Large scale eDNA database reveals potential for pseudo biodiversity

Large scale eDNA database reveals potential for pseudo biodiversity | 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 Large scale eDNA database reveals potential for pseudo biodiversity Minoru Kasada, Naoto Shinohara, Kenta Tanaka, Riku Fukasawa, Akifumi S. Tanabe, and 47 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7872521/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The spatial and temporal patterns of marine diversity are essential for the sustainable use of fishery resources and the effective management and conservation of marine ecosystems. We investigated the latitudinal diversity gradients of fish genera in Japanese waters using an environmental DNA (eDNA) data. A total of 1,765 environmental DNA surveys across Japan were obtained from the All Nippon eDNA Monitoring Network (ANEMONE) database. We found a negative correlation between fish genus richness and latitude, with a particularly pronounced gradient in summer. Seasonal change in the latitudinal gradient may reflect the increased occurrence of vagrant fish from tropical regions, rather than the commonly assumed pattern of seasonal migration (i.e., southwards in winter and northwards in summer). Furthermore, our results suggests that global warming may lead to a temporary increase in biodiversity by promoting the influx of tropical vagrant fish. However, such increases are unlikely sustainable, as these vagrant species may not establish long-term populations. We refer to this phenomenon as “pseudo biodiversity”, which may give the illusion of increase in biodiversity enhancement in short-term monitoring. This concept warrants attention as similar patterns of apparent biodiversity increase may occur under other circumstances as well. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Ocean sciences Fish diversity Environmental DNA Latitudinal diversity gradients Migration Monitoring network Biodiversity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Understanding the spatial and temporal distribution patterns of marine biodiversity is becoming increasingly important for the management and sustainable use of fishery resources, as well as for the conservation of marine ecosystems. Fish diversity, which contributes to ecosystem functioning, is critical to the provision of all ecosystem services (Costanza et al., 1997 ; 2017 ). However, assessing systematic patterns of biodiversity remains a key challenge, especially in the context of biodiversity loss (Sala and Knowlton 2006 ), as the marine ecosystem is inherently variable and strongly influenced by environmental changes and fishing pressure at global scales. Although ocean covers approximately 70 percent of the Earth’s surface and harbors tremendous biodiversity potential (Costello et al., 2010 ), its vastness and inaccessibility make it difficult to access the actual state of biodiversity loss compared to terrestrial ecosystems. Hence, it is critical to develop accurate and spatially extensive monitoring methods to understand current patterns of fish diversity. Effort to evaluate global-scale marine biodiversity patterns have been advanced by initiatives such as the Census of Marine Life (CoML, http://www.coml.org/ ), which has generated valuable resources for identifying and documenting marine species (Cosello et al., 2010; Tittensor et al., 2010 ). Yet, capturing biodiversity patterns require accounting for spatiotemporal variations. Seasonal migratory fish alter biodiversity patterns over time, appearing different area depending on the season (Plavan et al., 2010 ; Ichinomiya et al., 2022 ; Shinohara et al., 2022 ). In addition to migration, the temporal appearance of vagrant fish, those that move beyond their native range but fail to establish persistent populations, can also influence seasonal changes in biodiversity (Nakazono et al., 2002; Masuda et al., 2008; Nakamura et al., 2013 ). Spatial variation, meanwhile, is shaped by ocean currents and the structure of the littoral zone (Kai and Motomura 2022 ). Therefore, both temporal and spatial context must be considered to assess correctly the marine fish biodiversity. However, there is limited spatiotemporal biodiversity information available using a consistent method, hampering our ability to assess the recent impacts of human activities and climate change. Large-scale monitoring of fish diversity is constrained by high logistical and financial demands (Costello et al., 2010 ; Fujikura et al., 2010 ). A promising solution is environmental DNA (eDNA), which refers to DNA shed by organisms into their environment (Ficetola et al., 2008 ; Bohmann et al., 2014 ). Recent advances in eDNA analysis have made it a reliable, noninvasive, and highly sensitive monitoring tool for estimating diversity estimation and species composition, especially in aquatic ecosystems (Shinohara et al., 2022 ; Deiner et al., 2017 ; harper et al., 2019 ; Ionescu et al., 2022 ). Compared with traditional capture-based surveys, eDNA offers relatively lower cost and higher scalability, allowing for more frequent and extensive spatio-temporal sampling (Miya et al., 2020 ). Because eDNA can originate from a wide variety of sources, including intestinal and skin cells, urine, mucus, eggs, or sperm (Wang et al., 2021 ), it enables detection of multiple species from a single water sample (Ushio et al., 2018 ; Jeunen et al., 2023 ). To investigate the spatiotemporal changes in fish diversity patterns in the marine system, we utilized a data from the All Nippon eDNA Monitoring Network (ANEMONE, https://db.anemone.bio ), a publicly accessible nation-wide eDNA-based biodiversity monitoring program in Japan. Established in 2019, ANEMONE currently hosts more than 8,000 surveys with data continuously updated on the database (Figure. 1; Kondoh et al., 2024 ). The database covers over 3000 km 2 of coastal marine areas, including regions influenced by the Kuroshio and Oyashio currents, two major western boundary currents in the western North Pacific. As of September 2024, the database includes data from 2017 to 2022. Sampling sites are mainly located along the coastline of Japan, – an area of high biodiversity and ecosystem service value due to the intersection of the land and sea and the complex mosaic of topographic and environmental conditions (Kai and Motomura 2022 ). Japan harbors at least 3,790 fish species, reflecting a combination of various historical and environmental factors (Fujikura et al., 2010 ). As a major fishing nation, Japan also holds potential to sustainably enhance its fishery yields through appropriate management (Ichinokawa et al., 2017 ; Endo and Matsuura 2022 ). Thus, the ANEMONE provides an idea basis analyzing spatial and temporal patterns of marine fish diversity. To explore large-scale patterns of marine biodiversity, we focused on the inter-regional and inter-seasonal differences in the latitudinal diversity gradients (LDGs). The LDG is one of the most well-established global ecosystem patterns, generally showing that species richness increases toward lower latitudes, particularly in terrestrial systems (Hillebrand 2004 ; Jablonski et al., 2006 ). Similar patterns have often been reported for marine taxa in both coastal and deep-sea ecosystems (Rohde 1992 ; Clarke and Crame 1997 ; Roy et al., 1998 ; Hillebrand 2004 ; Linse et al., 2006 ; Yasuhara et al., 2009 ), although there are several exceptions (Valdovinos et al., 2003 ; Clarke et al., 2007 ). Despite extensive studies on LDGs, little is known about how they vary temporally, especially across seasons. Fish, being highly mobile, migrate in response to changes in environmental conditions. A prominent seasonal pattern is the northward migration in summer and return to southern waters in winter (e.g., Furukawa et al., 2020 ), which is expected to flatten or even reverse the LDGs in winter as more fish concentrate at higher latitudes. Alternatively, the seasonal influx of vagrant fish may also influence the LDGs. For example, many tropical fishes are observed in Japanese waters in summer, brought by the Kuroshio Current, but most do not survive the winter (Nakazono 2002 ; Masuda 2008 ; Nakamura et al., 2013 ). The transient increase in tropical species richness at higher latitudes may steepen the LDGs in summer, strengthening the expected steeper slope arising from the seasonal fish migration patterns. Given those dynamics, we investigated how LDGs of marine fish diversity change seasonally in Japanese waters. The Japanese Archipelago spans approximately 3,000 km from north to south and encompass a range of climate zones from subboreal to subtropical (Fujikura et al., 2010 ; Endo and Matsuura 2022 ), making it an ideal system to study LDGs. In this study, we aimed to (1) characterize the general LDGs of marine fish in Japanese waters, (2) examine the seasonal variations in LDGs, and (3) explore the potential drivers of the seasonal changes in the LDGs (i.e., the effect of seasonal migration and temporary inflow of fish), focusing on the roles of vagrant and migration fish. Results To assess the inter-regional fish LDGs, Japanese waters were divided into two regions: the Mainland and the Ryukyu area (Fig. 1 ). In the raw data (i.e., before rarefaction), the mean genus richness value in the Mainland area was 25.33 for 1,119 surveys (median = 23, range 4–78) and the value in the Ryukyu area was 41.52 for 155 surveys (median = 40, range 10–90). Latitude was negatively correlated with rarefied fish genus richness in the Mainland area (Fig. 2 a, Table 1 ), whereas significant patterns were not observed in the Ryukyu area (Fig. 2 b, Table 1 ). In the models, the coefficients in the Ryukyu area differed from those in the Mainland area. In summer and autumn, the Mainland area exhibits significantly higher biodiversity compared to the winter season. The interaction between seasonal variation and latitude was significant for the Mainland area (Fig. 1 a, Table 1 ). Remarkably, the interaction between summer and latitude was significant. The regression line for winter was less negative than summer (Fig. 2 a). Note that, we found the similar negative LDGs pattern for the data pooled for all sampling surveys, but the interaction between season and latitude was not observed from the data (Figure S1 , Table S1 ). Table 1 Coefficients of the generalized linear model (GLM) ***: p < 0.001, **: p < 0.01, *: p < 0.05. Mainland area Ryukyu area (Intercept) 27.670*** -56.020 Latitude -0.317** 3.451 Spring 17.672** 27.848 Summer 24.437*** 44.564 Autumn 24.344*** 20.295 Methods (qualitative vs quantitative) 0.265 -0.005 Latitude:Spring -0.385* -0.998 Latitude:Summer -0.508** -1.655 Latitude:Autumn -0.479* -0.679 To identify the factors driving the seasonal differences in the LDGs, we investigated two potential mechanisms. The first was presence of the season-specific fish, defined as genera observed exclusively only in one season at a given sampling site (e.g., a species present in summer but absent in the other seasons at the site). For each season, we calculated the proportion of survey sites in mainland Japan (i.e. excluding Ryukyu area) that harbored more than one season-specific genus. We found that the occurrence of season-specific genera was highest in summer with 16.92%, compared to 7.35% in spring, 10.33% in autumn, 4.69% in winter (Fig. 3 ). A one-sided binomial test, using the overall data all seasons as the reference, revealed that the proportion of season-specific fish was significantly larger only in summer (p = 0.976 in spring, p < 0.001 in summer, p = 0.647 in autumn, p = 1.000 in winter). In addition, those summer-specific fishes tended to occur at lower latitudes (Fig. 3 b), suggesting that many of them were vagrant tropical fish. The second factor examined was the contribution of migratory fish. We visualized the seasonal migration patterns of five genera of fish that are designated as useful fish in resource management by Japanese Fisheries Agency - Cololabis, Sardinops , Scomber , Seriola and Trachurus . However, the spatial distribution of these genera did not exhibit clear or consistent seasonal shifts across the study period (Fig. 4 ), suggesting that seasonal migration by these taxa may have limited influence on the observed seasonal variation in the LDGs. Discussion Large-scale eDNA data from ANEMONE revealed a general latitudinal diversity gradient (LDG) in fish genera along the Japanese coastline (Fig. 2 ). In the mainland area, fish genus richness was negatively correlated with latitude (Fig. 2 a), consistent with ell-established LDG patterns observed globally (Hillebrand, 2004 ; Mannion et al., 2014 ). In contrast, no clear pattern was observed in the Ryukyu area (Fig. 2 b), likely due to sampling bias in latitude. This limitation may be resolved as the database becomes more spatially complete. When all sampling data are pooled, the declining trend in genus richness with increasing latitude was still evident across Japanese waters (Figure S1 ). These results align with previous studies showing higher fish diversity in southern areas of Japan (Shinohara et al., 2001 ; Miya et al., 2022 ), supporting the establishment of an LDG in Japanese waters at the genus level. A key finding was the seasonal variations in the slope of LDG. In the mainland area, the interaction between summer and latitude was significant (Table 1 ), with the slope being steeper (more negative) in summer compared to winter. We attribute this seasonal steeping of the LDGs to the influx of vagrant tropical fish. The proportion of survey sites with season-specific genera was significantly higher in summer (Fig. 3 ); and these genera were more frequently observed at southern area, suggesting the arrival of tropical fish from southern regions. Such vagrant species are frequently observed along southern Japanese coasts during summer but do not survive the colder winter waters (Nakazono et al., 2002; Masuda et al., 2008; Nakamura et al., 2013 ). Their temporary presence inflates summer diversity in southern regions as reported in other studies (Motomura and Matsunuma 2022 ), particularly around Kyusyu and Shikoku islands (30°N − 35°N, Shinohara et al., 2001 ; Miya et al., 2022 ). Interestingly, the observed seasonal pattern in the LDG contradicts the pattern expected from the seasonal migration of fish. If fish migrated northward in summer and southward in winter, LDGs would be flatter in summer and steeper in winter. To test this, we examined the seasonal distribution of five migratory genera ( Cololabis, Sardinops, Scomber, Seriola and Trachurus ). However, their eDNA-based distributions showed little seasonal change (Fig. 4 ). This might be because eDNA reflects the presence of biological material from various sources (e.g., mucus, eggs, or sloughed cells), and not necessarily the real-time distribution of adult stocks. Taken together, the seasonal steeping of the LDGs is better explained by the temporally appearance of vagrant fish than the seasonal migration. This interpretation is further supported by our analysis of inland waters, where oceanic currents do not influence fish movement. Using the same GLM approach, we found no seasonal change in the LDG slope in inland waters (Figure S2, Table S2) while the negative LDG patterns were observed similarly to coastal waters, reinforcing the view that fish dispersal driven by oceanic currents underlies the summer steepening of LDGs in coastal waters. Notably, spring showed a significant deviation from other seasons in inland systems (Table S2), which warrants further investigation. Due to recent global warming, sea water temperature around Japan has been rising, especially in the Tsushima area, which has been reported to be one of the most rapidly warming water bodies on Earth during the modern warming period (Takahashi 2022 ), and has been found to have a significant impact on community structure and diversity (Lejeusne et al., 2010 ; Doney et al., 2012 ). We observed a positive correlation between sea surface temperature and genus richness (Fig. 5 ), suggesting a potential increase in biodiversity with warming. However, our findings caution against interpreting this pattern as an actual gain in biodiversity. Our results demonstrated that the observed increase of diversity in summer was driven by vagrant fish. While this may temporarily enhance ecosystem services (e.g., recreational diving), it may also disrupt local communities through increased competition. We call this phenomenon as ‘pseudo biodiversity’ —an apparent increase in diversity not captured by traditional indicators, and which may mislead assessments of ecosystem health. Moreover, continued warming may allow vagrant species to establish viable populations or hybridize with local species, introducing longer-term ecological changes (Takahashi, 2022 ). The consequences of such changes remain poorly understood and represent an important direction for future research. Our results also underscore the need for careful interpretation of eDNA-derived diversity indices. While genus or species richness is often used to infer environmental quality, our study shows that such indicators can be misleading if seasonal or spatial patterns are not accounted for. In particular, temporary summer diversity increases may mask underlying ecological conditions. Accurate environmental assessments thus require adjustment for season, latitude, temperature, and the influence of transient species. The fish distribution pattern in Japanese water, including the incidence of vagrant fish, is associated with several currents around the Japanese islands (Fig. 1 , Motomura et al., 2007 ; Sakurai 2007 ; Kai 2022 ; Nakayama 2022 ; Motomura and Matsunuma 2022 ). Therefore, future biodiversity assessments under climate change should incorporate current-biodiversity relationships. The ANEMONE database revealed seasonal changes in diversity with regards to the latitudinal line patterns. This was made possible by the availability of a large amount of data, over a wide area, and from a large database. However, the database still presents several challenges. As several institutes are involved, there are regional variations in data acquisition status. There is a particular shortage of sampling sites in the Okinawa area. Although the database is considered useful for elucidating fish distribution and assessing diversity, a cooperative system for more systematic data collection is also needed. Systematic data and long-term monitoring might reveal differences between the years or diversity pattern changes in time. Thus, monitoring should continue, the database should be enriched, and observations of long-term dynamics should be made. Continued operation of monitoring networks like ANEMONE will be the key to future diversity conservation in the seas surrounding Japan. Methods Data collection Data sets were obtained from the ANEMONE database (downloaded on 20 th September, 2024). The database contains data on both marine and inland water, although we focused on analyzing data on marine fish. To exclude inland water data, we utilized information on coastlines from Nature earth (https://www.naturalearthdata.com), and we have screened the surveys within 10km of the coastline. The data sampling sites were distributed between 24.46N to 45.54N and 123.8E to 145.6E, covering coastal areas throughout Japan. The data collection period was from December, 2017 to September, 2022. The raw data from the ANEMONE database consisted of sequence data, species information from the sequence data, and sampling location data. The eDNA data were collected using the manual for environmental DNA research from the eDNA Society (Minamoto et al., 2021). Data obtained from the same sampling site at different depths and times was analyzed as independent surveys. Genus data were used for fish biodiversity because 89.92% of the sequence data in the database was identified at the genus level, whereas only 63.03% was identified at the species level, particularly in sequences with a low number of reads. Undefined genus reads were still obtained; however, these were excluded when calculating genus-level fish diversity. Thus, the total number of surveys for the five-year time-period was 1274. Rarefaction The data in the ANEMONE database included two types of eDNA data due to the different methods used to obtain sequence reads (qualitative vs. quantitative). The eDNA data in 2017 and 2018 were obtained using universal polymerase chain reaction (PCR) primers (MiFish-U/E) for the metabarcoding of environmental DNA (eDNA) from fish (Miya et al., 2015). Because the concentrations of PCR inhibitors depend on certain uncontrollable conditions of the samples (Schrader et al., 2012), this method cannot be used to estimate the amount of eDNA. This method is referred to as the qualitative method. eDNA data from 2019 onwards were obtained using an expanded method developed by Ushio et al to detect fish biomass (Ushio et al., 2018). In the expanded method, internal standard DNA is added to eDNA samples from the field, and metabarcoding analysis is performed; therefore, we can quantify the eDNA copy number of fish using the copy numbers from the sequence reads of the added standard eDNA as a calibration of the absolute abundance. This method is referred to as the quantitative method. The quantitative method is more beneficial, as it allows us to obtain more accurate information on fish abundance/biomass; however, a small amount of eDNA may be missed because standard eDNA interferes with the eDNA sampled in the field. Consequently, with regards to the data collected as presence/absence data, the quantitative method may have a limited detection capability compared to the qualitative method. It is possible that the species richness estimated using the qualitative method will be greater than that estimated using the quantitative method. To avoid misinterpretation due to the method difference, the sensitivity difference between the methods was corrected. To solve this problem, a rarefaction method was used. The rarefaction method can be applied to downsize larger samples until they are the same size as the smallest sample (Hurlbert 1971). The rarefaction curve indicates the expected species richness in the random subsamples of the sample from the community. The expected species richness calculated using rarefaction should be corrected for differences in DNA detection sensitivity. To accomplish this, the ‘rarefy’ function of the vegan package (version 2.6.4) in R (version 4.3.2) was used. Note that no significant difference was observed between the qualitative and quantitative methods after rarefaction ( Wilcoxon signed-rank test, W = 8732, p = 0.6852). Data analysis To assess the inter-regional fish LDGs, Japanese waters were divided into two regions: the Mainland area and the Ryukyu area (Figure 1). The Ryukyu area includes the Ryukyu Islands (Figure 1), where a different faunal structure of the Pacific coast along the Japanese Archipelago has been reported (kai and Motomura 2022), and specific local populations are formed (Aoki et al., 2008; Imai et al., 2009; Kuriiwa et al., 2014). Therefore, the Ryukyu region was treated separately from the other regions, although we also analyzed the pooled data for all surveys (see Figure S1 and Table S1). The seasonal changes in LDGs in each region were also considered. A generalized linear model (GLM) was constructed to demonstrate the fish genus richness in each area, and the fish genus richness after rarefaction was settled on the response variables to correct the difference between qualitative and quantitative methods. In the models, latitude was chosen as an explanatory variable to explore LDGs, and the categorical variable ‘season’ was included with the four levels: winter (used as the reference level) from December to February, spring from March to May, summer from June to August, and autumn from September to November. The interaction between latitude and season was also included in the model. For the GLM analysis, a gamma error distribution with the identity-link function was used to account for the fact that the response variables were positive real numbers. Lastly, we examined whether the seasonal pattern differences in the LDGs were due to fish that are unique to the season (vagrant fish carried by ocean currents from outside regions) or to migratory fish. A season-specific genus was defined as a genus observed only in one season (e.g., the summer-specific genus at a sampling site was present in summer but absent in the other seasons). For season-specific fish, the ratio of survey sites with more than one season-specific genus to the total number of survey sites was investigated in mainland area (i.e. excluding Ryukyu area) in each season. In order to see the impact that migratory fish have on the LDGs patterns, the seasonal migration patterns of five genera of fish that are designated as useful fish in Japanese resource management by Japanese Fisheries Agency - Cololabis, Sardinops , Scomber , Seriola and Trachurus - were visualised in each season. These are genera widely distributed in Japan and have relatively well-known seasonal migration patterns (northwards in summer and southwards in winter) (Hiyama et al., 2002; Sassa et al., 2009; Okunishi et al., 2012; Suyama et al., 2012; Liu et al., 2022; Furukawa et al., 2020) Declarations Funding Declaration: This study was supported by a Grant-in-Aid for JSPS Fellows Number 19J00864, JST Grant Number JPMJPF2206 and JSPS KAKENHI Grant Number 19H05641 and 21H05312. Acknowledgments: This work utilized the ANEMONE database [https://db.anemone.bio/]. Observations by ANEMONE are carried out with the help of many collaborators. We thank the Fisheries Research Division, Tokushima Agriculture, Forestry, and Fisheries Technology Support Center, for their valuable support. We would like to express our gratitude to all those who have participated in data acquisition. Author Contributions: MinoruK, NS, RF and MichioK designed research, MinoruK, NS, RF, AST and MichioK performed research, AST contributed new reagents or analytic tools. HY conducted eDNA metabarcoding. MinoruK, NS, RF analyzed data. MinoruK, NS, RF and MichioK wrote the main manuscript text and MinoruK prepared the figures and the tables. The other authors contributed to data collection. Competing Interests: There are no conflicts of interest to declare. References Aoki, M., Naruse, T., Cheng, J.-H., Suzuki, Y. & Imai, H. 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14:31:07","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":24064,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7872521/v1/ff573ea5aeb547c8d526cc14.png"},{"id":95314519,"identity":"ae7facc7-d62d-4d69-8d69-b0e70b6cd6e9","added_by":"auto","created_at":"2025-11-06 15:52:58","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":105183,"visible":true,"origin":"","legend":"","description":"","filename":"1fe22bc8a13c4a19893d793e6bf7ec521structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7872521/v1/5872d3b9fa6a4f6b20860bc9.xml"},{"id":95305796,"identity":"09939e25-9143-44d8-90de-6726e64cfaa3","added_by":"auto","created_at":"2025-11-06 14:31:07","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":114626,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7872521/v1/d9a3df78d4404207fb5b95e3.html"},{"id":95305777,"identity":"a4acf70e-7096-4a9f-88d4-21c5f5adab42","added_by":"auto","created_at":"2025-11-06 14:31:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":205411,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSampling sites on All Nippon eDNA Monitoring Network (ANEMONE) database. The black and red dots represent the sampling sites in the Mainland and Ryukyu areas, respectively. Arrows indicate currents (modified from Endo and Matsuura 2022).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7872521/v1/5d934f361330761dffb1c60b.png"},{"id":95315057,"identity":"f1a575ab-edb0-471c-bc3b-00e0e64bd6d7","added_by":"auto","created_at":"2025-11-06 15:53:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":178816,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLatitudinal diversity gradients in a) the Mainland and b) the Ryukyu areas. The green, red, orange and blue coloring represents spring, summer, autumn and winter, respectively. The dots represent the surveys. The lines are regression lines and the grey areas are 95% confidence intervals.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7872521/v1/2b4419ef834452c23ec64bb2.png"},{"id":95305780,"identity":"665ce59e-0e3d-49f5-8c1f-2af0eac9786e","added_by":"auto","created_at":"2025-11-06 14:31:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72521,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe numbers of surveys in each latitude (binned with 1º) with (black) and without (gray) season-specific fish in a) spring, b) summer, c) autumn, and d) winter.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7872521/v1/74efe3af770ffc8a23987848.png"},{"id":95305787,"identity":"6655a4b0-4bd8-4935-8a50-382e75845fab","added_by":"auto","created_at":"2025-11-06 14:31:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":159175,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSeasonal fish distribution patterns of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCololabis,\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSardinops\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e,\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e Scomber\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSeriola\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTrachurus\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e. Colored and grey dots represent the sampling sites with a presence or absence of the target genus, respectively.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7872521/v1/bcb7937de10029b79924f0ea.png"},{"id":95305783,"identity":"49e4ef4f-565e-4527-81cd-66bde3d62c5c","added_by":"auto","created_at":"2025-11-06 14:31:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":78550,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between water temperature and genus richness. The correlation coefficient was 0.407 (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ep \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026lt; 0.001). The scatter plot was drawn from the 929 surveys where temperature data existed.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7872521/v1/3bb1568eac4c3bcf94656932.png"},{"id":95316248,"identity":"a342bf8f-c778-46d3-96ac-cb2ab5bc3675","added_by":"auto","created_at":"2025-11-06 15:58:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2024215,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7872521/v1/85e69ea7-bc22-43ba-8d78-41b534fa1060.pdf"},{"id":95305784,"identity":"8f39ae59-ec92-455e-bafb-06b81c489e63","added_by":"auto","created_at":"2025-11-06 14:31:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":5106952,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformationver2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7872521/v1/61380617987e490a2da8a9a9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Large scale eDNA database reveals potential for pseudo biodiversity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnderstanding the spatial and temporal distribution patterns of marine biodiversity is becoming increasingly important for the management and sustainable use of fishery resources, as well as for the conservation of marine ecosystems. Fish diversity, which contributes to ecosystem functioning, is critical to the provision of all ecosystem services (Costanza et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, assessing systematic patterns of biodiversity remains a key challenge, especially in the context of biodiversity loss (Sala and Knowlton \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), as the marine ecosystem is inherently variable and strongly influenced by environmental changes and fishing pressure at global scales.\u003c/p\u003e\u003cp\u003eAlthough ocean covers approximately 70 percent of the Earth\u0026rsquo;s surface and harbors tremendous biodiversity potential (Costello et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), its vastness and inaccessibility make it difficult to access the actual state of biodiversity loss compared to terrestrial ecosystems. Hence, it is critical to develop accurate and spatially extensive monitoring methods to understand current patterns of fish diversity.\u003c/p\u003e\u003cp\u003eEffort to evaluate global-scale marine biodiversity patterns have been advanced by initiatives such as the Census of Marine Life (CoML, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.coml.org/\u003c/span\u003e\u003cspan address=\"http://www.coml.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which has generated valuable resources for identifying and documenting marine species (Cosello et al., 2010; Tittensor et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Yet, capturing biodiversity patterns require accounting for spatiotemporal variations. Seasonal migratory fish alter biodiversity patterns over time, appearing different area depending on the season (Plavan et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ichinomiya et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shinohara et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition to migration, the temporal appearance of vagrant fish, those that move beyond their native range but fail to establish persistent populations, can also influence seasonal changes in biodiversity (Nakazono et al., 2002; Masuda et al., 2008; Nakamura et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Spatial variation, meanwhile, is shaped by ocean currents and the structure of the littoral zone (Kai and Motomura \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, both temporal and spatial context must be considered to assess correctly the marine fish biodiversity. However, there is limited spatiotemporal biodiversity information available using a consistent method, hampering our ability to assess the recent impacts of human activities and climate change.\u003c/p\u003e\u003cp\u003eLarge-scale monitoring of fish diversity is constrained by high logistical and financial demands (Costello et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Fujikura et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A promising solution is environmental DNA (eDNA), which refers to DNA shed by organisms into their environment (Ficetola et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Bohmann et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Recent advances in eDNA analysis have made it a reliable, noninvasive, and highly sensitive monitoring tool for estimating diversity estimation and species composition, especially in aquatic ecosystems (Shinohara et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Deiner et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; harper et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ionescu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Compared with traditional capture-based surveys, eDNA offers relatively lower cost and higher scalability, allowing for more frequent and extensive spatio-temporal sampling (Miya et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Because eDNA can originate from a wide variety of sources, including intestinal and skin cells, urine, mucus, eggs, or sperm (Wang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), it enables detection of multiple species from a single water sample (Ushio et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jeunen et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo investigate the spatiotemporal changes in fish diversity patterns in the marine system, we utilized a data from the All Nippon eDNA Monitoring Network (ANEMONE, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://db.anemone.bio\u003c/span\u003e\u003cspan address=\"https://db.anemone.bio\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a publicly accessible nation-wide eDNA-based biodiversity monitoring program in Japan. Established in 2019, ANEMONE currently hosts more than 8,000 surveys with data continuously updated on the database (Figure. 1; Kondoh et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The database covers over 3000 km\u003csup\u003e2\u003c/sup\u003eof coastal marine areas, including regions influenced by the Kuroshio and Oyashio currents, two major western boundary currents in the western North Pacific. As of September 2024, the database includes data from 2017 to 2022. Sampling sites are mainly located along the coastline of Japan, \u0026ndash; an area of high biodiversity and ecosystem service value due to the intersection of the land and sea and the complex mosaic of topographic and environmental conditions (Kai and Motomura \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Japan harbors at least 3,790 fish species, reflecting a combination of various historical and environmental factors (Fujikura et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). As a major fishing nation, Japan also holds potential to sustainably enhance its fishery yields through appropriate management (Ichinokawa et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Endo and Matsuura \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, the ANEMONE provides an idea basis analyzing spatial and temporal patterns of marine fish diversity.\u003c/p\u003e\u003cp\u003eTo explore large-scale patterns of marine biodiversity, we focused on the inter-regional and inter-seasonal differences in the latitudinal diversity gradients (LDGs). The LDG is one of the most well-established global ecosystem patterns, generally showing that species richness increases toward lower latitudes, particularly in terrestrial systems (Hillebrand \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Jablonski et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Similar patterns have often been reported for marine taxa in both coastal and deep-sea ecosystems (Rohde \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Clarke and Crame \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Roy et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Hillebrand \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Linse et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Yasuhara et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), although there are several exceptions (Valdovinos et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Clarke et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Despite extensive studies on LDGs, little is known about how they vary temporally, especially across seasons. Fish, being highly mobile, migrate in response to changes in environmental conditions. A prominent seasonal pattern is the northward migration in summer and return to southern waters in winter (e.g., Furukawa et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which is expected to flatten or even reverse the LDGs in winter as more fish concentrate at higher latitudes. Alternatively, the seasonal influx of vagrant fish may also influence the LDGs. For example, many tropical fishes are observed in Japanese waters in summer, brought by the Kuroshio Current, but most do not survive the winter (Nakazono \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Masuda \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Nakamura et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The transient increase in tropical species richness at higher latitudes may steepen the LDGs in summer, strengthening the expected steeper slope arising from the seasonal fish migration patterns. Given those dynamics, we investigated how LDGs of marine fish diversity change seasonally in Japanese waters. The Japanese Archipelago spans approximately 3,000 km from north to south and encompass a range of climate zones from subboreal to subtropical (Fujikura et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Endo and Matsuura \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), making it an ideal system to study LDGs.\u003c/p\u003e\u003cp\u003eIn this study, we aimed to (1) characterize the general LDGs of marine fish in Japanese waters, (2) examine the seasonal variations in LDGs, and (3) explore the potential drivers of the seasonal changes in the LDGs (i.e., the effect of seasonal migration and temporary inflow of fish), focusing on the roles of vagrant and migration fish.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTo assess the inter-regional fish LDGs, Japanese waters were divided into two regions: the Mainland and the Ryukyu area (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the raw data (i.e., before rarefaction), the mean genus richness value in the Mainland area was 25.33 for 1,119 surveys (median\u0026thinsp;=\u0026thinsp;23, range 4\u0026ndash;78) and the value in the Ryukyu area was 41.52 for 155 surveys (median\u0026thinsp;=\u0026thinsp;40, range 10\u0026ndash;90). Latitude was negatively correlated with rarefied fish genus richness in the Mainland area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), whereas significant patterns were not observed in the Ryukyu area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the models, the coefficients in the Ryukyu area differed from those in the Mainland area. In summer and autumn, the Mainland area exhibits significantly higher biodiversity compared to the winter season. The interaction between seasonal variation and latitude was significant for the Mainland area (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Remarkably, the interaction between summer and latitude was significant. The regression line for winter was less negative than summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Note that, we found the similar negative LDGs pattern for the data pooled for all sampling surveys, but the interaction between season and latitude was not observed from the data (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eCoefficients of the generalized linear model (GLM)\u003c/b\u003e ***: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMainland area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRyukyu area\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e(Intercept)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27.670***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-56.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLatitude\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.317**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.451\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpring\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.672**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.848\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSummer\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.437***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44.564\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAutumn\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.344***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.295\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMethods \u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(qualitative vs quantitative)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLatitude:Spring\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.385*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLatitude:Summer\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.508**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.655\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLatitude:Autumn\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.479*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.679\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo identify the factors driving the seasonal differences in the LDGs, we investigated two potential mechanisms. The first was presence of the season-specific fish, defined as genera observed exclusively only in one season at a given sampling site (e.g., a species present in summer but absent in the other seasons at the site). For each season, we calculated the proportion of survey sites in mainland Japan (i.e. excluding Ryukyu area) that harbored more than one season-specific genus.\u003c/p\u003e\u003cp\u003eWe found that the occurrence of season-specific genera was highest in summer with 16.92%, compared to 7.35% in spring, 10.33% in autumn, 4.69% in winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A one-sided binomial test, using the overall data all seasons as the reference, revealed that the proportion of season-specific fish was significantly larger only in summer (p\u0026thinsp;=\u0026thinsp;0.976 in spring, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in summer, p\u0026thinsp;=\u0026thinsp;0.647 in autumn, p\u0026thinsp;=\u0026thinsp;1.000 in winter). In addition, those summer-specific fishes tended to occur at lower latitudes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), suggesting that many of them were vagrant tropical fish.\u003c/p\u003e\u003cp\u003eThe second factor examined was the contribution of migratory fish. We visualized the seasonal migration patterns of five genera of fish that are designated as useful fish in resource management by Japanese Fisheries Agency - Cololabis, \u003cem\u003eSardinops\u003c/em\u003e, \u003cem\u003eScomber\u003c/em\u003e, \u003cem\u003eSeriola\u003c/em\u003e and \u003cem\u003eTrachurus\u003c/em\u003e. However, the spatial distribution of these genera did not exhibit clear or consistent seasonal shifts across the study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), suggesting that seasonal migration by these taxa may have limited influence on the observed seasonal variation in the LDGs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLarge-scale eDNA data from ANEMONE revealed a general latitudinal diversity gradient (LDG) in fish genera along the Japanese coastline (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the mainland area, fish genus richness was negatively correlated with latitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), consistent with ell-established LDG patterns observed globally (Hillebrand, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Mannion et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In contrast, no clear pattern was observed in the Ryukyu area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), likely due to sampling bias in latitude. This limitation may be resolved as the database becomes more spatially complete. When all sampling data are pooled, the declining trend in genus richness with increasing latitude was still evident across Japanese waters (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These results align with previous studies showing higher fish diversity in southern areas of Japan (Shinohara et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Miya et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), supporting the establishment of an LDG in Japanese waters at the genus level.\u003c/p\u003e\u003cp\u003eA key finding was the seasonal variations in the slope of LDG. In the mainland area, the interaction between summer and latitude was significant (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with the slope being steeper (more negative) in summer compared to winter. We attribute this seasonal steeping of the LDGs to the influx of vagrant tropical fish. The proportion of survey sites with season-specific genera was significantly higher in summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e); and these genera were more frequently observed at southern area, suggesting the arrival of tropical fish from southern regions. Such vagrant species are frequently observed along southern Japanese coasts during summer but do not survive the colder winter waters (Nakazono et al., 2002; Masuda et al., 2008; Nakamura et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Their temporary presence inflates summer diversity in southern regions as reported in other studies (Motomura and Matsunuma \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), particularly around Kyusyu and Shikoku islands (30\u0026deg;N\u0026thinsp;\u0026minus;\u0026thinsp;35\u0026deg;N, Shinohara et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Miya et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInterestingly, the observed seasonal pattern in the LDG contradicts the pattern expected from the seasonal migration of fish. If fish migrated northward in summer and southward in winter, LDGs would be flatter in summer and steeper in winter. To test this, we examined the seasonal distribution of five migratory genera (\u003cem\u003eCololabis, Sardinops, Scomber, Seriola\u003c/em\u003e and \u003cem\u003eTrachurus\u003c/em\u003e). However, their eDNA-based distributions showed little seasonal change (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This might be because eDNA reflects the presence of biological material from various sources (e.g., mucus, eggs, or sloughed cells), and not necessarily the real-time distribution of adult stocks. Taken together, the seasonal steeping of the LDGs is better explained by the temporally appearance of vagrant fish than the seasonal migration. This interpretation is further supported by our analysis of inland waters, where oceanic currents do not influence fish movement. Using the same GLM approach, we found no seasonal change in the LDG slope in inland waters (Figure S2, Table S2) while the negative LDG patterns were observed similarly to coastal waters, reinforcing the view that fish dispersal driven by oceanic currents underlies the summer steepening of LDGs in coastal waters. Notably, spring showed a significant deviation from other seasons in inland systems (Table S2), which warrants further investigation.\u003c/p\u003e\u003cp\u003eDue to recent global warming, sea water temperature around Japan has been rising, especially in the Tsushima area, which has been reported to be one of the most rapidly warming water bodies on Earth during the modern warming period (Takahashi \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and has been found to have a significant impact on community structure and diversity (Lejeusne et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Doney et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). We observed a positive correlation between sea surface temperature and genus richness (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), suggesting a potential increase in biodiversity with warming. However, our findings caution against interpreting this pattern as an actual gain in biodiversity. Our results demonstrated that the observed increase of diversity in summer was driven by vagrant fish. While this may temporarily enhance ecosystem services (e.g., recreational diving), it may also disrupt local communities through increased competition. We call this phenomenon as \u0026lsquo;pseudo biodiversity\u0026rsquo; \u0026mdash;an apparent increase in diversity not captured by traditional indicators, and which may mislead assessments of ecosystem health. Moreover, continued warming may allow vagrant species to establish viable populations or hybridize with local species, introducing longer-term ecological changes (Takahashi, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The consequences of such changes remain poorly understood and represent an important direction for future research.\u003c/p\u003e\u003cp\u003eOur results also underscore the need for careful interpretation of eDNA-derived diversity indices. While genus or species richness is often used to infer environmental quality, our study shows that such indicators can be misleading if seasonal or spatial patterns are not accounted for. In particular, temporary summer diversity increases may mask underlying ecological conditions. Accurate environmental assessments thus require adjustment for season, latitude, temperature, and the influence of transient species. The fish distribution pattern in Japanese water, including the incidence of vagrant fish, is associated with several currents around the Japanese islands (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Motomura et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Sakurai \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kai \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nakayama \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Motomura and Matsunuma \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, future biodiversity assessments under climate change should incorporate current-biodiversity relationships.\u003c/p\u003e\u003cp\u003eThe ANEMONE database revealed seasonal changes in diversity with regards to the latitudinal line patterns. This was made possible by the availability of a large amount of data, over a wide area, and from a large database. However, the database still presents several challenges. As several institutes are involved, there are regional variations in data acquisition status. There is a particular shortage of sampling sites in the Okinawa area. Although the database is considered useful for elucidating fish distribution and assessing diversity, a cooperative system for more systematic data collection is also needed. Systematic data and long-term monitoring might reveal differences between the years or diversity pattern changes in time. Thus, monitoring should continue, the database should be enriched, and observations of long-term dynamics should be made. Continued operation of monitoring networks like ANEMONE will be the key to future diversity conservation in the seas surrounding Japan.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eData collection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData sets were obtained from the ANEMONE database (downloaded on 20\u003csup\u003eth\u003c/sup\u003e September, 2024). The database contains data on both marine and inland water, although we focused on analyzing data on marine fish. To exclude inland water data, we utilized information on coastlines from Nature earth (https://www.naturalearthdata.com), and we have screened the surveys within 10km of the coastline. The data sampling sites were distributed between 24.46N to 45.54N and 123.8E to 145.6E, covering coastal areas throughout Japan. The data collection period was from December, 2017 to September, 2022. The raw data from the ANEMONE database consisted of sequence data, species information from the sequence data, and sampling location data. The eDNA data were collected using the manual for environmental DNA research from the eDNA Society (Minamoto et al., 2021). Data obtained from the same sampling site at different depths and times was analyzed as independent surveys. Genus data were used for fish biodiversity because 89.92% of the sequence data in the database was identified at the genus level, whereas only 63.03% was identified at the species level, particularly in sequences with a low number of reads. Undefined genus reads were still obtained; however, these were excluded when calculating genus-level fish diversity. Thus, the total number of surveys for the five-year time-period was 1274.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRarefaction\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe data in the ANEMONE database included two types of eDNA data due to the different methods used to obtain sequence reads (qualitative vs. quantitative). The eDNA data in 2017 and 2018 were obtained using universal polymerase chain reaction (PCR) primers (MiFish-U/E) for the metabarcoding of environmental DNA (eDNA) from fish (Miya et al., 2015). Because the concentrations of PCR inhibitors depend on certain uncontrollable conditions of the samples (Schrader et al., 2012), this method cannot be used to estimate the amount of eDNA. This method is referred to as the qualitative method. eDNA data from 2019 onwards were obtained using an expanded method developed by Ushio\u003cem\u003e et al\u003c/em\u003e to detect fish biomass (Ushio et al., 2018). In the expanded method, internal standard DNA is added to eDNA samples from the field, and metabarcoding analysis is performed; therefore, we can quantify the eDNA copy number of fish using the copy numbers from the sequence reads of the added standard eDNA as a calibration of the absolute abundance. This method is referred to as the quantitative method. The quantitative method is more beneficial, as it allows us to obtain more accurate information on fish abundance/biomass; however, a small amount of eDNA may be missed because standard eDNA interferes with the eDNA sampled in the field. Consequently, with regards to the data collected as presence/absence data, the quantitative method may have a limited detection capability compared to the qualitative method. It is possible that the species richness estimated using the qualitative method will be greater than that estimated using the quantitative method. To avoid misinterpretation due to the method difference, the sensitivity difference between the methods was corrected. \u003c/p\u003e\n\u003cp\u003eTo solve this problem, a rarefaction method was used. The rarefaction method can be applied to downsize larger samples until they are the same size as the smallest sample (Hurlbert 1971). The rarefaction curve indicates the expected species richness in the random subsamples of the sample from the community. The expected species richness calculated using rarefaction should be corrected for differences in DNA detection sensitivity. To accomplish this, the \u0026lsquo;rarefy\u0026rsquo; function of the vegan package (version 2.6.4) in R (version 4.3.2) was used. Note that no significant difference was observed between the qualitative and quantitative methods after rarefaction (\u003cem\u003eWilcoxon signed-rank test, W\u003c/em\u003e= 8732, \u003cem\u003ep\u003c/em\u003e = 0.6852). \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the inter-regional fish LDGs, Japanese waters were divided into two regions: the Mainland area and the Ryukyu area (Figure 1). The Ryukyu area includes the Ryukyu Islands (Figure 1), where a different faunal structure of the Pacific coast along the Japanese Archipelago has been reported (kai and Motomura 2022), and specific local populations are formed (Aoki et al., 2008; Imai et al., 2009; Kuriiwa et al., 2014). Therefore, the Ryukyu region was treated separately from the other regions, although we also analyzed the pooled data for all surveys (see Figure S1 and Table S1). The seasonal changes in LDGs in each region were also considered. A generalized linear model (GLM) was constructed to demonstrate the fish genus richness in each area, and the fish genus richness after rarefaction was settled on the response variables to correct the difference between qualitative and quantitative methods. In the models, latitude was chosen as an explanatory variable to explore LDGs, and the categorical variable \u0026lsquo;season\u0026rsquo; was included with the four levels: winter (used as the reference level) from December to February, spring from March to May, summer from June to August, and autumn from September to November. The interaction between latitude and season was also included in the model. For the GLM analysis, a gamma error distribution with the identity-link function was used to account for the fact that the response variables were positive real numbers. \u003c/p\u003e\n\u003cp\u003eLastly, we examined whether the seasonal pattern differences in the LDGs were due to fish that are unique to the season (vagrant fish carried by ocean currents from outside regions) or to migratory fish. A season-specific genus was defined as a genus observed only in one season (e.g., the summer-specific genus at a sampling site was present in summer but absent in the other seasons). For season-specific fish, the ratio of survey sites with more than one season-specific genus to the total number of survey sites was investigated in mainland area (i.e. excluding Ryukyu area) in each season.\u003c/p\u003e\n\u003cp\u003eIn order to see the impact that migratory fish have on the LDGs patterns, the seasonal migration patterns of five genera of fish that are designated as useful fish in Japanese resource management by Japanese Fisheries Agency - Cololabis, \u003cem\u003eSardinops\u003c/em\u003e, \u003cem\u003eScomber\u003c/em\u003e, \u003cem\u003eSeriola\u003c/em\u003e and \u003cem\u003eTrachurus\u003c/em\u003e - were visualised in each season. These are genera widely distributed in Japan and have relatively well-known seasonal migration patterns (northwards in summer and southwards in winter) (Hiyama et al., 2002; Sassa et al., 2009; Okunishi et al., 2012; Suyama et al., 2012; Liu et al., 2022; Furukawa et al., 2020)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by a Grant-in-Aid for JSPS Fellows Number 19J00864, JST Grant Number JPMJPF2206 and JSPS KAKENHI Grant Number 19H05641 and 21H05312.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work utilized the ANEMONE database [https://db.anemone.bio/]. Observations by ANEMONE are carried out with the help of many collaborators. We thank the Fisheries Research Division, Tokushima Agriculture, Forestry, and Fisheries Technology Support Center, for their valuable support. We would like to express our gratitude to all those who have participated in data acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMinoruK, NS, RF and MichioK designed research, MinoruK, NS, RF, AST and MichioK performed research, AST contributed new reagents or analytic tools. HY conducted eDNA metabarcoding. MinoruK, NS, RF analyzed data. MinoruK, NS, RF and MichioK wrote the main manuscript text and MinoruK prepared the figures and the tables. The other authors contributed to data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no conflicts of interest to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAoki, M., Naruse, T., Cheng, J.-H., Suzuki, Y. \u0026amp; Imai, H. 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Total Environ.\u003c/em\u003e \u003cstrong\u003e755\u003c/strong\u003e, 142622 (2021).\u003c/li\u003e\n \u003cli\u003eYasuhara, M., Hunt, G., Cronin, T. M. \u0026amp; Okahashi, H. Temporal latitudinal-gradient dynamics and tropical instability of deep-sea species diversity. \u003cem\u003eProc. Natl Acad. Sci. U. S. A.\u003c/em\u003e \u003cstrong\u003e106\u003c/strong\u003e, 21717-21720 (2009).\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\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":"Fish diversity, Environmental DNA, Latitudinal diversity gradients, Migration, Monitoring network, Biodiversity","lastPublishedDoi":"10.21203/rs.3.rs-7872521/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7872521/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The spatial and temporal patterns of marine diversity are essential for the sustainable use of fishery resources and the effective management and conservation of marine ecosystems. We investigated the latitudinal diversity gradients of fish genera in Japanese waters using an environmental DNA (eDNA) data. A total of 1,765 environmental DNA surveys across Japan were obtained from the All Nippon eDNA Monitoring Network (ANEMONE) database. We found a negative correlation between fish genus richness and latitude, with a particularly pronounced gradient in summer. Seasonal change in the latitudinal gradient may reflect the increased occurrence of vagrant fish from tropical regions, rather than the commonly assumed pattern of seasonal migration (i.e., southwards in winter and northwards in summer). Furthermore, our results suggests that global warming may lead to a temporary increase in biodiversity by promoting the influx of tropical vagrant fish. However, such increases are unlikely sustainable, as these vagrant species may not establish long-term populations. We refer to this phenomenon as “pseudo biodiversity”, which may give the illusion of increase in biodiversity enhancement in short-term monitoring. This concept warrants attention as similar patterns of apparent biodiversity increase may occur under other circumstances as well.","manuscriptTitle":"Large scale eDNA database reveals potential for pseudo biodiversity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 14:31:02","doi":"10.21203/rs.3.rs-7872521/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-24T10:46:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-24T06:13:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184888974408475100937946849320044787210","date":"2026-02-03T13:00:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-03T09:25:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296469504726601458789022791344503126777","date":"2026-02-03T00:07:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323106139710008706934090077208837330156","date":"2026-01-19T08:22:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-27T10:27:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-22T11:29:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-18T01:48:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-18T01:47:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-16T02:18:01+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":"33324553-8846-4a4b-87e6-27a00daeb7fd","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":57271493,"name":"Biological sciences/Ecology"},{"id":57271494,"name":"Earth and environmental sciences/Ecology"},{"id":57271495,"name":"Earth and environmental sciences/Ocean sciences"}],"tags":[],"updatedAt":"2026-05-08T10:53:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 14:31:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7872521","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7872521","identity":"rs-7872521","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}