Integration of artificial intelligence for sustainable freshwater fishery governance: an Okavango River ecosystem perspective

This qualitative study examined the integration of Artificial Intelligence (AI) in sustainable freshwater fishery management within the Okavango River ecosystem, combining primary field research with a comprehensive document review. The investigation explored how AI technologies, including machine learning and predictive analytics, can enhance fish stock assessment, habitat monitoring, and resource administration to achieve ecological and socio-economic sustainability. The study emphasizes the Okavango River's unique biodiversity and its critical importance to local communities while assessing AI's potential to transform traditional fishery management approaches. The research employs a dual-method approach, utilizing both face-to-face semi-structured interviews with key stakeholders (fishers, vendors, and officials) and a systematic review of relevant policy documents and documentary reviews. Thematic analysis of interview data and document content reveals key insights about AI adoption challenges, implementation opportunities, and practical applications in freshwater fisheries. Findings demonstrate AI's transformative potential in enabling real-time data collection, predictive population modeling, and overfishing prevention. However, significant barriers emerge, including technological infrastructure gaps, institutional resistance, and capacity-building needs among local stakeholders. By synthesizing field data with existing literature, this study makes a novel contribution to sustainable fishery management discourse, offering context-specific, AI-integrated strategies for the Okavango River ecosystem. The research proposes policy recommendations that address both technical implementation challenges and ethical considerations, grounded in empirical evidence from multiple data sources. Ultimately, this study highlights the critical role of AI in balancing ecosystem conservation with socio-economic development, while demonstrating how mixed-method approaches can strengthen research outcomes in environmental technology studies.

1 Introduction

Freshwater fisheries serve as vital pillars supporting global food security, economic livelihoods, and biodiversity conservation, with particular ecological significance in regions such as the Okavango River ecosystem (FAO, 2022). These critical ecosystems currently face mounting pressures from overexploitation, habitat destruction, climate variability, and outdated governance frameworks. Conventional management approaches, which are predominantly labor-intensive and reactive, prove increasingly inadequate in addressing contemporary environmental challenges (Welcomme, 2016). The emergence of Artificial Intelligence (AI) technologies, encompassing predictive analytics, machine learning, and automated monitoring systems, has been anticipated to have substantial possible for advancing fishery management through data-driven decision-making and proactive conservation approaches (Ertör, 2023). Though it is significant to note that several of these applications continue in pilot phases or have been confirmed primarily in data-rich, technologically advanced contexts, their transferability to multifaceted, transboundary social-ecological systems like the Okavango River ecosystem, with its discrete challenges, is largely uncharted and establishes a central focus of this analysis. Nevertheless, the application of AI in intricate, transboundary social-ecological systems, alike the Okavango River ecosystem, remains nascent. This gap is crucial for two main reasons. The Okavango characterizes a quintessential instance of a data-poor yet governance-rich environment, where management decisions have philosophical insinuations for biodiversity and anthropological livelihoods. Conservative data collection approaches are habitually too slow, costly, and coarse to arrest the system's vitality, predominantly its seasonal flood pulse.

AI offers an opportunity to leapfrog these confines, allowing real-time, fine-scale monitoring that is otherwise impossible, thus presenting a novel pathway to bridge the crucial data gap that hampers effective governance.

The then, and just as importantly, the Okavango context forces an essential critique of top-down technological solutionism. The river's governance is characterized by multi-level, transboundary organizations (e.g., OKACOM) and deep-seated Indigenous and Local Knowledge (ILK) systems. Purely importing AI tools intended for industrial fisheries would be morally and practically fraught. Consequently, exploring AI integration here is not simply a technical exercise; it is a critical case study for emerging a model of accountable and context-aware technological implementation. The teachings learned are globally pertinent, offering a model for how AI can be attached to support, rather than supplant, community-based management and indigenous knowledge in the Global South's vulnerable ecosystems. This study is thus located at the juncture of digital innovation and participatory environmental management, in search of understanding how innovative technology can be stewarded to aid local priorities and ecological resilience (Boettiger, 2018).

Given this setting, this study is steered toward the following primary research question: How can AI be integrated into the governance of the Okavango River freshwater fisheries to improve sustainability? To address this research question, the research sets out three specific objectives:

1. To analyse and document the existing challenges, monitoring practices, and governance gaps in Okavango fisheries from the perspective of local stakeholders.

2. To evaluate the alignment between existing fisheries governance practices and the necessities for data-driven and adaptive management.

3. To identify potential prerequisites and pathways for the accountable integration of AI tools that enhance traditional knowledge and address identified needs.

This qualitative-methods study employed a robust complementary-instrument approach to investigate AI's potential in sustainable fishery management within the Okavango River ecosystem. It is imperative to note that this research is qualitative without generating quantitative baseline data on catch per unit effort (CPUE), particular ecological or stock assessment parameters. Instead, its principal contribution lies in detailing stakeholder-identified difficulties, perceived ecological trends, and governance gaps which are vital for contextualizing future quantitative studies and for ensuring that any subsequent technological interferences are pertinent to on-the-ground realisms. The research methodology incorporates: (1) in-depth, face-to-face interviews using semi-structured guides with key stakeholders, including fishery managers, conservation experts, and (2) systematic document review of existing policies, technical reports, and relevant case studies. This methodological synergy enables comprehensive data triangulation, enhancing the study's validity and practical relevance.

2 Conceptual framework: connecting AI, adaptive capacity, and socio-ecological outcomes

This study is situated within the theoretical lens of Adaptive Governance, which emphasizes the importance of flexible, learning-oriented institutions for managing complex social-ecological systems (Folke et al., 2005). A core tenet of this theory is that enhancing a system's capacity to generate, process, and act upon information is critical for building resilience and achieving sustainable outcomes. We propose a conceptual framework (Figure 1) that positions AI not as a standalone solution, but as a potential catalyst that can augment key components of governance capacity, thereby influencing sustainability pathways.

Figure 1

Figure 1. Conceptual framework: connecting AI, adaptive capacity, and socio-ecological outcomes. Source: Authors' compilation.

The framework illustrates the following interconnected pillars:

AI as an Augmentation Tool: The framework postulates that AI technologies (e.g., automated monitoring, predictive analytics) do not directly influence sustainability. Instead, they operate by augmenting three fundamental dimensions of governance capacity:

Informational Capacity: The ability to generate, process, and synthesize data. AI can enhance this by providing high-resolution, real-time data on fish stocks, habitat health, and illegal activities, thereby addressing the critical data gaps identified in this study.

Analytical Capacity: The skill to interpret information and predict future conditions. AI can improve this with machine learning models that forecast stock dynamics under different climate or management scenarios, helping transition from reactive to proactive governance.

Operational Capacity: The capacity to execute and enforce decisions. AI can support this through decision-support tools that assist in targeting patrols or optimizing seasonal closures, as well as communication systems that enhance the reach and inclusivity of regulation dissemination.

The Central Role of Governance Mediators: The framework suggests that AI's impact on sustainability is not automatic. It is heavily mediated by two governance factors:

1. Institutional Flexibility: The capacity of formal and informal rules (e.g., the Inland Fisheries Act, community norms) to incorporate new information and adapt. Rigid, traditional laws will limit AI's potential, while adaptable legal frameworks can facilitate it.

2. Stakeholder Inclusivity & Equity: The extent to which AI's design and implementation involve co-creation and include diverse knowledge systems (e.g., Traditional Ecological Knowledge). Without this, AI risks reinforcing existing power imbalances and undermining trust, as shown by concerns related to surveillance.

Socio-Ecological Outcomes: The eventual goal of this augmented governance system is to shift the socio-ecological system toward more sustainable outcomes, specifically:

Ecological Resilience: Improved fish stock health, biodiversity conservation, and habitat integrity.

Social Equity: Enhanced livelihood security, fair access to resources, and strengthened community agency in management. This conceptual framework champions the entire study. It permits the study to structure the investigation around how AI might augment particular governance capacities, how present social dynamics and institutional structures facilitate this potential, and what this suggests for achieving the twofold goals of ecological and social sustainability in the Okavango River ecosystem.

Conceptual framework illustrating the role of AI in sustainable fishery governance. The model posits that AI technologies augment core governance capacities, whose impact on socio-ecological outcomes is mediated by critical governance factors. A feedback loop represents the adaptive learning process essential for resilient governance.

3 Materials and methods

3.1 Study area

This study focuses particularly on the Namibian section of the Okavango River ecosystem, a section of the larger transboundary freshwater ecosystem that spans Namibia, Botswana, and Angola. The Namibian part of the ecosystem is crucial for supporting subsistence fishing, riparian communities, and river biodiversity (Marcantonio, 2016). It is essential to note that the primary data collection and subsequent analysis of the Namibian legal framework (Section 4) are geographically restricted to this national context. Whereas the Okavango River ecosystem is a linked system, socio-economic pressures, governance structures, and implementation capacities vary significantly across the three riparian states. Thus, the findings and conclusions of this study are presented as a detailed case study of the Namibian context, and their direct applicability to the Angolan or Botswanan parts of the basin should not be assumed without further transboundary research. The aquatic flow is year-round, reaching its highest during the yearly flood pulse (March–June), favorable for floodplain agriculture and fish breeding cycles (Mosepele et al., 2022; Boyes, 2021). Significant fish species include Clarias gariepinus (sharptooth catfish) and Oreochromis andersonii (three-spotted tilapia), which are important for community livelihoods and food security (Tiyeho and Jacobs, 2022). The basin is under threat from climate variability, overfishing, and competing water demands, such as agriculture and tourism (Mbaiwa, 2011). Governance comprises multi-country frameworks like the Permanent Okavango River Basin Water Commission (OKACOM), making it a case for exploring AI-driven ideal sustainable fishery management within multifaceted socio-ecological systems (Folke, 2016; Young, 2017).

4 Data analysis and discussion

To explore the integration of AI in sustainable freshwater fishery governance within the Okavango River ecosystem, this study employed a qualitative-dominant research design to assess the potential of AI using both primary and secondary data sources. The methodological technique was grounded in in-depth, semi-structured interviews, with results contextualized by a descriptive analysis of secondary data, including policy and documentary review and fishery reports, which assisted in triangulating the primary qualitative data.

The qualitative findings from the interviews underwent a systematic thematic analysis following the six-phase framework outlined by Braun and Clarke (2006):

Familiarization: All audio recordings were transcribed verbatim and read repeatedly by the primary researcher to ensure deep familiarity with the data.

Generating Initial Codes: Two researchers independently conducted initial coding on a subset of transcripts (approximately 20%) using NVivo software. They met to compare and refine a unified codebook, which was then applied to the entire dataset.

Searching for Themes: The coded data were collated and analyzed to identify broader patterns of meaning and potential themes. This involved sorting the different codes into potential themes and gathering all data relevant to each potential theme.

Reviewing Themes: The preliminary themes were analyzed in two stages. First, in relation to the coded extracts to check if they formed a coherent pattern. Second, in relation to the entire dataset, to ensure the thematic map accurately reflected the meanings evident in the data as a whole.

Defining and Naming Themes: Ongoing analysis was conducted to refine the specifics of each theme and the overall story the analysis tells. Clear definitions and names for each theme were generated. Producing the Report: The final analysis was woven into a coherent narrative, presented in this manuscript (Sections 4.1.1 to 4.1.10), supported by data extracts and scholarly literature to ensure the validity of the interpretation. To ensure analytical reliability, the two researchers involved in the coding process maintained a coding consistency rate of over 85%. Discrepancies were discussed until consensus was reached. Furthermore, member checking was performed with 32 key informants (as detailed in Section 5.6) to validate the accuracy and relevance of the identified themes, thereby enhancing the trustworthiness of the findings. The findings of this research are structured across numerous key proportions. The following sections present these results, with the primary data and comparative analyses visualized in Tables 1, 2, and Figures 2–6.

Table 1

Table 1. Demographic information.

Table 2

Table 2. From data to design: linking research findings to AI recommendations.

Figure 2

Figure 2. Map of the Okavango River Basin, highlighting the Namibian segment and key fishing zones. Source: Authors' compilation.

Figure 3

Figure 3. Different types of fish species (e.g., Clarias gariepinus, Oreochromis andersonii) caught for sale at the Okavango River Basin, Namibia.

Figure 4

Figure 4. Current fishing practices at the Okavango River Basin, Namibia.

Figure 5

Figure 5. Common fishing tools used at the Okavango River ecosystem, Namibia.

Figure 6

Figure 6. Sand mining and gardening activities alongside the Okavango River ecosystem, Namibia.

4.1 Primary data

The following units present the thematic analysis of interview findings. A crucial finding across all themes is the absence of vigorous, a gap explicitly, systematic baseline data explicitly referenced by participants. Thus, the value of these themes is not in offering quantitative baselines but in delineating the specific informational and governance voids, as supposed by key stakeholders, where future data collection and AI potential tools could be most critically applied. Prime data were gathered utilizing a purposive sampling technique to ensure representation across different fishing contexts along the Okavango River ecosystem. The sample consisted of 32 participants, primarily comprising local fishers and fish vendors at 4 major catch sites (strategically located along the Namibian stretch of the river to encompass a range of fishing pressures and community contexts: the Shakave, Samaro, Ncamasere, and Ncaangana landing sites). An inadequate number of officials from the Ministry of Environment were also involved to gain initial regulatory perspectives.

A significant limitation of this sampling method, as identified in our study, is the limited diversity of stakeholders crucial to the topic of AI implementation. The analysis principally captures the end-user perspective (fishers, vendors, and officials) but does not systematically involve the viewpoints of senior government policymakers, technology developers, NGO conservation specialists, or representatives from transboundary bodies like OKACOM. Their expertise on feasibility, technical requirements, policy alignment, and funding is critical for a comprehensive understanding of AI integration pathways. Hence, the data and suggested research agenda presented in this analysis should be interpreted as a critical first step: a thorough assessment of community-level concerns, readiness, and conditions. This ground-level perspective is an essential, but not adequate, component for designing practical AI interventions. The primary data permitted a profound exploration of artisanal and restricted regulatory viewpoints, while also clearly defining a crucial need for future research to engage this wider ecosystem of stakeholders.

4.1.1 Species dependence in local livelihoods

The participant's list of fish species includes Tigerfish – Hydrocynus vittatus (Nyiru), catfish – Clarias gariepinus (Hongo), and Mbayena – Oreochromis andersonii (Tilapia/Bream), indicating a diverse reliance on various fish types for community livelihood and subsistence. The frequent mention of mbayena and catfish highlights their crucial role in daily life, likely due to their market value, constant presence, or nutritional importance. Less frequently mentioned species, such as Serranochromis robustus (Nono) and Serranochromis angusticeps (Nkusa), could represent periodic resources or have culturally particular uses, reflecting complex ecological relationships. These findings suggest two key understandings for Okavango fishery management. Primarily, the data highlight the high dependence on several species, which underscores the need for a comprehensive conservation strategy that avoids focusing solely on certain commercial fish species (Allan et al., 2005). Furthermore, the variances in species significance (for example, staple vs. supplementary) underscore a key data gap in present management: the absence of granular, species-specific population data that echoes their differential importance to livelihoods. This data gap, as acknowledged by the fishers' detailed knowledge, proposes a potential role for AI-powered monitoring tools. To assess both community livelihoods and ecological well-being (Sarfaraz et al., 2022). By providing the detailed, species-level analytics that existing methods lack. For instance, a reduction in mbayena or catfish populations would necessitate urgent action (Welcomme, 2011), while changes in fewer common species might highlight broader ecosystem changes (Cowx et al., 2010). Community-based management approaches should aim to guard this biodiversity while warranting fair access to resources (Ostrom, 2009). Future studies could investigate how the importance of diverse species varies across regions or cultural groupings to improve intervention methods (Béné et al., 2016).

4.1.2 Sequential shifts in catch composition: a five-year assessment

The participant's perception that “they are no longer catching fish as in earlier days” underlines a crucial data gap and proposes a qualitative, community-held baseline of ecological decline. This theme accentuates the pressing need for establishing a quantitative baseline against which such perceptions can be measured and validated, a primary function that AI-powered data aggregation could serve in the future (Allison and Ellis, 2001). The stated decrease in fish populations, notwithstanding most species still being extant, resonates with intensified harvesting burden motivated by the shift from supplemental, subsistence-based fishing to full livelihood dependence (Béné, 2003; Fabinyi et al., 2022b). This escalation in effort has compromised traditional river recovery periods that once allowed natural fish restocks, creating a concerning feedback loop where increased economic dependence on fisheries, excess fish restock, and, in turn, worsen livelihood insecurity (Cinner et al., 2009). This status quo exemplifies the classic poverty-environment trap well-documented in small-scale fisheries worldwide (Barnes et al., 2020).

As fishing transitions from a livelihood and seasonal activity into a main income source, the absence of traditional or institutional governance leads to overexploitation and continued river degradation (Ostrom et al., 1999). The participant's understanding of lost periodic recovery is mainly significant, signifying that stimulating culturally-rooted practices like rotational or seasonal bans on fishing could assist in restoring ecological balance (Poe et al., 2023). Nevertheless, such actions must now be improved to contemporary realities where communities have increased their dependency on consistent commercial fishing (Fabinyi et al., 2023). A potential pathway going forward could integrate traditional knowledge with modern tools: implementing locally-designed closed seasons informed by ancient spawning cycles, while introducing substitute income sources during these rest waiting periods (Johannes, 2002). The fishers' strong enunciation of lost periodic recovery periods ideas to a crucial need for predictive information on stock regeneration. In this context, AI could complement this development by modeling optimal rest durations and forecasting recovery rates during different management setups (McClanahan et al., 2015). Thus, providing a scientific basis for the closed seasons that fishers themselves have identified as missing. The participant's clear enunciation of the problem indicates community eagerness for co-management solutions that merge ecological restoration with livelihood protection (Armitage et al., 2009). Future studies should quantify these pragmatic declines via catch monitoring while recording traditional management practices that once maintained equilibrium (Pauly, 1990). Eventually, this instance underscores that sustainable determinations must address both the economic pressures and ecological limitations driving overharvesting (Folke et al., 2005).

4.1.3 Comparative challenges in contemporary vs. past fishing practices

Participants pronounced depending mainly on observational knowledge gathered over generations to monitor fish populations, with several noting they observe the river closely to determine when and where fish move (Berkes et al., 2000). Traditional indicators include water level changes, bird activity patterns, and lunar cycles (Moller et al., 2004). Though several respondents acknowledged these strategies struggle to detect steady declines, with one fisher noting: “The changes come slowly, you don't see the calamity until it's already here”. This dependence on phenomenological detection presents both strengths and limitations in present fishery management (Gadgil et al., 1993). The place-based accuracy of traditional knowledge provides a nuanced understanding of community microhabitats and species behaviors that conventional research often misses (Johannes, 2002). Yet as other stressors and climate change alter historical ecological patterns, these observational systems face increasing challenges in distinguishing concerning trends from normal fluctuations (Ruddle, 2000; Lazurko et al., 2023; McNie et al., 2023).

4.1.4 Practices and gaps in the current methods for fish population monitoring

The respondents' articulations establish a complete dependence on indigenous observation strategies. This dependence on qualitative, observational data, while valuable, explicitly confirms the absence of a systematic, quantitative baseline for fish populations. The 'gap' acknowledged here is not just in technology, but in the essential data required for adaptive management. This makes a compelling case for AI as a tool to assist in creating that baseline through approaches like automated species identification and population counting, rather than to merely enhance an existing scientific dataset (Reid et al., 2021). Indigenous communities employ cultural techniques that include inferring seasonal flood patterns to forecast fish movements, recognizing biological indicators like the appearance of pearl chad or red-neck tilapia, and tracking mass migrations, such as catfish movements from west to east (Eckert et al., 2020). The clear statement about the absence of technology underscores a critical gap between on-the-ground practices and modern fishery management tools (Johannes, 2002). Whereas these traditional systems underline invaluable generational wisdom and a nuanced understanding of local ecosystems, predominantly in correlating fish migrations with hydrological cycles, they face increasing vulnerabilities from climate variation disrupting historical patterns (Alexander et al., 2021a,b).

The dependence on particular indicator species creates likely fragility in the monitoring framework as environmental changes accelerate (Moller et al., 2004). The respondents‘ dependence on indigenous approaches, combined with their expressed concerns about their ability to detect a slow decrease, divulges a pure demand for supplementary monitoring data. This state provides an opportunity to develop hybrid answers that respectfully integrate indigenous knowledge with appropriate technologies. Primary steps, informed by the fishers' own indicator-based framework, could include digitizing flood-fish relationships for projection models, creating SMS-based reporting for indicator species observations, and periodic scientific justification through methods like eDNA sampling (Sigsgaard et al., 2020). These integrations must prioritize a participatory strategy that protects critical indicator species, maintains community ownership, and establishes two-way learning between scientific and indigenous systems (Tengö et al., 2014). Eventually, these traditional practices constitute an established ecological monitoring network that should be improved rather than substituted, creating culturally-grounded, scientifically-informed systems capable of addressing contemporary environmental challenges while preserving traditional expertise systems (Chambers et al., 2021). This balanced approach suggests the most promising path toward community-based, resilient fishery management that regards local knowledge while incorporating technological advancements where advantageous (Brondízio et al., 2021).

The analysis of interview data on monitoring practices revealed a consistent reliance on sophisticated Traditional Ecological Knowledge (TEK) and a clear gap in technological tools. The key themes, supporting quotes, and their implications for management are summarized in Table 3.

Table 3

Table 3. Thematic analysis of traditional monitoring practices and identified gaps.

4.1.5 Accessibility and trust in fishing regulations information channels

The participants' views highlight that fishing restrictions and rules are currently communicated through radio announcements from the Ministry of Environment, echoing a centralized, top-down communication method in fisheries governance (Song et al., 2020). While this approach warrants reliable delivery of regulations across communities, it raises critical questions about engagement and accessibility (Béné et al., 2021). The dependence merely on radio broadcasts may exclude fishers in zones with poor reception or those who speak minority languages not covered in the radio announcements (Gelcich et al., 2010). Moreover, this one-way communication method lacks instruments for fishers to give feedback or seek explanation, potentially leading to gaps in information dissemination, understanding, and compliance (Jentoft and Chuenpagdee, 2015).

This system mirrors a traditional government-led method that prioritizes consistency but may benefit from complementary approaches to improve effectiveness (Berkes, 2009). For instance, assimilating community-based broadcasting through community leaders or fisher cooperatives could bridge the gap between local practices and formal regulations (Ostrom, 2009). Furthermore, leveraging modest mobile technologies, such as SMS updates or voice communications in local dialects, could make crucial information more accessible while upholding the authority of official communication channels (Aker, 2011). The challenge lies in designing inclusive communication approaches that regard the Ministry's regulatory role while accommodating the various needs of fishing communities, mainly in remote areas where radio signals are not reliable (Cohen, 2022). Eventually, improving how fishing guidelines are shared and understood could reinforce both trust and compliance in fisheries management (Gutiérrez et al., 2011). Effective directive broadcasting entails balancing centralized authority with localized communication approaches that warrant all fishers receive, understand, and can respond to management rules (Cinner et al., 2012). A hybrid model that combines community intermediaries with radio broadcasts and basic digital tools may provide the most inclusive approach (Song et al., 2021).

4.1.6 Potential for mobile technology adoption catch reporting

The respondents' nuanced response, representing preparedness to use mobile phones for reporting violations to authorities but the hesitancy for individual catch documentation, discloses significant sociotechnical dynamics in technology approval for fishery management (Stocks et al., 2022). This choosy acceptance advocates for a fundamental displeasure between individual fisher incentives and institutional systems, where phones are perceived as tools for application rather than collaborative resource management (Ryan, 2023). The acknowledgment that some local members of the community already report violations validates existing digital literateness and infrastructure capacity (Aker, 2011), yet the resistance to individual catch reporting accentuates concerns about data ownership, alleged benefits, or possible surveillance implications (Micheli et al., 2022). This dichotomy reflects global patterns where technology implementation flops when users see no individual value or fear misappropriation of their data (Manski, 2023).

The interpretations feature how present systems prioritize punitive compliance over participatory engagement, with fishers more eager to report infractions to authorities than contribute to shared knowledge bases (Song et al., 2023). An operative mobile reporting system would need to address these confidence hindrances by representing strong individual and collective benefits. The fishers' choosy acceptance, using phones to report violations but not their own catch, proposes that system design must first address their perceived risks. Thus, preliminary features should focus on community-wide benefits, such as real-time stock assessments or market price alerts knotted to participation (Chavez et al., 2024), to build trust before introducing individual catch documentation. The preference for individual and institutional reporting also mirrors cultural customs of collective action, signifying that group-based reporting through fishing cooperatives might be more satisfactory than individual mandates (Partelow et al., 2020). Eventually, this instance exemplifies how technology integration must navigate multifaceted incentive structures and power dynamics where apparatuses designed for top-down monitoring will face resistance except reconfigured as reciprocal systems that bring tangible value to the locals while regarding communal decision-making processes (Fleming et al., 2021). The study findings call for co-designed solutions that blend livelihood supports with regulatory needs, possibly through dual-function apps that combine official reporting with catch optimisation tips or features like weather alerts, thereby creating mutual benefits for both management authorities and fishers (Turner et al., 2016).

4.1.7 Perceived trends and evidence of river health degradation

The participants' vivid account of the river/delta's ecological decline compared to the year 1977 emphasizes the philosophical impact of human activity on water health and fisheries, highlighting critical socio-environmental dynamics (Coulthard et al., 2021). Their metaphors identify expanding human settlements, pollution, and habitat destruction, mainly gardening, grass cutting, and land mining, as key drivers of degradation, directly linking these factors to compromised river health and declining fish populations (Fulton et al., 2019). This perspective underscores a fundamental chain in which land-use changes disrupt ecosystems, weakening both biodiversity and fishery sustainability (Fulton E. A. et al., 2021). The reference to 1977 suggests a long-term, generational awareness of environmental decline, positioning older fishers as witnesses to ecological shifts that younger generations may perceive as normal (Sáenz-Arroyo and Revollo-Fernández, 2021).

The respondents' attention on local actions, such as grass cutting, is distinct from the absence of institutional accountability, implying a sense of disempowerment or a normalization of degradation in addressing systemic issues like industrial contamination or weak regulatory enforcement (Howlett, 2023). Their general view, tying habitat health to fish viability, echoes Indigenous and Local Knowledge (ILK), which integrates social and ecological factors more impeccably than conventional management frameworks (Lam et al., 2020). This narrative aligns with political ecology moralities, where environmental degradation arises from unequal power dominance in resource accessibility, emulating global cases such as the Mekong Delta, where small-scale fishers stand the brunt of habitat loss (Suhardiman et al., 2018). The participants' emphasis on decreasing sustainability signals a consciousness of tipping points, where the river's capacity to regenerate is irrevocably damaged (Folke et al., 2004). To address these challenges, interventions must prioritize pollution control alongside habitat restoration (e.g., replanting grasses), while vigorously involving fishers in participatory monitoring to influence their observational expertise (Johnson et al., 2021). Their testimony similarly serves as a powerful advocacy tool, humanising the consequences of ecological neglect and urging policymakers to support enforcement against negative practices (Bennett, 2022a,b). Eventually, this situation highlights the significance of co-designed solutions that integrate governance reform with ecological recovery, thus safeguarding both environmental resilience and equitable accessibility to resources for fishing communities (Nightingale et al., 2023).

4.1.8 Community-led efficacy and compliance with conservation rules

The participants' revelation of an absence of formal community rules to protect fish stocks exposes a critical governance gap in local fisheries governance, where self-regulation has become a systematic default (Partelow, 2023). This laissez-faire strategy proposes either a breakdown of indigenous management systems or their unsuitability in response to contemporary pressures on the river ecosystem (Freed et al., 2022). The acknowledgment that fishing activities are self-regulated suggests individual rather than shared responsibility for resource conservation, which aligns with common-pool resource dilemmas where short-term individual gains often supersede long-term community benefits (Schlüter et al., 2019). The lack of institutionalized protection mechanisms points to potential historical factors such as the erosion of traditional ecological knowledge, displacement of local leadership structures, or the encroachment of peripheral fishing pressures that have undermined joint action (Albert et al., 2021). The state echoes patterns observed in other small-scale fisheries, where resource scarcity and modernization fail to maintain social cohesion, making voluntary compliance difficult to enforce (Tilley et al., 2021).

The respondents' matter-of-fact demonstration of this reality submits either notification to the status quo or ignorance of different management models, stressing an essential for transformed community engagement processes (Temper, 2023). This self-regulated system probably exacerbates overfishing, as individual fishers face no formal consequences or social sanctions for unsustainable practices (Fabinyi et al., 2022a). The absence of community regulations may also echo a lack of trust in joint systems, likely stemming from historical failures or seeming inequities in rule enforcement (Barnes et al., 2020). To address this narrative, interventions would necessitate the facilitation of local dialogue to co-create rules with community legitimacy, possibly rebuilding systems reminiscent of historically operative but now abandoned indigenous management practices (Packer, 2025). The instance accentuates how the absenteeism of formal regulation nurtures the tragedy of the commons (Epstein, 2023), while signifying that untapped possibilities exist for stimulating community-based management through participatory strategies that establish the tangible benefits of shared conservation efforts (Wyborn et al., 2021).

4.1.9 Stakeholder roles in fishery decision-making processes

The participants' views underline the importance of multi-stakeholder collaboration, inclusive in fisheries decision-making, reflecting a nuanced understanding of governance that balances community involvement with expert and institutional oversight (Chuenpagdee, 2020). By promoting community education campaigns led by authorities, the respondents emphasize the need for top-down knowledge distribution to ensure community stakeholders understand rules and their rationale (Nursey-Bray, 2021). However, their concurrent emphasis on involving the community, alongside authorities, technocrats, and knowledgeable persons, divulges an impetus for participatory management that values both technical expertise and local lived experience (Rathwell et al., 2023). This twofold focus proposes a consciousness of the limitations of morally community-led or ethically bureaucratic approaches; instead, it invites a hybrid model where guidelines and regulations are co-developed by those with administrative, scientific, and traditional knowledge (Österblom et al., 2024).

The reference to “knowledgeable” individuals, distinct from technocrats or authorities, may implicitly recognize the role of local or indigenous ecological expertise, whose perceptions are often side-lined in formal policy-making (Tengö et al., 2021). The emphasis on education campaigns also highlights an apparent gap in awareness, where communities may lack the information necessary to engage eloquently in complying or decision-making with sustainable practices (Mcdougall et al., 2023). This interpretation aligns with global evidence that effective fisheries governance demands not only enforceable regulations but also buy-in from indigenous users, which is best attained through shared ownership of policies and transparent communication (Ban et al., 2024). The respondents' vision thus reflects the ideologies of adaptive co-management, where decentralized decision-making is reinforced by administrative and scientific frameworks (Fabinyi et al., 2022a). Critically, their insights avoid romanticizing community autonomy or uncritically trusting top-down control, instead suggesting a pragmatic middle path (Cohen, 2024). For such a framework to thrive, however, power disparities must be addressed, ensuring that local voices are not tokenised and that technocrats regard indigenous knowledge (Marshall et al., 2022). This study suggests that fisheries management in the region could benefit from structured platforms for collaborative dialogue, where experts, authorities, and communities design and monitor rules and regulations, with education initiatives serving as a bridge to impartial participation and compliance (Alexander and Armitage, 2023).

4.1.10 Fisheries management unanticipated insights

The respondents' ultimate recommendations highlight a visionary yet realistic roadmap for restoring the delta's health and fisheries sustainability, combining traditional knowledge with innovative solutions (Alexander et al., 2021c). Their call for elected committees and community policing mirrors a demand for localized governance, recognizing that top-down enforcement alone has not been successful in curbing overexploitation (Minter et al., 2024). By promoting intergenerational leadership (both “young and old”), they underscore the need to bridge traditional ecological wisdom with younger generations' adaptability, promoting collective ownership of conservation efforts (Diver et al., 2022).

The outstanding proposal to allow delta recovery for a reasonable period emphasizes the importance of considering ecological timescales, challenging short-term economic priorities with a long-term restorative imperative (Lade et al., 2020). This aligns with global cases such as the Cauvery River in India, where fishing bans enabled partial ecosystem recovery, though success depended on compensating affected fishers (Mbaru et al., 2021), a nuance these respondents plan could integrate. Their emphasis on physical habitat restoration (regrowing vegetation, clearing invasive species) directly addresses the original causes of fish decline, echoing biome-specific conservation ideologies that prioritize native flora-fauna symbiosis (Hagger et al., 2022). Remarkably, numerous participants explicitly suggested integrating technology for monitoring, signifying a community-articulated opening for innovation. This suggestion to integrate AI technology directly replies to that stakeholder vision, potentially for monitoring fish stocks or detecting pollution (Taconet et al., 2022). Its triumph would hinge on community training and confidence (Vaughan et al., 2024), gaps that their parallel proposals for face-to-face education intend to fulfill. This twin strategy, pairing high-tech tools with grassroots education, discloses a consciousness that technology alone cannot determine change without cultural buy-in (Medina, 2021). The participants' all-inclusive vision weaves together governance (committees, policing), ecology (habitat restoration, period recovery), and innovation (AI, adaptation, and education), providing a template for integrated resource management (Fulton S. et al., 2021). Nevertheless, its practicability depends on external support (e.g., legal recognition of community policing, funding for AI tools) (Cohen, 2024) and reconciling immediate livelihood desires with long-term recovery (Coulthard et al., 2023), a tension requiring participatory policy collaborations (Chimbali et al., 2023). Eventually, these suggestions reject piecemeal solutions, asserting that community agency, ecological resilience, and technological adaptation must advance in tandem (Kavhu et al., 2025).

5 Documentary analysis

The study analyzed secondary data, including national fishery policies (MFMR (Ministry of Fisheries and Marine Resources), 2020), transboundary governance documents (Heyns, 2022), and global datasets (FAO, 2021). This analysis was conducted to address a critical question evolving from the interview findings: Does the existing legal framework enable or inhibit the types of community-informed, adaptive, and data-driven management solutions that stakeholders envision? The primary focus was the Inland Fisheries Resources Act (2003) and its Regulations, which provide the legal foundation for Namibian inland fisheries. The documentary analysis specifically assessed how this legal scaffold aligns with the governance gaps and socio-technical needs identified by fishers and other stakeholders in the preceding sections.

5.1 Synthesized analysis: the Namibian inland fisheries legal framework as a static scaffold for AI-driven governance

The Inland Fisheries Resources Act 1 of 2003 and its supplementing Regulations of 2003 collectively establish a comprehensive legal framework. Though when viewed through the lens of the interview findings, a serious disconnect emerges. The fishers' calls for localized governance (Section 4.1.10), hybrid monitoring (Section 4.1.4), and responsive rules stand in stark contrast to the framework's inherent structure. An in-depth analysis emphasizes that while this guideline provides a robust foundation for conservation, its functional structure remains static, rigid, and analog, making a legal environment that is incompatible with the community-centric, dynamic, and data-informed approaches proposed by the study's participants. This misalignment poses a serious challenge to implementing any of the stakeholder-derived suggestions, including the exploratory use of AI for governance (Nakamura and Amador, 2023; Achieng et al., 2023).

5.2 The core dissonance of foundational intent vs. operational obsolescence

The Inland Fisheries Act and its Regulations exemplify a core dissonance between forward intent and obsolete practice (Mosepele and Kolawole, 2017). While the Act establishes a progressive order by requiring evidence-based, ecosystem-oriented governance reinforced by multi-stakeholder management structures (Nakamura and Amador, 2023), the Guidelines enforce a static command-and-control system based on input restrictions such as bag limits, net sizes, and licensing processes (MFMR (Ministry of Fisheries and Marine Resources), 2020). This core dissonance directly impacts the feasibility of addressing fisher-identified problems. For instance, the fishers' observed reliance on self-regulation due to a lack of formal community rules (Section 4.1.8) is, in part, a consequence of this legal rigidity. The Act's progressive intent is hamstrung by Regulations that enforce a static, top-down system, failing to provide the legal flexibility for the elected committees and community policing that fishers themselves proposed. Similarly, the 'information gap' created by paper-based reporting directly undermines the potential for the responsive, evidence-based decision-making that communities seek (Achieng et al., 2023). In divergence, the Act's call for “the best scientific information” implicitly requires advanced gears like remote sensing, predictive analytics, and big data, capabilities that AI could provide to effectively govern the dynamic Okavango River system (FAO, 2021; Turhan, 2021). The Regulations, however, disregard this vision by institutionalizing operational obsolescence, leaving management systems incapable of responding to real-time ecological and livelihood challenges (Datta and Chaffin, 2022).

5.3 Structural and procedural barriers to technological integration

The current legal guidelines not only overlook provisions for AI but would likely hinder its future adoption through rigidity, ambiguity, and enforcement gaps. A preliminary analysis suggests that this static framework is mismatched with AI's capacity for real-time monitoring (Garren et al., 2021). Their definitions of fishing and gear are linked to physical tools, leaving AI-driven systems like drones or machine learning outside legal recognition (Knowledge4Policy, 2022), while their silence on the admissibility of digital evidence avoids recognizing satellite imagery or acoustic data in enforcement (Research4Committees, 2022). Additionally, the lack of provisions for data-related offenses leaves AI systems vulnerable to manipulation without legal protections (Knowledge4Policy, 2022). These structural barriers explicitly hinder the integration of the very tools that could bridge the gaps fishers described. The respondents' nuanced willingness to use mobile technology (Section 4.1.6) is rendered moot by a legal framework that lacks provisions for digital evidence. Furthermore, the slow, centralized process for changing rules, identified in this legal analysis, prevents management from responding to the “slowly coming changes” and lost 'recovery periods' that fishers perceptively identified (Section 4.1.2). The law, as it stands, institutionalizes the reactivity that the community's knowledge seeks to overcome (Garren et al., 2021).

5.4 The path forward: from a static scaffold to an adaptive, enabling framework

Considering a future path for potential AI integration, this analysis indicates that technical advancements alone would be insufficient; a parallel evolution of the legal framework would likely be required. The existing Act and Regulations provide a critical scaffold, but future feasibility studies should investigate the need to evolve this into an enabling framework. Any future legislative review could, for instance, explore amending the Act to explicitly acknowledge digital monitoring tools and consider legally recognizing evidence from automated systems (Briand, 2023). These are potential long-term goals, contingent upon the success of prior pilot projects and detailed cost-benefit analyses. Alongside, the Guidelines must be overhauled to operationalise this vision by enacting digital data flows, establishing interoperable repositories, leading to adaptive processes for real-time, AI-informed governance, and embedding equity through all-inclusion design with indigenous communities (Cisneros-Montemayor et al., 2021). In the absence of such reforms, Namibia's fisheries management remains entombed in outdated models, but with them, the Okavango River Basin could become a global paradigm of resilient, sustainable, and technologically empowered freshwater governance (Petrossian and Ward, 2022; Obura et al., 2021).

5.5 Integrated governance assessment

Regulatory documents were analyzed systematically using content analysis matrices to relate the stated objectives with actual execution outcomes (Nguyen, 2020). A policy gap analysis exposed three critical disconnects: (a) a misalignment between enforcement capacity on the ground and seasonal fishing bans (Wilson et al., 2006), (b) discrepancies in transboundary data-sharing procedures (Kamau, 2019), and (c) inadequate integration of traditional ecological knowledge into formal governance plans (Bachmann et al., 2022). These findings highlight systemic challenges in aligning regulatory goals with practical implementation, accentuating the need for better management, capacity building, and incorporation of indigenous knowledge systems into fisheries management frameworks (Molefe, 2023; Patel, 2024).

5.6 Triangulation and validation

The research findings were cross-validated using a multi-method approach to enhance credibility and contextual relevance (Khandekar et al., 2023). A key component of this was triangulation, which involves using different data sources to corroborate findings. This was achieved through several methods:

Data Triangulation: The themes emerging from the qualitative interviews were systematically compared with independent, secondary data sources, including logbook records of fishing operations (Chen and Roberts, 2019) and policy documents. The consistencies and discrepancies between these different types of data helped to elucidate patterns in effort, compliance, and catch reporting (Lopez et al., 2020), providing a cross-check on the interview data.

Methodological Triangulation: The ground-truthing of historical trends identified in interviews was undertaken through focus group discussions, which provided a different forum for veteran fishers to offer long-term observations that could challenge or authenticate the initial interpretations (Martinez et al., 2022).

Stakeholder Feedback: Member checking was performed with 16 key informants from the original participant pool (Harrison and Palmer, 2020). It is acknowledged that this does not constitute independent validation, as it relies on the same participants. Therefore, its purpose is best understood not as a test of objectivity but as a participatory step to enhance the accuracy and contextual relevance of the interpretations, ensuring that the researchers‘ framing and summaries resonated with the participants' intended meanings and were not misrepresentative (Johnson et al., 2021).

This multi-pronged systematic approach, underlining qualitative (face-to-face interview and documentary analysis) perceptions, and participatory validation, not only consolidated the study's trustworthiness (Tracy, 2024) but also pinpointed four significant areas for AI intervention; these were derived directly from cross-referencing interview themes with documentary evidence. For instance, the theme of self-regulation and illegal fishing (Section 4.1.8) aligns with the need for improving surveillance (e.g., detecting illegal fishing hotspots). Similarly, the documented discrepancies in logbook records (Chen and Roberts, 2019) and fishers' resistance to self-reporting (Section 4.1.6) highlight a need for optimizing catch documentation. The fishers' observed shifts in catch (Section 4.1.2) and the policy gaps in seasonal bans (Section 4.5) collectively underscore the potential of forecasting stock dynamics and enhancing seasonal bans (e.g., aligning closures with ecological and socioeconomic realities) (Cohen et al., 2019). Momentously, these interventions were designed with a strong contextual foundation, warranting that they complement (rather than displace) local ecological knowledge and fit within present governance structures (Reyes and Kenter, 2021). For instance, AI instruments for surveillance were suggested to enhance, not to replace, community patrols (Bennett et al., 2022), while predictive models included traditional indicators of fish behavior deliberated in face-to-face interviews (Okeke and and Zhang, 2023). This method emphasizes the importance of assimilating technological novelty with place-based knowledge to forge actionable, impartial solutions (Brondizio et al., 2021).

6 Challenges of integrating AI in fisheries governance for the Okavango River ecosystem

A fundamental challenge preceding technical implementation is the near-total absence of quantitative baseline data on current fishing effort, precise catch rates, and key ecosystem health indicators. This lack makes it difficult to calibrate AI models or to identify specific, measurable targets for intervention. Integrating AI into fisheries management in the Okavango River offers immense potential but faces substantial socio-technical and ecological challenges. Therefore, the initial integration of AI must focus on foundational data generation and establishing key performance indicators in collaboration with communities. The regions' rural context poses practical hindrances, unreliable electricity, poor internet connectivity, inadequate digital literacy among fishers, and high costs of maintaining innovative systems (Kamutuezu et al., 2021; Nwokolo et al., 2024).

Beyond the socio-technical challenges lies a critical gap in implementation planning. This study, by its nature, does not define the specific technical requirements, infrastructure upgrades, or detailed training curricula needed for AI adoption. However, any future initiative must address these foundational elements. Critical unanswered questions that must be the focus of subsequent feasibility studies include: the specification of hardware (e.g., sensor types, server capacity), the stability and expansion of internet connectivity to remote areas, the development of multi-level digital literacy and technical training programs for fishers and ministry staff, and the creation of realistic, phased timelines that acknowledge the long-term nature of such a socio-technical transition. The absence of these details in the current discussion underscores that AI integration is a complex, long-term endeavor, not a simple technological plug-in.

Ecological complexities, such as recurrent flooding and various biodiversity, could further complicate AI's accuracy, as present models may not align with indigenous realities without significant, locally sourced data. Beyond technical hurdles, community trust emerges as a serious factor: fishers may perceive AI as surveillance or as undermining long-standing traditional practices, except that it is presented through advantageous, transparent, participatory processes (Islam et al., 2024; co-design and indigenous participation literature). Monetary sustainability is equally ambiguous, with risks of neglect if technical support and funding lapse after pilot phases. Thus far, these challenges are surmountable through co-design methods that merge AI with indigenous knowledge, employ accessible instruments (e.g., voice-based reporting in local languages), and begin with small-scale, high-value pilot projects to build trust and establish tangible benefits (Ecology and Society on Kakadu dashboards; Participatory technology development). Eventually, successful integration demands positioning AI as a supportive complement to community governance rather than a replacement, certifying that technological invention progresses hand in hand with social inclusion, equity, and local empowerment.

7 Opportunities for integrating AI in fisheries governance of the Okavango River ecosystem

The findings from this study suggest several potential opportunities where AI could be investigated as a tool for fisheries governance in the Okavango River. A suggested research agenda should explore AI's potential for real-time ecological monitoring. Future pilot projects could assess the viability of AI-driven surveillance systems. Substantial opportunity for further study lies in democratizing participation and knowledge through voice-based tools. These potential applications represent a starting point for future feasibility studies that must examine technical, economic, and social viability before any policy implementation can be considered (Mosepele et al., 2024; Martínez-Capel et al., 2017). AI presents strong instruments for real-time ecological monitoring, allowing continuous tracking of pollution, water quality, and habitat changes with far better accuracy than manual approaches (Belarbi et al., 2026; Yalew et al., 2023). In a floodplain system as dynamic as the Okavango, such instruments could forecast breeding or fish migration cycles and predict ecological burden, providing opportunities for proactive rather than reactive governance (Nyadzi et al., 2021; Ramos et al., 2024). AI-driven surveillance systems, such as computer vision drones, also offer capable avenues for combating illegal fishing, consolidating community policing efforts, while lessening the physical and financial pressures on local monitors (Bravo-Peña, 2025; Loh and Wackernagel, 2004). Besides, machine learning could scrutinize catch data to detect overexploitation hotspots, helping management bodies respond with targeted, evidence-based interventions (Mbaiwa et al., 2019; King and Chonguiça, 2016).

Added substantial opportunity lies in democratizing participation and knowledge. AI systems equipped with local language processing could provide voice-based tools in indigenous languages, bridging literacy gaps and making reporting more inclusive (Denney, 2022). Extrapolative models could be translated into reachable visuals, supporting community education and locally shared decision-making (UNDP, 2024). By allowing participatory data systems, such as mobile apps for ecological observations and crowdsourced catch records, AI could also assimilate traditional ecological knowledge with scientific understandings, addressing community concerns about ecosystem recovery and habitat restoration (Mosepele et al., 2024; Nyadzi et al., 2021).

AI's predictive and modeling competencies further open doors for long-term sustainability planning. Simulation instruments could exemplify different conservation scenarios, including lengthy recovery timelines, consolidation communities to assess trade-offs before approving interventions (Belarbi et al., 2026; Yalew et al., 2023). Significantly, these technologies could strengthen, rather than replace, existing governance structures by equipping elected committees with more dependable data and preserving elders' ecological wisdom over digitized knowledge systems (Martínez-Capel et al., 2017; Ramos et al., 2024). If implemented carefully, AI could create a synergistic model where progressive technologies intensify the efficiency of community-based management. This demands co-design with community stakeholders, just access, and culturally grounded implementation strategies to ensure benefits are widely shared (Denney, 2022; King and Chonguiça, 2016). When aligned with community values, AI becomes not just a technical solution but a cooperative instrument that reinforces resilience, supports restoration goals, and builds an adaptive, future-ready fisheries management system for the Okavango River (Mbaiwa et al., 2019; Loh and Wackernagel, 2004). It is critical to state that these are not proven solutions for this context, but illustrative examples of technologies that could be tested. Their success would be entirely contingent on overcoming the significant challenges outlined in Section 5.

The opportunities described herein are conceptual. Translating them into reality would require a dedicated implementation strategy that this study does not provide. For example, the proposal for ‘voice-based tools in indigenous languages' necessitates a detailed plan for software development, linguistic translation, acoustic model training, and device distribution. Similarly, “AI-driven surveillance systems” would require a full assessment of drone regulations, maintenance logistics, and data processing pipelines. The promising potential of AI can only be realized after these practical, logistical, and financial implementation details are thoroughly researched and addressed in a dedicated planning phase.

The analysis of interview data on monitoring practices revealed a consistent reliance on sophisticated Traditional Ecological Knowledge (TEK) and a clear gap in technological tools. The key themes, supporting quotes, and their implications for management are summarized in Table 3.

This structured analysis demonstrates that the present monitoring system, while rich in knowledge, is primarily qualitative and faces modern pressures. This directly informs the need for hybrid monitoring solutions that are culturally grounded and technologically augmented.

7.1 Ethical considerations for AI in Okavango fishery governance

This study recognizes that the integration of AI into community-based resource management raises significant ethical considerations that must be proactively addressed. While the primary data collection for this research received ethical approval from the University of Namibia (Reference Number: RU00042) and involved informed consent for the interviews, the ethical implications of the potential technological systems discussed herein extend far beyond these standard research protocols.

1. Data Privacy, Ownership, and Consent: The potential deployment of AI systems for catch reporting or ecological monitoring would generate vast amounts of data, including potentially sensitive information about fishing locations, yields, and community resources. Our findings, particularly the fishers' hesitancy toward individual catch documentation (Section 4.1.6), underscore deep-seated concerns about data usage. A critical ethical imperative is to establish clear, co-designed protocols for data sovereignty, ensuring that communities retain ownership and control over their data. Informed consent for any future data collection must be ongoing, contextual, and explain not only the benefits but also the risks of data sharing and algorithmic decision-making.

2. Surveillance and Power Dynamics: AI-enabled tools like drones or automated catch monitoring can easily be perceived as, or devolve into, systems of surveillance. This risk is acute in contexts with existing power imbalances between fishing communities and regulatory bodies. To prevent this, any AI system must be governed by an ethical charter, developed in partnership with communities, that explicitly prohibits the weaponization of data for punitive enforcement and mandates its use for cooperative, community-beneficial management. The goal must be to augment local governance (as requested in Section 4.1.10), not to replace it with digital surveillance.

3. Algorithmic Bias and Equity: AI models are trained on data, and if that data is unrepresentative or reflects historical biases, the resulting recommendations can perpetuate or exacerbate existing inequalities. An ethical approach requires ongoing audits of AI systems for fairness and bias, ensuring that management recommendations do not disproportionately harm specific sub-groups within the community, such as small-scale subsistence fishers. Therefore, ethical integration of AI is not a secondary concern but a prerequisite for success. Future work must place these considerations at the forefront, co-designing ethical frameworks and data governance models with the communities of the Okavango River ecosystem before any technological implementation is considered. Without this foundation, AI systems risk violating trust, undermining autonomy, and causing harm, regardless of their technical sophistication.

8 Conclusion and recommendations

The integration of AI into Okavango River ecosystem fisheries management offers a nuanced opportunity to improve sustainable governance while regarding the region's unique socio-ecological setting (Bennett, 2022a). The study findings demonstrate that AI's true value lies not in substituting human know-how and traditional knowledge systems, but in deliberately augmenting them (Kruijssen et al., 2018). Though the technical competences for real-time monitoring, unlawful fishing detection, and predictive modeling are well-established (Alvarez et al., 2021), the study divulges that human dimensions, including data literacy, community trust, and institutional support, appear as the central determinants of successful implementation (Cisneros-Montemayor et al., 2022). The Okavango River case presents universally applicable understandings, particularly for the Global South, where biodiversity conservation must be balanced with livelihood preservation (Keane et al., 2021).

To move from principle to practice, a formal protocol for integrating Traditional Ecological Knowledge (TEK) with Artificial Intelligence (AI) must be established, detailing structured co-design workshops with fishers, elders, scientists, and developers; a hybrid data governance model ensuring community sovereignty; and the creation of ‘translation tools' like shared dashboards. The objective is a seamless, two-way workflow where local observations directly inform AI models, and model outputs, in turn, validate and contextualize community knowledge.

Rather than definitive policy recommendations, this study proposes a cautious and evidence-based pathway for future exploration. The findings highlight several critical prerequisites for any future AI integration:

1. A Foundational Research and Development Phase: The immediate priority is not implementation, but investigation. This must begin with small-scale, co-designed pilot projects focused on addressing the specific data gaps and stakeholder concerns identified in this study (e.g., testing SMS-based reporting for market prices or a limited drone surveillance trial). The primary goal of these pilots should be to evaluate feasibility, build trust, and generate preliminary data for a robust cost-benefit analysis.

2. A Shift from Prescription to Exploration: AI development must occur through frank co-design processes with fishing communities, with the initial objective of creating functional prototypes and assessing their acceptability, not deploying policy-mandated systems.

3. Prioritize Analysis Over Adoption: Continued investment is vital, but should be directed first toward funding rigorous feasibility studies and literacy programs to bridge technological gaps, rather than large-scale procurement.

Forward-looking, the study recommends three key research directions to precede any policy change: (1) conducting the aforementioned feasibility studies and cost-benefit analyses; (2) initiating longitudinal pilot projects to assess AI's long-term ecosystem and social impacts (Cooke et al., 2021); and (3) developing flexible models for technology transfer (Ban et al., 2022).

The policy and AI recommendations proposed in this study are not speculative; they are directly derived from the specific gaps and needs identified by stakeholders and documented in the thematic analysis. Table 4 explicitly traces the lineage from empirical findings to proposed interventions.

Table 4

Table 4. Thematic analysis of traditional monitoring practices and identified gaps.

This traceability matrix ensures that every proposed intervention is grounded in empirical evidence, strengthening the practical relevance and defensibility of the study's conclusions.

The knowledge of the Okavango River demonstrates that 21st-century fishery governance requires neither blind trust in technology nor resistance to new ideas, but rather the thoughtful combination of artificial and human intelligence (Bennett et al., 2023). As a testing ground for this emerging approach, the Okavango's delicate balance between ecology and economy offers valuable lessons for environmental stewardship worldwide, suggesting that the most sustainable solutions emerge when advanced technology supports rather than replaces community knowledge and needs (Berkes, 2021). The way forward demands flexibility and patience, but the potential benefits, resilient river systems managed through the collaboration of traditional wisdom and appropriate technology, make the Okavango River both an example and a testing ground for participatory conservation in the digital era (Folke et al., 2023). Considering the study's finding that a lack of baseline data is a key obstacle, the primary and most vital recommendation is to start pilot projects that focus on co-designing data collection systems. AI should be used not as a complete solution but as a tool to help establish these baselines. Future research must focus on gathering quantitative data on catch rates and ecosystem health to validate stakeholder perceptions and create a strong foundation for predictive or optimisation models.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

FJ: Data curation, Visualization, Writing – original draft, Resources, Project administration, Investigation, Validation, Conceptualization, Writing – review & editing, Formal analysis, Methodology, Funding acquisition, Supervision. PS: Writing – original draft, Investigation, Writing – review & editing. FH: Writing – original draft, Writing – review & editing, Formal analysis. TI: Writing – original draft, Writing – review & editing.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Acknowledgments

The authors gratefully acknowledge the Okavango River fishers who participated in this study and the Ministry of Environment for their support.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Gen AI was used in the creation of this manuscript.

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