Birds and many other living organisms rely on large old trees for their survival. However, these trees are rapidly disappearing from many landscapes. The regrowth of such trees takes hundreds of years, and in many disturbed landscapes, wildlife populations cannot persist without temporary human-made replacement structures. In 2022, Mirra et al. [1] conducted a study on the AI-based generation of 3D models, or visual abstractions, of large old trees. An AI agent analysed a dataset of these trees to identify features that appeal to animals and generated forms that approximated these features. The researchers also evaluated the usefulness of the AI-generated forms as artificial replacements for trees using morphological and cost criteria.

Building on this research, we developed our pavilion entry for the IASS 2024 design competition. Our focus was on creating (1) a design strategy to translate the AI-generated visual abstractions into buildable tensegrities, and (2) a fabrication method that facilitates the transport, assembly, and disassembly of these structures using repurposed and biodegradable materials. We named this prototype “FloaTree”. We have constructed prototype tests in Melbourne, Australia, and will build the full-scale pavilion during the annual IASS symposium in Zurich, Switzerland, in August 2024.

Biodiversity is in a state of global collapse. Among the main drivers of this crisis is habitat degradation that destroys living spaces for animals, birds, and other species. Design and provision of human-made replacements for natural habitat structures can alleviate this situation. Can emerging knowledge in ecology, design, and artificial intelligence (AI) help? Current strategies to resolve this issue include designing objects that reproduce known features of natural forms. For instance, conservation practitioners seek to mimic the function of rapidly disappearing large old trees by augmenting utility poles with perch structures. Other approaches to restoring degraded ecosystems employ computational tools to capture information about natural forms and use such data to monitor remediation activities. At present, human-made replacements of habitat structures cannot reproduce significant features of complex natural forms while supporting efficient construction at large scales. We propose an AI agent that can synthesise simplified but ecologically meaningful representations of 3D forms that we define as visual abstractions. Previous research used AI to synthesise visual abstractions of 2D images. However, current applications of such techniques neither extend to 3D data nor engage with biological conservation or ecocentric design. This article investigates the potential of AI to support the design of artificial habitat structures and expand the scope of computation in this domain from data analysis to design synthesis. Our case study considers possible replacements of natural trees. The application implements a novel AI agent that designs by placing three-dimensional cubes – or voxels – in the digital space. The AI agent autonomously assesses the quality of the resulting visual abstractions by comparing them with three-dimensional representations of natural trees. We evaluate the forms produced by the AI agent by measuring relative complexity and features that are meaningful for arboreal wildlife. In conclusion, our study demonstrates that AI can generate design suggestions that are aligned with the preferences of arboreal wildlife and can support the development of artificial habitat structures. The bio-informed approach presented in this article can be useful in many situations where incomplete knowledge about complex natural forms can constrain the design and performance of human-made artefacts.