential for taxonomic bias. Mol. Ecol. Resour. 14, 1160\u0026ndash;1170; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/1755-0998.12265\u003c/span\u003e\u003cspan address=\"10.1111/1755-0998.12265\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeagle, B. E., Jarman, S. N., Coissac, E., Pompanon, F. \u0026amp; Taberlet, P. DNA metabarcoding and the cytochrome c oxidase subunit I marker: Not a perfect match. Biol. Lett. 10; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1098/rsbl.2014.0562\u003c/span\u003e\u003cspan address=\"10.1098/rsbl.2014.0562\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBookwalter, J., Niyas, A. M. M., Caballero-L\u0026oacute;pez, B., Villari, C. \u0026amp; Claramunt-L\u0026oacute;pez, B. Fecal matters: implementing classical Coleoptera species lists with metabarcoding data from passerine bird feces. J. Insect Conserv. 27, 557\u0026ndash;569; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10841-023-00479-7\u003c/span\u003e\u003cspan address=\"10.1007/s10841-023-00479-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalther, B. A., Chou Ta-Ching \u0026amp; Lee, P.-F. Population density, home range, and habitat use of Crested Serpent-Eagles (\u003cem\u003eSpilornis cheela hoya\u003c/em\u003e) in southern Taiwan: Using distance-based analysis and compositional analysis at different spatial cales. The Journal of Raptor Res. 48, 195\u0026ndash;209; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3356/JRR-13-36.1\u003c/span\u003e\u003cspan address=\"10.3356/JRR-13-36.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatanabe, S., Nakanishi, N. \u0026amp; Izawa, M. Seasonal abundance in the floor-dwelling frog fauna on Iriomote Island of the Ryukyu Archipelago, Japan. J. Trop. Ecol. 21, 85\u0026ndash;91; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/S0266467404002068\u003c/span\u003e\u003cspan address=\"10.1017/S0266467404002068\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGokula, V. Breeding ecology of the crested serpent eagle \u003cem\u003eSpilornis cheela\u003c/em\u003e (Latham, 1790) (Aves: Accipitriformes: Accipitridae) in Kolli hills, Tamil Nadu, India. TAPROBANICA: The Journal of Asian Biodiversity 4, 77; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4038/tapro.v4i2.5059\u003c/span\u003e\u003cspan address=\"10.4038/tapro.v4i2.5059\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrassman, L. I., Tewes, M. E., Silvy, N. J. \u0026amp; Kreetiyutanont, K. Spatial organization and diet of the leopard cat (\u003cem\u003ePrionailurus bengalensis\u003c/em\u003e) in north-central Thailand. J. Zool. 266, 45\u0026ndash;54; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/S095283690500659X\u003c/span\u003e\u003cspan address=\"10.1017/S095283690500659X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, O., Lee, S., Nam, D.-H. \u0026amp; Lee, H. Y. Food Habits of the Leopard Cat (\u003cem\u003ePrionailurus bengalensis euptilurus\u003c/em\u003e) in Korea. Mammal Study 39, 43\u0026ndash;46; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3106/041.039.0107\u003c/span\u003e\u003cspan address=\"10.3106/041.039.0107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajaratnam, R., Sunquist, M., Rajaratnam, L. \u0026amp; Ambu, L. Diet and habitat selection of the leopard cat (\u003cem\u003ePrionailurus bengalensis borneoensis\u003c/em\u003e) in an agricultural landscape in Sabah, Malaysian Borneo. J. Trop. Ecol. 23, 209\u0026ndash;217; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/S0266467406003841\u003c/span\u003e\u003cspan address=\"10.1017/S0266467406003841\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShehzad, W. \u003cem\u003eet al.\u003c/em\u003e Carnivore diet analysis based on next-generation sequencing: Application to the leopard cat (\u003cem\u003ePrionailurus bengalensis\u003c/em\u003e) in Pakistan. Mol Ecol 21, 1951\u0026ndash;1965; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1365-294X.2011.05424.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-294X.2011.05424.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEriksson, P., Mourkas, E., Gonz\u0026aacute;lez-Acuna, D., Olsen, B. \u0026amp; Ellstr\u0026ouml;m, P. Evaluation and optimization of microbial DNA extraction from fecal samples of wild Antarctic bird species. Infect. Ecol. Epidemiol. 7, 1386536; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/20008686.2017.1386536\u003c/span\u003e\u003cspan address=\"10.1080/20008686.2017.1386536\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOehm, J., Thalinger, B., Eisenk\u0026ouml;lbl, S. \u0026amp; Traugott, M. Diet analysis in piscivorous birds: What can the addition of molecular tools offer? \u003cem\u003eEcol. Evol.\u003c/em\u003e 7, 1984\u0026ndash;1995; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ece3.2790\u003c/span\u003e\u003cspan address=\"10.1002/ece3.2790\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOehm, J., Juen, A., Nagiller, K., Neuhauser, S. \u0026amp; Traugott, M. Molecular scatology: How to improve prey DNA detection success in avian faeces? Mol. Ecol. Resour. 11, 620\u0026ndash;628; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1755-0998.2011.03001.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1755-0998.2011.03001.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiaz, T. et al. EcoPrimers: Inference of new DNA barcode markers from whole genome sequence analysis. Nucleic Acids Res. 39; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkr732\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkr732\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeray, M. \u003cem\u003eet al.\u003c/em\u003e A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: Application for characterizing coral reef fish gut contents. Front. Zool. 10, 1\u0026ndash;14; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1742-9994-10-34\u003c/span\u003e\u003cspan address=\"10.1186/1742-9994-10-34\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeyer, C. P. \u003cem\u003eMolecular Systematics of Cowries (Gastropoda: Cypraeidae) and Diversification Patterns in the Tropics\u003c/em\u003e. Biological Journal of the Linnean Society vol. 79 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeller, J., Meyer, C., Parker, M. \u0026amp; Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851\u0026ndash;861; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/1755-0998.12138\u003c/span\u003e\u003cspan address=\"10.1111/1755-0998.12138\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJusino, M. A. \u003cem\u003eet al.\u003c/em\u003e An improved method for utilizing high-throughput amplicon sequencing to determine the diets of insectivorous animals. Mol. Ecol. Resour. 19, 176\u0026ndash;190; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/1755-0998.12951\u003c/span\u003e\u003cspan address=\"10.1111/1755-0998.12951\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHebert, P. D. N., Cywinska, A., Ball, S. L. \u0026amp; DeWaard, J. R. Biological identifications through DNA barcodes. \u003cem\u003eProceedings of the Royal Society B: Biological Sciences\u003c/em\u003e 270, 313\u0026ndash;321; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1098/rspb.2002.2218\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2002.2218\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiya, M. \u003cem\u003eet al.\u003c/em\u003e MiFish, a set of universal primers for metabarcoding environmental DNA from fishes: detection of \u0026gt;\u0026thinsp;230 species from aquarium tanks and coral reefs in the subtropical western North Pacific. Genome 58, 257; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.1251156\u003c/span\u003e\u003cspan address=\"10.1126/science.1251156\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCox, M. P., Peterson, D. A. \u0026amp; Biggs, P. J. SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data. BMC bioinformatics, 11; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-2105-11-485\u003c/span\u003e\u003cspan address=\"10.1186/1471-2105-11-485\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagoč, T. \u0026amp; Salzberg, S. L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957\u0026ndash;2963; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btr507\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btr507\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460\u0026ndash;2461; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btq461\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btq461\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamacho, C. \u003cem\u003eet al.\u003c/em\u003e BLAST+: Architecture and applications. BMC Bioinformatics 10; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-2105-10-421\u003c/span\u003e\u003cspan address=\"10.1186/1471-2105-10-421\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 46, D8\u0026ndash;D13; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkx1095\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkx1095\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePereira, A., Xavier, R., Perera, A., Salvi, D. \u0026amp; Harris, D. J. DNA metabarcoding to assess diet partitioning and feeding strategies in generalist vertebrate predators: a case study on three syntopic lacertid lizards from Morocco. Biological Journal of the Linnean Society 127, 800\u0026ndash;809; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/biolinnean/blz061\u003c/span\u003e\u003cspan address=\"10.1093/biolinnean/blz061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChao, A. \u003cem\u003eet al.\u003c/em\u003e Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45\u0026ndash;67; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1890/13-0133.1\u003c/span\u003e\u003cspan address=\"10.1890/13-0133.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHacker, C. E. \u003cem\u003eet al.\u003c/em\u003e Use of DNA metabarcoding of bird pellets in understanding raptor diet on the Qinghai-Tibetan Plateau of China. \u003cem\u003eAvian Res.\u003c/em\u003e 12; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40657-021-00276-3\u003c/span\u003e\u003cspan address=\"10.1186/s40657-021-00276-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsieh, T. C., Ma, K. H. \u0026amp; Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451\u0026ndash;1456; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/2041-210X.12613\u003c/span\u003e\u003cspan address=\"10.1111/2041-210X.12613\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeagle, B. E. \u003cem\u003eet al.\u003c/em\u003e Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data? Mol. Ecol. 28, 391\u0026ndash;406; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/mec.14734\u003c/span\u003e\u003cspan address=\"10.1111/mec.14734\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright, B. E. Use of chi-square tests to analyze scat-derived diet composition data. Marine Mammal Science 26, 395\u0026ndash;401; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1748-7692.2009.00308.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1748-7692.2009.00308.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOksanen, J. \u003cem\u003eet al.\u003c/em\u003e The vegan package. Community ecology package 10, 719 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson, M. J. Permutational Multivariate Analysis of Variance (PERMANOVA). Wiley StatsRef: Statistics Reference Online 1\u0026ndash;15; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/9781118445112.STAT07841\u003c/span\u003e\u003cspan address=\"10.1002/9781118445112.STAT07841\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3907562/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3907562/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSmall islands lack predators because species at higher trophic levels often cannot survive. However, two top predators\u0026mdash;the Iriomote cat \u003cem\u003ePrionailurus bengalensis iriomotensis\u003c/em\u003e, and the Crested Serpent Eagle \u003cem\u003eSpilornis cheela perplexus\u003c/em\u003e\u0026mdash;live on small Iriomotejima Island in the Ryukyu Archipelago. To understand how these predators coexist on the island with limited resources, we focused on their seasonal diets which are considered crucial for survival in such an island ecosystem. To compare the diets of them, we used DNA metabarcoding analysis of their fecal samples. In the summer, we identified 16 prey items from Iriomote cat fecal samples, and 15 from Crested Serpent Eagle fecal samples. In the winter, we identified 37 and 14 prey items, respectively. Using a non-metric multidimensional scaling and a permutational multivariate analysis of variance, our study reveals significant differences in the diet composition at the order level between the predators during both seasons. Furthermore, although some prey items at the species-to-order level overlapped between them, the frequency of occurrence of most prey items differed in both seasons. These results suggest that this difference in diets was one of the reasons why the Iriomote cat and the Crested Serpent Eagle coexisted on such a small island.\u003c/p\u003e","manuscriptTitle":"Seasonal Diet Partition among Top Predators of a Small Island, Iriomotejima Island in the Ryukyu Archipelago, Japan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-12 19:16:54","doi":"10.21203/rs.3.rs-3907562/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-02-29T10:19:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-22T08:52:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52e9769e-5731-45df-a0ca-2b85151d29ec","date":"2024-02-11T23:44:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-11T12:03:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-11T11:58:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-02-08T11:39:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-08T11:36:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-01-29T02:13:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"81924d9f-5ea4-4e22-94a9-51f2d827d110","owner":[],"postedDate":"February 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28699550,"name":"Biological sciences/Ecology/Ecological networks"},{"id":28699551,"name":"Biological sciences/Ecology/Ecosystem ecology"},{"id":28699552,"name":"Biological sciences/Ecology/Molecular ecology"}],"tags":[],"updatedAt":"2024-04-08T15:09:00+00:00","versionOfRecord":{"articleIdentity":"rs-3907562","link":"https://doi.org/10.1038/s41598-024-58204-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-04-02 15:01:11","publishedOnDateReadable":"April 2nd, 2024"},"versionCreatedAt":"2024-02-12 19:16:54","video":"","vorDoi":"10.1038/s41598-024-58204-6","vorDoiUrl":"https://doi.org/10.1038/s41598-024-58204-6","workflowStages":[]},"version":"v1","identity":"rs-3907562","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3907562","identity":"rs-3907562","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}