3.01 AI and environmental knowledge

Defining environmental knowledge systems

The discourse surrounding environmental stewardship is often dominated by quantitative data streams from satellites, sensors, and complex climate models. While indispensable, this approach can overlook a profound source of ecological wisdom: local and traditional environmental knowledge. This knowledge, which includes what is often referred to as Traditional Ecological Knowledge (TEK), is far from being mere folklore. It is a cumulative body of knowledge and practice, evolving by adaptive processes and often handed down through generations. It represents a living system of understanding the intricate relationships between humans and their environment.

Projects that aim to preserve the intangible cultural heritage related to specific ecosystems are fundamentally engaged in the work of safeguarding this local knowledge. This knowledge is not always found in textbooks but in the oral histories of residents. It is the understanding of how to read a river’s subtle signs – the colour of the water, the behaviour of birds – to know where fish will be. It is the knowledge of which plants have medicinal properties, how to practise sustainable agriculture in a specific microclimate, or how to interpret celestial and atmospheric patterns to predict weather. This form of knowledge is place-based, deeply contextual, and relational, offering a holistic perspective.

The synergy of two knowledge systems

At first glance, the worlds of Artificial Intelligence (AI) and local environmental knowledge may seem antithetical. AI operates on algorithms, computational logic, and the statistical analysis of vast, often decontextualised datasets. Local knowledge, conversely, is rooted in qualitative observation, intergenerational wisdom, and a deep, intuitive understanding built from continuous interaction with a specific environment. However, a more nuanced perspective reveals a powerful potential for synergy. The convergence of these two distinct ways of knowing seeks to bridge the analytical prowess of AI with the time-tested, place-based wisdom of local communities.

This integration can be conceptualised through frameworks that create a synergy between AI and local knowledge systems, which utilise AI’s machine learning capabilities to analyse complex environmental data while incorporating local knowledge to ensure that practices are ecologically sound and culturally relevant. AI’s capacity for pattern recognition, data analysis, and predictive modelling can augment and amplify the reach and impact of local knowledge in addressing contemporary environmental challenges. For example, AI can analyse vast datasets of ecological observations, traditional environmental indicators, and climate data to identify vulnerable ecosystems, predict environmental changes, and optimise conservation interventions.

This synergy moves beyond a simple archival function. It is not merely about using AI to store local knowledge; it is about creating a hybrid mode of understanding that can generate novel insights that neither system could produce in isolation. Consider a practical scenario. Local knowledge might hold an indicator like, “The river is likely to flood when a specific species of bird flies further inland than usual”. This is a rich, longitudinal, and context-specific piece of data. Simultaneously, scientific instruments provide quantitative data, such as satellite imagery showing snowmelt in distant mountains or sensor readings of rising river levels.

An AI model can be trained on all of these disparate data streams. Through machine learning, it can learn to correlate the local knowledge indicator – the specific bird behaviour – with the quantitative data from sensors and satellites. The model might discover that the bird behaviour is a more reliable early indicator of a specific type of flash flood than the satellite data alone, which might have a longer lag time. The result is the development of a more robust, locally-attuned, and effective early warning system. The ultimate outcome is a profound shift from the passive preservation of local knowledge to its active operationalisation within modern scientific frameworks, creating more resilient environmental management strategies.

AI’s role in documentation and analysis

The initial step in this process is the documentation and analysis of local knowledge, which often exists in oral or narrative form. AI offers powerful tools to assist in this crucial phase. A core activity of many community projects is the collection of oral histories from residents. The transcripts from these interviews represent a rich but unstructured source of qualitative data. AI, specifically through Natural Language Processing (NLP), can be employed to systematically analyse this text. Techniques like AI-assisted thematic analysis can quickly process large volumes of interview transcripts to identify and categorise key themes, such as recurring mentions of changes in river flow, the historical prevalence of certain fish species, or traditional land management practices. This automates a part of the qualitative analysis process that would traditionally require immense manual effort, allowing researchers to more efficiently pinpoint critical areas of ecological knowledge within the narratives.

This application is not unique to river ecosystems. Parallel uses of AI are emerging in the field of ethnobiology. Researchers are using AI to analyse vast datasets on the medicinal use of plants, helping to classify species, predict new therapeutic applications, and validate traditional medicinal claims found in historical texts and oral traditions. In both ethnobotany and the documentation of river-based local knowledge, AI serves as a powerful analytical tool that helps to structure, interpret, and unlock the value held within traditional knowledge systems, preparing it for integration with other forms of scientific data.