User Guide for Rhino.Ecologic®

Preface

Rhino.Ecologic introduces an ecological simulation framework designed to operate directly within the parametric design environment Rhino/Grasshopper. It extends computational design by embedding dynamic vegetation modeling, biodiversity assessment, and biomass simulation into the same spatial and algorithmic systems used to generate geometry.

Rather than treating ecological performance as a post-design evaluation step, Rhino.Ecologic integrates plant community dynamics into the generative loop itself. Geometry, soil configuration, solar exposure, and environmental context become active variables in long-term ecological development. Biodiversity and biomass are no longer abstract sustainability targets; they become measurable, adjustable parameters within computational workflows.

At its core, Rhino.Ecologic is built around a spatially explicit, individual-based ecological model operating on a voxelized representation of design geometry. This structure allows plant communities to grow, compete, and evolve in response to environmental constraints derived directly from the project model. The system does not approximate ecological outcomes statistically; it simulates underlying processes and makes them accessible within Grasshopper.

This document serves both as a guide and as a methodological reference. It explains how the system operates, how it integrates with parametric modeling, and how it can be deployed in professional design contexts. It is written for architects, landscape architects, urban designers, computational specialists, and interdisciplinary teams seeking to incorporate ecological intelligence into their design processes.

Rhino.Ecologic does not replace ecological expertise, nor does it reduce ecological systems to simplified metrics. Instead, it provides a structured computational environment in which ecological dynamics can inform spatial decision-making from the earliest stages of design.

Core capabilities

1. Ecological analysis of 3D geometry models. Analyze landscapes, urban areas, and building envelopes. Output includes spatio-temporal species distribution maps for terrain, building, and urban forms.
2. Biodiversity prediction. Generate dynamic biodiversity maps showing potential species that may inhabit a given design space.
3. Environmental mapping. Output geometry-specific soil and precipitation distribution and local illumination maps, which inform plant growth and species viability.
4. Data integration for advanced analysis. Export ecological data linked to 3D geometry in a format suitable for advanced data analysis methods, including machine learning workflows.

The chapters that follow move from conceptual framing to system architecture, ecological modeling logic, applied workflows, and interoperability with optimization and landscape information modeling tools. Together, they outline a framework in which ecological processes are treated not as constraints imposed from outside the design process, but as integrated components of it.

How to use this guide

This document is structured to reflect the dual nature of Rhino.Ecologic: it is both a computational system and an applied ecological framework. The chapters move from conceptual positioning to operational architecture, and from ecological modeling logic to applied integration within professional workflows.

The guide is not organized as a step-by-step tutorial. Instead, it is layered to support different levels of engagement with the system.

• Chapter 1 – Introduction establishes the conceptual framework and positions ecological simulation within parametric design.
• Chapter 2 – Plugin Overview defines the system architecture, installation requirements, and interface logic inside Rhino and Grasshopper.
• Chapter 3 – Rhino.Ecologic Grasshopper Components documents all components by tab, detailing inputs, outputs, data types, and operational behavior.
• Chapter 4 – The Ecological Core: The Joschinski Model explains the underlying individual-based community model governing plant dynamics.
• Chapter 5 – Tutorials presents structured workflows demonstrating system behavior under applied design conditions.
• Chapter 6 – Case Studies documents professional and academic implementations of Rhino.Ecologic.
• Chapter 7 – Interoperability outlines integration with optimization frameworks and landscape information modeling tools.

The document may be read sequentially to understand the full framework, or selectively depending on project requirements. Designers focused on implementation may begin with the system interface and workflows. Readers interested in methodological depth may begin with the ecological core.

Throughout, the emphasis remains consistent: Rhino.Ecologic operates as a structured ecological simulation layer embedded within computational design systems. The guide reflects this position.

We recommend the following approach based on your familiarity and project phase:

Reference sections

• Troubleshooting (Section 3.9)
• Glossary for definitions of key concepts and terminology.
• References to both cited work and suggested further reading are provided in this guide.

This modular structure allows you to read the guide front-to-back or dip into individual chapters as needed. Use it as a reference, a learning resource, and a companion throughout your computational design process using Rhino.Ecologic.

Table of contents

Chapter 1: Introduction to Rhino.Ecologic

1.1 Bridging architecture and ecology

The relationship between architecture and ecology is neither new nor incidental. Across cultures and centuries, built environments have been shaped in dialogue with living systems, from early vegetated structures such as Newgrange to the Hanging Gardens of Babylon and the hydraulic landscapes of Angkor Wat. These examples demonstrate that ecological intelligence has long informed spatial design, even if not formalized as such.

In the twentieth century, this relationship was reframed through environmental science and social movements. The American Environmental Justice Movement, among others, emphasized the inseparability of ecological integrity and human well-being, advocating for sustainable and equitable environments (Bullard 1990; Ehrlich 1968). Today, accelerating climate change, urban expansion, and biodiversity loss have made ecological literacy a prerequisite for responsible design practice.

At the same time, ecology as a discipline has developed increasingly sophisticated modeling approaches. Species Distribution Models (SDMs) have enabled spatial prediction of species occurrence based on environmental variables (Guisan et al. 2000; Elith et al. 2006). Yet SDMs remain largely correlative: they estimate probability surfaces rather than simulate the ecological processes that generate community dynamics. They rarely capture succession, competition, or spatial interaction between individuals over time.

Mechanistic and hybrid models, such as FATE-HD (Boulangeat et al. 2013), address these limitations by simulating ecological processes directly. However, despite their analytical power, such models have largely remained detached from spatial design environments. Designers typically encounter ecological performance only after geometry has been defined, through static evaluation tools or external simulations.

This separation constitutes a critical gap: while ecological modeling has advanced, it has not been structurally embedded within the generative systems used to shape space.

Rhino.Ecologic addresses this gap. It integrates an extended, urban-compatible ecological model directly into Rhino and Grasshopper, enabling biodiversity, species distribution, biomass accumulation, and environmental interaction to be simulated within the same parametric frameworks that generate architectural and landscape geometry (Vogler et al. 2025; Joschinski et al. 2024).

By embedding ecological processes inside the computational design loop, Rhino.Ecologic transforms ecology from a post-design validation layer into an active design parameter. Geometry, soil configuration, solar exposure, and environmental context become drivers of long-term ecological development rather than static inputs. The question shifts from “Does this design support biodiversity?” to “How does biodiversity emerge from this design?”

In doing so, Rhino.Ecologic repositions ecological modeling not as an external constraint, but as a generative component of spatial practice, bridging architecture and ecology within a unified computational environment.

Rhino.Ecologic generates spatio-temporal maps of:

• Soil and precipitation conditions
• Light availability
• Species distribution
• Biodiversity
• Biomass accumulation

1.2 Why ecological modeling matters in landscape architecture

As environmental pressures intensify across cities worldwide, landscape architecture is undergoing a structural transformation. The discipline can no longer focus solely on aesthetics, spatial organization, or human usability. It must now engage climate resilience, ecosystem stability, resource cycles, and the integration of non-human life as fundamental design parameters. This shift demands not only new strategies, but new tools capable of representing ecological processes within spatial decision-making.

Urban landscapes are hybrid systems: constructed, managed, and inhabited environments that simultaneously function as ecological habitats. They influence microclimates, hydrological flows, soil dynamics, and species movement. Yet traditional design workflows often rely on static assumptions: fixed planting schemes, simplified soil descriptions, and generalized species lists detached from long-term ecological dynamics. Such approaches rarely account for succession, shading effects, competition, dispersal, or the temporal development of plant communities.

Ecological modeling offers a fundamentally different approach by simulating how living systems evolve in response to spatial configuration and environmental constraints. By representing interactions among soil depth, solar exposure, precipitation, plant traits, and spatial structure, modeling frameworks make ecological performance measurable, testable, and adjustable within the design process itself.

This capability changes the temporal horizon of design. Green roofs can be evaluated not only for immediate coverage, but for long-term biomass development and species turnover. Planting strategies can be assessed for resilience under varying light conditions and soil depths, while urban districts can be tested for habitat connectivity, biodiversity potential, and adaptive capacity before construction. Trade-offs between biomass and diversity, density and ecological space, exposure and understorey viability become visible rather than speculative.

Ecological modeling does more than generate metrics; it reshapes design thinking. When vegetation is simulated as a dynamic system rather than inserted as a final layer, spatial decisions begin to anticipate growth, competition, and succession. Designers move from arranging static forms to configuring ecological conditions. Ecology becomes a co-author of spatial development rather than a constraint applied after geometry is fixed.

In this context, Rhino.Ecologic operates as an integrative bridge. By embedding ecological simulation within Rhino and Grasshopper, it enables designers to explore how spatial configurations influence biodiversity, biomass, and environmental performance over time.

The result is a shift from reactive validation to anticipatory design: from checking ecological impact to shaping ecological trajectories. Landscape architecture, in this framework, evolves from managing planted surfaces to orchestrating living systems.

1.3 From data to design: ecological insight through simulation

Rhino.Ecologic differs from conventional environmental analysis tools not only in the data it processes, but in how ecological simulation is embedded within the design workflow. Rather than operating as a post-design evaluation layer, Rhino.Ecologic integrates ecological dynamics directly into the generative loop of parametric modeling.

At the core of this integration lies the coupling of a voxel-based spatial representation with an individual-based ecological simulation engine, the Joschinski Model (Joschinski et al., 2024). Together, these systems transform three-dimensional geometry into an ecological domain in which plant communities grow, compete, reproduce, and decline over time.

Design geometry is discretized into a structured volumetric grid. Each voxel stores environmental attributes derived from geographic location and spatial configuration, including soil depth, soil type, precipitation, and solar exposure. This discretized structure does more than store information; it defines the spatial topology of ecological interaction. Plants are not abstract coverage values but autonomous individuals responding locally to light availability, soil constraints, and neighboring competition.

Within this framework, ecological processes unfold across annual time steps. Germination, growth, biomass allocation, shading, reproduction, dispersal, and mortality are simulated sequentially. Solar radiation intercepted by plant crowns is converted into biomass according to species-specific efficiencies. Maintenance costs are subtracted, resources are allocated, and competition for light and space emerges from overlapping geometries rather than imposed ranking systems. Community structure is not prescribed; it develops from spatial interaction.

This integration fundamentally alters the design workflow. Each iteration of a 3D model becomes not only a geometric configuration but an ecological scenario. Changing a building mass, adjusting soil depth, reallocating biophilic areas, or modifying exposure conditions alters the long-term trajectory of vegetation development. Biodiversity and biomass are no longer abstract targets but measurable outcomes of spatial decisions.

Such a workflow enables designers to:

• Test adaptive green infrastructure strategies (roofs, terraces, corridors)
• Evaluate long-term biodiversity development
• Align planting schemes with site-specific conditions
• Anticipate trade-offs between density, exposure, and ecological performance

Crucially, the system shifts ecological data from static constraint to active design driver. Instead of asking whether a finished proposal satisfies environmental criteria, designers can explore how ecological performance emerges from parametric variation. Geometry, environmental data, and ecological processes operate within a continuous feedback loop.

In this sense, Rhino.Ecologic does not merely visualize ecological conditions; it operationalizes them. It embeds ecological intelligence within computational design systems, allowing spatial decisions to be evaluated not only in terms of form and function, but in terms of long-term biological development.

The outcome is a unified framework in which aesthetics, performance, and ecology are no longer sequential concerns but interdependent components of a single generative process.