The scale of the blind spot is staggering. Livestock collectively dwarf all wild mammals in biomass, making them dominant ecological actors that shape land use, biogeochemical cycles and disease dynamics on a planetary scale. The sector is a leading source of greenhouse gas emissions, a primary driver of deforestation for pasture and feed crops, and a major contributor to nitrogen and phosphorus pollution that creates vast hypoxic dead zones in coastal ecosystems. Yet each of these impacts is typically monitored by a different agency using different methodologies. Agricultural ministries track livestock numbers and feed production, environmental agencies monitor water quality and deforestation rates, and climate scientists model greenhouse gas fluxes. While each dataset is valuable in isolation, their separation prevents a comprehensive, spatially explicit understanding of the entire cause-and-effect chain, making it difficult to correlate a regional increase in cattle density with downstream water quality degradation or a localised loss of forest-dependent species.

One of the most urgent One Health challenges obscured by these silos is antimicrobial resistance, a global health crisis driven by the overuse of antibiotics in both human medicine and agriculture. The livestock sector is a significant consumer of antimicrobials, exerting powerful selective pressure for the evolution of resistant bacteria. Effectively combatting AMR requires an integrated data ecosystem that links agricultural data on antibiotic sales, environmental data on resistant bacteria in soil and water, and public health data on resistant infections in humans. Currently, these datasets are almost always managed by different agencies with no standardised way to connect them, making it incredibly difficult to trace the pathways of resistance from farms to the environment and ultimately to human clinics.

The proposed solution avoids the immense technical and political challenges of creating a centralised data repository. Instead, the paper advocates for a federated data network, a model that respects the sovereignty of existing data systems while enabling powerful, integrated analysis. Unlike systems that ultimately cache data centrally for performance, this model adheres to the strict definition of federation: data remains entirely with its original custodian. Making such a truly federated network a reality requires overcoming significant interoperability hurdles, which can be achieved through two key technical components: a standardised API gateway that queries distributed databases in parallel to minimise latency, and an AI-driven harmonisation engine that dynamically maps terminologies in real-time, functioning as an intelligent intermediary that translates a single user query into the specific dialects of independent databases.

The technical core of the federated network would be the API gateway. An API is a set of rules that allows different software applications to communicate, and the gateway acts as a universal translator and traffic controller. For instance, a researcher might submit a query asking for all records of wild bird species from GBIF and all reported outbreaks of H5N1 avian influenza in poultry farms within a 50 kilometre radius of those sightings. The gateway receives this request, knows how to communicate with the GBIF API to retrieve the bird data and with a veterinary health agency’s API to retrieve the outbreak data, dispatches these requests simultaneously, gathers the disparate results and presents them back to the researcher in a single, unified format.

Artificial intelligence serves as the engine that makes this integration meaningful. The historical barriers to combining heterogeneous data, including disparate formats, inconsistent terminologies and different spatial resolutions, would no longer be insurmountable. Natural Language Processing models can be trained to extract structured information such as disease, location and species from unstructured veterinary field reports. Computer vision algorithms can analyse satellite imagery to automatically classify land use, identifying pasture expansion in biodiversity hotspots. Machine-learning models can learn to align datasets with different temporal or spatial scales, creating a cohesive analytical product from often messy, real-world inputs. The institutional will, not technical feasibility, would now be the primary barrier.

The feasibility of automating data integration at this scale is supported by recent breakthroughs in applying Large Language Models to structured database management. LLMs have evolved beyond text generation to master table processing tasks, including column type annotation and entity resolution. Foundation models can autonomously wrangle messy data, performing complex cleaning operations that previously required custom scripts. Frameworks like StructGPT allow models to reason directly over heterogeneous interfaces, while specific applications in semantic typing prove that AI can accurately map local database schemas to global standards without manual intervention. By leveraging these proven capabilities, the proposed network transforms data federation from a labour-intensive manual task into an automated, scalable pipeline.

The integration of livestock, biodiversity and public health data, powered by AI, does not just enable better analysis of past events; it opens the door to a new paradigm of predictive environmental science and public health management. One of the most promising applications is the development of dynamic risk maps for zoonotic disease spillover. Instead of reacting to an outbreak after it has occurred, an integrated system could continuously pull data on livestock density, deforestation rates from satellite imagery, wildlife distribution from GBIF and other sources, and climate variables. A machine-learning model could then analyse these real-time data streams to identify and flag geographic hotspots where the probability of a pathogen spilling over from wildlife to livestock and potentially to humans is dangerously high, allowing public health and veterinary authorities to target surveillance and intervention efforts with unprecedented precision.

This approach could also revolutionise sustainable agriculture policy. Rather than applying uniform regulations, policy-makers could use integrated data to model the precise ecological impact of different agricultural practices at the farm, landscape, regional and national scales. For example, a model could predict how a proposed expansion of a specific type of grazing system would affect local water quality, soil carbon sequestration and the habitat of an endangered bird species. This would enable the design of evidence-based, spatially explicit policies that optimise food production while minimising environmental harm.

Ultimately, the goal is to create a digital twin of our agro-ecological systems, a dynamic, data-driven model that reflects the complex interactions between human activities and the natural world. Such a system would provide the predictive power needed to navigate the challenges of the Anthropocene, allowing us to anticipate and mitigate problems before they become crises. The complex environmental and public health challenges of the Anthropocene cannot be solved with fragmented data. A true One Health approach requires an integrated view of the world, one that treats a farm, a forest and a city not as separate entities but as interconnected components of a single socio-ecological system. The data to build this integrated view already exist, locked in institutional silos. A federated data network powered by modern AI offers a pragmatic and powerful way to break down these silos, particularly at the critical interface of livestock production and environmental health, representing a necessary paradigm shift towards the collaborative data infrastructure needed for a sustainable future.

Subject of Research: AI-driven federated data network for integrating livestock and biodiversity data

Article Title: Connecting the herd and the habitat: A plea for an AI-driven framework for integrating livestock and biodiversity data

Article References: Doi, H. (2026). Connecting the herd and the habitat: A plea for an AI-driven framework for integrating livestock and biodiversity data. One Ecosystem, 11, Article e174196. https://doi.org/10.3897/oneeco.11.e174196

Image Credits: AI Generated

DOI: 10.3897/oneeco.11.e174196

Keywords: livestock, biodiversity, One Health, antimicrobial resistance, federated data network, AI, zoonotic disease, GBIF, digital twin, sustainable agriculture, Connecting, herd