Repurposed smart tuna-fishing buoys provide real-time ocean intelligence for ecological and blue economy applications

Repurposed smart tuna-fishing buoys provide real-time ocean intelligence for ecological and blue economy applications | 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 Repurposed smart tuna-fishing buoys provide real-time ocean intelligence for ecological and blue economy applications Mario Lebrato, Kathryn Gavira-O’Neill, Teresa Losada, Alvaro Bravo, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9001386/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Repurposed echosounder buoys developed for industrial tuna fishing are emerging as powerful ecological monitoring tools, providing real-time biomass data across marine habitats. We present a novel field concept study applying this cross-sector innovation, by using smart-buoys to track the biomass of critical habitats, detect potential fishing impacts, and support biodiversity accountability within the blue economy. Repurposed through the Satlink project ReCon, smart-buoys measure vertical biomass distribution and transmit continuous data streams via satellite (INMARSAT) to shore-based systems. Our deployments lasted 72–170 h in both fixed-point station and drifting configurations, covering one shelf area, two coral reefs, and two seamounts/pinnacles in the Western Indian Ocean (WIO) region (Bazaruto Archipelago, Mozambique), yielding > 1,000 hourly total biomass records. Integration with the Benguerra Island-based BCSS Ocean Observatory provided simultaneous weather and oceanographic baselines for over 20 in-situ variables, while concurrent scuba verification surveys recorded 14–20 megafauna taxa per site. The resulting datasets revealed diel cycles with 20–40% higher nocturnal biomass, coinciding with cooler bottom waters and transient chlorophyll-a pulses, and illustrated how site-level heterogeneity challenges broad ecosystem status labels, such as an overfished reef displaying higher evenness and diversity than a nominally healthy reef. This demonstration highlights a real-time scalable monitoring platform that bridges fisheries technology and ecology, providing a novel class of verification tools for MPAs, fisheries management, biodiversity crediting, and ESG-aligned interventions. Crucially, by providing continuous and verifiable ecological indicators, it addresses a fundamental supply/demand-side barrier for blue finance and biodiversity markets: investor confidence, accountability, and trust in real-time outcomes. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Ocean sciences tuna-fishing smart buoys blue economy ecosystem monitoring biomass biodiversity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Global commitments to sustainable oceans are driving a rapid expansion of marine monitoring and innovation in the “blue economy”, the sustainable use of ocean resources for economic growth, improved livelihoods, and ocean ecosystem health. Initiatives such as the United Nations Sustainable Development Goal 14 (“Life Below Water”) call for conserving and sustainably using the oceans and coasts ( 1 ). More recently, the Kunming-Montreal Global Biodiversity Framework set ambitious targets to protect 30% of marine and coastal areas by 2030 and enhance biodiversity monitoring and reporting ( 2 ). Achieving these goals will require new tools for continuous ecological assessment and for verifying the outcomes of conservation interventions. In parallel, innovative finance mechanisms like blue bonds and emerging biodiversity credit markets are being explored to fund marine conservation and climate adaptation, for example, the world’s first sovereign blue bond (issued by Seychelles in 2018) is supporting marine protected areas and sustainable fisheries ( 3 ). These financing approaches demand robust, real-time indicators of ecosystem status and human impacts to ensure accountability and success within a legal market framework ( 4 , 5 , 6 ). There is a growing need for affordable, scalable ocean observing solutions that can provide actionable data on marine life and environments (“ocean intelligence”), to inform policy and management, and build trust to encourage investment in the blue economy sector. One promising avenue is the repurpose of industrial fishing technologies as ocean observation platforms. The tropical tuna purse-seine fishery has deployed tens of thousands of drifting fish aggregating devices (dFADs) equipped with smart-buoys that integrate GPS and echo-sounders ( 7 ), relaying data via satellite to shore-based systems. These echosounder buoys, developed to help commercial fleets locate fish schools under floating rafts, have become a ubiquitous “sensor network” across the open oceans. Recent estimates indicate that over 1.4 million such buoys were released between 2007 and 2021, covering ~ 37% of the global ocean surface ( 7 ). Each buoy can remotely report the approximate fish biomass beneath the FAD by using active acoustics, the buoy’s downward-facing echosounder pings the water column and converts the returning echoes into an index or estimate of biomass tonnage ( 8 ). Modern units can profile biomass in multiple depth layers (e.g. 0-115 m) and transmit these data via satellite in near real-time. The fishing industry’s adoption of these smart-buoys has thus effectively created a distributed ocean acoustic sensor array of unprecedented scale, yielding tens of millions of hourly observations on pelagic biomass ( 9 ). This presents a unique opportunity to “re-use” or reinterpret industrial data streams for science and conservation: the same instruments that aid fishing can be turned into cost-effective ecological monitors. Indeed, fisheries scientists have begun to utilize fishers’ echosounder buoy data to study tuna ecology and population dynamics ( 9 , 10 ). Machine-learning approaches now allow biomass estimates from raw acoustic signals with increasing accuracy ( 11 ). Beyond tuna behavior, these buoys could also detect other mid-trophic organisms or even plankton layers, offering insight into broader ecosystem trends if their data are properly calibrated and analyzed. Crucially, however, sometimes deployed FAD buoys can drift away from the fishing areas and wash ashore where they cannot be recovered by the fleets ( 7 ). Retrieving and repurposing these drifting sensors once their use in industrial fisheries ends is an emerging strategy that aligns with circular economy principles and could vastly expand our ocean observing capacity. In this context, Satlink´s project ReCon was launched as a pioneer circular economy initiative to give “second life” to end-of-use tuna fishing buoys ( 12 ). Originally developed by the Spanish technology company Satlink in partnership with tuna fleets, ReCon establishes a global network to recover satellite echo-sounder buoys that have drifted out of fishing grounds and refurbish them for scientific and environmental applications. Instead of becoming techno-marine debris, reclaimed smart-buoys can be redeployed to serve conservation programs, marine research, or even hazard monitoring. With minimal modifications, for instance, adding additional sensors or reprogramming data streams, such devices can act as autonomous ocean observation nodes providing real-time biological and environmental data. This approach exemplifies a blue circular economy concept: valorizing industrial fishing end-of-life technology into tools for sustainability. The present work builds on this concept by formally evaluating repurposed tuna buoys as observation platforms within a coastal marine observatory. Ocean observatories are evolving from traditional fixed stations to more integrative networks that combine physical, chemical, and biological monitoring at multiple scales. Around the world, long-term ocean time-series stations have proven essential for detecting ecosystem change and distinguishing natural variability from human impacts ( 13 ). They allow scientists to track trends in key variables, from ocean climate (temperature, currents, chemistry) to biological indicators (plankton, fish, megafauna), and thereby inform resource management and climate adaptation strategies. However, establishing and maintaining permanent observatory infrastructure is costly and logistically challenging, especially in remote biodiversity-rich regions, pivotal in blue economy principles. In Africa, for example, the first permanent ocean observatory was only recently established at the Bazaruto Center for Scientific Studies (BCSS) in Mozambique ( 14 ), aiming to provide permanent multi-ecosystem time-series data in the Western Indian Ocean (WIO) region. The BCSS Ocean Observatory, located in the Bazaruto Archipelago, serves as a regional hub (Mozambique Channel/EEZ) for open-access environmental monitoring, logging data on dozens of oceanographic and ecological parameters at high frequency. The Observatory recently signed an agreement with Satlink to recover and repurpose smart-buoys that wash ashore along the coastlines of East African nations, including South Africa, Mozambique, Tanzania, Kenya, and Madagascar, for use in ecological research and blue economy initiatives ( 15 ) Integrating biological sensing, such as acoustic monitoring of fish, into such observatory systems is a crucial next step for holistic marine ecosystem assessment ( 16 ). Traditionally, biological data (like fish abundance or biodiversity surveys) are collected by periodic manual sampling that are sparse in time and space. By contrast, smart-buoy sensor arrays can provide continuous, in situ observations of marine life, bridging a major gap in our monitoring capability. For instance, a network of echo-sounder buoys moored at key habitats could deliver real-time indices of fish biomass or plankton dynamics, complementing the physical and chemical data streams of observatories. Likewise, if outfitted with passive acoustic hydrophones, buoys could record the ambient soundscape, capturing biological sounds (such as fish choruses or marine mammal calls) and anthropogenic noise, which is increasingly recognized as an important indicator of ecosystem health and human pressure ( 16 , 17 , 18 ). Use-cases for such technology span multiple facets of the blue economy and marine policy. In conservation and fisheries management, real-time biomass tracking can help evaluate the effectiveness of MPAs or fishing closures by detecting changes in fish abundance and behavior inside versus outside protected zones. An array of buoys at reef and seamount sites could signal whether fish biomass is rebuilding after protection, or conversely, alert managers to unusual drops that might indicate poaching or ecosystem disturbance. The ability to continuously monitor hotspots can improve stock assessments and early warning of stock depletions, complementing traditional fisheries surveys ( 19 , 20 ). In marine biodiversity finance, outcome-based funding models require credible indicators that biodiversity gains are being achieved ( 21 ). Blue bonds and biodiversity crediting schemes rely on quantifiable improvements, such as increases in fish biomass, recovery of endangered species, or restoration of habitats, to validate that invested funds lead to positive ecological impact ( 22 ). Acoustic technology can provide precisely this kind of verification data: for instance, tracking the return of spawning fish populations to a restored reef, or documenting the presence of cetaceans and other acoustic fauna in an area under protection. Furthermore, by detecting the acoustic signatures of human activities (boat noise, sonar, etc), the same tools can monitor compliance with no-fishing zones or identify illegal activities in real-time, thereby strengthening enforcement ( 23 , 24 ). On the climate front, continuous observations of biomass and soundscapes contribute to understanding how ocean warming and overfishing together alter marine food webs and biological carbon storage ( 25 ). In aggregate, networks of smart-buoys feeding data into observatories would support a more transparent and accountable ocean governance, where progress toward international targets (like SDG14 and the biodiversity framework) can be measured and reported with confidence. This aligns with the vision of a “New Blue Economy” that is knowledge-based and data-driven, looking to the ocean not just for extraction, but for information to solve societal challenges. Here we present a field demonstration of repurposed smart-buoys as real-time ocean observatory instruments, deployed in the Bazaruto Archipelago (Mozambique) as part of the ongoing collaboration between the BCSS Ocean Observatory ( https://bcssmz.org/ocean-observatory-public-database/ ) and Satlink’s project ReCon ( https://satlink.es/en/science-and-sustainability/proyecto-recon ) in the WIO region. To our knowledge, this is the first study to integrate repurposed echosounder buoys into an observatory framework for simultaneous ecosystem monitoring and blue economy applications. We show how these smart-buoys were used to continuously track/map real-time biomass profiles at coral reef and seamount sites, while synchronizing with independent biodiversity surveys and environmental sensors time-series measurements. This cross-sector innovation bridges industrial fisheries technology and marine ecology, illustrating a scalable approach to augment ocean monitoring in data-poor regions. Our results highlight the novelty and potential of a circular economy paradigm in ocean science, leveraging recycled industrial hardware to meet the growing global demand for ocean intelligence, conservation impact tracking, and biodiversity-positive “blue investment”. Results Deployment performance and data return All deployments of repurposed smart-buoys returned continuous acoustic biomass time-series with high fidelity (Table S1 ). Instruments were deployed across four habitats (+ a fifth one for testing), for 72–170 h each, yielding over 1,000 hourly biomass data points in total (Fig. 1 ). Transmission rates exceeded 95% of expected records, with minimal gaps due to telemetry. Integration with the observatory’s cloud-based system (Figure S1 ), and environmental time-series (Fig. 2 ) allowed near real-time visualization of biomass estimates, demonstrating operational feasibility under remote field conditions. Drifting deployments covered ~ 10 km 2 of horizontal transects, while moored deployments remained stable for fixed-point time-series (Fig. 3 , 4 ). Biomass dynamics across sites Biomass estimates revealed both diel variability and site-specific contrasts (Fig. 3 ). Across all sites, nocturnal hours consistently exhibited 20–40% higher biomass than daytime, indicative of diel vertical-horizontal migration and nocturnal aggregation. Median biomass differed strongly among habitats: Site #4 “Healthy” reef and Site #1 “Overfished” reef yielded the highest medians (~ 57.9 and ~ 52.6 tons/day, respectively), while Site #3 “Overfished” seamount was intermediate (~ 43.3 tons/day), and Site #2 “Healthy” pinnacles the lowest (~ 22.2 tons/day). Peak hourly biomass exceeded 200 tons/day at reef-associated sites. Spatial drift transects confirmed short-scale heterogeneity, with biomass “hotspots” consistently detected around hard rock/reef substrates, near structural slopes and seamount flanks, while offshore areas exhibited reduced signals (Fig. 4 ). This demonstrates that buoy-based monitoring can resolve fine-scale habitat-linked differences in benthopelagic biomass. Biodiversity structure and ground-truthing Concurrent scuba diving megafauna surveys across three consecutive days per site recorded between 14 and 20 taxa (Fig. 5 ). Species richness was highest at Site #1 “Overfished” reef (20 taxa) and Site #4 “Healthy” reef (20 taxa), lower at Site #2 “Healthy” pinnacles (19 taxa), and lowest at Site #3 “Overfished” seamount (14 taxa). Shannon diversity indices ranged from 1.64 (Site #3) to 2.56 (Site #1), while Simpson diversity ranged from 0.73 (Site #4) to 0.90 (Site #1). Dominance patterns varied: Site #4 “Healthy” reef showed the strongest skew (Berger-Parker = 0.45), while Site #1 “Overfished” reef was least dominated (0.17). Evenness was highest at Site #1 (0.86) and lowest at Site #4 (0.58). Several IUCN-listed taxa were confirmed across sites, including various shark species listed as “Vulnerable”, and large teleost fish (e.g., tuna, trevallies), providing ecological validation of the biomass signals. These surveys confirmed that buoy-derived acoustic estimates correspond to real ecological assemblages, though with habitat-specific differences in richness, evenness, and dominance. Comparative ecological metrics When aggregated by habitat status, “Healthy” versus “Overfished” contrasts were inconsistent (Figure S2). Effect size analysis showed that evenness was significantly higher in “Overfished” sites (Cliff’s Δ = -0.72, 95% CI -0.97 to -0.22, p = 0.04). Richness was moderately higher in “Healthy” sites (Δ = +0.47, p = 0.20), but not significant. Dominance was higher in “Healthy” sites (Δ = +0.67, p = 0.065), driven by Site #4. Biomass showed no systematic difference (Δ = +0.02, p = 1.0). Shannon and Simpson trended higher in “Overfished” sites, but without significance (Δ = -0.17 and − 0.44). These outcomes highlight that site-specific ecological fingerprints dominate the patterns, with one “Overfished” reef (Site #1) showing unexpectedly high diversity and evenness, while one “Healthy” reef (Site #4) showed skewed dominance. This underscores the value of site-level monitoring rather than relying solely on categorical designations. Integration with environmental baselines Biomass peaks frequently coincided with cooler seabed temperature pulses ([ΔT = -0.5 to -1.2°C]) and short-lived increases in chlorophyll-a ([ΔChl-a = + 0.2–0.4 mg m⁻³]) recorded in the Ocean Observatory time-series (Fig. 2 ). Nocturnal biomass increases were synchronized with calm nighttime conditions and reduced tidal currents, suggesting that both physical and biological drivers modulated aggregation. While correlations between biomass and environmental parameters were modest, the patterns indicate that buoy-derived biomass integrates dynamically with local oceanographic processes. This demonstrates the potential for multi-sensor observatories to provide ecosystem-scale baselines. Proof-of-concept outcomes The deployments established a proof of concept for repurposed buoy technology as a tool for high-resolution biomass monitoring in coastal and offshore habitats. The system operated reliably, captured both diel and spatial variability, and produced ecological signals that were consistent with in-situ biodiversity observations. Statistical analyses confirmed the sensitivity of the approach to site-level differences in richness, diversity, and dominance. Together, these results demonstrate the feasibility of combining smart-buoy technology with ecological ground-truthing and environmental baselines. The approach is scalable, cost-effective, and directly relevant for applications in marine conservation, fisheries assessment, and blue economy verification. Discussion Advancing ocean observing technology This study demonstrates that repurposed smart-buoys, originally designed for industrial tuna fisheries, can be adapted into reliable biomass observatories that operate at ecosystem scale (Fig. 1 , 6 ). Their ability to deliver continuous and reliable acoustic biomass time-series, integrated with real-time telemetry, expands the toolkit for marine monitoring, particularly in under-observed regions like the Western Indian Ocean (WIO) and Mozambique Channel regions. These geographies, despite hosting some of the world’s most biodiverse coral reef and pelagic ecosystems, remain chronically under-monitored, limiting both scientific understanding and evidence-based governance. Traditional biomass assessments remain largely dependent on ship-based surveys and net sampling, which are costly, spatially limited, and temporally episodic ( 26 , 27 ). Moreover, conventional surveys cannot resolve diel or sub-daily dynamics, nor capture short-lived events such as productivity pulses or storm-driven mixing, which are central to ecosystem function. By contrast, smart-buoy deployments can persist for days-weeks-months at a fraction of the operational cost, creating opportunities for continuous and distributed monitoring that complements autonomous vehicles ( 28 ), moored arrays ( 29 ), and satellite data streams ( 30 ). This proof-of-concept thus positions smart-buoy-enabled biomass monitoring as part of the new generation of distributed multi-platform observatories envisioned by the UN Decade of Ocean Science for Sustainable Development ( 31 ). Similar to how Argo floats revolutionized physical oceanography ( 32 ), the adaptation of fisheries technologies into ecological observatories can democratize access to biomass data, especially in remote locations where resource constraints limit traditional monitoring capacity. Embedding these technologies into regional observatories offers a pathway toward building global equity in ocean observing capacity. By generating verifiable ecological indicators in near real-time, buoy-based observatories also create opportunities for transparency and accountability, which are increasingly demanded by blue economy finance, ESG frameworks, and biodiversity-positive investment markets. Ecological insights and site-specific heterogeneity Although our deployments were limited to four sites (+ a fifth test site) and short durations, the ecological signals they revealed are instructive of the technology concept for ecological verifications (Fig. 3 – 5 ). This distinction is important: the value lies less in the specific ecological patterns reported here, and more in demonstrating how repurposed smart-buoys can detect and quantify contrasts that traditional categorical labels fail to capture. The results illustrate strong site-specific heterogeneity in biomass and community structure, which challenges simplistic assumptions about ecosystem status. For example, an overfished reef (Site #1) exhibited high richness, Shannon diversity, and evenness, while a nominally healthy reef (Site #4) was skewed by dominance of a few taxa (Fig. 5 , S2). These findings illustrate that categorical status labels alone are insufficient to describe ecological reality, although longer time-series are needed to verify an ecosystem status. Instead, they highlight the necessity of observatory-style, fine-scale monitoring to reveal ecological fingerprints shaped by habitat complexity, productivity regimes, and historical exploitation. Recognizing such fine-scale heterogeneity is critical for ecosystem-based management, where adaptive governance must account for the interplay of local drivers and cumulative pressures rather than relying on simplified status categories. Such contrasts are consistent with long-standing observations in coral reef and pelagic ecosystems where local drivers such as reef geomorphology, upwelling, or fishing selectivity, produce outcomes that diverge from broad management categories ( 33 , 34 ). Similar patterns have been reported in relation to structural complexity, near-island productivity hotspots, and fishing market proximity ( 33 , 35 , 36 ). The implication is not that management labels like “healthy” or “overfished” are irrelevant, but rather that their ecological manifestations must be empirically tested with continuous, high-resolution observations. Smart-buoy-enabled biomass data, coupled with biodiversity surveys, can provide exactly this form of fine-scale resolution. Beyond their scientific role, these observations also support demand-side confidence in blue economy interventions, by supplying the real-time verification that underpins accountability in biodiversity crediting and conservation finance. From biomass data to ecological indicators Transforming raw acoustic biomass data into quantitative ecological indicators represents a central methodological advance of this work (Fig. 5 , 6 ). While diversity indices such as Shannon H′, Simpson diversity, richness, evenness, and dominance are widely applied in community ecology, they have rarely been operationalized in tandem with continuous, high-frequency biomass monitoring. By applying these indices to smart-buoy-derived time-series, we demonstrate that biomass observations can be converted into standardized ecological descriptors suitable for both science and management contexts. This integration is particularly novel because acoustic biomass data are typically restricted to fisheries stock assessments, whereas here they are reframed as ecological verification tools with applications in conservation, biodiversity crediting, and adaptive governance. Across sites, richness, Shannon diversity, evenness, and dominance indices revealed consistent contrasts, with evenness emerging as the most sensitive indicator of fishing pressure (Fig. 5 , S2). This sensitivity suggests that evenness may function as a leading indicator, capable of detecting ecological shifts before changes in richness or overall diversity become apparent. This outcome accords with ecological theory and empirical studies showing that predator depletion can redistribute biomass across mesopredator guilds, elevating community evenness while reducing trophic integrity ( 37 , 38 ). Importantly, our statistical framework emphasizes effect size estimation (Cliff’s Δ) with bootstrap-derived confidence intervals, which provides robust contrasts and reproducible interpretations despite modest sample sizes. Such an effect-size approach is particularly valuable for proof-of-concept deployments, where limited replication is inherent but robust contrasts are still essential to evaluate feasibility. This combination of continuous biomass streams with non-parametric effect sizes highlights a pathway toward scalable, indicator-based monitoring frameworks in data-limited regions. Statistical contrasts between healthy and overfished sites were further quantified using non-parametric effect sizes (Figure S2). Evenness was significantly higher in overfished habitats (Cliff’s Δ = -0.72), while richness and dominance trended higher in healthy habitats. These outcomes highlight the importance of effect size metrics in proof-of-concept work, where sample sizes are modest and p-values alone are insufficient ( 39 ). In a policy context, these indicators provide not only ecological insight but also the verifiable metrics required to build trust and accountability in biodiversity crediting and blue economy finance. Integration with multi-ecosystem observatories Linking acoustic biomass with physical and biogeochemical drivers is essential to understand ecosystem functioning under anthropogenic and environmental change (Fig. 2 ). In our concept demonstration, biomass peaks coincided with nocturnal periods, cooler bottom waters, and short-lived chlorophyll-a pulses. These patterns suggest coupling between diel migration, productivity-driven aggregation, and physical conditions. Such fine-scale coupling between physical drivers and ecological responses is rarely captured by traditional ship-based transects or coarse satellite datasets, underscoring the added value of buoy-based observatories. Although correlations were modest, such integrative signals are precisely what long-term observatories are designed to capture: ecosystem rewiring under the combined pressures of climate variability, fishing, and habitat change ( 40 ). Modest correlations are unsurprising given the short deployment durations, yet their detection underscores the potential for stronger, more diagnostic relationships to emerge with sustained, long-term monitoring. This capacity to connect ecological and environmental signals is particularly critical in tropical and subtropical regions, where long-term baselines are scarce but vulnerability to climate extremes is high ( 41 ). These ecosystems are also prone to threshold or tipping-point dynamics, where small shifts in temperature or productivity can trigger disproportionate ecological responses, further justifying the need for high-frequency observatories. By embedding biomass monitoring in a multi-sensor platform such as an ocean observatory, it exemplifies how localized time-series monitoring can act as nodes within global systems of resilience monitoring ( 42 ). Such integration positions local observatories as nodes in a global observing system capable of detecting not just trends but also nonlinear ecological responses to climate shocks. Implications for the blue-economy and governance The blue economy requires verification tools that can quantify ecological baselines, detect change, and ensure accountability in conservation and resource management (Fig. 6 ; Text S1). Continuous biomass monitoring provides exactly such functionality. For MPAs and fisheries, biomass and biodiversity indices can serve as performance metrics and indicators for adaptive management, moving beyond static assessments toward real-time evaluation of ecological outcomes ( 43 , 44 ). Monitoring arrays can also integrate other sensors, including seawater carbon chemistry, passive acoustic monitoring (PAM), samples-based systems, eDNA identification, and devices capable of integrating artificial intelligence (AI) technology to classify biomass and biodiversity, instead of using scuba diving based approaches (Fig. 6 ). More broadly, observatory data streams can support ESG (Environmental, Social, Governance) frameworks in ocean finance, including blue bonds, biodiversity credits, and carbon offsets. By offering transparent, open-access biomass indicators, observatories can enhance trust and accountability in markets that currently lack robust ecological verification ( 45 ). For nations like Mozambique and others in the WIO region, where fisheries and tourism are key sectors, this integration provides a foundation for sustainable economic planning while simultaneously addressing global equity in ocean science. Our findings underscore that ecological supply, the availability of biomass and biodiversity baselines, is only half the equation. The decisive factor in scaling blue economy finance is demand: buyers, investors, and governments require real-time, verifiable evidence that interventions are delivering measurable outcomes. Repurposed smart-buoy observatories directly respond to this trust gap by providing digital monitoring, reporting, and verification that aligns with market requirements and emerging biodiversity crediting frameworks. As a proof-of-concept, this study has limitations: short deployments, only four sites (+ a fifth test site), and biodiversity surveys restricted to three days per site. These constraints mean results should be seen as illustrative, not representative. However, they also demonstrate that even with minimal effort, smart-buoy systems can return reliable biomass streams and reveal ecologically meaningful contrasts. Future pathways include extending deployments to seasonal and interannual scales, integrating with genomic and acoustic AI species identification (Fig. 6 ; Text S1), and coupling smart-buoy observations with satellite and autonomous platforms ( 46 ). Advances in machine learning and AI will further enhance the capacity to classify and interpret biomass signals in real time ( 47 ). By scaling horizontally across sites and vertically across methods, smart-buoy-enabled observatories can evolve from local demonstration projects to operational monitoring networks. Our smart-buoys technology demonstration establishes a new pillar of ocean observation, ecological verification, and blue-economy metrics. By bridging fisheries technology with ecological monitoring, the approach creates a scalable pathway for high-resolution biomass tracking, biodiversity assessment, and environmental integration. The applications combining technology with an ocean observatory highlight both feasibility under challenging field conditions and the ecological heterogeneity that only observatories can reveal. As distributed observatory networks expand globally, smart-buoy-enabled biomass monitoring can underpin both scientific discovery and policy innovation, linking resilience research with blue economy verification. In this sense, the approach represents not only a technical innovation but a conceptual step toward accountable, ecosystem-scale stewardship of the ocean commons. Methods Study site and context This study was conducted in 2025 (between 1st June to 17th July) as a sub-project of the BCSS Ocean Observatory platform (MCTES license 002/IICDTI/BCSS/MCTES/2023, INAMAR license 1437/INAMAR/MA/2021; https://bcssmz.org/ , https://bcssmz.org/ocean-observatory-public-database/ 48–49). The Observatory provides open-access time-series data across the Mozambique Exclusive Economic Zone (EEZ), with a focus on the Mozambique Channel and the Bazaruto Archipelago. Deployments were enabled through a collaboration between BCSS and Satlink under project ReCon ( 51 ), a circular economy initiative that repurposes decommissioned smart-buoys from the tuna fishing industry for scientific research. Over 20 Satlink smart-buoys were recovered after washing ashore along coastlines of East African countries, including South Africa, Mozambique, Tanzania, Kenya, and Madagascar ( 15 ). Recovery was supported by the public and coastal communities, often during organized clean-ups or opportunistic reporting of stranded devices. All smart-buoys were transported to the BCSS Research Station on Benguerra Island, where they were repurposed. Field operations were conducted across five representative ecosystems (shelf sandbanks, coral reefs, seamounts, and pinnacles), distributed along a north-south gradient outside MPAs and within the Bazaruto Archipelago (Fig. 1 ). Site-specific ocean/weather time-series data (Fig. 2 ), habitat classifications, seafloor topography/2D-3D maps, fishing activity, and presence of significant IUCN Red List species ( https://www.iucnredlist.org/ ) with LC, NT, VU, EN, CR status (Fig. 1 C; Table S1 ) were obtained from the BCSS Ocean Observatory database repository ( 50 ), using relevant time-series datasets ( 52 , 53 ). High-resolution 3D seabed bathymetry High-resolution bathymetric mapping was conducted from 2020 to 2024 at the BCSS Ocean Observatory platform as part of the general research operation in the Bazaruto Archipelago (3D maps used in Fig. 1 , 3 , 4 , 5 , S2). The surveys were conducted in a 10 m vessel (NovaCat) using the MaxSea TimeZero Professional software platform in combination with an AIRMAR TM275 transducer. The TM275 is a dual-channel, high-performance transducer capable of operating at both CHIRP low and CHIRP high frequencies, allowing for detailed detection of bottom structures and enhanced depth penetration. The transducer was transom-mounted and connected to a dedicated onboard computer system (DELLlaptop/KONE screen) via a NMEA2000 network, ensuring real-time data acquisition and live integration with MaxSea’s navigation and sounder interface modules. The transducer continuously recorded depth data along systematic transects across the study area, while the MaxSea TimeZero software simultaneously processed this input into a dynamic bathymetric data stream. Data were displayed in 2D/3D seabed visualization mode. Navigation was guided using real-time chart overlays and a vessel-mounted GPS unit also connected via NMEA2000, ensuring precise positional logging and sub-meter geolocation accuracy. Depth data were collected at 1 Hz intervals, with acoustic returns processed and visualized using MaxSea’s “Sounder & Nav” and “3D/2D” modules. Terrain features were examined in high-resolution using vertical exaggeration and contour generation functions. Contour intervals were automatically calculated by the software based on depth range and survey scale, typically in the range of 0.5 to 1 m. Resulting 3D terrain models and contour maps were exported in high-resolution GeoTIFF format with accompanying metadata. These outputs were then imported into QGIS (version desktop 3.42.2) for spatial georeferencing and further integration into a GIS workflow compatible with both QGIS and ESRI ArcGIS environments. Georeferencing was performed using the Georeferencer tool in QGIS, based on matching coordinate pairs extracted from the GPS-logged navigation track recorded during the survey. Control points were manually assigned by aligning known landmarks, survey grid intersections, or GPS timestamps embedded within the MaxSea data. A minimum of 6 spatial reference points were used per map to ensure accurate geotransformation using a polynomial transformation model. The coordinate reference system (CRS) was standardized to WGS84 (EPSG:4326) for global compatibility. Once georeferenced, the bathymetric raster files were saved as GeoTIFFs with world files (.tfw) and embedded coordinate systems. These files were then imported into QGIS project layers, allowing overlay of additional spatial features such as smart-buoy station locations (point shapefiles) and drift transects, as well as diver paths (polyline shapefiles). Stations seabed and surface ocean time-series (ocean/weather) Time-series of surface ocean chlorophyll-a (Chl. a ) concentrations were extracted daily for the Bazaruto Archipelago using NASA’s Ocean Color, Chlorophyll- a (L2, 1 km, Daily) dataset, available via the NASA Earthdata Ocean Color Web (Fig. 2 ). Near-surface Chl. a (mg/m³) was derived from remote sensing reflectance using Level 3 NetCDF files at a spatial resolution of ~ 0.041° latitude × 0.041° longitude. The region was divided into four hydrographic sectors to capture inshore-offshore variability and data were isolated near the stations used in this study. Data processing was conducted in R ( RStudio desktop) using the packages sf, tidyverse , raster , fields , ncdf4 , lubridate , and xlsx . Daily NetCDF files were parsed using a loop to extract Chl. a values within predefined latitude-longitude bounds for each sector. For each sector and day, the mean and standard deviation of available Chl. a pixels were computed. The remote sensing algorithm used to estimate Chl. a requires concurrent data from ≥ 3 spectral bands in the 440–670 nm range and is subject to data gaps from atmospheric interference. Time-series data of in situ seabed temperature/light at each station were acquired using HOBO® data loggers (ONSET Corp.) deployed at the stations (Fig. 1 ; Table S1 ). Instruments included the UA-002-64 and MX2202 Pendant loggers for temperature and light, the MX801-CT logger for conductivity and temperature, and the MX2501 logger for pHtotal and temperature. The UA-002-64 measures temperature using a thermistor (range: -20°C to 70°C; resolution: 0.14°C; accuracy: ±0.53°C at 25°C) and includes a photodiode light sensor for detecting diel light/dark cycles. The MX2202 provides high-resolution temperature (± 0.5°C; 0.04°C resolution) and lux-based light readings (0-167,731 lux; ±10% typical accuracy), with spectral response from 300–1100 nm. Time-series data of surface ocean/weather conditions (16 variables) were acquired through an integrated system combining in situ instrumentation/sensors, satellite smart-buoys, third-party data services, and model-based products (Fig. 2 ). Local environmental parameters were monitored for calibration/corrections using a DAVIS Vantage Vue & WeatherLink Live Bundle with AirLink, installed at the BCSS Ocean Observatory. The solar-powered station transmits weather data every minute via low-power radio and includes a rain collector (0.2 mm resolution, 116 cm²), temperature/humidity sensors, anemometer, and wind vane. Internal sensor specifications include barometric pressure (0.1 mb resolution, ± 0.3%), relative humidity (± 2%), and temperature (± 0.3°C). These in situ observations are complemented with high-resolution data (up to 90 m, calibrated down to ~ 10–20 m using in situ data) from Meteomatics and Copernicus services. Data fusion includes radar and satellite sources (e.g., GOES-16/17, Meteosat-8/11, Himawari-8, EUMETSAT, SEVIRI), deterministic and ensemble forecasts (ECMWF, GEM/CMC, DWD ICON-D2, UK MetOffice), neural network models (FourCastNet, GraphCast), and atmospheric/oceanographic archives (CAMS, ERA5, CMEMS, HRRR, GHRSST). Surface and subsurface calibrations use BCSS Ocean Observatory spot sensor data and discrete sampling. The integrated system supports downscaling via terrain-ocean-astronomy-soil interactions and draws from global APIs for live atmospheric/oceanic conditions (e.g., MOS model hourly forecasts, CORINE land cover database, Heliosat, NOAA SWPC, and others). All data streams are centralized and time-synchronized under the BCSS Ocean Observatory data/platform infrastructure. Smart-buoy and platform technical details The smart-buoys used were autonomous drifting devices designed to collect, transmit, and visualize real-time ocean and biomass data (Fig. 1 , S1). They integrate a multi-sensor transducer, GPS/GNSS tracking, and a low-power satellite telemetry system operating on the INMARSAT network (satellite communication modules), with position updates at programmable intervals (hourly). The smart-buoy body is constructed with impact-resistant marine-grade materials and incorporates energy-efficient solar panels with an internal battery, supporting deployment cycles from weeks to months. The central feature is a multi-frequency echosounder operating typically at 200 kHz (190.5 kHz in this study) or 38 kHz, used to estimate acoustic backscatter of biomass beneath the device with a nominal beam aperture of 30º. These acoustic returns are processed onboard using proprietary algorithms, producing biomass estimates in tons per 11.2 m depth layer, typically down to 115 m. The echosounder scans a vertical water column beneath the buoy, with detection thresholds tuned to discriminate fish aggregations by size and density. These same acoustic data are used for machine-learning-based biomass estimates via an AI platform. The smart-buoys are managed via Satlink's proprietary platform, a software-based interface (ELB3010 Manager 2022) used to configure deployment parameters, view buoy trajectories, retrieve acoustic biomass time-series, and export data. This dashboard (Figure S1 ) enables the user to remotely monitor each buoy’s location, drift path, and biomass readings through a live-feed interface. Data are updated in real-time, with environmental overlays (see previous section). The biomass algorithm is calibrated and validated to approximate fish biomass in non-extractive applications, particularly for relative abundance comparisons across time and space. While initially designed for skipjack and yellowfin tuna detection, the system reliably detects mesopelagic and reef-associated biomass aggregations, making it suitable for shallow waters ecological monitoring when combined with ground-truthing. Fixed-point deployments The buoys were deployed in fixed-point stations configuration for 3 to 6 days intervals (72 to 140 hour non-stop), weather-dependent, across five stations outside MPAs and within the Bazaruto Archipelago, including one initial test deployment over a shelf sandbank and five subsequent deployments across ecologically distinct habitats, namely coral reefs, pinnacles, and seamounts (Fig. 3 , S2; Table S1 ). Each deployment used a standardized mooring system designed to stabilize the buoy under moderate to high current regimes. The anchoring unit consisted of a 50 kg cement block fitted with a corrosion-resistant, rubber-coated stainless-steel handle for secure attachment. This anchor was connected via 3 cm diameter braided marine-grade rope to a surface support float using galvanized D-shackles. A secondary 2 m tether of identical rope connected the surface support float to the Satlink smart-buoy, forming a tandem surface configuration. This arrangement was engineered to minimize submersion risk and lateral oscillation of the buoy due to wind drag and surface currents. Without this stabilization, excessive smart-buoy tilt or partial sinking can trigger power inefficiencies or antenna misalignment, resulting in premature battery depletion and data transmission failure. The tether lengths were optimized to allow slight flexibility for drift movement while ensuring vertical acoustic ping orientation for the echosounder. All deployments were manually diver-verified for tension, orientation, and obstruction-free sonar line of sight at the seafloor. Drifting deployments The smart-buoys were also deployed in controlled drifting mode around the mooring central point to map horizontally the distribution of biomass (Fig. 4 ; Table S1 ). While fixed-point deployments track biomass passage over time and depth per location, drifting allows spatial resolution over hundreds of square meters. Drifts were conducted during daylight for up to 10 h during 3 to 6 days intervals (30 to 60 hours segmented into 6-minute/drift intervals ~ 100 drifts/day), weather-dependent, around each station. The smart-buoy was repositioned manually by boat at random locations within a pre-defined operational quadrat (Fig. 4 ). Drift paths were random and annotated for further processing and georeferencing using MaxSea TimeZero software high-precision GPS aboard the support vessel. This setup recorded drifts GPS independently of the Satlink platform because the smart-buoy only transmits its position hourly. Scuba diving megafauna surveys Marine megafauna structure and diversity associated with the biomass detected during daylight hours were assessed through standardized scuba-based visual surveys. Informed consent was obtained from all scuba divers involved in this study. Daily censuses were conducted during the first 3 days of each deployment using waterproof slates to record observations of megafauna, including reef-associated teleost fish, elasmobranchs, sea turtles, and other large mobile species (Figure S1 ). Each survey began precisely at the mooring location of the smart-buoy (Fig. 5 ; Table S1 ), using GPS coordinates to ensure spatial consistency. From this fixed starting point, two scuba divers swam parallel along a pre-defined 30-minute transect path, maintaining a ∼5 m separation to maximize survey coverage. Transects followed consistent bearings to map as much habitat as possible (Fig. 5 ). Divers remained within consistent depth strata determined by local bathymetry, enabling detection of both benthopelagic and pelagic species. All target organisms observed within a 5 m swath on each diver’s side were recorded individually. At each station, three replicate surveys were conducted per day; morning, midday, and late afternoon, timed to coincide with smart-buoy drift mapping windows and biomass data acquisition (Table S1 ). Software and statistical data handling/graphics All data analyses were conducted primarily in R-Studio (v2022.02.3), with statistical testing using the vegan , tidyverse , and ggplot2 packages. Exploratory data handling and figure prototyping were supported in Python (v3.11) using pandas , nunpy , scipy , seaborn , and matplotlib , and MATLAB R2020b (v3.7). Non-parametric Mann-Whitney U tests and Cliff´s Delta were used as statistical tests. All final figures were assembled and standardized for publication in Adobe Illustrator CS6. Prior to analysis, all datasets were screened for completeness, checked for normality and variance homogeneity where relevant, and inspected for outliers using platform-specific diagnostics. Declarations Acknowledgements The work was technically supported by Satlink (https://satlink.es/) and the project ReCon (51), proprietary of the smart-buoys and the technology. We thank the BCSS Ocean Observatory platform (MCTES license 002/IICDTI/BCSS/MCTES/2023, INAMAR license 1437/INAMAR/MA/2021; https://bcssmz.org/, https://bcssmz.org/ocean-observatory-public-database/; 48), for facilitating fieldwork logistics. The Ocean Observatory is further supported by the Mozambican Government, main partners including: Ministerio do Mar, Aguas Interiores e Pescas (MIMAIP), Instituo Oceanografico de Mozambique (InOM), and Ministerio da Ciencia, Tecnologia, e Ensino Superior (MCTES). Author Contributions All authors contributed to the interpretation of the results and provided feedback to the paper. M.L., K.G., T.L., C.M., A.B., and E.K. designed the project. M.L., N.N., M.G., M.J., N.C. conducted the fieldwork. M.L. wrote the paper with all co-authors’ comments. Data Availability The databases are open-access and available from the corresponding author. Original data were derived from the BCSS Ocean Observatory (https://bcssmz.org/) in DOI links https://doi.org/10.82174/bcssmz.ocean.observatory.data.access and https://doi.org/10.82174/bcssmz.ocean.observatory.data.repository, where data are permanently archived under citable DataCite DOIs. Funding Funding was provided by the Bazaruto Center for Scientific Studies (BCSS)-Kisawa Sanctuary (Mozambique) resort-to-research (R2R) initiative (14), by Satlink (https://satlink.es/) through the project ReCon (51), and by Universal Plastic (UP- https://universalplastic.io/) via NextGenerationEU funds in project “SEDIA”. References United Nations. Transforming our world: the 2030 Agenda for Sustainable Development. UN General Assembly Resolution A/RES/70/1 (United Nations), 2015). Convention on Biological Diversity (CBD). Kunming-Montreal Global Biodiversity Framework. 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Maria da Graca, Teodote Matimbe, Calum Murie, Norton Cossa, Nelson Nhamussua, Ocean/weather time-series (2017-present) for 28 variables in 12 fixed-point stations/ecosystems of the Mozambique Channel and the Bazaruto Archipelago. https://doi.org/10. (2025). 82174/bcssmz.ocean.observatory.timeseries.ocean.weather.28variables Lebrato, M. et al. Underwater activity megafauna time-series (2021-present) in 5 ecosystems of the Bazaruto Archipelago, https://doi.org/10. (2025). 82174/bcssmz.ocean.observatory.timeseries.underwater.activity.survey.megafauna Additional Declarations No competing interests reported. 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Bazaruto Center for Scientific Studies (BCSS), Benguerra Island","correspondingAuthor":false,"prefix":"","firstName":"Ekaterina","middleName":"","lastName":"Kalashnikova","suffix":""},{"id":611514810,"identity":"18f2c555-38c3-422d-ad90-75b5b0f29e25","order_by":8,"name":"Calum Murie","email":"","orcid":"","institution":"Underwater Africa","correspondingAuthor":false,"prefix":"","firstName":"Calum","middleName":"","lastName":"Murie","suffix":""}],"badges":[],"createdAt":"2026-03-01 12:23:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9001386/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9001386/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105491172,"identity":"dc5ead67-9292-4121-bc96-b886e38307ba","added_by":"auto","created_at":"2026-03-26 15:29:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1165650,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSmart-buoys biomass monitoring and spatial deployment in the Bazaruto Archipelago. \u003c/strong\u003e(A) Deployment locations of the arrays across the five study and test sites (see Table S1 for details). The BCSS Ocean Observatory (research station) on Benguerra Island serves as the coordination platform for biomass-weather-ocean data reception/analyses. All data are relayed to the observatory in real-time. (B) Schematic of the smart-buoy system repurposed for ecological surveys. Devices detect and profile total biomass using integrated vertical echosounder arrays and an AI-driven classification protocol. Future applications include biodiversity crediting, blue bonds/carbon market verification, ESG impact tracking, and ecosystem observation for ecological purposes. (C) High-resolution 3D bathymetric seabed map showing an example of the two complementary sampling strategies used: fixed-point smart-buoy moorings continuously profiling biomass over time, and drifting survey paths (example) using intervals. Reef types, depth intervals, and biodiversity/conservation-related key information are also included.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9001386/v1/0559f25eff341e05001e9a26.png"},{"id":105491174,"identity":"ccdc2d41-1641-4df7-ae0b-c90482750385","added_by":"auto","created_at":"2026-03-26 15:29:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2128170,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnvironmental (ocean/weather) time-series data recorded across the five study and test sites.\u003c/strong\u003e Measurements were conducted in parallel to the smart-buoy biomass deployments (thick black line on x-axis) using the BCSS Ocean Observatory platform based on Benguerra Island (see Table S1 for details; 48, 49, 50). The time-series spans surface, seabed and atmospheric variables over a 45-day period, offering a multiscale perspective on spatiotemporal variability across the ecosystems. These continuous observations were used to characterize environmental gradients, assess exposure patterns, and contextualize data from the smart-buoys and the biological surveys.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9001386/v1/61a1a57680fa03fff7f0ff8f.png"},{"id":105566650,"identity":"5e14fd2a-5fe9-4f16-9543-5a905ba7e6f6","added_by":"auto","created_at":"2026-03-27 12:56:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1421858,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDepth-resolved biomass dynamics using a fixed-point station configuration across the four study and test sites.\u003c/strong\u003e (A) High-resolution 3D bathymetric seabed maps showing fixed-point smart buoy moorings (see Table S1for details). (B) Time-series of vertically integrated heatmaps of biomass binned by depth (11.2 m) against deployment time over deployment interval. (C) Time-series of integrated (total) biomass over the same deployment interval.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9001386/v1/a66ac1364c439d62569d9837.png"},{"id":105567176,"identity":"52afdd5e-76ac-4deb-80f7-e4a7ac6d80c6","added_by":"auto","created_at":"2026-03-27 12:58:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1118552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDepth-integrated spatial biomass dynamics using a drifting configuration across the four study sites.\u003c/strong\u003e (A) High-resolution 3D bathymetric seabed maps showing drift grid and trajectory of the smart-buoys. See Table S1 for details. (B) Depth-integrated spatial biomass distribution heatmaps over the drifts interval. (C) Time-series of integrated (total) biomass over the same drifts’ interval. No test site data (SHELF) are included because no drifts were conducted at that site.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9001386/v1/ce86f4cd618fde08df0671f2.png"},{"id":105570320,"identity":"2b8f2d70-5f8d-46cf-9d5b-02e9384e8bbe","added_by":"auto","created_at":"2026-03-27 13:16:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1179701,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMegafauna community structure/diversity underlying smart-buoy biomass observations.\u003c/strong\u003e (A) Approximate start and end points of scuba diving surveys (x3 per site) conducted at each site to assess seabed and water column megafaunal community composition (see Table S1 for details). (B) Proportional family-level (all surveys) and species-level (per survey) composition of megafauna per site, as well as IUCN red list species proportions per site by status (https://www.iucnredlist.org/). Diversity metrics are also included (species nº, Shannon-diversity index \u003cem\u003eH´\u003c/em\u003e, and Berger-Parker dominance index \u003cem\u003ed\u003c/em\u003e). No test site data (SHELF) are included because no diving surveys were conducted at that site.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9001386/v1/6be6e2ec98799319a631c272.png"},{"id":105566813,"identity":"e5c476df-68fc-4639-b9e4-dcc47c608150","added_by":"auto","created_at":"2026-03-27 12:57:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1178169,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBlue economy framework for smart-buoy applications across local to global scales.\u003cbr\u003e\n \u003c/strong\u003eConceptual framework designed to support marine monitoring from local to global scales. The system integrates fixed-point and mobile sensors (e.g., echosounders, hydrophones, benthic landers, video platforms), along with scientific diving and satellite feeds, to deliver real-time data on biomass, biodiversity, and ocean/weather conditions. This observatory configuration supports a wide range of ecological (e.g., habitat restoration, fisheries closures, MPA performance) and governance applications (e.g., blue bond verification, ESG-aligned investment, SDG-based ocean accounting), enabling science-based decision-making and data-driven marine management. This figure is expanded in detail in Text S1.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-9001386/v1/88d2568f5c36e1194b1a6a87.png"},{"id":105752629,"identity":"5f8809fe-f005-4de4-a6ea-81575ab5501a","added_by":"auto","created_at":"2026-03-30 16:03:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9384382,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9001386/v1/57c34e57-36d5-422c-bae3-08af7ef15b6a.pdf"},{"id":105566016,"identity":"81e8382a-974f-404b-b5dc-d18cbb3225d1","added_by":"auto","created_at":"2026-03-27 12:55:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1157678,"visible":true,"origin":"","legend":"","description":"","filename":"4.ManuscriptNatureScientificReportsSupplementaryFINAL02.03.2026.docx","url":"https://assets-eu.researchsquare.com/files/rs-9001386/v1/1150b849cd1fc9b709c82ab0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Repurposed smart tuna-fishing buoys provide real-time ocean intelligence for ecological and blue economy applications","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal commitments to sustainable oceans are driving a rapid expansion of marine monitoring and innovation in the \u0026ldquo;blue economy\u0026rdquo;, the sustainable use of ocean resources for economic growth, improved livelihoods, and ocean ecosystem health. Initiatives such as the United Nations Sustainable Development Goal 14 (\u0026ldquo;Life Below Water\u0026rdquo;) call for conserving and sustainably using the oceans and coasts (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). More recently, the Kunming-Montreal Global Biodiversity Framework set ambitious targets to protect 30% of marine and coastal areas by 2030 and enhance biodiversity monitoring and reporting (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Achieving these goals will require new tools for continuous ecological assessment and for verifying the outcomes of conservation interventions. In parallel, innovative finance mechanisms like blue bonds and emerging biodiversity credit markets are being explored to fund marine conservation and climate adaptation, for example, the world\u0026rsquo;s first sovereign blue bond (issued by Seychelles in 2018) is supporting marine protected areas and sustainable fisheries (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These financing approaches demand robust, real-time indicators of ecosystem status and human impacts to ensure accountability and success within a legal market framework (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). There is a growing need for affordable, scalable ocean observing solutions that can provide actionable data on marine life and environments (\u0026ldquo;ocean intelligence\u0026rdquo;), to inform policy and management, and build trust to encourage investment in the blue economy sector.\u003c/p\u003e \u003cp\u003eOne promising avenue is the repurpose of industrial fishing technologies as ocean observation platforms. The tropical tuna purse-seine fishery has deployed tens of thousands of drifting fish aggregating devices (dFADs) equipped with smart-buoys that integrate GPS and echo-sounders (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), relaying data via satellite to shore-based systems. These echosounder buoys, developed to help commercial fleets locate fish schools under floating rafts, have become a ubiquitous \u0026ldquo;sensor network\u0026rdquo; across the open oceans. Recent estimates indicate that over 1.4\u0026nbsp;million such buoys were released between 2007 and 2021, covering\u0026thinsp;~\u0026thinsp;37% of the global ocean surface (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Each buoy can remotely report the approximate fish biomass beneath the FAD by using active acoustics, the buoy\u0026rsquo;s downward-facing echosounder pings the water column and converts the returning echoes into an index or estimate of biomass tonnage (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Modern units can profile biomass in multiple depth layers (e.g. 0-115 m) and transmit these data via satellite in near real-time. The fishing industry\u0026rsquo;s adoption of these smart-buoys has thus effectively created a distributed ocean acoustic sensor array of unprecedented scale, yielding tens of millions of hourly observations on pelagic biomass (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This presents a unique opportunity to \u0026ldquo;re-use\u0026rdquo; or reinterpret industrial data streams for science and conservation: the same instruments that aid fishing can be turned into cost-effective ecological monitors. Indeed, fisheries scientists have begun to utilize fishers\u0026rsquo; echosounder buoy data to study tuna ecology and population dynamics (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Machine-learning approaches now allow biomass estimates from raw acoustic signals with increasing accuracy (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Beyond tuna behavior, these buoys could also detect other mid-trophic organisms or even plankton layers, offering insight into broader ecosystem trends if their data are properly calibrated and analyzed. Crucially, however, sometimes deployed FAD buoys can drift away from the fishing areas and wash ashore where they cannot be recovered by the fleets (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Retrieving and repurposing these drifting sensors once their use in industrial fisheries ends is an emerging strategy that aligns with circular economy principles and could vastly expand our ocean observing capacity.\u003c/p\u003e \u003cp\u003eIn this context, Satlink\u0026acute;s project ReCon was launched as a pioneer circular economy initiative to give \u0026ldquo;second life\u0026rdquo; to end-of-use tuna fishing buoys (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Originally developed by the Spanish technology company Satlink in partnership with tuna fleets, ReCon establishes a global network to recover satellite echo-sounder buoys that have drifted out of fishing grounds and refurbish them for scientific and environmental applications. Instead of becoming techno-marine debris, reclaimed smart-buoys can be redeployed to serve conservation programs, marine research, or even hazard monitoring. With minimal modifications, for instance, adding additional sensors or reprogramming data streams, such devices can act as autonomous ocean observation nodes providing real-time biological and environmental data. This approach exemplifies a blue circular economy concept: valorizing industrial fishing end-of-life technology into tools for sustainability. The present work builds on this concept by formally evaluating repurposed tuna buoys as observation platforms within a coastal marine observatory.\u003c/p\u003e \u003cp\u003eOcean observatories are evolving from traditional fixed stations to more integrative networks that combine physical, chemical, and biological monitoring at multiple scales. Around the world, long-term ocean time-series stations have proven essential for detecting ecosystem change and distinguishing natural variability from human impacts (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). They allow scientists to track trends in key variables, from ocean climate (temperature, currents, chemistry) to biological indicators (plankton, fish, megafauna), and thereby inform resource management and climate adaptation strategies. However, establishing and maintaining permanent observatory infrastructure is costly and logistically challenging, especially in remote biodiversity-rich regions, pivotal in blue economy principles. In Africa, for example, the first permanent ocean observatory was only recently established at the Bazaruto Center for Scientific Studies (BCSS) in Mozambique (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), aiming to provide permanent multi-ecosystem time-series data in the Western Indian Ocean (WIO) region. The BCSS Ocean Observatory, located in the Bazaruto Archipelago, serves as a regional hub (Mozambique Channel/EEZ) for open-access environmental monitoring, logging data on dozens of oceanographic and ecological parameters at high frequency. The Observatory recently signed an agreement with Satlink to recover and repurpose smart-buoys that wash ashore along the coastlines of East African nations, including South Africa, Mozambique, Tanzania, Kenya, and Madagascar, for use in ecological research and blue economy initiatives (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIntegrating biological sensing, such as acoustic monitoring of fish, into such observatory systems is a crucial next step for holistic marine ecosystem assessment (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Traditionally, biological data (like fish abundance or biodiversity surveys) are collected by periodic manual sampling that are sparse in time and space. By contrast, smart-buoy sensor arrays can provide continuous, in situ observations of marine life, bridging a major gap in our monitoring capability. For instance, a network of echo-sounder buoys moored at key habitats could deliver real-time indices of fish biomass or plankton dynamics, complementing the physical and chemical data streams of observatories. Likewise, if outfitted with passive acoustic hydrophones, buoys could record the ambient soundscape, capturing biological sounds (such as fish choruses or marine mammal calls) and anthropogenic noise, which is increasingly recognized as an important indicator of ecosystem health and human pressure (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUse-cases for such technology span multiple facets of the blue economy and marine policy. In conservation and fisheries management, real-time biomass tracking can help evaluate the effectiveness of MPAs or fishing closures by detecting changes in fish abundance and behavior inside versus outside protected zones. An array of buoys at reef and seamount sites could signal whether fish biomass is rebuilding after protection, or conversely, alert managers to unusual drops that might indicate poaching or ecosystem disturbance. The ability to continuously monitor hotspots can improve stock assessments and early warning of stock depletions, complementing traditional fisheries surveys (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In marine biodiversity finance, outcome-based funding models require credible indicators that biodiversity gains are being achieved (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Blue bonds and biodiversity crediting schemes rely on quantifiable improvements, such as increases in fish biomass, recovery of endangered species, or restoration of habitats, to validate that invested funds lead to positive ecological impact (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Acoustic technology can provide precisely this kind of verification data: for instance, tracking the return of spawning fish populations to a restored reef, or documenting the presence of cetaceans and other acoustic fauna in an area under protection. Furthermore, by detecting the acoustic signatures of human activities (boat noise, sonar, etc), the same tools can monitor compliance with no-fishing zones or identify illegal activities in real-time, thereby strengthening enforcement (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). On the climate front, continuous observations of biomass and soundscapes contribute to understanding how ocean warming and overfishing together alter marine food webs and biological carbon storage (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). In aggregate, networks of smart-buoys feeding data into observatories would support a more transparent and accountable ocean governance, where progress toward international targets (like SDG14 and the biodiversity framework) can be measured and reported with confidence. This aligns with the vision of a \u0026ldquo;New Blue Economy\u0026rdquo; that is knowledge-based and data-driven, looking to the ocean not just for extraction, but for information to solve societal challenges.\u003c/p\u003e \u003cp\u003eHere we present a field demonstration of repurposed smart-buoys as real-time ocean observatory instruments, deployed in the Bazaruto Archipelago (Mozambique) as part of the ongoing collaboration between the BCSS Ocean Observatory (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bcssmz.org/ocean-observatory-public-database/\u003c/span\u003e\u003cspan address=\"https://bcssmz.org/ocean-observatory-public-database/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Satlink\u0026rsquo;s project ReCon (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://satlink.es/en/science-and-sustainability/proyecto-recon\u003c/span\u003e\u003cspan address=\"https://satlink.es/en/science-and-sustainability/proyecto-recon\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in the WIO region. To our knowledge, this is the first study to integrate repurposed echosounder buoys into an observatory framework for simultaneous ecosystem monitoring and blue economy applications. We show how these smart-buoys were used to continuously track/map real-time biomass profiles at coral reef and seamount sites, while synchronizing with independent biodiversity surveys and environmental sensors time-series measurements. This cross-sector innovation bridges industrial fisheries technology and marine ecology, illustrating a scalable approach to augment ocean monitoring in data-poor regions. Our results highlight the novelty and potential of a circular economy paradigm in ocean science, leveraging recycled industrial hardware to meet the growing global demand for ocean intelligence, conservation impact tracking, and biodiversity-positive \u0026ldquo;blue investment\u0026rdquo;.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDeployment performance and data return\u003c/h2\u003e \u003cp\u003eAll deployments of repurposed smart-buoys returned continuous acoustic biomass time-series with high fidelity (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Instruments were deployed across four habitats (+\u0026thinsp;a fifth one for testing), for 72\u0026ndash;170 h each, yielding over 1,000 hourly biomass data points in total (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Transmission rates exceeded 95% of expected records, with minimal gaps due to telemetry. Integration with the observatory\u0026rsquo;s cloud-based system (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), and environmental time-series (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e) allowed near real-time visualization of biomass estimates, demonstrating operational feasibility under remote field conditions. Drifting deployments covered\u0026thinsp;~\u0026thinsp;10 km\u003csup\u003e2\u003c/sup\u003e of horizontal transects, while moored deployments remained stable for fixed-point time-series (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBiomass dynamics across sites\u003c/h3\u003e\n\u003cp\u003eBiomass estimates revealed both diel variability and site-specific contrasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Across all sites, nocturnal hours consistently exhibited 20\u0026ndash;40% higher biomass than daytime, indicative of diel vertical-horizontal migration and nocturnal aggregation. Median biomass differed strongly among habitats: Site #4 \u0026ldquo;Healthy\u0026rdquo; reef and Site #1 \u0026ldquo;Overfished\u0026rdquo; reef yielded the highest medians (~\u0026thinsp;57.9 and ~\u0026thinsp;52.6 tons/day, respectively), while Site #3 \u0026ldquo;Overfished\u0026rdquo; seamount was intermediate (~\u0026thinsp;43.3 tons/day), and Site #2 \u0026ldquo;Healthy\u0026rdquo; pinnacles the lowest (~\u0026thinsp;22.2 tons/day). Peak hourly biomass exceeded 200 tons/day at reef-associated sites. Spatial drift transects confirmed short-scale heterogeneity, with biomass \u0026ldquo;hotspots\u0026rdquo; consistently detected around hard rock/reef substrates, near structural slopes and seamount flanks, while offshore areas exhibited reduced signals (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This demonstrates that buoy-based monitoring can resolve fine-scale habitat-linked differences in benthopelagic biomass.\u003c/p\u003e\n\u003ch3\u003eBiodiversity structure and ground-truthing\u003c/h3\u003e\n\u003cp\u003eConcurrent scuba diving megafauna surveys across three consecutive days per site recorded between 14 and 20 taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Species richness was highest at Site #1 \u0026ldquo;Overfished\u0026rdquo; reef (20 taxa) and Site #4 \u0026ldquo;Healthy\u0026rdquo; reef (20 taxa), lower at Site #2 \u0026ldquo;Healthy\u0026rdquo; pinnacles (19 taxa), and lowest at Site #3 \u0026ldquo;Overfished\u0026rdquo; seamount (14 taxa). Shannon diversity indices ranged from 1.64 (Site #3) to 2.56 (Site #1), while Simpson diversity ranged from 0.73 (Site #4) to 0.90 (Site #1). Dominance patterns varied: Site #4 \u0026ldquo;Healthy\u0026rdquo; reef showed the strongest skew (Berger-Parker\u0026thinsp;=\u0026thinsp;0.45), while Site #1 \u0026ldquo;Overfished\u0026rdquo; reef was least dominated (0.17). Evenness was highest at Site #1 (0.86) and lowest at Site #4 (0.58). Several IUCN-listed taxa were confirmed across sites, including various shark species listed as \u0026ldquo;Vulnerable\u0026rdquo;, and large teleost fish (e.g., tuna, trevallies), providing ecological validation of the biomass signals. These surveys confirmed that buoy-derived acoustic estimates correspond to real ecological assemblages, though with habitat-specific differences in richness, evenness, and dominance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eComparative ecological metrics\u003c/h3\u003e\n\u003cp\u003eWhen aggregated by habitat status, \u0026ldquo;Healthy\u0026rdquo; versus \u0026ldquo;Overfished\u0026rdquo; contrasts were inconsistent (Figure S2). Effect size analysis showed that evenness was significantly higher in \u0026ldquo;Overfished\u0026rdquo; sites (Cliff\u0026rsquo;s Δ = -0.72, 95% CI -0.97 to -0.22, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04). Richness was moderately higher in \u0026ldquo;Healthy\u0026rdquo; sites (Δ = +0.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20), but not significant. Dominance was higher in \u0026ldquo;Healthy\u0026rdquo; sites (Δ = +0.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.065), driven by Site #4. Biomass showed no systematic difference (Δ = +0.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.0). Shannon and Simpson trended higher in \u0026ldquo;Overfished\u0026rdquo; sites, but without significance (Δ = -0.17 and \u0026minus;\u0026thinsp;0.44). These outcomes highlight that site-specific ecological fingerprints dominate the patterns, with one \u0026ldquo;Overfished\u0026rdquo; reef (Site #1) showing unexpectedly high diversity and evenness, while one \u0026ldquo;Healthy\u0026rdquo; reef (Site #4) showed skewed dominance. This underscores the value of site-level monitoring rather than relying solely on categorical designations.\u003c/p\u003e\n\u003ch3\u003eIntegration with environmental baselines\u003c/h3\u003e\n\u003cp\u003eBiomass peaks frequently coincided with cooler seabed temperature pulses ([ΔT = -0.5 to -1.2\u0026deg;C]) and short-lived increases in chlorophyll-a ([ΔChl-a\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.2\u0026ndash;0.4 mg m⁻\u0026sup3;]) recorded in the Ocean Observatory time-series (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Nocturnal biomass increases were synchronized with calm nighttime conditions and reduced tidal currents, suggesting that both physical and biological drivers modulated aggregation. While correlations between biomass and environmental parameters were modest, the patterns indicate that buoy-derived biomass integrates dynamically with local oceanographic processes. This demonstrates the potential for multi-sensor observatories to provide ecosystem-scale baselines.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eProof-of-concept outcomes\u003c/h2\u003e \u003cp\u003eThe deployments established a proof of concept for repurposed buoy technology as a tool for high-resolution biomass monitoring in coastal and offshore habitats. The system operated reliably, captured both diel and spatial variability, and produced ecological signals that were consistent with in-situ biodiversity observations. Statistical analyses confirmed the sensitivity of the approach to site-level differences in richness, diversity, and dominance. Together, these results demonstrate the feasibility of combining smart-buoy technology with ecological ground-truthing and environmental baselines. The approach is scalable, cost-effective, and directly relevant for applications in marine conservation, fisheries assessment, and blue economy verification.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAdvancing ocean observing technology\u003c/h2\u003e \u003cp\u003eThis study demonstrates that repurposed smart-buoys, originally designed for industrial tuna fisheries, can be adapted into reliable biomass observatories that operate at ecosystem scale (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Their ability to deliver continuous and reliable acoustic biomass time-series, integrated with real-time telemetry, expands the toolkit for marine monitoring, particularly in under-observed regions like the Western Indian Ocean (WIO) and Mozambique Channel regions. These geographies, despite hosting some of the world’s most biodiverse coral reef and pelagic ecosystems, remain chronically under-monitored, limiting both scientific understanding and evidence-based governance. Traditional biomass assessments remain largely dependent on ship-based surveys and net sampling, which are costly, spatially limited, and temporally episodic (\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e). Moreover, conventional surveys cannot resolve diel or sub-daily dynamics, nor capture short-lived events such as productivity pulses or storm-driven mixing, which are central to ecosystem function. By contrast, smart-buoy deployments can persist for days-weeks-months at a fraction of the operational cost, creating opportunities for continuous and distributed monitoring that complements autonomous vehicles (\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e), moored arrays (\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e), and satellite data streams (\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e). This proof-of-concept thus positions smart-buoy-enabled biomass monitoring as part of the new generation of distributed multi-platform observatories envisioned by the UN Decade of Ocean Science for Sustainable Development (\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e). Similar to how Argo floats revolutionized physical oceanography (\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e), the adaptation of fisheries technologies into ecological observatories can democratize access to biomass data, especially in remote locations where resource constraints limit traditional monitoring capacity. Embedding these technologies into regional observatories offers a pathway toward building global equity in ocean observing capacity. By generating verifiable ecological indicators in near real-time, buoy-based observatories also create opportunities for transparency and accountability, which are increasingly demanded by blue economy finance, ESG frameworks, and biodiversity-positive investment markets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEcological insights and site-specific heterogeneity\u003c/h2\u003e \u003cp\u003eAlthough our deployments were limited to four sites (+ a fifth test site) and short durations, the ecological signals they revealed are instructive of the technology concept for ecological verifications (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e–\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). This distinction is important: the value lies less in the specific ecological patterns reported here, and more in demonstrating how repurposed smart-buoys can detect and quantify contrasts that traditional categorical labels fail to capture. The results illustrate strong site-specific heterogeneity in biomass and community structure, which challenges simplistic assumptions about ecosystem status. For example, an overfished reef (Site #1) exhibited high richness, Shannon diversity, and evenness, while a nominally healthy reef (Site #4) was skewed by dominance of a few taxa (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, S2). These findings illustrate that categorical status labels alone are insufficient to describe ecological reality, although longer time-series are needed to verify an ecosystem status. Instead, they highlight the necessity of observatory-style, fine-scale monitoring to reveal ecological fingerprints shaped by habitat complexity, productivity regimes, and historical exploitation. Recognizing such fine-scale heterogeneity is critical for ecosystem-based management, where adaptive governance must account for the interplay of local drivers and cumulative pressures rather than relying on simplified status categories. Such contrasts are consistent with long-standing observations in coral reef and pelagic ecosystems where local drivers such as reef geomorphology, upwelling, or fishing selectivity, produce outcomes that diverge from broad management categories (\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e). Similar patterns have been reported in relation to structural complexity, near-island productivity hotspots, and fishing market proximity (\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e). The implication is not that management labels like “healthy” or “overfished” are irrelevant, but rather that their ecological manifestations must be empirically tested with continuous, high-resolution observations. Smart-buoy-enabled biomass data, coupled with biodiversity surveys, can provide exactly this form of fine-scale resolution. Beyond their scientific role, these observations also support demand-side confidence in blue economy interventions, by supplying the real-time verification that underpins accountability in biodiversity crediting and conservation finance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFrom biomass data to ecological indicators\u003c/h2\u003e \u003cp\u003eTransforming raw acoustic biomass data into quantitative ecological indicators represents a central methodological advance of this work (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). While diversity indices such as Shannon H′, Simpson diversity, richness, evenness, and dominance are widely applied in community ecology, they have rarely been operationalized in tandem with continuous, high-frequency biomass monitoring. By applying these indices to smart-buoy-derived time-series, we demonstrate that biomass observations can be converted into standardized ecological descriptors suitable for both science and management contexts. This integration is particularly novel because acoustic biomass data are typically restricted to fisheries stock assessments, whereas here they are reframed as ecological verification tools with applications in conservation, biodiversity crediting, and adaptive governance. Across sites, richness, Shannon diversity, evenness, and dominance indices revealed consistent contrasts, with evenness emerging as the most sensitive indicator of fishing pressure (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, S2). This sensitivity suggests that evenness may function as a leading indicator, capable of detecting ecological shifts before changes in richness or overall diversity become apparent. This outcome accords with ecological theory and empirical studies showing that predator depletion can redistribute biomass across mesopredator guilds, elevating community evenness while reducing trophic integrity (\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e). Importantly, our statistical framework emphasizes effect size estimation (Cliff’s Δ) with bootstrap-derived confidence intervals, which provides robust contrasts and reproducible interpretations despite modest sample sizes. Such an effect-size approach is particularly valuable for proof-of-concept deployments, where limited replication is inherent but robust contrasts are still essential to evaluate feasibility. This combination of continuous biomass streams with non-parametric effect sizes highlights a pathway toward scalable, indicator-based monitoring frameworks in data-limited regions. Statistical contrasts between healthy and overfished sites were further quantified using non-parametric effect sizes (Figure S2). Evenness was significantly higher in overfished habitats (Cliff’s Δ = -0.72), while richness and dominance trended higher in healthy habitats. These outcomes highlight the importance of effect size metrics in proof-of-concept work, where sample sizes are modest and p-values alone are insufficient (\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e). In a policy context, these indicators provide not only ecological insight but also the verifiable metrics required to build trust and accountability in biodiversity crediting and blue economy finance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIntegration with multi-ecosystem observatories\u003c/h2\u003e \u003cp\u003eLinking acoustic biomass with physical and biogeochemical drivers is essential to understand ecosystem functioning under anthropogenic and environmental change (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In our concept demonstration, biomass peaks coincided with nocturnal periods, cooler bottom waters, and short-lived chlorophyll-a pulses. These patterns suggest coupling between diel migration, productivity-driven aggregation, and physical conditions. Such fine-scale coupling between physical drivers and ecological responses is rarely captured by traditional ship-based transects or coarse satellite datasets, underscoring the added value of buoy-based observatories. Although correlations were modest, such integrative signals are precisely what long-term observatories are designed to capture: ecosystem rewiring under the combined pressures of climate variability, fishing, and habitat change (\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e). Modest correlations are unsurprising given the short deployment durations, yet their detection underscores the potential for stronger, more diagnostic relationships to emerge with sustained, long-term monitoring. This capacity to connect ecological and environmental signals is particularly critical in tropical and subtropical regions, where long-term baselines are scarce but vulnerability to climate extremes is high (\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e). These ecosystems are also prone to threshold or tipping-point dynamics, where small shifts in temperature or productivity can trigger disproportionate ecological responses, further justifying the need for high-frequency observatories. By embedding biomass monitoring in a multi-sensor platform such as an ocean observatory, it exemplifies how localized time-series monitoring can act as nodes within global systems of resilience monitoring (\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e). Such integration positions local observatories as nodes in a global observing system capable of detecting not just trends but also nonlinear ecological responses to climate shocks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImplications for the blue-economy and governance\u003c/h2\u003e \u003cp\u003eThe blue economy requires verification tools that can quantify ecological baselines, detect change, and ensure accountability in conservation and resource management (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e; Text S1). Continuous biomass monitoring provides exactly such functionality. For MPAs and fisheries, biomass and biodiversity indices can serve as performance metrics and indicators for adaptive management, moving beyond static assessments toward real-time evaluation of ecological outcomes (\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e). Monitoring arrays can also integrate other sensors, including seawater carbon chemistry, passive acoustic monitoring (PAM), samples-based systems, eDNA identification, and devices capable of integrating artificial intelligence (AI) technology to classify biomass and biodiversity, instead of using scuba diving based approaches (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). More broadly, observatory data streams can support ESG (Environmental, Social, Governance) frameworks in ocean finance, including blue bonds, biodiversity credits, and carbon offsets. By offering transparent, open-access biomass indicators, observatories can enhance trust and accountability in markets that currently lack robust ecological verification (\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e). For nations like Mozambique and others in the WIO region, where fisheries and tourism are key sectors, this integration provides a foundation for sustainable economic planning while simultaneously addressing global equity in ocean science.\u003c/p\u003e \u003cp\u003eOur findings underscore that ecological supply, the availability of biomass and biodiversity baselines, is only half the equation. The decisive factor in scaling blue economy finance is demand: buyers, investors, and governments require real-time, verifiable evidence that interventions are delivering measurable outcomes. Repurposed smart-buoy observatories directly respond to this trust gap by providing digital monitoring, reporting, and verification that aligns with market requirements and emerging biodiversity crediting frameworks. As a proof-of-concept, this study has limitations: short deployments, only four sites (+ a fifth test site), and biodiversity surveys restricted to three days per site. These constraints mean results should be seen as illustrative, not representative. However, they also demonstrate that even with minimal effort, smart-buoy systems can return reliable biomass streams and reveal ecologically meaningful contrasts. Future pathways include extending deployments to seasonal and interannual scales, integrating with genomic and acoustic AI species identification (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e; Text S1), and coupling smart-buoy observations with satellite and autonomous platforms (\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e). Advances in machine learning and AI will further enhance the capacity to classify and interpret biomass signals in real time (\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e). By scaling horizontally across sites and vertically across methods, smart-buoy-enabled observatories can evolve from local demonstration projects to operational monitoring networks.\u003c/p\u003e \u003cp\u003eOur smart-buoys technology demonstration establishes a new pillar of ocean observation, ecological verification, and blue-economy metrics. By bridging fisheries technology with ecological monitoring, the approach creates a scalable pathway for high-resolution biomass tracking, biodiversity assessment, and environmental integration. The applications combining technology with an ocean observatory highlight both feasibility under challenging field conditions and the ecological heterogeneity that only observatories can reveal. As distributed observatory networks expand globally, smart-buoy-enabled biomass monitoring can underpin both scientific discovery and policy innovation, linking resilience research with blue economy verification. In this sense, the approach represents not only a technical innovation but a conceptual step toward accountable, ecosystem-scale stewardship of the ocean commons.\u003c/p\u003e \u003c/div\u003e "},{"header":"Methods","content":"\u003ch2\u003eStudy site and context\u003c/h2\u003e\u003cp\u003eThis study was conducted in 2025 (between 1st June to 17th July) as a sub-project of the BCSS Ocean Observatory platform (MCTES license 002/IICDTI/BCSS/MCTES/2023, INAMAR license 1437/INAMAR/MA/2021; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bcssmz.org/\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bcssmz.org/ocean-observatory-public-database/\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e 48–49). The Observatory provides open-access time-series data across the Mozambique Exclusive Economic Zone (EEZ), with a focus on the Mozambique Channel and the Bazaruto Archipelago. Deployments were enabled through a collaboration between BCSS and Satlink under project ReCon (\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e), a circular economy initiative that repurposes decommissioned smart-buoys from the tuna fishing industry for scientific research. Over 20 Satlink smart-buoys were recovered after washing ashore along coastlines of East African countries, including South Africa, Mozambique, Tanzania, Kenya, and Madagascar (\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e). Recovery was supported by the public and coastal communities, often during organized clean-ups or opportunistic reporting of stranded devices. All smart-buoys were transported to the BCSS Research Station on Benguerra Island, where they were repurposed. Field operations were conducted across five representative ecosystems (shelf sandbanks, coral reefs, seamounts, and pinnacles), distributed along a north-south gradient outside MPAs and within the Bazaruto Archipelago (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Site-specific ocean/weather time-series data (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), habitat classifications, seafloor topography/2D-3D maps, fishing activity, and presence of significant IUCN Red List species (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iucnredlist.org/\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with LC, NT, VU, EN, CR status (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC; Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e) were obtained from the BCSS Ocean Observatory database repository (\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e), using relevant time-series datasets (\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eHigh-resolution 3D seabed bathymetry\u003c/h2\u003e\u003cp\u003eHigh-resolution bathymetric mapping was conducted from 2020 to 2024 at the BCSS Ocean Observatory platform as part of the general research operation in the Bazaruto Archipelago (3D maps used in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, S2). The surveys were conducted in a 10 m vessel (NovaCat) using the MaxSea TimeZero Professional software platform in combination with an AIRMAR TM275 transducer. The TM275 is a dual-channel, high-performance transducer capable of operating at both CHIRP low and CHIRP high frequencies, allowing for detailed detection of bottom structures and enhanced depth penetration. The transducer was transom-mounted and connected to a dedicated onboard computer system (DELLlaptop/KONE screen) via a NMEA2000 network, ensuring real-time data acquisition and live integration with MaxSea’s navigation and sounder interface modules. The transducer continuously recorded depth data along systematic transects across the study area, while the MaxSea TimeZero software simultaneously processed this input into a dynamic bathymetric data stream. Data were displayed in 2D/3D seabed visualization mode. Navigation was guided using real-time chart overlays and a vessel-mounted GPS unit also connected via NMEA2000, ensuring precise positional logging and sub-meter geolocation accuracy. Depth data were collected at 1 Hz intervals, with acoustic returns processed and visualized using MaxSea’s “Sounder \u0026amp; Nav” and “3D/2D” modules. Terrain features were examined in high-resolution using vertical exaggeration and contour generation functions. Contour intervals were automatically calculated by the software based on depth range and survey scale, typically in the range of 0.5 to 1 m. Resulting 3D terrain models and contour maps were exported in high-resolution GeoTIFF format with accompanying metadata. These outputs were then imported into QGIS (version desktop 3.42.2) for spatial georeferencing and further integration into a GIS workflow compatible with both QGIS and ESRI ArcGIS environments. Georeferencing was performed using the Georeferencer tool in QGIS, based on matching coordinate pairs extracted from the GPS-logged navigation track recorded during the survey. Control points were manually assigned by aligning known landmarks, survey grid intersections, or GPS timestamps embedded within the MaxSea data. A minimum of 6 spatial reference points were used per map to ensure accurate geotransformation using a polynomial transformation model. The coordinate reference system (CRS) was standardized to WGS84 (EPSG:4326) for global compatibility. Once georeferenced, the bathymetric raster files were saved as GeoTIFFs with world files (.tfw) and embedded coordinate systems. These files were then imported into QGIS project layers, allowing overlay of additional spatial features such as smart-buoy station locations (point shapefiles) and drift transects, as well as diver paths (polyline shapefiles).\u003c/p\u003e\u003ch2\u003eStations seabed and surface ocean time-series (ocean/weather)\u003c/h2\u003e\u003cp\u003eTime-series of surface ocean chlorophyll-a (Chl. \u003cem\u003ea\u003c/em\u003e) concentrations were extracted daily for the Bazaruto Archipelago using NASA’s Ocean Color, Chlorophyll-\u003cem\u003ea\u003c/em\u003e (L2, 1 km, Daily) dataset, available via the NASA Earthdata Ocean Color Web (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Near-surface Chl. \u003cem\u003ea\u003c/em\u003e (mg/m³) was derived from remote sensing reflectance using Level 3 NetCDF files at a spatial resolution of ~ 0.041° latitude × 0.041° longitude. The region was divided into four hydrographic sectors to capture inshore-offshore variability and data were isolated near the stations used in this study. Data processing was conducted in R (\u003cem\u003eRStudio\u003c/em\u003e desktop) using the packages sf, \u003cem\u003etidyverse\u003c/em\u003e, \u003cem\u003eraster\u003c/em\u003e, \u003cem\u003efields\u003c/em\u003e, \u003cem\u003encdf4\u003c/em\u003e, \u003cem\u003elubridate\u003c/em\u003e, and \u003cem\u003exlsx\u003c/em\u003e. Daily NetCDF files were parsed using a loop to extract Chl. \u003cem\u003ea\u003c/em\u003e values within predefined latitude-longitude bounds for each sector. For each sector and day, the mean and standard deviation of available Chl. \u003cem\u003ea\u003c/em\u003e pixels were computed. The remote sensing algorithm used to estimate Chl. \u003cem\u003ea\u003c/em\u003e requires concurrent data from ≥ 3 spectral bands in the 440–670 nm range and is subject to data gaps from atmospheric interference.\u003c/p\u003e\u003cp\u003eTime-series data of in situ seabed temperature/light at each station were acquired using HOBO® data loggers (ONSET Corp.) deployed at the stations (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Instruments included the UA-002-64 and MX2202 Pendant loggers for temperature and light, the MX801-CT logger for conductivity and temperature, and the MX2501 logger for pHtotal and temperature. The UA-002-64 measures temperature using a thermistor (range: -20°C to 70°C; resolution: 0.14°C; accuracy: ±0.53°C at 25°C) and includes a photodiode light sensor for detecting diel light/dark cycles. The MX2202 provides high-resolution temperature (± 0.5°C; 0.04°C resolution) and lux-based light readings (0-167,731 lux; ±10% typical accuracy), with spectral response from 300–1100 nm.\u003c/p\u003e\u003cp\u003eTime-series data of surface ocean/weather conditions (16 variables) were acquired through an integrated system combining in situ instrumentation/sensors, satellite smart-buoys, third-party data services, and model-based products (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Local environmental parameters were monitored for calibration/corrections using a DAVIS Vantage Vue \u0026amp; WeatherLink Live Bundle with AirLink, installed at the BCSS Ocean Observatory. The solar-powered station transmits weather data every minute via low-power radio and includes a rain collector (0.2 mm resolution, 116 cm²), temperature/humidity sensors, anemometer, and wind vane. Internal sensor specifications include barometric pressure (0.1 mb resolution, ± 0.3%), relative humidity (± 2%), and temperature (± 0.3°C). These in situ observations are complemented with high-resolution data (up to 90 m, calibrated down to ~ 10–20 m using in situ data) from Meteomatics and Copernicus services. Data fusion includes radar and satellite sources (e.g., GOES-16/17, Meteosat-8/11, Himawari-8, EUMETSAT, SEVIRI), deterministic and ensemble forecasts (ECMWF, GEM/CMC, DWD ICON-D2, UK MetOffice), neural network models (FourCastNet, GraphCast), and atmospheric/oceanographic archives (CAMS, ERA5, CMEMS, HRRR, GHRSST). Surface and subsurface calibrations use BCSS Ocean Observatory spot sensor data and discrete sampling. The integrated system supports downscaling via terrain-ocean-astronomy-soil interactions and draws from global APIs for live atmospheric/oceanic conditions (e.g., MOS model hourly forecasts, CORINE land cover database, Heliosat, NOAA SWPC, and others). All data streams are centralized and time-synchronized under the BCSS Ocean Observatory data/platform infrastructure.\u003c/p\u003e\u003ch2\u003eSmart-buoy and platform technical details\u003c/h2\u003e\u003cp\u003eThe smart-buoys used were autonomous drifting devices designed to collect, transmit, and visualize real-time ocean and biomass data (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, S1). They integrate a multi-sensor transducer, GPS/GNSS tracking, and a low-power satellite telemetry system operating on the INMARSAT network (satellite communication modules), with position updates at programmable intervals (hourly). The smart-buoy body is constructed with impact-resistant marine-grade materials and incorporates energy-efficient solar panels with an internal battery, supporting deployment cycles from weeks to months. The central feature is a multi-frequency echosounder operating typically at 200 kHz (190.5 kHz in this study) or 38 kHz, used to estimate acoustic backscatter of biomass beneath the device with a nominal beam aperture of 30º. These acoustic returns are processed onboard using proprietary algorithms, producing biomass estimates in tons per 11.2 m depth layer, typically down to 115 m. The echosounder scans a vertical water column beneath the buoy, with detection thresholds tuned to discriminate fish aggregations by size and density. These same acoustic data are used for machine-learning-based biomass estimates via an AI platform. The smart-buoys are managed via Satlink's proprietary platform, a software-based interface (ELB3010 Manager 2022) used to configure deployment parameters, view buoy trajectories, retrieve acoustic biomass time-series, and export data. This dashboard (Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e) enables the user to remotely monitor each buoy’s location, drift path, and biomass readings through a live-feed interface. Data are updated in real-time, with environmental overlays (see previous section). The biomass algorithm is calibrated and validated to approximate fish biomass in non-extractive applications, particularly for relative abundance comparisons across time and space. While initially designed for skipjack and yellowfin tuna detection, the system reliably detects mesopelagic and reef-associated biomass aggregations, making it suitable for shallow waters ecological monitoring when combined with ground-truthing.\u003c/p\u003e\u003ch2\u003eFixed-point deployments\u003c/h2\u003e\u003cp\u003eThe buoys were deployed in fixed-point stations configuration for 3 to 6 days intervals (72 to 140 hour non-stop), weather-dependent, across five stations outside MPAs and within the Bazaruto Archipelago, including one initial test deployment over a shelf sandbank and five subsequent deployments across ecologically distinct habitats, namely coral reefs, pinnacles, and seamounts (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, S2; Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Each deployment used a standardized mooring system designed to stabilize the buoy under moderate to high current regimes. The anchoring unit consisted of a 50 kg cement block fitted with a corrosion-resistant, rubber-coated stainless-steel handle for secure attachment. This anchor was connected via 3 cm diameter braided marine-grade rope to a surface support float using galvanized D-shackles. A secondary 2 m tether of identical rope connected the surface support float to the Satlink smart-buoy, forming a tandem surface configuration. This arrangement was engineered to minimize submersion risk and lateral oscillation of the buoy due to wind drag and surface currents. Without this stabilization, excessive smart-buoy tilt or partial sinking can trigger power inefficiencies or antenna misalignment, resulting in premature battery depletion and data transmission failure. The tether lengths were optimized to allow slight flexibility for drift movement while ensuring vertical acoustic ping orientation for the echosounder. All deployments were manually diver-verified for tension, orientation, and obstruction-free sonar line of sight at the seafloor.\u003c/p\u003e\u003ch2\u003eDrifting deployments\u003c/h2\u003e\u003cp\u003eThe smart-buoys were also deployed in controlled drifting mode around the mooring central point to map horizontally the distribution of biomass (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). While fixed-point deployments track biomass passage over time and depth per location, drifting allows spatial resolution over hundreds of square meters. Drifts were conducted during daylight for up to 10 h during 3 to 6 days intervals (30 to 60 hours segmented into 6-minute/drift intervals ~ 100 drifts/day), weather-dependent, around each station. The smart-buoy was repositioned manually by boat at random locations within a pre-defined operational quadrat (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Drift paths were random and annotated for further processing and georeferencing using MaxSea TimeZero software high-precision GPS aboard the support vessel. This setup recorded drifts GPS independently of the Satlink platform because the smart-buoy only transmits its position hourly.\u003c/p\u003e\u003ch2\u003eScuba diving megafauna surveys\u003c/h2\u003e\u003cp\u003eMarine megafauna structure and diversity associated with the biomass detected during daylight hours were assessed through standardized scuba-based visual surveys. Informed consent was obtained from all scuba divers involved in this study. Daily censuses were conducted during the first 3 days of each deployment using waterproof slates to record observations of megafauna, including reef-associated teleost fish, elasmobranchs, sea turtles, and other large mobile species (Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Each survey began precisely at the mooring location of the smart-buoy (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e), using GPS coordinates to ensure spatial consistency. From this fixed starting point, two scuba divers swam parallel along a pre-defined 30-minute transect path, maintaining a ∼5 m separation to maximize survey coverage. Transects followed consistent bearings to map as much habitat as possible (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Divers remained within consistent depth strata determined by local bathymetry, enabling detection of both benthopelagic and pelagic species. All target organisms observed within a 5 m swath on each diver’s side were recorded individually. At each station, three replicate surveys were conducted per day; morning, midday, and late afternoon, timed to coincide with smart-buoy drift mapping windows and biomass data acquisition (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eSoftware and statistical data handling/graphics\u003c/h2\u003e\u003cp\u003eAll data analyses were conducted primarily in R-Studio (v2022.02.3), with statistical testing using the \u003cem\u003evegan\u003c/em\u003e, \u003cem\u003etidyverse\u003c/em\u003e, and \u003cem\u003eggplot2\u003c/em\u003e packages. Exploratory data handling and figure prototyping were supported in Python (v3.11) using \u003cem\u003epandas\u003c/em\u003e, \u003cem\u003enunpy\u003c/em\u003e, \u003cem\u003escipy\u003c/em\u003e, \u003cem\u003eseaborn\u003c/em\u003e, and \u003cem\u003ematplotlib\u003c/em\u003e, and MATLAB R2020b (v3.7). Non-parametric Mann-Whitney U tests and Cliff´s Delta were used as statistical tests. All final figures were assembled and standardized for publication in Adobe Illustrator CS6. Prior to analysis, all datasets were screened for completeness, checked for normality and variance homogeneity where relevant, and inspected for outliers using platform-specific diagnostics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was technically supported by Satlink (https://satlink.es/) and the project ReCon (51), proprietary of the smart-buoys and the technology. We thank the BCSS Ocean Observatory platform (MCTES license 002/IICDTI/BCSS/MCTES/2023, INAMAR license 1437/INAMAR/MA/2021; https://bcssmz.org/, https://bcssmz.org/ocean-observatory-public-database/; 48), for facilitating fieldwork logistics. The Ocean Observatory is further supported by the Mozambican Government, main partners including: Ministerio do Mar, Aguas Interiores e Pescas (MIMAIP), Instituo Oceanografico de Mozambique (InOM), and Ministerio da Ciencia, Tecnologia, e Ensino Superior (MCTES).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the interpretation of the results and provided feedback to the paper. M.L., K.G., T.L., C.M., A.B., and E.K. designed the project. M.L., N.N., M.G., M.J., N.C. conducted the fieldwork. M.L. wrote the paper with all co-authors\u0026rsquo; comments.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe databases are open-access and available from the corresponding author. Original data were derived from the BCSS Ocean Observatory (https://bcssmz.org/) in DOI links https://doi.org/10.82174/bcssmz.ocean.observatory.data.access and https://doi.org/10.82174/bcssmz.ocean.observatory.data.repository, where data are permanently archived under citable DataCite DOIs. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding was provided by the Bazaruto Center for Scientific Studies (BCSS)-Kisawa Sanctuary (Mozambique) resort-to-research (R2R) initiative (14), by Satlink (https://satlink.es/) through the project ReCon (51), and by Universal Plastic (UP- https://universalplastic.io/) via NextGenerationEU funds in project \u0026ldquo;SEDIA\u0026rdquo;.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUnited Nations. \u003cem\u003eTransforming our world: the 2030 Agenda for Sustainable Development. 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(2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e82174/bcssmz.ocean.observatory.timeseries.underwater.activity.survey.megafauna\u003c/span\u003e\u003cspan address=\"http://82174/bcssmz.ocean.observatory.timeseries.underwater.activity.survey.megafauna\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"tuna-fishing, smart buoys, blue economy, ecosystem monitoring, biomass, biodiversity","lastPublishedDoi":"10.21203/rs.3.rs-9001386/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9001386/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRepurposed echosounder buoys developed for industrial tuna fishing are emerging as powerful ecological monitoring tools, providing real-time biomass data across marine habitats. We present a novel field concept study applying this cross-sector innovation, by using smart-buoys to track the biomass of critical habitats, detect potential fishing impacts, and support biodiversity accountability within the blue economy. Repurposed through the Satlink project ReCon, smart-buoys measure vertical biomass distribution and transmit continuous data streams via satellite (INMARSAT) to shore-based systems. Our deployments lasted 72\u0026ndash;170 h in both fixed-point station and drifting configurations, covering one shelf area, two coral reefs, and two seamounts/pinnacles in the Western Indian Ocean (WIO) region (Bazaruto Archipelago, Mozambique), yielding\u0026thinsp;\u0026gt;\u0026thinsp;1,000 hourly total biomass records. Integration with the Benguerra Island-based BCSS Ocean Observatory provided simultaneous weather and oceanographic baselines for over 20 in-situ variables, while concurrent scuba verification surveys recorded 14\u0026ndash;20 megafauna taxa per site. The resulting datasets revealed diel cycles with 20\u0026ndash;40% higher nocturnal biomass, coinciding with cooler bottom waters and transient chlorophyll-a pulses, and illustrated how site-level heterogeneity challenges broad ecosystem status labels, such as an overfished reef displaying higher evenness and diversity than a nominally healthy reef. This demonstration highlights a real-time scalable monitoring platform that bridges fisheries technology and ecology, providing a novel class of verification tools for MPAs, fisheries management, biodiversity crediting, and ESG-aligned interventions. Crucially, by providing continuous and verifiable ecological indicators, it addresses a fundamental supply/demand-side barrier for blue finance and biodiversity markets: investor confidence, accountability, and trust in real-time outcomes.\u003c/p\u003e","manuscriptTitle":"Repurposed smart tuna-fishing buoys provide real-time ocean intelligence for ecological and blue economy applications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 15:29:35","doi":"10.21203/rs.3.rs-9001386/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-05T02:21:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91962794860109681637521397040959798456","date":"2026-03-26T11:19:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-24T16:13:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322500809211882244988663400485285262296","date":"2026-03-24T11:45:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-24T11:11:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-24T11:10:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-11T10:06:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-09T19:53:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-04T09:51:32+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":"b298f732-ceda-4c42-bcb8-f314d52120a7","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65067441,"name":"Biological sciences/Ecology"},{"id":65067442,"name":"Earth and environmental sciences/Ecology"},{"id":65067443,"name":"Earth and environmental sciences/Environmental sciences"},{"id":65067444,"name":"Earth and environmental sciences/Ocean sciences"}],"tags":[],"updatedAt":"2026-03-26T15:29:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 15:29:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9001386","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9001386","identity":"rs-9001386","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}