Ecological Simulation Framework for Rhino/Grasshopper
Interview with Verena Vogler of McNeel Europe, Rhino
Can you briefly describe what McNeel Europe will be presenting at CDFAM, and explain the main objectives behind the development of Rhino.Ecologic?
At CDFAM, the audience will learn about Rhino.Ecologic®, a novel ecological simulation framework for Grasshopper developed by the R&D team at McNeel Europe (https://www.rhino3d.com/en/mcneel/contact/emea/). The main objective of Rhino.Ecologic is to bring ecological modeling directly into Grasshopper’s parametric design environment, enabling design professionals across architecture and urban systems to evaluate ecological performance as part of iterative computational workflows within Rhino .
Unlike traditional ecological assessment tools that operate as external post-processing software, Rhino.Ecologic embeds ecological simulation directly into parametric modeling workflows, preserving full geometric and data interoperability.
The plugin provides location- and time-specific outputs, including 3D species distribution maps as well as biomass and biodiversity simulations. These results are delivered as structured data that can be further processed within Grasshopper definitions or extended through custom scripts and third-party tools. In this way, Rhino.Ecologic supports ecologically informed design systems while remaining fully integrated into existing computational design and software development ecosystems.
Rhino.Ecologic is developed by a small interdisciplinary core team: Eleftherios Kourkopoulos and myself as architects and computational designers, and Jens Joschinski as a computational ecologist. This collaboration bridges ecological science and computational design engineering, ensuring that the system remains scientifically grounded while fully integrated into parametric design environments.
The current version is in alpha testing with over 100 participants from more than 60 institutions worldwide, approximately 85% from the AEC industry and the remainder from academia. We are currently preparing the beta release.
Rhino.Ecologic logo. © McNeel Europe, 2026.
How does Rhino.Ecologic integrate with existing Rhino and Grasshopper workflows, and what kinds of ecological simulations does it enable?
Rhino.Ecologic integrates directly into Rhino and Grasshopper as a free plugin, allowing ecological simulation components to integrate directly into existing parametric Grasshopper definitions and iterative design workflows. This enables designers to evaluate ecological performance alongside geometric modeling, and other computational processes without leaving the Grasshopper environment.
Rhino.Ecologic User Interface (UI) in Grasshopper. © McNeel Europe, 2026.
What distinguishes Rhino.Ecologic from many other tools is that it brings ecological simulation into the core design process rather than treating it as a post-design analysis step. Designers can input 3D geometries such as buildings, terrain, or open spaces and assign environmental data layers representing site-specific conditions. The model simulates how plant communities develop over time in response to factors such as light competition, succession dynamics, soil constraints, and spatial limitations. Outputs are dynamic and time-specific, providing insight into how species interactions and ecosystem performance evolve throughout the design lifecycle.
The plugin supports location-based ecological simulations including 3D species distribution mapping as well as biomass and biodiversity modeling. By producing structured, data-driven results, Rhino.Ecologic allows users to explore how design interventions such as adjusting soil conditions, vegetation placement, or site geometry can influence ecological outcomes from the earliest project stages onward.
Simulation of plant growth (Plant Volume) over time at a specific geographic site.
© McNeel Europe, 2026.
Could you walk us through the typical data flow when setting up a simulation in Rhino.Ecologic, what kinds of inputs are required, and what outputs can users expect?
A typical Rhino.Ecologic workflow begins with three primary inputs: a 3D model imported to or created in Rhino or Grasshopper, the geographic coordinates of the site, and the desired voxel resolution. The model geometry is voxelized into a spatially indexed volumetric data structure, where each voxel cell becomes part of a graph representation used for downstream ecological simulation.
Rhino.Ecologic data flow diagram (DFD) (Vogler et al., 2025) showing data inputs and outputs. ©McNeel Europe, 2026.
This voxel graph integrates geometric properties, environmental layers (solar exposure, precipitation, soil volume, soil type, soil depth), and species-specific parameters. On this structured state space, an individual-based community model, the “Joschinski Model” , simulates stochastic plant population dynamics across yearly time steps. This model simulates plant individuals and tracks growth, light interception and investment into biomass. The result is a plant community with spatially variable species compositions. Beyond geometry and site inputs, users can build and modify a custom plant library, defining species-specific ecological traits that directly influence growth dynamics and community interactions within the simulation.
Outputs can be grouped into three categories:
- Geometric data (spatial structure and volumetric properties)
- Environmental data (site-specific climatic and soil conditions)
- Spatio-temporal ecological data (species distribution, biomass, abundance, plant volume per voxel over time)
Importantly, results are not just visual artifacts; they are structured datasets that remain fully accessible within Grasshopper for further processing, scripting, or machine-learning workflows.
Rhino.Ecologic spatio-temporal ecological data outputs (Species, Biomass, Abundance, Plant Volume). ©McNeel Europe, 2026.
To support interpretation, Rhino.Ecologic includes data-analysis components that apply statistical and ML techniques to explore correlations, feature importance, and predictive relationships between environmental inputs and ecological outcomes. In this way, the system bridges simulation and data-driven insight generation.
Graphs such as biomass-over-time curves and correlation maps can be generated for analysis and interpretation. © McNeel Europe, 2026.
Can you give some examples of how this has, or might be applied in the future?
Our alpha testers are already using Rhino.Ecologic to quantify and evaluate urban greenery within complex design scenarios, including green roofs, courtyards, façades, and larger urban developments. For example, PhD and Master students in the Emergent Technologies and Design (EmTech program) at the Architectural Association (AA) in London directed by Milad Showkatbakhsh have been applying Rhino.Ecologic to assess biodiversity potential and biomass development in early-stage urban design projects (link). Their work demonstrates how ecological metrics can move from abstract sustainability goals to measurable design parameters that directly inform spatial decisions.
This is also the second consecutive year that we are supporting the EmTech program, where Rhino.Ecologic is integrated into advanced computational design research and teaching. In that academic setting, the tool is not only used to evaluate projects, but to explore how ecological simulation can reshape design methodologies at the intersection of architecture, technology, and environmental systems.
Looking ahead, our vision is to make ecological intelligence native to the design process. Rather than assessing environmental impact at the end of a project, we see a future where biodiversity, biomass, and habitat quality become active drivers within parametric workflows. By embedding ecological simulation directly into computational design environments, Rhino.Ecologic aims to help shift AEC practice toward a more data-informed, adaptive, and ecologically informed design systems
How might designing for/with autonomous organic agents such as bees inform our approaches to designing for/with autonomous AI agents in computational design?
Designing with autonomous organic agents such as bees or plant communities shifts design thinking from deterministic form generation to condition-based system design. In ecological modeling, we do not design individual behaviors directly; instead, we define environmental gradients, resource fields, and interaction rules, and observe how local interactions scale into emergent spatial patterns.
We are currently exploring machine-learning workflows that leverage simulation outputs as high-resolution training datasets. These include surrogate modeling to accelerate iteration, feature importance analysis to identify dominant ecological drivers, and predictive models that estimate biomass or biodiversity outcomes based on parametric site inputs. In this sense, the ecological engine serves both as a simulation environment and as a data generator for learning-based methods.
What are you hoping to share with and learn from the community at CDFAM Barcelona through your participation this year?
At CDFAM Barcelona, I’m hoping to exchange ideas with the community around the latest developments in real-time 3D simulation and data-driven design tools that help capture and evaluate multiple performance aspects of digital models. I’m particularly interested in how researchers and developers address challenges related to large-scale datasets, efficient visualization of millions of data points, accelerating geometry-processing pipelines, and robustly linking simulation data back to complex design models. Beyond gaining new technical insights, I’m looking forward to learning how others frame and solve these problems, as this often helps identify better questions and new directions for future work. Finally, I’m excited to meet people working at the intersection of computational design, simulation, and software development, and to contribute to the ongoing exchange of knowledge within the CDFAM community.
References:
Vogler, V., Kourkopoulos, E., Fraguada, L., Mimet, A., Joschinski, J. (2025). Integrating Ecological Modeling into the 3D CAD System Rhinoceros. JoDLA Journal of Digital Landscape Architecture, Issue 10-2025, 86–100. Berlin/Offenbach: Wichmann Verlag im VDE VERLAG. e-ISSN 2511-624X, https://doi.org/10.14627/537754009.
Vogler, V., Kourkopoulos, E., Joschinski, J., Eckelt, K. (2025). Developing Volumetric Data Models for ML Training Datasets Using Grasshopper. JoDLA Journal of Digital Landscape Architecture, Issue 10-2025, 101–113. Berlin/Offenbach: Wichmann Verlag im VDE VERLAG. e-ISSN 2511-624X, https://doi.org/10.14627/537754010.
Joschinski J., Boulangeat I., Calbi M., Hauck T.E, Vogler V., Mimet A. (2024). The Ecolopes Plant Model: a high-resolution model to simulate plant community dynamics in cities and other human-dominated and managed environments. bioRxiv 2024.09.23.614561.
