Utilizing eDNA methods in biodiversity studies of river affected by anthropogenic pollution: A case study on the Batanghari River in Indonesia

doi:10.21203/rs.3.rs-4462558/v1

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Utilizing eDNA methods in biodiversity studies of river affected by anthropogenic pollution: A case study on the Batanghari River in Indonesia

https://doi.org/10.21203/rs.3.rs-4462558/v1

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The amalgamation of global climate change, escalating anthropogenic activities, and species invasions has resulted in a decrease in the biodiversity of aquatic organisms. The Batanghari River, one of Indonesia's longest rivers, is impacted by anthropogenic activities including pollution from mercury-containing waste originating from illegal gold mining (PETI), industrial pollutants, and domestic waste. Studies have highlighted a decrease in organismal biodiversity within the river, yet a comprehensive assessment of its current status is lacking. eDNA metabarcoding is a promising tool for understanding biodiversity of river affected by anthropogenic pollution, offering broader insights compared to traditional methods. We used eDNA metabarcoding to analyze biodiversity distribution in the Batanghari River, yielding 22,180,436 reads from 405 eDNA samples across 5 locations and 15 sites. Significant variations in beta diversity were observed among locations. Approximately 80% of reads were classified to the species level, with the remainder identified as unidentified taxa. Our findings underscore disparities in species richness and community composition between upstream and downstream areas, likely influenced by anthropogenic stressors. This method revealed the presence of several extinct and endangered species at multiple sampling locations. Understanding species diversity and distribution is crucial for advancing river ecology and conservation practices.

Biodiversity

Batanghari River

Freshwater biodiversity

Environmental DNA

The advent of global climate change introduces new challenges affecting all ecosystems, including freshwater environments, resulting in an accelerated decline in biodiversity across numerous freshwater ecosystems, including rivers (Prakash 2021; Turney, Ausseil, and Broadhurst 2020; Worm and Lotze 2021). Climate change is anticipated to trigger significant transformations, such as the warming of waters, which in turn affects water quality and the overall well-being of freshwater ecosystems (Mann et al. 2022; van Vliet et al. 2013). Multiple studies provide compelling evidence that biodiversity is already undergoing changes in response to climate change (Gillette et al. 2022; Kariyawasam, Kumar, and Ratnayake 2021; Paul et al. 2021; Retnaningtyas 2021). In addition to climate change, other anthropogenic impacts also exert pressure on river ecosystems (Häder et al. 2020). Despite occupying a smaller area than Earth's oceans, rivers pose a significant hurdle in endeavors aimed at conserving biodiversity and sustaining vital ecosystem services (Deiner et al. 2016; Gu et al. 2023). A significant hurdle lies in the efficient monitoring and conservation of these essential yet constrained ecosystems (Li et al. 2023).

Recently, the utilization of environmental DNA (eDNA) has emerged as a promising technique for investigating ecological communities and tackling intricate environmental inquiries (Collins et al. 2022; Harper et al. 2019; Schenekar 2023; Danziger, Olson, and Frederich 2022). Furthermore, eDNA metabarcoding has gained widespread acceptance for characterizing, modelling, and forecasting organism populations in dynamic freshwater environments. It offers broader biodiversity assessments compared to traditional methods, detecting hidden species with minimal reliance on observer expertise (Evans and Lamberti 2018; Garlapati et al. 2019; Compson et al. 2020; Jeunen et al. 2024). Its applications span various aquatic environment, including studying biodiversity under different anthropogenic stress levels and identifying fish, arthropoda, and mollusca and other aquatic organism in laboratory settings (Ruppert, Kline, and Rahman 2019; Rishan, Kline, and Rahman 2023; Zhang 2019; Li et al. 2018; Prié et al. 2021). However, in aquatic including river environments, eDNA applications often lack focus on major taxonomic groups crucial for biodiversity. Research emphasizing perturbation-sensitive taxa is essential for reliable biodiversity assessments and refining conservation practices. Chordata, Arthropoda, and Mollusca, contribute significantly to river biodiversity and play vital ecological and economic roles (Cuthbert et al. 2022; Quave et al. 2010). Despite the abundant biodiversity found in Indonesia's freshwater or river ecosystems, there is a lack of comprehensive genetic records available in public databases, resulting in incomplete representation of data in these databases (Madduppa et al. 2021; Madduppa et al. 2022). The constrained quantity and taxonomic coverage of Indonesian river biodiversity data within public databases likely stem from the relatively limited extent of genetic research and observations conducted within the country. This underscores the importance of fostering research initiatives, especially through the integration of molecular tools, primarily DNA barcoding, alongside morphological identification. Such an approach is essential for accurately identifying and documenting new species and broadening the representation of taxa within reference barcode databases. Additionally, employing eDNA methods can aid in assessing the scale of river biodiversity.

The Batanghari River comprises numerous significant branches along with tributaries and lakes that function as breeding habitats for juvenile fish (Nurdawati, Muflikhah, and Sunarno 2017). Nonetheless, the array of issues confronting the Batanghari River, such as mercury-containing waste from illegal gold mining (PETI), industrial discharges, and household waste (Desrizal, Carlo, and Syah 2019; Kaban et al. 2020; Yanova et al. 2022), has raised serious concerns regarding its biodiversity. Consequently, various aquatic organisms, including fish, arthropods, and mollusks in the Batanghari River, have been listed as at-risk species on the IUCN Red List due to their global population status (IUCN 2023). Hence, this research aimed to assess the biodiversity of the Batanghari River and investigate the relative abundance and distribution of ecologically and economically significant organisms associated with the river across fisheries management areas in Indonesia, using eDNA methods. We specifically focused on analyzing differences between sites in three phyla (Chordata, Arthropods, and Mollusca), which comprise the majority of taxa in river communities. The data obtained from this study will help to reveal biodiversity patterns and provide essential information for species conservation and fisheries management.

eDNA Batanghari river sample collection

A total 405 samples of environmental DNA (eDNA) were gathered from the Batanghari River at 15 locations spanning five main watersheds: Batanghari Hulu (Teluk Kayu Putih), Batanghari Tebo, Batanghari Tabir, Batanghari Tembesi, and Batanghari Hilir (Muara Kumpeh), encompassing the entire river course from its source to its mouth (Fig. 1). Collection was executed using 4-liter water bottles, with each sample filtered sequentially through sterile filter papers of decreasing pore sizes: 3.0 µm, 1.2 µm, and 0.22 µm from Pall Corporation, each with a diameter of 47 mm, via a peristaltic pump. Filtration was paused if filter clogging impeded flow. To ensure the integrity of samples, all equipment utilized between samples and sites underwent sterilization using a 10% bleach solution and distilled water. Filtration was conducted at the conclusion of each day's sampling. Subsequent to filtration, each filter paper was housed in a 2 mL cryotube containing 1.5 mL of ZymoBIOMICS DNA/RNA shield and then transported to the Genomic Laboratory of the National Research and Innovation Institute (BRIN, Indonesia) for storage at 4°C pending further analysis.

DNA extraction

DNA extraction was carried out from the filtered water samples using ZymoBIOMICS DNA extraction kits according to the manufacturer's protocol (Zymo Research Corporation, USA). This protocol involves several key steps, including cell lysis, DNA precipitation, binding DNA to the column, washing, and elution.Subsequently, DNA samples underwent mini horizontal gel electrophoresis. Each sample was loaded into a 1.5% (w/v) agarose gel and subjected to electrophoresis powered with 95 volts for 50 minutes. Following electrophoresis, the gel was stained using 2 µL of 1st Base FloroSafe DNA Stain (First Base Axil Scientific, Singapore).To assess DNA concentration and purity, spectrophotometric measurements were conducted using a NanoDrop™ One/OneC Microvolume UV-Vis Spectrophotometer (Thermo Scientific). The extracted DNA samples were then stored at -20°C for subsequent analysis.

PCR, library preparation, and next-generation sequencing

PCR amplification was conducted in a total volume of 50 µL utilizing the Phusion High-Fidelity PCR Master Mix, a commercial kit from New England Biolabs, following the manufacturer's guidelines. Primers mlCOIintF-XT (forward): 5’ GGWACWRGWTGRACWITITAYCCYCC-3’ (Wangensteen and Turon 2017) and jgHCO2198 (reverse): 5’-TAIACYTCIGGRTGICCRAARAAYCA-3’ (Geller et al. 2013) were employed. The PCR mixture was composed of 2.5 µL 10X buffer, 2.5 µL MgCl2, 0.5 µL mlCOIintF primer, 0.5 µL jgHCO2198 primer, 0.5 µL dNTPs, 0.15 µL Taq polymerase, 17.35 µL double-distilled water (ddH2O), and 1 µL of the sample template. Reactions were carried out using a Veriti Thermal Cycler (Thermo Fisher Scientific). Thermal cycling conditions encompassed an initial pre-denaturation phase at 94°C for 1 minute, followed by 30 cycles of denaturation at 94°C for 30 seconds, annealing at 42°C for 30 seconds, and extension at 72°C for 1 minute, with a final extension step at 72°C for 8 minutes. The PCR products from all three replicates of each sample were pooled together and purified using Zymo® Select-a-Size DNA Clean & Concentrator Kits, following the manufacturer's instructions for amplicons greater than 150 bp. The purified amplicons were then eluted in 25 µl of water. Quantification of the purified DNA was performed using a Qubit 3 fluorometer with the HS dsDNA kit, using 2 µl of PCR product from each sample. Subsequently, the remaining PCR product from each sample was shipped to the Novogene Genome Sequencing Company on dry ice for library preparation. The TruSeq DNA PCR-Free Sample Preparation Kit (Illumina) was used for library preparation, followed by sequencing on an Illumina NovaSeq 6000 sequencer.

Bioinformatics and data analysis

The Anacapa pipeline was used to analyse all eDNA sequences (Curd et al. 2019). a COI database was created using the CRUX package (https://github.com/limey-bean/Anacapa/) to create a de novo sequence reference library combining records from publicly accessible databases, such as the European Molecular Biology Laboratory (EMBL) and (NCBI). Anacapa then de-multiplexed the amplicon read based on the primer sequences specified in the laboratory analysis section and removed the primers from the reads once the database construction was completed. Anacapa used the DADA2 algorithm (Callahan et al. 2016) to denoise and correct raw sequence data, merge paired-end reads, and assign high quality reads to Amplicon Sequence Variants (ASVs) using ASV Parsing. Anacapa employed Bowtie 2 and the Bayesian Least Common Ancestor technique to assign ASVs to taxa in the final step of the pipeline, using a default likelihood threshold value recommended by (Gao et al. 2017). We eliminated singletons from most analyses to obtain the most conservative measure of diversity. To investigate patterns of taxonomic diversity, ASV tables output by DADA2 were converted to BIOM format and imported into R for diversity analysis, statistical testing, and data visualization.

The number of reads per sample varied between 77,551 and 96,155 (Suppl. 1). Taxa from specific orders and families were excluded from downstream analysis, including Primata, Rodentia, Artiodactyla, Chiroptera, and the family Bovidae from the class Mammalia, as well as Passeriformes, Columbiformes, and Galliformes from the class Aves. To summarize the taxonomic composition of each sample, taxa were aggregated at seven hierarchical levels (Kingdom, Phylum, Class, Order, Family, Genus, and Species). Additionally, taxa representing less than 2% at the phylum and order levels, and less than 10% at the species level of the overall community were filtered out. Patterns of alpa diversity were assessed by calculating taxonomic richness for all Amplicon Sequence Variants (ASVs) and three major phyla (Chordata, Arthropoda, and Mollusca) across each sampling location. Multivariate analysis, specifically PERMANOVA (Permutational Multivariate Analysis of Variance), was conducted using the Vegan package (Oksanen et al. 2023) within R version 4.4.0 (Team 2024). The analysis was based on the presence/absence of Amplicon Sequence Variants (ASVs), using Jaccard similarity, as well as the abundance of reads, employing Bray–Curtis dissimilarity. These differences were observed using both the Bray–Curtis and Jaccard indices. Statistical significance was evaluated through 9999 permutations with a confidence level set at α = 0.05.

Assessment of biodiversity using COI markers and eDNA technique

In total, 22,180,436 paired-end reads were produced from the COI amplicons acquired from the 405 samples gathered at 15 sites spanning 5 sampling locations, aligning with 398,409 unique sequences. This study identified six kingdom levels of eukaryote, including Animalia, Chromista, Protista, fungi, plantae, and Protozoa. Animalia dominated the taxonomic composition at all sampling sites (97.04%), followed by Plantae (1.56%), Chromista (0.95%), Protista (0.38%), and others. Based on read abundance, the Kingdom animalia was dominant in Batanghari Tebo and prominent in Batanghari Hulu (Teluk Kayu Putih) (Fig. 2 and Fig. 3)

Chordata dominated at the phylum level, based on read abundance, in all sampling locations. (Fig. 3). Most of the phylum Chordata dominated by fish contributed a greater proportion (18,906) than Arthropoda (5,930), Mollusca (1,517), and Porifera (1,438). According to read abundance, phylum Chordata and Arthropoda was more abundant in Teluk Kayu Putih and Batanghari Tebo than in other locations.

At the location level, when including all ASVs showed that the upstream area was higher than the downstream area, Batanghari hulu had the highest number of ASVs (9,161), followed by Batanghari Tebo (7,478 ASVs), Batanghari Tabir (6,301 ASVs), and Batanghari Hilir (3,888 ASVs) and lowest number was recorded in Batanghari Tembesi (3,270 ASVs) (Table 1). In addition, when considering only ASVs from the phyla Chordata, Arthropoda, and Mollusca, Batanghari Hulu had the highest number of ASVs (7,961 ASVs), followed by Batanghari Tebo (6,444 ASVs) and Batanghari Tabir (5,663 ASVs) and Batanghari Tembesi had the lowest number (2,729 ASVs) (Table 1). Beta diversity analysis revealed not significant variations in taxonomic composition both among different sampling locations as determined by PERMANOVA (p < 0.05).

Table 1

Taxonomic richness from environmental DNA (eDNA) data from 5 sampling locations across Batanghari river
Location	Taxonomic richness
	Total sample ASVs	Chordata	Arthropoda	Mollusca
Batanghari Hulu (Teluk Kayu Putih)	9,161	5,773	1,755	433
Batanghari Tebo	7,478	4,492	1,601	351
Batanghari Tabir	6,301	4,317	1,068	278
Batanghari Tembesi	3,270	1,643	855	231
Batanghari Hilir (Muara Kumpeh)	3,888	2,681	651	224

Assessment of eukaryotic taxa interest using eDNA and a COI barcode

This section describes a snapshot of the distribution of three phyla (Chordata, Arthropoda, and Mollusca) based on read abundance across the sampling locations and included economically valuable and/or protected species in these phylum.

Phylum chordata

The database resulting from this study included 18,906 ASVs and 14,727 sequence reads, covering five classes, 93 orders, 199 families, 284 genera, and 587 species. The distribution of phylum Chordata at the class level is shown in Fig. 5. In this phylum we focused on Actinopteri class. There were high read abundances of Actinopteri at all sites with 303 species. Family Cypriniformes (71.87%) were observed at all sites with the highest number of reads from at all sites, followed Perciformes (12.28%), Cichliformes (7.17%), and unidentified (3.39%) (Fig. 4).

The highest abundance of fish was observed in the upper reaches of the river, specifically in Batanghari Hulu and Batanghari Tebo (Fig. 5). Notably, economically valuable fish species were identified, including Scleropages formosus, Chitala borneensis, Chitala lopis, Notopterus notopterus, and Anguilla bicolor (Table 2). According to the IUCN 2023 classification, Anguilla bicolor is categorized as near threatened, Scleropages formosus as an endangered species, and Chitala lopis as an extinct species, albeit with very low abundance detected through this eDNA method. Several native ornamental fish species in the Batanghari River, such as Chromobotia macracanthus, Syncrossus hymenophysa, and Syncrossus reversus, were also identified using this method, albeit with very low abundances. Additionally, introduced fish species not native to the Batanghari River were detected, including species from the order Cichliformes such as Oreochromis niloticus, Oreochromis mossambicus, Oreochromis aureus, and Oreochromis hybrids. The fish species Cichlasoma festae and Cichlasoma urophthalmum from the order Characiformes, as well as Chanos chanos from the order Gonorynchiformes, including cultivated fish and non-native species to the Batanghari River, were also detected using the eDNA method. The majority of fish species were detected in Batanghari Hulu, Kalyu Putih Bay, and Bataghari Tabir regions.

Table 2

Diversity of identified species in Batanghari river using eDNA
Orders	Species
Acanthuriformes	Boesemania microlepis; Panna microdon
Anabantiformes	Anabas testudineus
Anguilliformes	Anguilla bicolor
Beloniformes	Hemirhamphodon pogonognathus; Xenentodon canciloides
Blenniiformes	Phenablennius heyligeri
Characiformes	Cichlasoma festae; Cichlasoma urophthalmum
Cichliformes	Oreochromis niloticus; Oreochromis mossambicus; Oreochromis aureus; Oreochromis hybrids
Cypriniformes	Albulichthys albuloides; Amblyrhynchichthys truncatus; Balantiocheilos melanopterus; Barbichthys laevis; Barbonymus gonionotus; Barbonymus schwanenfeldii; Boraras maculatus; Crossocheilus cobitis; Crossocheilus gnathopogon; Crossocheilus langei; Crossocheilus oblongus; Crossocheilus obscurus; Crossocheilus pseudobagroides; Cyclocheilichthys apogon; Cyclocheilichthys enoplos; Cyclocheilichthys heteronema; Cyclocheilichthys repasson; Cyprinus carpio; Diplocheilichthys pleurotaenia; Eirmotus furvus; Eirmotus isthmus; Epalzeorhynchos kalopterus; Hampala ampalong; Hampala macrolepidota ; Hypsibarbus huguenini; Labeo chrysophekadion; Labeo erythropterus Valenciennes; Labiobarbus fasciatus; Labiobarbus leptocheila; Labiobarbus ocellatus; Leptobarbus hoevenii; Lobocheilos ixocheilos; Lobocheilos schwanenfeldii; Luciosoma setigerum; Luciosoma spilopleura; Luciosoma trinema; Macrochirichthys macrochirus; Malayochela maassi; Mystacoleucus marginatus; Neobarynotus microlepis; Osteochilus bleekeri; Osteochilus borneensis; Osteochilus intermedius; Osteochilus kerinciensis; Osteochilus melanopleura; Osteochilus microcephalus; Osteochilus scapularis; Osteochilus schlegelii; Osteochilus spilurus; Osteochilus vittatus; Osteochilus waandersii; Oxygaster anomalura; Paedocypris progenetica; Parachela cyanea ; Parachela hypophthalmus; Parachela oxygastroides; Pectenocypris micromysticetus; Puntioplites bulu; Puntioplites waandersi ; Puntius gemellus; Puntius hexazona; Puntius johorensis; Puntius lateristriga; Puntius lineatus; Puntius tetrazona; Raiamas guttatus; Rasbora argyrotaenia; Rasbora brittani; Rasbora cephalotaenia; Rasbora dorsiocellata; Rasbora dusonensis; Rasbora einthovenii; Rasbora elegans; Rasbora gracilis; Rasbora jacobsoni; Rasbora kalbarensis; Rasbora kalochroma; Rasbora myersi; Rasbora pauciperforata; Rasbora paucisqualis; Rasbora subtilis; Rasbora sumatrana; Rasbora trilineata; Rasborichthys helfrichii; Rohteichthys microlepis; Schismatorhynchus heterorhynchos; Sundadanio axelrodi; Thynnichthys polylepis; Thynnichthys thynnoides; Tor tambroides; Trigonostigma hengeli; Barbucca diabolica; Homaloptera ocellata; Homalopteroides nebulosus; Homalopterula gymnogaster; Neohomaloptera johorensis; Nemacheilus kapuasensis; Nemacheilus papillos; Nemacheilus longipinnis; Nemacheilus pfeifferae; Nemacheilus selangoricus; Vaillantella euepipterus; Vaillantella maassi; Acantopsis dialuzona; Kottelatlimia katik; Kottelatlimia pristes; Lepidocephalichthys tomaculum; Pangio alcoides; Pangio anguillaris; Pangio atactos; Pangio bitaimac; Pangio cuneovirgata; Pangio malayana; Pangio oblonga; Pangio semicincta; Pangio shelfordii; Chromobotia macracanthus; Syncrossus hymenophysa; Syncrossus reversus
Cyprinodontiformes	Aplocheilus panchax
Gobiiformes	Oxyeleotris marmorata; Oxyeleotris urophthalmoides; Bunaka gyrinoides; Butis humeralis; Brachygobius doriae; Eugnathogobius siamensis; Eugnathogobius festivus; Glossogobius giuris; Parapocryptes serperaster; Periophthalmodon septemradiatus; Caragobius urolepis
Gonorynchiformes	Chanos chanos
Gymnotiformes	Scleropages formosus; Chitala borneensis; Chitala lopis; Notopterus notopterus
Mugiliformes	Liza subviridis
Perciformes	Gymnochanda filamentosa; Gymnochanda limi; Paradoxodacna piratica; Parambassis apogonoides; Parambassis macrolepis; Parambassis wolffii; Datnioides microlepis; Datnioides polota; Polydactylus macrophthalmus; Polynemus dubius; Polynemus multifilis; Boesemania microlepis; Panna microdon; Toxotes jaculator; Toxotes microlepis; Nandus nebulosus; Pristolepis fasciata; Pristolepis grootii; Phenablennius heyligeri; Bunaka gyrinoides; Butis humeralis; Oxyeleotris marmorata; Oxyeleotris urophthalmoides; Brachygobius doriae; Eugnathogobius siamensis; Eugnathogobius festivus; Glossogobius giuris; Parapocryptes serperaster; Periophthalmodon septemradiatus; Helostoma temminkii; Belontia hasselti; Betta coccina; Betta cracens; Betta falx; Betta pugnax; Betta raja; Betta renata; Betta simorum; Luciocephalus aura; Luciocephalus pulcher; Osphronemus goramy; Parosphromenus bintan; Parosphromenus sumatranus; Sphaerichthys osphromenoides ; Trichopodus leerii; Trichopodus trichopterus; Trichopsis vittata ;Channa bankanensis; Channa cyanospilos; Channa gachua; Channa lucius; Channa marulioides; Channa melasoma; Channa micropeltes; Channa pleurophthalmus; Channa striata
Pleuronectiformes	Achiroides melanorhijnchus; Brachirus panoides; Cynoglossus feldmanni; Cynoglossus waandersii
Siluriformes	Acrochordonichthys rugosus; Akysis fontaneus; Akysis heterurus; Breitensteinia cessator; Parakysis grandis; Pseudobagarius macronema; Bagarius yarrelli; Glyptothorax platypogon; Belodontichthys dinema; Ceratoglanis scleronema; Hemisilurus heterorhynchus; Hemisilurus moolenburghi; Kryptopterus bicirrhis; Kryptopterus cryptopterus; Kryptopterus eugeneiatus; Kryptopterus limpok; Kryptopterus macrocephalus; Kryptopterus palembangensis; Kryptopterus schilbeides; Ompok fumidus; Ompok leiacanthus; Ompok rhadinurus; Phalacronotus apogon; Silurichthys indragiriensis; Wallago leerii; Chaca bankanensis; Plotosus canius; Plotosus lineatus; Clarias batrachus; Clarias leiacanthus; Clarias meladerma; Clarias nieuhofii; Clarias olivaceus; Encheloclarias cf. kelioides; Encheloclarias velatus; Arius maculatus; Batrachocephalus mino; Cephalocassis borneensis; Cephalocassis melanochir; Cryptarius truncatus; Hemiarius stormii; Nemapteryx nenga; Netuma bilineata; Osteogeneiosus militaris; Plicofollis argyropleuron; Plicofollis dussumieri; Plicofollis polystaphylodon; Laides hexanema; Pseudeutropius brachypopterus; Pseudeutropius moolenburghae; Helicophagus typus; Helicophagus waandersii; Pangasius djambal; Pangasius nasutus; Pangasius polyuranodon; Pseudolais micronemus; Bagrichthys hypselopterus; Bagrichthys macracanthus; Bagrichthys macropterus; Bagroides melapterus; Hemibagrus hoevenii; Hemibagrus nemurus; Hemibagrus velox; Hemibagrus wyckii; Hyalobagrus flavus; Leiocassis hosii; Leiocassis poecilopterus; Mystus abbreviatus; Mystus bimaculatus; Mystus castaneus; Mystus singaringan; Mystus wolffii; Nanobagrus fuscus; Nanobagrus stellatus; Nanobagrus torquatus; Pseudomystus heokhuii; Pseudomystus leiacanthus; Pseudomystus rugosus; Pseudomystus stenomus
Synbranchiformes	Monopterus albus; Macrognathus circumcinctus; Macrognathus maculatus; Macrognathus tapirus; Mastacembelus erythrotaenia; Mastacembelus unicolor; Bihunichthys monopteroides; Chendol keelini; Nagaichthys filipes
Syngnathiformes	Doryichthys deokhatoides; Doryichthys martensii
Tetraodontiformes	Auriglobus amabilis; Pao leiurus; Pao palembangensis

Phylum Arthropoda

The database resulting from this study included 5,930 ASVs and 2, 820 sequence reads, covering 4 class 19 order 85 families 228 genera 460 species. The distribution of phylum Arthropoda at the Order shown in Fig. 6. There high read abundances of in phylum Arthropoda was Decapoda, covering 28 families 35 genus 401 species. There high read abundances of Decapoda showed at upstream Batanghari river such as Batanghari Hulu (Teluk Kayu Putih, Batanghari Tebo dan Sebagian ke arah Batanghari hilir yaitu Batang hari Tembesi) sites. A high percentage of Phylum Arthoropod were unidentified at Batanghari Hulu site (Fig. 6).

The Palaemonidae family, which is freshwater, has been discovered in the Batanghari River. This includes species such as Macrobrachium rosenbergii, Macrobrachium sp, Macrobrachium lar, Macrobrachium Pilimanus, Macrobrachium Malayanum, Macrobrachium lanchesteri, and Palaemon sp. Additionally, small freshwater shrimp from the Atyidae family, such as Caridina excavatoides, Caridina propinqua, Caridina excavatoides, Caridina sumatrensis, Caridina gracilipes, Caridina sp, Atyopsis spinipes, and Atyopsis moluccensis, have also been observed in the Upper Batanghari river. Among freshwater crabs, species like Parathelphusa pantherina, Parathelphusa maculate, Parathelphusa maindroni, Parathelphusa tridentata, Parathelphusa batamensis, Parathelphusa sp, and Geosesarma sp were identified. Additionally, members of the freshwater lobster family Parastacidae, namely Cherax quadricarinatus and Cherax sp, were found in the Batanghari River. These species are highly valued due to their economic importance and increasing demand for human consumption, with some being cultivated by local farmers. Besides the mentioned species, other findings in Sugai Batanghari include Potamon sumatrense, Procambarus sp, Periclimenes sp, Coenobita Sp, and Parhippolyte sp.

Phylum Mollusca

The database resulting from this study included 1,517 ASVs and 1,363 sequence reads, covering 4 class 24 ordo 40 families 102 genera 153 species. There high read abundances of in phylum Molusca was Gastropda and Bivalva. The distribution of phylum molusca at the class shown in Fig. 7. There high read abundances of those classes showed at upstream Batanghari river such as Batangharu Hulu (Teluk Kayu Putih, Batanghari Tebo dan Batang hari Tembesi) sites (Fig. 8 and Fig. 9). The family Thiaridae with species Tarebia granifera, Brotia costula, Melanoides tuberculate, Melanoides punctate, and Thiara sp. In additon, family Ampullariidae with species Pomacea canaliculate, Pila polita, Pila ampullacea, and family Viviparidae with species Filopaludina sumatrensis were common find in the Batanghari river. Order Pachychilidae was found in the Batanghari river with Pachychilidae sp. Unidentified Gastropoda species also found in this study around (12.28%) in all sampling sites. The Thiaridae family showed the highest abundance across all sites in Batanghari river compared to other families within the gastropod class.

Using this eDNA method, other classes that were detectable include Bivalves. Within the bivalve class, only those belonging to the order Unionida and the family Unionidae were identified, which includes species such as Rectidens sumatrensis and Pilsbryoconcha exillis. These species were frequently encountered in the Batanghari River, particularly in the upstream section, but were rarely found in the downstream Batanghari River. Approximately 15.38% of the species within the Bivalve class were unidentified.

This research underscores the effectiveness of eDNA metabarcoding in shedding light on the biodiversity studies of river affected by anthropogenic pollution such as Batanghari River ecosystem in Indonesia. We also emphasized its potential in enhancing monitoring and conservation efforts for ecologically and economically significant species, including those crucial for Batanghari river fisheries. eDNA metabarcoding serves as a valuable complement to traditional methods such as visual census and morphology-based identification, enabling the collection of data on freshwater biodiversity that was previously inaccessible through conventional means (Piggott et al. 2021; Minamoto et al. 2012; Porter and Hajibabaei 2018; Beng and Corlett 2020). Despite its relatively limited application in the sampling sites of the Batanghari River, a hotspot for freshwater biodiversity spanning much of its course, this study represents one of the few endeavors in Indonesia to utilize eDNA metabarcoding, yielding promising outcomes.

In general, 80% of the reads were successfully classified to the species level, whereas 20% remained unclassified. The presence of numerous unknown taxa suggests that many of the species identified in this study are not documented in global databases. However, considering some previous studies discovery of new taxa in the region, the limited research on biodiversity in Indonesian rivers, such as the Batanghari River, and the vast unexplored areas, it is possible that some of these unidentified ASVs may represent previously undiscovered freshwater species. This potential discovery could greatly improve our understanding of freshwater biodiversity. It highlights the significant portion of Indonesia's diverse biodiversity that remains unexplored and emphasizes the insufficient representation of taxa from the Batanghari River and surrounding areas in global COI databases. Moreover, the substantial abundance of unidentified taxa in this study emphasizes the need to address the gap in global metazoan databases to ensure accurate taxonomic classification. Nevertheless, this study has provided essential foundational sequence data that can be compared with species currently documented in these databases, proving valuable for future biodiversity and conservation research efforts.

The Batanghari River, Sumatra's longest river, is believed to harbor a vast array of multicellular species, of which only a small fraction has been identified so far (Marnis et al. 2024). These underscore the significant gap in our understanding of the river's biodiversity and emphasize the need for further research. It is imperative to focus research efforts on areas with extensive biodiversity that have been insufficiently explored, such as the upstream to downstream stretches of the river. Additionally, attention should be directed towards habitats facing significant anthropogenic pressures, such as illegal gold mining and expanding palm oil plantations along the downstream Batanghari River (Febrianti et al. 2023; Marnis et al. 2024). Despite ongoing efforts, biodiversity records in the region suffer from various shortcomings, including ineffective methods for building comprehensive inventory catalogues. For instance, biodiversity monitoring primarily relies on visual surveys conducted by observers with varying levels of expertise, which might not capture the full extent of biodiversity. In this context, our study has made significant strides by introducing a new method for biodiversity inventory and has underscored the vast disparity between our current knowledge and the unknown aspects of the river's biodiversity. Although still relatively new in Indonesia and arguably not yet developed to its full potential for Indonesian river, the application of eDNA metabarcoding can speed up auditing process for species identification by providing a general picture regarding the magnitude of biodiversity. Furthermore, while some studies applied eDNA in the freshwater areas have successfully identified plankton residing in the Cliwung river Indonesia (Aprilia et al., 2023), fish species and vertebrates in The Maninjau and Singkarak lakes (Roesma et al., 2021a, 2021b; Roesma et al., 2023), this approach has not been widely utilized to explore the biodiversity inhabiting concealed other freshwater environments like in Batanghari rivers. Our study outcomes have reinforced the significance of freshwater biodiversity, particularly within the Batanghari River in Indonesia, highlighting the necessity for additional taxonomic investigations aimed at offering comprehensive taxonomic data accompanied by DNA information. Furthermore, the results further underscore the crucial role of eDNA in uncovering concealed Batanghari river biodiversity, which encompasses the discovery of novel organisms (Penaluna et al. 2023; Jerde 2021) and the addition of updated information to public databases (Kulaš et al. 2022; Parrondo et al. 2018; Belle, Stoeckle, and Geist 2019; Coble et al. 2019).

The exploration of taxonomic composition in freshwater biodiversity hotspots like Indonesia poses significant challenges, especially when considering species that are under-researched, have small populations, or are rare and uncommon (Madduppa et al. 2021; Madduppa et al. 2022). The challenges in exploring the taxonomic composition of freshwater biodiversity hotspots, such as Indonesia, arise from various factors. Firstly, many species in freshwater ecosystems have not received sufficient attention due to limited resources, funding, and scientific focus (Darwall et al. 2018; Harper et al. 2021). This lack of investigation hinders our understanding of their taxonomic makeup and ecological functions. Secondly, some species exhibit cryptic diversity, where morphologically similar species have genetic distinctions (Grupstra et al. 2024; Cheng et al. 2024). Identifying and characterizing these species requires advanced molecular techniques, which may not be readily accessible. Thirdly, habitat loss, degradation, and fragmentation pose significant threats to freshwater ecosystems, leading to declines in species populations and overall biodiversity (Mayer and Pšenička 2024; Reid et al. 2019). This makes it challenging to study and conserve less prevalent species that are vulnerable to environmental changes. Additionally, conducting sampling activities in freshwater environments presents logistical challenges, including remote locations, rugged terrain, and fluctuating water conditions. These factors may limit access to certain habitats and species for research purposes. To address these challenges, collaborative efforts among researchers, conservationists, policymakers, and local communities are essential. Investing in biodiversity research, utilizing advanced modern molecular techniques such as eDNA, implementing effective conservation measures, and advocating for habitat restoration and protection are crucial steps toward enhancing our understanding and safeguarding the taxonomic composition of freshwater biodiversity hotspots like Indonesia.

The diversity analyses conducted in our study underscored the disparities in species richness and biotic community composition between the upstream and downstream locations of the Batanghari River. Our results were consistent with the widely accepted idea that areas with a history of environmental pollution, like downstream river areas, often have reduced levels of biodiversity. The results indicated that eDNA could expedite the identification of previously unnoticed diversity. The relatively comprehensive data from ecological surveillance and visual census of Batanghari river showed that this area suffers from a variety of anthropogenic stresses, including large numbers of introduced species induced by fish farming activities (Kaban et al. 2019; Kaban et al. 2020). In addition, numerous previous studies in these areas are poorly documented in terms of accessions deposited in public databases, including the BOLD Systems database (www.boldsystems.org) and the NCBI sequence database (https://www.ncbi.nlm.nih.gov/).

The utilization of eDNA metabarcoding involves applying both standard and taxon-specific COI mini barcodes, typically with lengths ranging up to approximately 200 bp but usually below 400 bp, has been developed for examining degraded DNA materials found in aquatic environments. Through this analysis, we successfully identified various taxa spanning three major phyla (Chordata, Arthropoda, and Mollusca) using short mitochondrial DNA COI sequences. However, we observed an unequal distribution of these phyla across the sampled areas. Regarding the sampling methodology, the relatively limited number of taxa detected in the water samples could be attributed to factors such as the minimal amount of DNA material released into the environment by present organisms, rapid degradation processes post-DNA shedding, or local water current movements consistently transporting DNA within the water column.

An analysis conducted across all sites in the Batanghari River globally revealed a higher proportion of reads originating from the phylum Chordata compared to other phyla, particularly mammals and fish. We identified a total of 587 chordate species, with 303 fish species surpassing the previously reported 297 fish species documented or presumed to inhabit this river between 1994 and 2003 (Hui and Kottelat 2009). Furthermore, this number exceeds the species count identified in 2022 using a combination of conventional methods and DNA barcoding (Marnis et al. 2024). Given that our study marked the first utilization of an eDNA approach in these regions, the findings imply that eDNA has the capability to unveil concealed and cryptic diversity that may remain unnoticed by traditional monitoring and DNA barcoding techniques.

The dominant fish order across all locations in the Batanghari River is Cyprinidae. This finding closely resemble those of previous studies conducted using both DNA barcoding (Marnis et al. 2024) and conventional technology (Hui and Kottelat 2009). However, our research utilizing the eDNA approach revealed a higher number of families, genera, and species, including unidentified species accounting for 3.39%. This presents an opportunity for the discovery of new fish species within the Batanghari River. Furtherome, in our study, we detected Scleropages formosus, Chitala borneensis, Chitala lopis, Notopterus notopterus, and Anguilla bicolor. These fish species have been exploited for an extended period, resulting in their classification within different at-risk categories on the IUCN Red List due to their global population status (IUCN 2023). Another significant observation was the substantial presence of cichliformes and Characiformes reads, suggesting the dominance of introduced fish species in the Batanghari River.

Regarding Arthropods, we observed a substantial portion of unidentified taxa at Batanghari Hulu, Teluk Kayu Putih, which includes freshwater shrimp, freshwater crab, and freshwater lobster. Batanghari River is one of the River in Sumatra with the potential for freshwater shrimp diversity and high freshwater crabs (Purnamasari 2013; Susilo 2013). The highest count of reads for the Order Decapoda was also found in the upper stretches of the Batanghari River, specifically at Batanghari Hulu (Teluk Kayu Putih), with Batanghari Tebo, also situated in the upper stretches of the river, showing similar patterns. The scarcity of shrimp and other Decapoda presence in the downstream Batanghari river might be attributed to the elevated levels of antopogenic pollution in the downstream river (dos Anjos Guimarães et al. 2023; Nguyen and Fisher 2014; Hukom et al. 2020). Nonetheless, numerous species of shrimp inhabit the lower stretches of the Batanghari River, including: Macrobrachium rosenbergii, also referred to as the giant river prawn or giant freshwater prawn, holds considerable commercial importance as a palaemonid freshwater prawn species. It is widely distributed across tropical and subtropical regions of the Indo-Pacific, ranging from India to Southeast Asia and Northern Australia. While the larvae of this species develop exclusively in brackish water, juveniles and adults primarily inhabit areas with lower salinity levels and freshwater. Adult M. rosenbergii migrate to estuaries for breeding purposes (Pillai et al. 2022). This observation may account for the higher prevalence of this species in the lower stretches of the river compared to the upper reaches of the Batang Hari River. Apart from Macrobrachium rosenbergii, Macrobrachium lar, which exhibit nearly identical characteristics to M. rosenbergii, were more prevalent in the lower sections of the Batang Hari River compared to the upper regions. Moreover, Macrobrachium malayanum, the most prevalent species in the rivers of the South Malaya forest, is distributed from Thailand to Borneo, including Sumatra (Johnson 1963). This species inhabits habitats with varying water currents, ranging from strong to slow. Macrobrachium lanchesteri thrives in pond and swamp environments and exhibits exceptional resilience in extreme conditions compared to other shrimp species. Its presence poses a potential threat to various native shrimp species (Wowor et al. 2009). Other genus of Macrobrachium is Macrobrachium pilimanus prefers habitats characterized by strong currents and rocky substrates. It is one of the freshwater shrimp species capable of surviving in areas with heavy currents, spanning both lowland and highland regions (Wowor 2010). This type of shrimp is commonly found in the upper reaches of the Batang Hari River.

The Atyidae family comprises Caridina sumatrensis, Caridina excavatoides, Caridina propinqua, Caridina gracilipes, and Caridina sp. These species are exclusively found in rivers within oil palm plantations and containing aquatic vegetation. This is supported by research indicating that the Atyidae family is typically associated with environments featuring aquatic, rocky, and subterranean vegetation. Caridina sumatrensis is distributed in Sumatra, the Malay Peninsula, and the Philippines, inhabiting rivers and streams (Johnson 1963). C. excavatoides is found in the Malay Peninsula and Sumatra. Caridina propinqua' distribution spans Malay Peninsula, Philippines, Sri Lanka, India, China, and Japan, (Cai, Ng, and Choy 2007). Caridina gracilipes is distributed across Sulawesi, Borneo, Taiwan, Philippines, Malay Peninsula, and mainland China, with its habitat primarily in the lower reaches of rivers and streams influenced by seawater (Yule and Sen 2004). Moreover, in this research, all Caridina genera are classified as Least Concern according to the IUCN 2023 assessment.

The current study identified seven freshwater crab species, a greater number than those identified in previous studies using conventional techniques (Nursyahra and Purnamasari 2018), including Parathelphusa pantherina, Parathelphusa maculate, Parathelphusa maindroni, Parathelphusa tridentata, Parathelphusa batamensis, Parathelphusa sp, and Geosesarma sp. The crabs' habitat has faced significant risks due to anthropogenic activities, including domestic and urban wastewater, and they have endured prolonged periods of exploitation. Consequently, these habitats are classified into various at-risk categories on the IUCN Red List. For example, Parathelphusa maindroni has been categorized as vulnerable since 2008 (Esser 2008). Interestingly, we found sequence reads of Parathelphusa batamensis species listed as endangered since 2008 (Esser 2008) and Parathelphusa pantherine is also listed as an endangered species since 2018 (Schubart 2018).

The presence of Cherax quadricarinatus in the Batanghari River resulted from human-mediated translocation, resulting in its designation as an introduced species (Austin 2010). C. quadricarinatus originates from permanent freshwater streams, billabongs, and lakes located along the northern coast of the Northern Territory, northeastern Queensland, and Papua New Guinea. In various rivers and freshwater habitats spanning multiple countries, including Indonesia (Patoka et al. 2016), this species formed indigenous populations, leading to its classification as an invasive species.

The application of eDNA metabarcoding has demonstrated its efficacy as a tool for identifying species across a range of Batanghari river invertebrate groups, including Mollusca. In this study, we identified 24 orders, 40 families, 102 genera, and 153 species belonging to the Phylum Mollusca. Unidentified Mollusca taxa were present across all sites, with the bivalve group exhibiting a notably higher percentage of unidentified species compared to Gastropods. In the Batanghari River, the Thiaridae family demonstrated the highest prevalence among all sites, surpassing other families within the gastropod class. The abundant presence of Gastropods belonging to the Thiaridae family is linked to the abundance of their food sources, encompassing detritus, moss, and diverse algae. Moreover, factors such as their adaptability to tranquil, slow-moving, and swiftly flowing waters contribute to their proliferation. Furthermore, members of the Thiaridae family are capable of thriving in water bodies characterized by high turbidity and elevated levels of Total Suspended Solids (Djajasasmita 1985, 1999). The family Ampullariidae and Viviparidae from Gastropods class were general find in the Batanghari river. The Viviparidae family thrives in a variety of aquatic environments such as swamps, rivers swift water currents, and typically resides in communal groups within their habitat (Viza 2018). Furthermore, the Ampullariidae family possesses the broadest distribution and demonstrates the capacity to endure dry environmental conditions for a comparatively extended period. In our study, Pomacea canaliculata was discovered, a species previously identified as invasive in small rivers flowing into the Batanghari River in several prior investigations (Susilowati 2017; Isnaningsih and Marwoto 2011). This species exhibits a remarkable capacity to adjust to varying environmental conditions, enabling it to inhabit diverse habitats. This adaptability is believed to contribute to its status as an invasive species.

This study revealed limited diversity within the bivalve order, with only species classified in the order Unionida and family Unionidae being detected, while the other orders remained unidentified. This might be attributed to the fact that many of the bivalve species identified in this study are not recorded in databases. Additionally, the limited variety of bivalves might also be associated with various activities carried out by communities surrounding the Batanghari River, such as the discharge of liquid waste from households and industries alongside illegal gold mining operations, potentially contributing to river contamination. These activities could lead to water pollution, resulting in a deterioration of water quality (Desrizal, Carlo, and Syah 2019; Kaban et al. 2020; Yanova et al. 2022).

Monitoring extensive areas of the Batanghari River presents a significant challenge. Utilizing eDNA protocols to effectively detect specific important species could be an efficient approach to support the achievement of river protection objectives. Environmental DNA (eDNA) metabarcoding surveys have shown their utility across various ecosystems and have the potential to provide an efficient and cost-effective complement to traditional ecosystem surveys. This could benefit river conservation managers and fisheries officers responsible for monitoring their designated areas.

eDNA metabarcoding demonstrates its sensitivity and efficiency in investigating the biodiversity of river affected to anthropogenic pollution, especially the organisms inhabiting the expansive spatial distribution of the Batanghari River. Employing eDNA methods can supplement traditional methodologies. Increased accessibility to reference sequences, particularly from similar geographic areas, will enhance the breadth and reliability of eDNA identification. eDNA metabarcoding has the potential to serve as a remarkable tool for identifying species, monitoring habitats, conducting biodiversity surveys, and aiding environmental conservation initiatives in vast and lengthy river systems.

Data availability

Kindly direct any inquiries regarding data availability to the authors.

Acknowledgments

We express our sincere gratitude to the local governments in Jambi province for their invaluable support during the sampling and environmental monitoring activities in the Batanghari River. Additionally, we extend our heartfelt thanks to Despa Surianis, Dwi Setianingsih, Ahmad Junaidi, Surya Roza, Rulif Aziz Kusnita, Riana Yulianti, and Lukman for their technical assistance in this study.

Funding

The funding for this project was provided by the 2022 Rumah Program Manajemen Sumber Daya Air dan Danau Prioritas research grant, administered by BRIN's Research Organization for Earth Sciences and Maritime under Grant Number SP DIPA-124.01.1.690501/2022.

Declarations of competing interest

The authors report no conflicts of interest. The authors alone are responsible for doing the research and writing the paper.

Funding

Author Contribution

H.M and K.H: Conceptualization and design; H.M: funding acquisition; J.D, D.F, E.T, S.L, B.I, E.P.H, M.S, and A.T.S: Performed the experiments; H.M and K.H: analysis and interpretation of data; H.M: drafting of the paper; H.M: critical revision of the paper; all the authors approved the final version; and all authors agree to be accountable for all aspects of the work.

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No competing interests reported.

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Utilizing eDNA methods in biodiversity studies of river affected by anthropogenic pollution: A case study on the Batanghari River in Indonesia

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Abstract

Figures

Introduction

Materials and methods

eDNA Batanghari river sample collection

DNA extraction

PCR, library preparation, and next-generation sequencing

Bioinformatics and data analysis

Results

Assessment of biodiversity using COI markers and eDNA technique

Assessment of eukaryotic taxa interest using eDNA and a COI barcode

Phylum chordata

Phylum Arthropoda

Phylum Mollusca

Discussion

Conclusion

Abbreviations

Declarations

Funding

Author Contribution

Acknowledgement

References

Additional Declarations

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