AI-Enabled Transdisciplinary Observability Framework for Biodiversity and Human Environment Systems

Effective biodiversity governance depends on the ability to detect weak early signals embedded within everyday field incidents. Yet the capture of these incidents remains fragmented across health, biodiversity, agriculture, climate, and disaster management systems, with existing surveillance systems suffering from isolated IT infrastructure lacking interoperability and common data standards. Current observational practices rely on siloed reporting channels, limited multimodal inputs, and little visibility into cross domain patterns that often reveal ecosystem stress long before formal indicators emerge.

This work introduces a generic configurable incident observability protocol grounded in systems thinking practices that treat frontline observations as part of broader ecological feedback loops. Drawing on boundary framing, distributed sensing, and causal pattern recognition, the protocol positions incident capture as core environmental intelligence. It adapts observability engineering concepts to real world ecosystems where data is heterogeneous, multimodal, and collected under resource constraints.

The solution framework defines a modular reference architecture built around configurable frontline adapters that support voice, image, video, sensor feeds, and text. Instead of rigid forms, spoken or visual reports such as “suspected cobra bite” are transformed into structured fields through staged interpretation that combines language understanding, lightweight image models, and on device inference. A shared schema ensures consistency, auditability, and cross domain comparison. Modes include passive contextless capture, targeted experiment modes, high resolution risk window monitoring, configurable audit layers, incident tiering, and relays for integration with agency and stakeholder systems.

These capabilities make the framework effective for use cases like snakebite reporting, where a single event reflects clinical, ecological, and agricultural conditions. A frontline capture becomes a datapoint that can reveal links to habitat stress, cropping cycles, or water scarcity. This extends to herbivore crop raids, predator spillovers, water contamination points, crop stress anomalies, and shifts in species presence. It strengthens existing monitoring systems by improving resolution and surfacing early cross-sector patterns that siloed tools overlook.

By establishing a multimodal, AI assisted, cross domain observability protocol, this work aims to surface earlier ecological and social signals, reduce underreporting, improve classification accuracy, and support anticipatory decision making across sectors responsible for biodiversity and human environment systems.