Assessing biodiversity in marine nearshore ecosystems is crucial for effective management, especially in the context of climate change and overexploitation of marine resources. Conventional methods often fall short in providing comprehensive information for managing seagrass ecosystems. However, the emergence of environmental DNA (eDNA) techniques has transformed the field by enabling non-invasive surveys that are cost-effective and provide detailed information with high resolution. In this study, we utilized eDNA to assess fish diversity and compared its effectiveness to conventional techniques such as catch assessment surveys and underwater surveys. We sampled three habitats (A: mangrove-seagrass, B: seagrass only, and C: coral-seagrass) with 4 replicates. Site A recorded 8 fish species, site B had 16 species, and site C, characterized by coral and seagrass habitats, exhibited the highest fish diversity with 45 species (mean H' index = 2.455), underscoring its ecological importance. To ensure accurate taxonomic identification, we utilized an updated MiFish reference database containing a larger number of fish species compared to the initial library. This expanded reference database with 9,569 fish species, facilitated more precise identification and enhanced the reliability of our findings. Notably, the eDNA technique outperformed conventional methods by detecting 23 additional fish species that went undetected using traditional surveys. Moreover, our study documented five fish species previously unknown to occur within the study region, further emphasizing the value of eDNA analysis in uncovering hidden biodiversity. These findings strongly advocate for integrating eDNA techniques into the monitoring and assessment of biodiversity in shallow tropical habitats of the Western Indian Ocean. By leveraging eDNA surveys, we can gain valuable insights into fish diversity, discover hidden species, and make informed decisions for the conservation and management of these ecologically significant areas. Methods Study site Located in Kenya’s South Coast region and covering ~7 km2, Gazi Bay’s seagrass meadows are surrounded by a dynamic fishing community (Hemminga et al., 1995; Tuda & Wolff, 2018; Pendleton et al., 2012). It is located, (4°25’S, and 39°30’E), ~55 km from Mombasa City. The bay is shallow with a mean depth of ~5 m, ~1.75-3.5 km wide and 3.25 km long with a surface area of ~10 km2 (Bouillon et al., 2007)⁠. Being a shallow tropical coastal water system (Musembi et al., 2019)⁠, it is surrounded by fringing mangrove forests on the landward side, coral reefs sheltering the bay from the eastern seaward side, freshwater inflow from two rivers and extensive seagrass bed on the shallow continental waters (Figure 1). The bay opens into the Indian Ocean through a relatively wide but shallow (3-8 m deep) entrance in the southern part and are two creeks (Western and Eastern creeks). The western creek is characterised by two freshwater inflows: River Kidogoweni to the north and River Mkurumunji to the west. Among the most active landing sites on Kenya's South Coast with dominance in fishing and fishery-related activities, Gazi Bay has long supported small-scale artisanal multi-species and multi-gear fishing (Kimani et al., 1996; Musembi et al., 2019). Samples and data collection All reusable apparatus and reagents used in this study were sterilized by autoclaving at 121°C for 15 minutes. Heat-labile apparatuses were UV sterilized for 1 hour, while heat-labile reagents were filter-sterilized using a 0.22 µm nitrocellulose filter membrane. All working surfaces and equipment were decontaminated using 10% bleach and 70% ethanol. Sample processing including DNA extraction, quantification, and amplification, was performed by a single individual in separate rooms. The sampling activities were conducted on 12th November 2020, and started just before the ebb current. The sampling scheme was customized following the guidance of the eDNA Society's sampling standardization method (Miya & Sado, 2019). Three sampling sites (10m*8m transect) within the bay were identified and labeled as Site A, B, and C (Figure 1) with their respective GPS coordinate (Table S1). The sampling sites were selected based on the proximity between seagrass and other habitats. Site A represented the seagrass-mangrove habitat, Site B represented the seagrass-only habitat, and Site C represented the seagrass-coral reef habitat. At each site, we randomly collected 1-liter water samples from the sea surface using a sterile Nansen bottle in four replicates at 2-minute intervals, as recommended by Ficetola et al. (2015). In addition to the sampling process, three 0.5-liter bottles filled with nuclease-free water were intentionally left open during the collection to serve as negative controls. These control samples were handled in the same manner as the actual samples, including exposure to the surrounding environment, but without any target organisms. By including these negative controls, we could monitor and account for any potential contamination or false positive results that might arise from the sampling and laboratory procedures. The physical-chemical parameters of the seawater were measured, and the description of the habitat was recorded (see Table S2). The sampling process took approximately 10 min per site. The collected water samples were immediately preserved in a cooler box with ice packs and transported to the molecular biology laboratory at the Kenya Marine and Fisheries Research Institute (KMFRI) for filtration. Once the seawater sampling was completed, an underwater visual survey and multi-gear fish catch survey followed up immediately. Underwater visual surveys were conducted to directly observe fish species present in the study site covering a predetermined transect of 30m * 30m coinciding with the seawater sampling points. Divers equipped with snorkels visually surveyed the underwater habitats, including seagrass beds, mangroves, and coral reefs immediately after seawater sampling. They carefully recorded the fish species observed and their abundance. Multi-gear fish catch survey in this study involved the use of various fishing gears, including basket traps, hand lines, and reef seines. This was conducted by the local fishers as part of their routine work. Basket traps were deployed by submerging them in submerged seagrass locations within the bay for 6 hours to capture fish. Hand lines, consisting of a line with a baited hook, were used to catch fish manually. Reef seines, which are large nets with weights and floats, were dragged along water columns to capture fish. These fishing gears were deployed in different locations and depths within the bay guided by the fishers to sample the fish population. Catch landing was carefully documented and four voucher specimens of the most dominant fish (Siganus sutor (Valenciennes, 1835)) in the landed catch were obtained and preserved in a cooler box.

DNA extraction, amplification and library preparation

The water samples were processed using a manifold filtration system, and sterile 0.45µm nitrocellulose filter papers were used to filter the samples. These filters were then stored at -80°C until further processing. Total genomic DNA was extracted from the filter papers, as well as from four fish voucher specimens (Siganus sutor (Valenciennes, 1835)) and three DNA extraction negative controls. The DNA extraction was performed using a CTAB-based method, following the protocol described by Miya and Sado (2019). To assess the concentration and purity of the extracted DNA, spectrophotometry was performed using an Eppendorf Bio Spectrometer with software version 4.3.5.0. The DNA samples were evaluated for their quality and quantity. For amplification of the hypervariable region of the 12S rRNA gene, a universal primer pair MiFish-U-F: GTCGGTAAAACTCGTGCCAGC and MiFish-U-R: CATAGTGGGGTATCTAATCCCAGTTTG was used (Miya et al., 2015). The expected amplicon length was approximately 172 bp, ranging from 163 to 185 bp. In addition, specific primers F1: TCAACCAACCACAAAGACATTGGCAC and R1: TAGACTTCTGGGTGGCCAAAGAATCA (Tabassum et al., 2017) targeting the Cytochrome C oxidase subunit I were used for the identification of the voucher specimens. The amplification reaction was conducted in a 12 μl reaction volume comprising the following components: 6.0 μl of 2 × KAPA HiFi HotStart ReadyMix (KAPA Biosystems), 1.4 μl of each primer (5 μM primer F/R), 2.6 μl of sterile distilled water, and 2.0 μl of DNA template. The amplification reaction was designed in accordance with Miya et al., (2015) while amplification was performed with an initial denaturation step at 95°C for 5 min, followed by 35 cycles of denaturation at 95°C for 30 sec, annealing at 55°C for 30 sec, extension at 72°C for 1 min, and a final extension step at 72°C for 10 min. The PCR products from three rounds of amplification were pooled together. Negative control samples from the field, as well as from the DNA extraction and amplification steps, were also pooled into one sample. The pooled samples, including the amplicons and the negative controls, were sent to Inqaba Biotechnical Industries, a commercial next-generation sequencing (NGS) service provider in Pretoria, South Africa, for sequencing. The amplicons were purified, end-repaired, and ligated to Illumina-specific adapter sequences using the NEBNext Ultra II DNA library prep kit. After quantification, the samples were individually indexed using NEBNext Multiplex Oligos for Illumina (Dual Index Primers Set 1), and an additional purification step was performed using AMPure XP beads. The libraries were quantified using Agilent Technologies 2100 Bioanalyzer, normalized, and sequenced on the Illumina MiSeq platform using a MiSeq v3 (600 cycles) kit. Additionally, the four voucher specimens were subjected to Sanger sequencing for further confirmation and

its population is characterized as Brazilian, Chinese, and Indian companies that presented financial information to external users through securities markets’ regulatory agencies in Brazil, China, and India and that implemented CDM projects during the 2005–2012 period, ranking in the “registered” status on the UNFCCC website.

Quantitative data were obtaining to test the statistical hypothesis proposed in the study from information referring to the companies and CDM projects that made up the sample as follows: (i) the financial information referring to the equity (E) of companies that have their shares listed in the capital markets of Brazil, China, and India; and (ii) the emission reduction estimates of CDM projects, available from the UNFCCC website.

The data collection, referring to the financial information of the companies that have made themselves available via regulatory bodies in the securities markets of the countries under study, was carried out through Thomson Reuters Eikon’s Electronic and Financial Database on July 30, 2013. Thus, when the data collection was carried out, financial information was obtained and converted into euros, referring to the equity (E) of 380 Brazilian companies, 2,584 Chinese companies, and 4,219 Indian companies, for the period under review.

The collection of data concerning CDM projects with the status “registered” on the UNFCCC site, on the other hand, was carried out using the Bloomberg Economic and Financial Database on July 29, 2013, at which time a total of 289 projects registered by the Brazilian DNA, 3,651 projects registered by the Chinese DNA, and 1,296 projects registered by the Indian DNA were available for analysis for the 2005–2012 period. On November 18, 2004, just one project was registered by the Brazilian DNA, entitled “Brazil NovaGerar Landfill Gas to Energy Project” (UNFCCC, 2014). This project was eliminated from the research because of its set limits defined between 2005 and 2012, the first stage of the Kyoto Protocol.

However, it was necessary to carry out new searches directly on the UNFCCC site for supplementary information that was crucial to implementing the research, given the fact that it did not include full descriptions concerning the names of the receiving agencies in each country (host party), in the Bloomberg Economic and Financial database, on the date mentioned above, information that was characterized as the only link between the CDM project database (Bloomberg) and the financial information database (Thomson Reuters Eikon). These searches were carried during the October 2013–May 2014 period.

Subsequently, on September 1, 2014, new searches were carried out on the UNFCCC website to update the information referring to CDM projects registered by the agency during the 2005–2012 period.

Thus, this research was carried out based on CDM projects located in the “registered” status section of the UNFCCC site over the 2005–2012 period, the records of which were finalized by the body prior to September 1, 2014, containing 299 projects registered by the DNA of Brazil, 3,682 projects registered by the DNA of China, and 1,371 projects registered by the DNA of India, adding up to 5,353 projects, that is, 74.69% of the total implemented projects in all the developing countries that ratified the Kyoto Protocol.

To allow the measurement to be applied to the fair value of estimates of project emission reduction approved by the companies that make up the research sample, we obtained the interest rate EURIBOR – Euro Interbank Offered Rate (average annual rates) from the Bloomberg Financial and Economic Database on July 29, 2013 to adjust the future flows of economic benefits of CER estimates to the present value.

Cytochrome c oxidase subunit I gene (COI) sequences of roughly 509 bp length for various species of the Infraorder Pentatomomorpha were generated. K2P divergences within and between species and genera were calculated and compared using newly generated sequences and the ones available on online portals. Mean interspecific (within-genus) genetic divergence (14.23%) was ∼ eight times greater than mean intraspecific (within-species) divergence (1.79%). Distance-based as well as character-based approaches were used towards constructing (COI) trees. In total, 20 sequences were of the species that were previously not part of the Barcode Of Life Database (BOLD), hence representing additions to the barcode library of Indian Heteroptera. Some of the analyzed species are well-known agricultural pests. All the COI sequences and the associated specimen data have been deposited on BOLD.