The framework presented in this paper offers a structured and tailored approach to GIS planning for aquatic animal health (AAH) management, which is critical for implementing projects effectively, especially for GIS end users working in complex aquatic environments. In fact, when compared to terrestrial contexts, aquatic environments present specific challenges for infectious disease surveillance and control. Pathogen transmission can occur rapidly and over large distances due to hydrodynamic connectivity, such as water currents, tides, or river flows, which may spread pathogens far beyond the initial outbreak site (Alaliyat and Yndestad, 2015). Environmental variables, including temperature, salinity, dissolved oxygen, and nutrient levels, can fluctuate over short timeframes and significantly influence host susceptibility, pathogen persistence and spread, and outbreak seasonality (Urke et al., 2021; Ding et al., 2024). For instance, in the Italian case study, bathymetry was considered since the area did not have a deep seabed, which is a factor to consider due to the poor water recirculation and the consequent persistence of the pathogen. Furthermore, in aquaculture, the boundaries of production units are often more permeable than in terrestrial farming, making containment strategies less straightforward. These factors make it necessary to adapt GIS tools to integrate spatial layers and dynamic environmental data (e.g., hydrological models), which are generally less relevant or absent in terrestrial epidemiology. Through a step-by-step process based on a series of chronological phases (Figure 2), the presented framework is meant to ensure that the developed GIS projects are sustainable, efficient, and aligned with the developers’ intended goals, as well as applicable and reliable for projects of all sizes, including small or one-time efforts. Unlike other existing but more generic models and protocols, the proposed framework combines technical-practical aspects (e.g., inclusion of a master data plan) with organizational and communicative components typical of the AAH research and production sector, that is to say it is meant to put standard theory into practice in the specific context. Moreover, and in line with the “holistic approach” wished by the European Food Safety Authority (EFSA (European Food Safety Authority) et al., 2022), the interdisciplinary and collaborative effort through which the framework was produced allowed combining the experts’ different perspectives and practical insights (Figure 1), ensuring that the final framework was not only robust from a technical point of view but that it also addressed real-world challenges typical of AAH surveillance and management. It also enabled the collection and provision of practical solutions for organizing, maintaining, and analyzing spatial datasets, especially ensuring they are suitable for aquatic environments (Open GIS Consortium, Inc 1999). The two case studies describing the practical applications of the framework, both in the marine aquaculture in Italy and in the freshwater aquaculture in Brazil, demonstrated its versatility and its capacity to be adapted to different environments and to guide the development of GIS projects that are aligned with stakeholders’ expectations, even when using simulated data, as may be the case when testing a new model or information system (Ishiwaka et al., 2021; Karras et al., 2023; Steele et al., 2024) or when real data are not yet available because data collection has not yet been completed (e.g., case study in the Toscana Region, Italy).

One of the main strengths of the proposed framework is its ability to guide end users, especially those with still limited GIS expertise, through all the stages of GIS project planning and implementation in complex domains such as the AAH one (Tomlinson, 2007; Longley et al., 2015). In particular, it encourages the inclusion of fundamental GIS planning elements that are often overlooked. In fact, by following the framework’s phases, GIS end users of the two Italian and Brazilian studies were able to define goals, select appropriate data sources, structure their systems, and develop outputs that addressed their stakeholders’ needs. As shown by both case studies, the use of the framework facilitated interdisciplinary collaboration by introducing a shared workflow that improved communication between GIS technicians, epidemiologists, and decision-makers. This collaborative alignment contributed to the development of outputs, such as thematic maps, data inventories, and risk assessments, that were in accordance with the established needs, technically robust, and directly applicable to the decision-making processes. Overall, even if not all components of the framework were fully implemented in the two case studies, its structured methodology proved to be an asset for orienting and enhancing the design and execution of GIS projects in the two diverse aquatic environments. The adoption of its approach promoted project consistency and coherence, reducing the risk of fragmented development or redundant efforts, while also fostering a GIS project planning culture (Dennis et al., 2015).

Further potential areas foreseen for the adoption and circulation of the framework, and deriving benefits, include:

Table 1 summarizes the abovementioned benefits experienced from the application of the framework within the two case studies, as well as those foreseen for its future and further implementations.

While the two case studies confirmed the framework’s overall applicability and usefulness, they also highlighted areas where GIS project planning in the AAH domain can be further strengthened. In fact, the limitations observed underscored project-level execution deficiencies reflecting a poor GIS planning culture that is widely spread. In both applications, two framework components were not fully leveraged: the GIS strategic plan and the GIS data plan. The omission of a GIS strategic plan, which should provide users with a long-term direction for resource use and allocation, may compromise the continuity and scalability of GIS projects, particularly when one single operator is fully responsible for the technical duties or when institutional long-term vision is lacking (Dennis et al., 2015). Similarly, the inclusion of a GIS data plan document in the GIS planning ensures a structured way to strategically manage, document, and plan the use of key geospatial data within the organization, with emphasis on background maps, shared layers, and reusable datasets (EFSA (European Food Safety Authority) et al., 2022). Especially within the application in the Brazilian freshwater environment, for example, reliance on geospatial information extracted from published literature significantly limited spatial accuracy and the ability to assess the effective value of a structured GIS data plan. This presents an opportunity to further demonstrate the role of such planning documents in ensuring reliable, reusable, and well-documented data infrastructures (Tomlinson, 2007; Longley et al., 2015).

Moreover, both the investigated case studies confirmed that, while in-house GIS personnel are essential for the launch and development of the project, complex tasks like advanced spatial analysis, model development, or system integration often require specialized expertise (McDaid Kapetsky and Aguilar-Manjarrez, 2007). The framework encourages project managers to recognize when external consultants are needed, ensuring that projects are not hindered by in-house personnel’s skill gaps or limited technical capacity. Indeed, the effectiveness of the GIS project’s output is expected to be maximized thanks to the collaborative effort between external experts and in-house human resources (Sinton, 2015; Fang et al., 2018). Furthermore, it must be noted that both the applications primarily focused on the production of static maps, without fully exploring the broader range of GIS functionalities outlined in Annex 5 - GIS functional aspects, including spatial querying, inventory management, or spatial modelling. And even if all maps, including when static, represent spatial phenomena that are naturally dynamic, especially in aquatic environments, and their cartographic representation does not always need to be dynamic to fulfill an analytical or communicative purpose, this limited exploitation of the GIS possibilities suggests, once more, that GIS is largely perceived merely as a cartographic tool, while its full potential as a dynamic decision-support system is widely overlooked. In both case studies, the production of static maps was enough to support the main goals of the pilot application of the framework; that is, to test its coherence, usability, and alignment with stakeholders’ needs. However, it is important to emphasize that the primary objective of the framework is not the production of maps per se but, rather, the establishment of a structured and replicable approach for developing GIS projects that support AAH by providing a coherent, well-structured, and context-sensitive guideline. That said, we do recognize the importance of more advanced and dynamic mapping approaches (e.g., exploratory spatial and temporal data analysis or epidemiological modelling, web-based GIS applications, interactive dashboards, risk assessment, habitat suitability mapping, hydrodynamic and connectivity modelling, as well as integration with early warning and decision-support systems) to be attempted in future tentative applications; for which these first explorative case studies lay the foundation. Moreover, it must be admitted that the current version of the framework does not provide any definition of “most effective” layout for output maps. This should be considered and included to further develop Phase five: Communication of the outcomes in future editions of the framework.

Finally, although the framework is technically comprehensive, its current length and textual format may be discouraging for end users to fully engage. A modular or interactive version, supported by visual examples, could improve its accessibility and promote a broader adoption.

A summarized list of the experienced limitations is also presented in Table 1.

Planning is largely acknowledged as a foundational step for the success of any GIS project, particularly in complex domains such as the AAH one. Therefore, this framework was developed to promote structured planning as the cornerstone of GIS project development and it should be considered as a flexible and adaptable tool that guides users, especially those with limited GIS experience, through the multifaceted decision-making processes required for such a challenging task. Indeed, as demonstrated by the two practical applications described, not all the potential dimensions and challenges associated with GIS projects emerge in every individual implementation. Therefore, the process of applying the framework becomes an opportunity for learning in itself; users become more and more familiar with its structure, utility, and adaptability as they use it in real-world situations. Furthermore, it is important to underscore that the framework takes shape from the interdisciplinary experiences of the involved experts, and that it is claimed to be a working guide to be adapted according to the specific project needs and local conditions, since every possible technical, environmental, and institutional contexts that GIS practitioners might encounter cannot be addressed, especially in the AAH domain. In fact, the proposed framework helps users identify and reflect on the critical components that should be considered for a coherent and complete GIS project development, while still leaving room for local adaptation and innovation. Furthermore, and once more, the authors would like to emphasize the importance of the inclusion of both a GIS strategic plan and a GIS data plan in the project development. Without these two key components, projects may become fragmented, unsustainable, or dependent on a single operator, thereby undermining its own long-term continuity and scalability. On the contrary, embodying them in a GIS development approach helps ensure robust data management, clearer prioritization of resources, and better integration across working teams and systems. Additionally, the use of external GIS consultants should be carefully considered. While internal staff can tackle day-to-day operations, complex spatial analyses, data modeling, or web applications often require specialized expertise. External consultants help overcome inherent limitations of in-house GIS technicians; even though they may not deliver cutting-edge solutions, they are likely to offer practical and resource-appropriate strategies that work well with the institution’s budget, personnel, and time constraints. Finally, the adoption of a GIS data plan is strategic to maximize the reuse of data across multiple projects. Which is in line with what was wished for by other authors and by the experts involved in previous Aquae Strength activities: that is a planning approach based on a forward-thinking vision encompassing the reuse of resources, integration of GIS solutions into other applications or processes, and accessibility and longevity of GIS tools and data (Goodspeed et al., 2016; Pfenninger et al., 2017; EFSA (European Food Safety Authority) et al., 2022). By identifying datasets that are applicable to various GIS applications (e.g., farm registry, aquatic area management), organizations can establish dedicated GIS projects that are exclusively focused on capture, maintenance, and quality control of these shared datasets. This approach ensures that data integrity is consistently guaranteed by the GIS project dedicated to data management, while the other GIS projects do not need to deal with data collection and validation, thus focusing solely on their own specific objectives, exploiting the reliable and up-to-date datasets already created and consistently managed. This enhances operational efficiency, optimizes the use of resources by preventing a waste of effort and ensuring that high-quality data is available across all GIS applications. In particular, the adoption of a GIS data plan is especially important for projects that involve data with cross-cutting applications, as it allows organizations to maximize the efficiency and long-term value of their investments in data collection and management.