User guide for Rhino.Ecologic®

Version: 0.1.19 beta
Compatible with: Rhino 8+, Grasshopper Platform: Windows
Release date BETA version: 25.07.2026
Author User Guide: Verena Vogler

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.

Rhino.Ecologic has the following 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 & 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:

New users & first-time installers

Start with:

• Installation Guide (separate document)
• Quickstart Guide (separate document)
• Chapter 1 – Introduction
• Chapter 3 – Grasshopper Components

This path enables you to run your first ecological simulation quickly.

Designers preparing simulations

Focus on:

• Chapter 3 – Grasshopper Components
• Chapter 4 – The Ecological Core
• Chapter 5 – Tutorials

This path helps you understand:

• how geometry becomes ecological space
• how environmental constraints affect growth
• how to configure simulation parameters meaningfully
  

Analysts & researchers

Jump to:

• Chapter 4 – The Ecological Core
• Section 3.8 – Species Library
• Section 3.7 – Statistics
• Chapter 7 – Interoperability

This is particularly relevant if you:

• build custom species libraries
• integrate ML workflows
• couple Rhino.Ecologic with optimization tools
• require reproducible ecological simulations

Interoperability & real-world applications

Explore:

• Chapter 6 – Case Studies to see real-world use scenarios and inspiration.
• Chapter 7 – Interoperability for integration with optimization and landscape tools.

Reference sections

• Troubleshooting (Section 3.9)
• Glossary for definitions of key concepts and terminology.
• References — 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

  • 1.1 Bridging Architecture and Ecology
  • 1.2 Why Ecological Modeling Matters in Landscape Architecture
  • 1.3 From Data to Design: Ecological Insight Through Simulation

• Chapter 2: Rhino.Ecologic Plugin Overview

  • 2.1 Installation
  • 2.2 User Interface Overview
  • 2.3 Interaction Model and Data Flow
  • 2.4 Grasshopper Components
  • 2.5 Component Architecture
  • 2.6 From Geometry to Ecological Simulation

• Chapter 3: Rhino.Ecologic Grasshopper Components

  • 3.1 Parameters Tab
  • 3.2 Project Tab
  • 3.3 Analysis Tab
  • 3.4 Data Tab
  • 3.5 Visualization Tab
  • 3.6 Utilities Tab
  • 3.7 Statistics Tab
  • 3.8 Species Library Tab
  • 3.9 Troubleshooting

• Chapter 4: The Ecological Core — The Joschinski Model

  • 4.1 Conceptual Foundation
  • 4.2 Spatial Representation and Environmental Integration
  • 4.3 Plant Representation and Trait-Based Dynamics
  • 4.4 Succession, Stochasticity, and Emergence
  • 4.5 Core Logic and Theory
  • 4.6 Scale, Calibration, and Intended Use
  • 4.7 From Geometry to Ecology

• Chapter 5: Rhino.Ecologic Tutorials

  • 5.1 Tutorial 1: Core Workflow
  • 5.2 Tutorial 2: Advanced Workflow

• Chapter 6: Rhino.Ecologic Case Studies

  • 6.1 Henning Larsen – Fælledby, Copenhagen
  • 6.2 Emergent Technologies & Design (EmTech), AA School London

• Chapter 7: Interoperability with Other Software Solutions

  • 7.1 Coupling with Wallacei
  • 7.2 Coupling with Rhino.Lands

• Glossary

• References

• Acknowledgements
  
  

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 bridges ecology, computational design, and software engineering.

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. 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.

System Architecture Rhino.Ecologic. © McNeel Europe 2026.

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.

Chapter 2: Rhino.Ecologic plugin overview

Where Chapter 1 established the conceptual and methodological foundation of Rhino.Ecologic, this chapter introduces the software as an operational system within Rhino and Grasshopper. It explains how the plugin is installed, how it integrates into the user interface, and how ecological simulation components are structured within the parametric workflow.

Rhino.Ecologic operates natively inside Grasshopper. It does not replace Rhino’s modeling environment; rather, it extends it with ecological data types, analysis pipelines, and simulation logic that interact directly with geometry and environmental parameters.

This chapter provides the technical entry point into the system.

2.1 Installation

2.1.1 System requirements:

• Rhino & Grasshopper: Rhino 8+ (Windows)
  
• Grasshopper: bundled (requires GH build ≥ 1.0.0007)
  
• OS: Windows 10/11 (64-bit)
  
• Runtime: .NET 7 (Rhino 8 environment)
  

⚠️ Caution: Rhino.Ecologic is currently supported on Windows only.

2.1.2 Installation procedure:

Rhino.Ecologic is distributed as a YAK package and can be installed via: the Rhino _PackageManager

1. Open Rhino 8.
   
2. Run _PackageManager.
   
3. Search for Rhino.Ecologic.
   
4. Install the latest compatible version.
   
5. Create a Rhino.Ecologic account: https://rhinoecologic.kinsta.cloud/create-rhino-eclogic-account/
   
6. Restart Rhino.
   

After successful installation, a new tab labeled Rhino.Ecologic® will appear in the Grasshopper ribbon.

💡 Tip: For detailed step-by-step instructions, refer to the separate Installation Guide.

2.2 User interface overview

Once installed, Rhino.Ecologic integrates directly into the Grasshopper interface as a dedicated toolbar tab. The system follows standard Grasshopper conventions, ensuring familiarity for users experienced in parametric workflows.

The plugin introduces structured component groups organized by function: Parameters, Project, Analysis, Data, Visualization, Utilities, Statistics, and Species Library.

Each tab represents a logical stage in the ecological simulation pipeline from defining a project to running analyses, extracting data, and visualizing results.

Rhino.Ecologic does not operate as a monolithic component. Instead, it follows Grasshopper’s modular paradigm: users assemble ecological workflows visually by connecting components that pass structured data objects.

Rhino.Ecologic® toolbar in Grasshopper.

2.3 Interaction model and data flow

Rhino.Ecologic inherits Grasshopper’s visual programming logic:

• Components process data from left to right
• Outputs from one component become inputs to another
• Data can be passed as Items, Lists, or Trees

Geometry and ecological data coexist within the same parametric graph. The typical ecological workflow follows this structure:

Create Project → Configure Parameters → Run Ecological Analysis → Extract Data → Visualize or Optimize

Key interaction principles:

• Preview: Toggle viewport visualization per component.
  
• Bake: Commit geometry to Rhino.
  
• Access Modes: Adjust Item/List/Tree per input.
  
• Units: Numerical inputs inherit Rhino document units unless otherwise specified.
  

Because ecological simulation can be computationally intensive, it is recommended to:

• Disable previews on heavy components when not needed.
  
• Use appropriate voxel resolutions.
  
• Run analyses only when geometry and parameters are stable.
  
  

2.4 Grasshopper components

Before we start introducing the Rhino.Ecologic Grasshopper components, let’s briefly review some general knowledge about Grasshopper components. In Grasshopper, there are six main types of components, each serving a different purpose within a definition:

• Parameter components store, reference, and pass data without performing operations on it. They are used to reference Rhino geometry or create constant values, for example with Point, Curve, Number, Boolean, Text, Geometry, or Data parameters. The component name is often just the data type.
  

• Primitive or input components create base data directly inside Grasshopper and provide user-adjustable input values, such as Number Sliders, Panels (for text), Toggles (True/False), or Colour Swatches.

• Operator or logic components perform calculations, comparisons, and data operations to manipulate numbers, vectors, or text—for example using +, −, ×, ÷, Greater Than, Equality, Boolean logic gates, Domain, or Vector Math.

• Geometry components generate and modify geometry, enabling users to build or transform Rhino geometry parametrically; common examples include Construct Point, Line, Loft, Extrude, Offset, or Project.

• Data management components are used to organize, filter, and structure data, especially when working with complex data trees and lists; typical examples are List Item, Cull Pattern, Split Tree, Merge, Flatten, Graft, or Path Mapper.
  

• Finally, scripting and utility components extend functionality or automate workflows, allowing for custom logic, algorithms, or automation through components like C# Script, Python Script, VB Script, Evaluate Expression, Timer, or Data Recorder.
  
  

2.5 Component architecture Rhino.Ecologic

Rhino.Ecologic extends Grasshopper by introducing new data types that represent ecological entities: Project, Voxel, Plant, Plant Library, and Soil Set.

These are not simple values; they are structured data containers that store geometry-linked environmental and ecological attributes.

The plugin components fall into four conceptual categories:

1. Initialization components: Define project geometry, voxel resolution, and environmental context.
   
2. Analysis components: Execute environmental and ecological analysis pipelines.
   
3. Data extraction components: Deconstruct structured ecological outputs for inspection and further processing.
   
4. Interpretation components: Visualize, summarize, optimize, and integrate results into design workflows.
   

This layered structure mirrors the ecological modeling logic: Spatial domain definition, environmental condition mapping, individual-based plant simulation, post-processing and interpretation.

By organizing components in this way, Rhino.Ecologic maintains transparency between geometry, environmental input, and ecological outcome.

2.6 From geometry to ecological simulation

At the operational level, Rhino.Ecologic transforms geometric models into ecological simulation domains through voxelization. Once a project is initialized:

1. Geometry is discretized into volumetric cells.
   
2. Environmental attributes are computed per voxel.
   
3. Plant communities are simulated within this structured spatial network.
   
4. Results are stored within the project object for further analysis.
   

The system therefore functions as a bridge between parametric geometry and dynamic ecological modeling without requiring users to leave the Grasshopper environment.

With the system installed and the interface understood, the next chapter documents each Rhino.Ecologic component in detail. Chapter 3 provides a structured reference of all tabs, inputs, outputs, and operational parameters within the plugin.

Chapter 3: Rhino.Ecologic Grasshopper components

Chapter 3 provides a structured reference of all Rhino.Ecologic components organized by toolbar tab. Each section documents component purpose, inputs, outputs, and workflow integration. The components are grouped into the following functional tabs: 00 Parameters, 01 Project, 02 Analysis, 03 Data, 04 Visualizations, 05 Utilities, 06 Statistics and 07 Species Libraries.

💡 Tip: For a step-by-step introduction to creating, importing, and saving projects, refer to the Quick Start Guide, which provides a structured walkthrough of the complete setup and analysis workflow.

3.1 Parameters tab

The Parameters tab introduces core Rhino.Ecologic data types. These parameter components function as structured data containers and enable the transfer of ecological entities across the Grasshopper definition without modification.

Unlike numerical or geometric parameters, these components encapsulate simulation-specific objects such as Project, Voxel, Plant, Plant Library, and Soil Set. You will find them located at the far left side of the first tab under Parameters. Both Parameter components store, reference, or pass data without performing any operation on it.

3.1.1 Parameter tab overview

GH Icon	Feature	Category/ Subcategory	Description
	Project (P)	Rhino.Ecologic® / Parameters	Represents a Rhino.Ecologic project. The component stores and passes the data of your Rhino.Ecologic project.
	Voxel (V)	Rhino.Ecologic® / Parameters	Represents a space boundary with specific attributes. This component stores and passes all data associated with each voxel in your Rhino.Ecologic project.
	Plant (PL)	Rhino.Ecologic® / Parameters	Represents a description of a plant.
	Plant Library (PLL)	Rhino.Ecologic® / Parameters	Represents a set of plants comprising a library.
	Soil Set (S)	Rhino.Ecologic® / Parameters	Represents a set of soils that can provide a nurturing environment for a plant.

3.2 Project tab

The Project tab appears as the second tab in the menu, next to Parameters. This tab includes Grasshopper components for creating a new Rhino.Ecologic project, importing existing projects, and saving your project locally.

The Project tab defines the ecological simulation domain. It is the entry point for transforming geometric input into a structured Rhino.Ecologic project object.

All ecological analyses in Rhino.Ecologic operate on a Project data type. This object encapsulates: Project metadata (Title, Location, Longitude and Latitude), referenced Geometry, Voxelized spatial domain (Voxel graph), environmental and ecological attributes, and the simulation state (Time Stamp).

The Project tab provides three core operations:

1. Create Project a new project from geometry
   
2. Import Project an existing serialized project
   
3. Save Project a project state for reuse or versioning
   
   

3.2.1 Project component overview

GH Icon	Feature	Category/ Subcategory	Description	Input parameters	Output
	Create Project (CP)	Rhino.Ecologic® / Project	Constructs a new Rhino.Ecologic project for analysis. The components converts geometry to mesh, voxelizes the bounding volume, and separates the graph into subgraphs such as interior and exterior envelopes.object.	Title, Location, Longitude, Latitude, Geometry, Biophilic Area, Voxel Size	Rhino.Ecologic Project
	Import Project (IP)	Rhino.Ecologic® / Project	Imports a project from a binary (.bph) file. Json deserialization is supported in the debug version.	Project Path	Rhino.Ecologic Project
	Save Project (SP)	Rhino.Ecologic® / Project	Saves a project as a binary (.bhp) file. Json serialization is supported in the debug version.	Project, Save Directory, Serialize	Rhino.Ecologic Project

3.2.2 Create Project

The Create Project component converts a 3D model into a Rhino.Ecologic Project, forming the computational basis for all subsequent environmental and ecological analyses.

The component processes the input geometry and generates a Rhino.Ecologic project instance, including a unique project ID and associated metadata such as Title, Location, Latitude, and complete project Geometry. The geometry is voxelized at a user-defined resolution, producing voxel IDs and mesh representations for each cell. These voxel entities define the spatial domain of ecological simulation.

Project initialization requires:

1. A 3D model in Rhino or Grasshopper
   
2. A project name
   
3. Geographic location data
   

Supported geometry types include: BReps, SubDs, and Meshes.

The Geometry input references the 3D model defining the spatial envelope. The Voxel Size parameter determines the resolution of the volumetric discretization.

💡 Tip: A voxel size of 1 meter is recommended. Rhino document units should be set to meters to ensure ecological plausibility and correct environmental scaling.

Project initialization based on referenced 3D geometry.

During project creation:

• Geometry is converted into mesh format
  
• the bounding volume is discretized into a structured voxel grid
  
• each voxel receives a unique identifier
  
• subgraphs are constructed (e.g., interior, exterior, envelope)
  

The resulting voxel graph establishes the spatial topology on which environmental mapping and plant community dynamics operate.

The generated Rhino.Ecologic Project object stores project metadata, a voxel graph, the spatial envelope, and associated geometric representations.

This object serves as the primary input for all analysis components in subsequent workflow stages.

The Biophilic Areas input allows restriction of ecological analysis to specified regions within the overall geometry. If left empty, environmental and ecological analyses are executed across the entire project domain. If bounding geometries are provided, only voxels intersecting these volumes are included in the simulation. This enables targeted ecological evaluation within defined spatial subsets of the model.

To analyze only specific regions of your project, reference a bounding box (or multiple boxes) around those areas to the Biophilic Areas input.

3.2.3 Import and Save Project

The Import Project and Save Project components support project persistence across sessions and workflows. The Import Project component loads an existing serialized Rhino.Ecologic project, restoring its full state and enabling continuation of a previously executed analysis. The Save Project component serializes the current project state and stores it as a .reproj file. This enables project reuse, version control, archival storage, and exchange between collaborators.

Save a Rhino.Ecologic Project.

Import an existing Rhino.Ecologic Project.

💡 Tip: To repeat this exercise using your own setup, you can use the Tutorial template 🦗 Grasshopper files Rhino.Ecologic_QuickStart.gh and Rhino.Ecologic_Import_Export.gh, and the 🦏 Rhino 3D model example file Rhino.Ecologic_Example_Wood_Cabins.3dm, which are available by hovering over the Rhino.Ecologic tab at the top of the Grasshopper menu.

3.2.4 Project tab inputs

The table below details the inputs of the Project tab, including their type, access, and units.

3.2.5 Project tab output

The project components outputs a Rhino.Ecologic Project (P), a structured data object encapsulating geometry, voxel graph, metadata, and simulation state.

This object forms the required input for all downstream environmental and ecological analysis components.

The table below details the outputs of the Project tab, including their type, access, and units.

3.3 Analysis tab

The Analysis tab is positioned adjacent to the Project tab and contains components responsible for environmental computation, ecological simulation (see Chapter 4), and post-processing of project data.

Its central component, Run Ecological Analysis, executes the full environmental and ecological analysis pipeline on the active voxelized project domain. The component processes solar exposure, soil conditions, precipitation distribution, and plant community dynamics based on the current project configuration.

Environmental and ecological behavior can be parameterized using the following components: Biodiversity Parameters, Solar Parameters, and Soil Depth Parameters. These parameter components define project-specific simulation settings and control model behavior prior to execution of the analysis pipeline.

3.3.1 Analysis tab component overview

The following components constitute the Analysis tab and define the environmental, ecological, and data-processing pipeline executed on the active Rhino.Ecologic Project.

GH Icon	Feature	Category/ Subcategory	Description	Input parameters	Output
	Run Ecological Analysis (EA)	Rhino.Ecologic®/ Analysis	Runs a configurable pipeline of analysis. This pipeline includes solar, soil, precipitation and biodiversity analysis, allowing for configuration (which analyses are run).	Project, Parameters, Run, Custom Library	Rhino.Ecologic Project, Log
	Biodiversity Parameters (BP)	Rhino.Ecologic®/ Analysis	Creates a set of parameters for the biodiversity analysis.	Simulation Duration	Parameters
	Solar Parameters (SP)	*Rhino.Ecologic®/ Analysis	Creates a set of customized analysis parameters to configure the solar analysis execution including radiation and daylight analysis.*	Hour Interval, Annual Frequency, Noon Hour, Day of Year, Plot Scale, Accumulative, Analysis Type, Analysis Span, Max Voxel Count, Max Depth, Min Surface Area	Parameters
	Soil Depth Parameters (SDP)	Rhino.Ecologic®/ Analysis	Creates a set of customized analysis parameters to configure the soil depth analysis execution.	Lower Height Depth	Parameters
	Run Data Analysis (DA)	Rhino.Ecologic®/ Analysis	Runs the data analysis pipeline and produces visualizations.	Rhino.Ecologic Project, Run	Bitmaps, Vector, Log

3.3.2 Run Ecological Analysis

The Run Ecological Analysis component executes the environmental and ecological simulation pipeline on the active Rhino.Ecologic Project.

Default configuration: In its default configuration, the component operates using internal default settings for Parameters and Custom Library. The environmental and ecological analysis pipeline is triggered by setting the Run boolean from False (inactive) to True (active). Execution includes: Inclination analysis, Solar analysis, Precipitation analysis, Biodiversity analysis including Species distribution, Abundance, Biomass, and Plant Volume.

Execution of the environmental and ecological analysis pipeline using default parameter and library settings.

Customized configuration: The analysis pipeline can be parameterized through the following components: Biodiversity Parameters, Solar Parameters, and Soil Depth Parameters.

In addition, a Custom Library may be supplied to define project-specific plant species and trait configurations.

Biodiversity Parameter customization: Simulation duration are defined through the Biodiversity Parameter component. In the example below, the simulation duration is set to 100 timesteps, corresponding to a total period of 100 years.

Customized environmental and ecological analysis using modified Biodiversity Parameter.

Soil Depth Parameter customization: Soil depth behavior and vertical constraints are defined through the Soil Depth Parameters component.

Customized environmental and ecological analysis using modified Soil Depth Parameters.

Solar Parameter customization: Solar exposure and irradiation analysis are configured through the Solar Parameters component. Adjustable parameters include: Analysis Type from Sunlight Hours to Irradiation, Analysis Span from Days to Years, and specifications for a particular Day of Year and Noon Hour. These parameters define the temporal and methodological scope of solar computation.

Customized environmental and ecological analysis using modified Solar Parameters.

Custom Library integration: A Custom Library may be provided as a Plant Library containing site-specific species definitions. Rhino.Ecologic includes predefined ecosystem-based example libraries (see Section 3.8 – Species Library Tab).

3.3.3 Run Data Analysis

The Run Data Analysis component performs statistical post-processing of simulation results generated by Run Ecological Analysis. The components processes environmental data (soil, solar, precipitation), geometry- derived data, and spatio-temporal ecological including biomass, species type and distribution, abundance (biodiversity), and plant volume.

Execution requires a valid Rhino.Ecologic Project (P) input. The analysis pipeline is triggered by setting the Run boolean to True.

Outputs include: Bitmap charts (raster images), and Vector charts exported as SVG. These outputs provide aggregated representations of simulation results suitable for documentation, reporting, and comparative scenario evaluation.

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Execution of the data analysis pipeline.

3.3.4 Analysis tab Inputs

The tables below provide further information about Type, Acess, and Units for each input of the Analysis tab.

Inputs for a customized Run Ecological Analysis setup (optional):

3.3.5 Analysis tab Outputs

The table provides Type, Acess, and Units for each output of the Analysis tab.

💡 Tip: To repeat this exercise using your own setup, you can use the Tutorial template 🦗 Grasshopper files Rhino.Ecologic_Growth_Simulation.gh, Rhino.Ecologic_Custom_Parameters.gh, and Rhino.Ecologic_Custom_Plant_Library.gh, and the 🦏 Rhino 3D model example file Rhino.Ecologic_Example_Wood_Cabins.3dm, which are available by hovering over the Rhino.Ecologic tab at the top of the Grasshopper menu.

3.4 Data tab

The Data tab is positioned adjacent to the Analysis tab and contains components for accessing, extracting, and modifying environmental and ecological data within a Rhino.Ecologic project.

All components operate on the voxelized project domain and return data at the predefined spatial resolution (Voxel Size). The extracted data represents the environmental conditions and ecological state computed during the analysis stage.

The Data tab provides two primary functions:

1. Deconstruction of project and voxel data into accessible attributes
2. Assignment and modification of project and voxel-level data

The resulting data can be used for downstream processing, custom visualization, and integration with external workflows within Grasshopper.

3.4.1 Data component overview

The following components constitute the Data tab and define the data extraction and manipulation workflows available for a Rhino.Ecologic Project.

GH Icon	Feature	Category/ Subcategory	Description	Input parameters	Output
	Deconstruct Project	Rhino.Ecologic®/ Data	Deconstructs a project object to its constituent parts: Geometry-, solar-, precipitation-, soil-, and biodiversity related attributes. It only filters and outputs the voxels set by the user, e.g. only the exterior envelope voxels.	Rhino.Ecologic Project	Title, Id, Location, Longitude, Latitude, Elevation, North, Timestamp, Voxels, Mesh
	Deconstruct Voxel	Rhino.Ecologic®/ Data	Deconstructs a voxel to its constituent parts.	Voxel, Time Step	Id, Mesh, Position, Size, Inclination, Solar, Soil Depth, Soil Volume, Soil Type, Precipitation, Timestamp, Species, Biomass, Abundance, Plant Volume
	Set Project Data	Rhino.Ecologic®/ Data	Sets the value of the selected project field: Title, Location, Longitude, Latitude, Elevation, Geometry, and Voxels	Project, Parameter, Value	Rhino.Ecologic Project
	Set Voxel Data	Rhino.Ecologic®/ Data	Sets the value of the selected voxel field.	Voxels, Analysis Type, Analysis Values	Voxels

3.4.2 Deconstruct Project

The Deconstruct Project component extracts project-level data from a Rhino.Ecologic Project (P) object.

The component separates the project into its constituent data layers, including: Geometry, Solar data, Precipitation data, Soil attributes, and Biodiversity attributes.

In addition, the component supports filtering of voxel subsets (e.g., exterior or envelope voxels), enabling selective access to spatial regions within the project domain.

Extraction of Rhino.Ecologic project data using the Deconstruct Project component.

3.4.3 Deconstruct Voxel

The Deconstruct Voxel component extracts voxel-level data from an analysed Rhino.Ecologic Project (P).

The component returns data across the following categories:

• Geometry: Id, Mesh, Position, Size, Inclination
• Environmental: Solar, Soil Depth, Soil Volume, Soil Type, Precipitation
• Ecological: Timestamp, Species, Biomass, Abundance / Biodiversity, Plant Volume

Voxel data corresponds to the environmental and ecological state computed during the analysis stage and reflects the spatial discretization defined by the project’s voxel resolution.

Extraction of voxel data using the Get Voxels by Type component. Selection of Exterior Envelope returns results for the project envelope.

Temporal selection is controlled through the Time Step input, which defines the simulation year for which ecological data is returned.

Ecological results retrieved for individual simulation timesteps.

3.4.4 Set Project Data

The Set Project Data component modifies project-level attributes of a Rhino.Ecologic Project (P).

The component provides access to core project properties, including:Title, ID, Location, Longitude, Latitude, Elevation, North, Timestamp, Voxels, and Mesh of your project.

These attributes define the project’s metadata, spatial configuration, and environmental reference parameters.

Modification of project-level attributes using the Set Project Data component. Geometry inputs may be redefined to evaluate subsets of the original project domain.

3.4.5 Set Voxel Data

The Set Voxel Data component modifies voxel-level attributes for a specified Analysis Type.

Supported analysis types include, for example: Soil Depth, Solar, or Biomass.

Custom values can be assigned through the Analysis Values input, enabling controlled modification of environmental or ecological parameters at voxel resolution.

Modification of voxel-level attributes. In this example, soil depth is adjusted and the voxel data is updated accordingly.

3.4.6 Data tab Inputs

The following table defines the Type, Access, and Units for each input in the Data tab.

3.4.7 Data tab Outputs

The following table defines the Type, Access, and Units for each output in the Data tab.

⚠️ Caution: Species, Abundance, and Biomass are mapped to plant origin locations and occur only in voxels containing soil. For visualization, filter voxels with the Exterior Envelope property.

💡 Tip: Example workflows are available in the tutorial templates Rhino.Ecologic_QuickStart.gh and Rhino.Ecologic_Data_Inspection.gh, together with the Rhino model Rhino.Ecologic_Example_Wood_Cabins.3dm. These can be accessed via the Rhino.Ecologic tab in the Grasshopper interface.

3.5 Visualization tab

The Visualization tab sits next to the Data tab and includes the Grasshopper components Visualize Analysis and Graph Display. Visualizing your data has the following advantages:

1. It helps you quickly spot patterns and outliers
2. You can compare scenarios across time steps
3. You can validate assumptions and communicate results clearly to stakeholders

It also improves QA/QC by revealing data gaps or errors, supports faster design decisions, and allows customization (e.g., gradients, voxel types, attributes) for task-specific insights. Finally, visuals can be exported (bitmap/SVG) for reports and presentations.

The Visualize Analysis component (Section 2.5.1) displays the data from your analysis, while Graph Display (Section 2.5.3) generates graphical representations of the analyzed data, which can be exported as either bitmap images or vector-based SVG files. In the following section, you will see how both components are used. Additionally, you will learn how to display your analysis results in Rhino using standard Grasshopper components (Section 2.5.2).

3.5.1 Visualization tab component overview

The tables below describe the Grasshopper components in the Visualization tab.

GH Icon	Feature	Category/ Subcategory	Description	Input parameters	Output
	Visualize Analysis (VA)	Rhino.Ecologic®/ Visualization	Visualize the data of the analysis.	Project, Voxel Type, Attribute, Time Step, Show	Visualization
	Graph Display (GD)	Rhino.Ecologic®/ Visualization	Visualizes graphs in the form of a bitmap or svg.	Project Data	Graph Display

3.5.1 Visualize Analysis

To visualize your data, drag the Visualize Analysis Grasshopper component onto the canvas and connect it to your Rhino.Ecologic project. Set the Voxel Types, Analysis Type/ Attributes, and Time Steps inputs to visualize your analysis results in Rhino.

Setup of the Visualize Analysis component with changing Value List inputs.

3.5.2 Visualization of data using standard Grasshopper components

Alternatively, you can use standard Grasshopper components such as Domains from the Math tab and Gradient from the Params tab to visualize the data outputs in Rhino. Using standard Grasshopper components for visualizing analysis results offers the advantage of allowing you to adjust gradient colors and types as needed.

Access Solar data associated with your project’s geolocation: Daylight Hours and Irradiation.

Access Precipitation data associated with your project’s geolocation.

Access Soil data of your project: Soil Depth, Soil Volume and Soil Type.

Access ecological analysis data for your project. Modify the Time Steps to simulate ecological changes over time. This example shows the Biomass for each voxel cell at the level of the Exterior Envelope. The simulation results are shown without trees, including only herbaceous vegetation.

The next example of the Biomass simulation shows trees, shrubs, and herbs. As the trees mature, they shade the lower layers, reducing the growth of smaller plants. The dark green voxels represent the locations where trees begin to grow.

This example shows the Plant Volume simulation results for a Species Library consisting of trees, shrubs, and herbs. The key difference compared to Biomass is that Plant Volume quantifies the geometric volume occupied by vegetation in each voxel, whereas Biomass measures the accumulated biological dry mass.

Obtain ecological simulation results for a specific species by applying a filter. Use standard Grasshopper methods to extract the desired species from the Species Distribution results.

3.5.3 Graph Display

⚠️ Caution: Only available in the Debug Version.

Run a post-process analysis on the ecological analysis data using Run Data Analysis, then visualise the results with Graph Display. To choose which time step to display, connect a native Grasshopper List Item component to Graph Display and set the index to the desired time step. Graphs can be displayed and exported either as a Bitmap image or as a Vector graphic.

Explain different graphs and what they mean, the model behind and why this information is useful, at least half a page

Visualisation of post-processed analysis results at a selected time step.

3.5.4 Visualization tab Inputs

The table shows the Type, Access, and Units for each input in the Visualisation tab.

3.5.5 Visualization tab Outputs

The table provides information about the Type, Access, and Units for the Visualisation tab outputs.

💡 Tip: To repeat this exercise using your own setup, you can use the Tutorial template 🦗 Grasshopper files Rhino.Ecologic_QuickStart.gh, Rhino.Ecologic_Visualisation.gh and Rhino.Ecologic_Data_Analysis.gh, and the 🦏 Rhino 3D model example file Rhino.Ecologic_Example_Wood_Cabins.3dm, which are available by hovering over the Rhino.Ecologic tab at the top of the Grasshopper menu.

3.6 Utilities tab

The Utilities components provide supporting tools for managing, inspecting, and refining project data within the workflow. They allow users to adjust project settings, filter and access simulation outputs, and prepare data for analysis or visualization. The Utilities tab sits next to the Visualization tab and includes the following Grasshopper components: Get Annual Sun Positions, Get Daily Sun Positions, Get Biophilic Voxels, Get Plant Voxels, Get Voxels by Type, and an educational component on soil types called Get Soil Description. The functionality of each of these components is described below.

3.6.1 Utilities tab component overview

The table below gives an overview of the Grasshopper components available in the Utilities tab.

GH Icon	Feature	Category/ Subcategory	Description	Input parameters	Output
	Get Annual Sun Positions (ASP)	Rhino.Ecologic®/ Utilities	Calculates the sun positions of the location specified in the project.	Project, Annual Frequency, Hour Interval	Sun Positions
	Get Daily Sun Positions (DSP)	Rhino.Ecologic®/ Utilities	Gets the sun positions of the location specified in the project on the specific day.	Project, Day	Sun Positions
	Get Biophilic Voxels (GBV)	Rhino.Ecologic®/ Utilities	Gets voxels marked as biophilic.	Project, Day	Voxels
	Get Plant Voxels (GPV)	Rhino.Ecologic®/ Utilities	Gets all the voxels that have valid biomass values.	Project, Time Step	Voxels, Time Step
	Get Voxel Value (GVV)	Rhino.Ecologic®/ Utilities	Gets a specific analysis value of the voxel.	Voxel, Analysis Type, Time Step	Value
	Get Voxels By Type (SVD)	Rhino.Ecologic®/ Utilities	Sets the value of the selected voxel field.	Voxel, Analysis Type, Analysis Values	Voxels
	Get Soil Description (SD)	Rhino.Ecologic®/ Utilities	Gets a soil description for a given soil type.	Soil Type	Name, Description

3.6.2 Get Annual Sun Positions

Connect the Get Annual Sun Positions component to your analysed Rhino.Ecologic project to output all annual sun positions as a list of points. Adjust the Annual Frequency (number of days; 365 by default) and the Hour Interval (time step between hours of the day; 1 hour by default).

Retrieve the annual sun positions of your project as a list of points.

3.6.3 Get Daily Sun Positions

Connect the Get Daily Sun Positions component to your analysed project and specify a day of the year (in serial). The component outputs that day’s sun positions as a list of points.

Retrieve the daily sun positions of your project as a list of points.

3.6.4 Get Biophilic Voxels

Connect the Get Biophilic Voxels component to your analysed project to return only the voxels located within the biophilic areas.

Retrieve the biophilic voxels associated with the Biophilic Area of your Rhino.Ecologic Project.

3.6.5 Get Plant Voxels

Use the Get Plant Voxels component to filter voxels that contain vegetation. It returns every voxel with a valid (positive) biomass value at the selected time step, marking where plants are growing.

Connect your analysed project and set the Time Step (in years) to retrieve the data. This lets you simulate plant growth over time and visualise herbs, shrubs, and trees as true 3D voxel representations.

The Get Plant Voxels component enables time-based plant growth simulations for your Project.

3.6.6 Get Voxel Value

Use the Get Voxel Value component to read a single analysis value from one voxel. Connect a Voxel, choose the Analysis Type, and set the Time Step; the component returns the corresponding Value.

3.6.7 Get Voxels by Type

The Get Voxels by Type component filters the voxel graph by a specific voxel type — Exterior, Exterior Envelope, Interior Envelope, or Interior. Connect your analysed project and a Value List to select the type.

• The Exterior voxel graph contains all voxels at the defined resolution that lie within the bounding box surrounding your 3D project.
• The Exterior Envelope voxel graph includes all voxel cells that form the outer envelope of your 3D model, including both terrain and building geometry.
• The Interior Envelope voxel graph represents the voxel cells located on the inner surfaces of enclosed spaces.
• The Interior voxel graph contains all voxels located fully within a closed volume of your 3D model.
  

Retrieve a specific voxel type from your project. In the example shown, the Exterior voxel graph contains a total of 247,016 voxels, while the Exterior Envelope voxel graph contains 22,209 voxels.

3.6.8 Get Soil Description

Rhino.Ecologic uses 10 functionally distinct Soil Types to capture the key ecological properties influencing vegetation, water dynamics, and biodiversity, while avoiding unnecessary taxonomic complexity and ensuring efficient, robust simulations. Connect the Get Soil Description component and a Soil Type to return expert information for each soil category used in Rhino.Ecologic: Clay, Loamy Sand, Sandy Loam, Loam, Silt Loam, Silt, Sandy Clay Loam, Silty Clay Loam, Sandy Clay, and Silty Clay.

• Clay: Very heavy soil with high water and nutrient capacity, but no aeration and strong penetration resistance. Relatively rare. Examples: lake sediments or quiet zones of river deltas (locally), heavily disturbed sites with exposed subsoil; contaminated brownfields (functionally equivalent).

• Loamy Sand: Sand-dominated soil with some loam; higher nutrient and water-holding capacity than pure sand. Examples: heathlands, dry pine forests.

• Sandy Loam: Loam-dominated soil with a higher sand content; good aeration and drainage but lower nutrient retention than loam. Examples: sandy riverbanks, vineyard soils in warm climates, intensive green roofs.

• Loam: Balanced soil with a mix of sand, silt, and clay, providing good aeration, drainage, and water and nutrient retention. Easy for roots to penetrate. Examples: gardens and parks, productive farmland.

• Silt Loam: Silt-dominated, fine-textured soil with good drainage and high water and nutrient retention, but prone to erosion. Examples: fertile agricultural plains, loess soils, river deltas.

• Silt: Fine but non-cohesive soil with high water retention and nutrient accessibility, but prone to erosion. Rare in pure form. Examples: exposed subsoils and river bank sediments in loess regions (e.g. Danube loess belt).

• Clay Loam: Clay-rich soil with high water and nutrient retention, but prone to compaction and difficult root penetration. Poor drainage and stagnant water may limit plant growth. Examples: heavily irrigated farmland, muddy pastures.

• Sandy Clay Loam: Substantial clay content but enough sand to improve aeration and drainage compared to clay loam. High nutrient retention but moderate penetration resistance. Examples: Old agricultural soils on sandy substrates, mixed alluvial deposits; some urban soils.

• Silty Clay Loam: Fine-textured soil with high water and nutrient retention but reduced drainage, intermediate between loam and clay loam. Examples: floodplains, fertile valley soils, brownfields and other compacted vacant lots.

• Sandy Clay: Clay-dominated soil with noticeable sand fraction; very high nutrient and water capacity, but still poor drainage and accessibility. Examples: disturbed industry/construction sites.

• Silty Clay: Very fine-textured, cohesive and with extremely high water/nutrient retention. Minimal aeration, high penetration resistance. Often seasonally waterlogged. Examples: backswamps, lake margins.

The class NoSoil describes the absence of any substrate that supports growth.

Get more information about the various Soil Types used in the ecological analysis by connecting a Panel to the Description output.

3.6.8 Utilities tab Inputs

The table provides information about the Type, Access, and Units for each input in the Utilities tab.

3.6.9 Utilities tab Outputs

The table summarizes the Type, Access, and Units for each output in the Utilities tab.

💡 Tip: To repeat this exercise using your own setup, you can use the Tutorial template 🦗 Grasshopper files Rhino.Ecologic_Growth_Simulation.gh and Rhino.Ecologic_Sun_Positions.gh, and the 🦏 Rhino 3D model example file Rhino.Ecologic_Example_Wood_Cabins.3dm, which are available by hovering over the Rhino.Ecologic tab at the top of the Grasshopper menu.

3.7 Statistics tab

The Statistics tab sits next to the Utilities tab and includes the following Grasshopper components: Get Biodiversity Summaries, Get Inclination Summaries, Get Precipitation Summaries, Get Soil Summaries, Get Solar Summaries, and Get Voxel Summaries.

Each component runs a descriptive analysis on one group of project attributes and returns its summary statistics — typically the minimum, maximum, average, and median — which you can use for project reporting and comparison.

3.7.1 Statistics tab component overview

The table below gives an overview of the Grasshopper components available in the Statistics tab.

GH Icon	Feature	Category/ Subcategory	Description	Input parameters	Output
	Get Biodiversity Summaries (BS)	Rhino.Ecologic®/ Statistics	Performs descriptive analysis on the biodiversity attributes of a project and returns its summary statistics.	Project	Minimum, Maximum, Average, Median
	Get Inclination Summaries (IS)	Rhino.Ecologic®/ Statistics	Performs descriptive analysis on the inclination attributes of a project and returns its summary statistics.	Project	Minimum, Maximum, Average, Median
	Get Precipitation Summaries (PS)	Rhino.Ecologic®/ Statistics	Performs descriptive analysis on the precipitation attributes of a project and returns its summary statistics.	Project	Minimum, Maximum, Average, Median
	Get Soil Summaries (SS)	Rhino.Ecologic®/ Statistics	Performs descriptive analysis on the soil attributes of a project and returns its summary statistics.	Project	Minimum Soil Depth, Maximum Soil Depth, Average Soil Depth, Median Soil Depth, Minimum Soil Volume, Maximum Soil Volume, Average Soil Volume, Median Soil Volume, Soil Type Distribution (Types), Soil Type Distribution (Count)
	Get Solar Summaries (SS)	Rhino.Ecologic®/ Statistics	Performs descriptive analysis on the solar attributes of a project and returns its summary statistics.	Project	Minimum Soil Depth, Maximum, Average, Median
	Get Voxel Summaries (VS)	Rhino.Ecologic®/ Statistics	Performs descriptive analysis on the voxel attributes of a project and returns its summary statistics.	Project	Biophilic Areas Count, Type Distribution (Types), Type Distribution (Count), Voxel Index, Degree Centrality

3.7.2 Get Summaries components

Connect your analysed Rhino.Ecologic project to any Get Summaries component to generate descriptive statistics for that attribute group — Biodiversity, Inclination, Precipitation, Soil, Solar, or Voxel.

Obtain statistical summaries such as Biodiversity, Inclination, and Solar summaries for your Rhino.Ecologic project.

Obtain statistical summaries for Soil (Soil Depth, Soil Volume, and Soil Type) in your Rhino.Ecologic project. In addition, the component provides a list of all soil types used in the analysis, along with the count for each soil type.

Obtain Voxel statistical summaries for your Rhino.Ecologic project. These include the count of biophilic voxels, information on voxel type distribution, voxel identifiers, and the calculated degree for each voxel node.

3.7.3 Statistics tab Inputs

The table provides information about the Type, Access, and Units for the input in the Statistics tab.

3.7.4 Statistics tab Outputs

The table summarizes the Type, Access, and Units for each output in the Statistics tab.

💡 Tip: To repeat this exercise using your own setup, you can use the Tutorial template 🦗 Grasshopper file Rhino.Ecologic_Statistics.gh and the 🦏 Rhino 3D model example file Rhino.Ecologic_Example_Wood_Cabins.3dm, which are available by hovering over the Rhino.Ecologic tab at the top of the Grasshopper menu.

3.8 Species Library tab

The Species Library tab sits next to the Statistics tab, at the outer edge of the menu, and includes the following Grasshopper components: Compute Preset Values, Create Plant, Create Plant Library, Create Soil Set, Deconstruct Plant, Deconstruct Plant Library, Deconstruct Soil Set, Import Plant Library, and Save Plant Library.

Ecological analyses are strongly influenced by site-specific and geographic conditions, and species composition may vary significantly between different urban ecosystems. As a result, working with a project-specific, custom species library can be advantageous.

Use these components to define and manage custom species for ecological analysis. Parameterize each species using individual traits such as life span, maturation time, and rooting depth, and organize them into user-defined Custom Libraries. Export a custom library to reuse it as a reference in other projects, and deconstruct both individual species definitions and complete libraries to inspect and access their underlying parameters.

The components also support the creation of Soil Sets, which define collections of soil types and parameterize the soil conditions relevant to plant growth. The functionality of each component is described below.

3.8.1 Species Library tab component overview

The table below gives an overview of the Grasshopper components available in the Species Library tab.

GH Icon	Feature	Category/ Subcategory	Description	Input parameters	Output
	Compute Preset Values (CPV)	Rhino.Ecologic®/ Species Library	Provides reasonable guesses for traits that are difficult to find in literature.	Life Span, Growth Form, Shade Tolerance	Maturation Time, Rooting Depth, Size, Germination Rate, Shade Tolerance
	Create Plant (CPL)	Rhino.Ecologic®/ Species Library	Creates a plant definition.	Name, Growth Form, Life Span, Maturation Time, Shade Tolerance, Shape, Max Height, Width, Opacity, Ratio Crown To Stp, Crown Width, Rooting Depth, Size, Soil Set, Germination Rate	Plant
	Create Plant Library (CPLL)	Rhino.Ecologic®/ Species Library	Creates a plant library provided a series of plant definitions.	Name, Plants	Plant Library
	Create Soil Set (CSS)	Rhino.Ecologic®/ Species Library	Creates a soil set for a plant definition.	Accepted Soils	Soil Set
	Deconstruct Plant (DPL)	Rhino.Ecologic®/ Species Library	Deconstructs a plant to its constituent parts.	Plant	Name, Max Height, Life Span, Maturation Time, Rooting Depth, Size, Soil Set, Dormancy, Dormancy Break Rate, Germination Success, Max Biomass, Leaf Surface Area, Biomass Allocation, Seed Allocation, Conversion Efficiency, Opacity, Maintenance Costs
	Deconstruct Plant Library (DPLL)	Rhino.Ecologic®/ Species Library	Soils contained in the soil set.	Project Data	Graph Display
	Deconstruct Soil Set (DSS)	Rhino.Ecologic®/ Species Library	Visualizes graphs in the form of a bitmap or svg.	Soil Set	Soils
	Import Plant Library (EPLL)	Rhino.Ecologic®/ Species Library	Imports a plant library.	Library Path	Plant Library
	Save Plant Library (SPLL)	Rhino.Ecologic®/ Species Library	Saves a plant library to a file.	Plant Library, Save Directory, Save	-

3.8.2 Compute Preset Values

Defining a plant species means specifying many traits, which can be difficult for non-ecologists. The Compute Preset Values component gives a guided starting point: choose a Growth Form (Grass, Herb, Shrub, Tree Deciduous, Tree Evergreen) and a Shade Tolerance (Low, Medium, High), and it returns matching values for Maturation Time, Rooting Depth, Plant Size, Germination Rate, and Shade Tolerance (as a numeric value), which you can adjust as needed.

The Compute Preset Values component calculates values for a plant definition from a chosen preset.

3.8.3 Create Plant

Use the Create Plant component to build a plant definition — a Rhino.Ecologic data type that acts as a container for the traits describing a species. Plant is also available as a parameter component on the far left of the first tab under Parameters.

Provide the following inputs: Name, Growth Form, Life Span, Maturation Time, Shade Tolerance, Shape, Max Height, Width, Opacity, Ratio Crown to Stem, Crown Width, Rooting Depth, Size, Soil Set, and Germination Rate. From these, further ecological and physiological characteristics are inferred automatically (see also the Deconstruct Plant component).

💡 Tip: To simplify the definition process, use the Compute Preset Values component described above to generate initial parameter values. Many plant traits can also be obtained from publicly available sources, such as botanical databases and general reference resources (for example, Wikipedia).

In this example, the plant Holcus lanatus (Yorkshire fog) is created.

3.8.4 Create Plant Library

Use the Create Plant Library component to define the list of plant species relevant to your study site. First parameterize each Plant individually by defining its traits, then combine them into a library by providing a library Name and the list of Plants. The output is a Plant Library, a Rhino.Ecologic data type you can feed into subsequent ecological analysis components.

The Create Plant Library component composes a set of plant definitions into one library.

The graph illustrates the relationship between plant size and plant age for both herbs and trees.

3.8.5 Create Soil Set

Each Plant supports a specific set of soil types, which are a prerequisite for successful growth, so the supported soils must be defined by the user. Use the Create Soil Set component to select all soil types compatible with a given Plant from a predefined list.

The Accepted Soils input defines the soil types supported by the plant. The component outputs a list of the selected soil types as a Soil Set.

3.8.6 Deconstruct Plant

Use the Deconstruct Plant component to break a Plant into its constituent parameters — useful when you import a Plant from a previous project and want to inspect the traits that define it. The outputs are: Name, Max Height, Life Span, Maturation Time, Rooting Depth, Size, Soil Set, Dormancy, Dormancy Break Rate, Germination Success, Max Biomass, SLA, Biomass Allocation, Seed Allocation, Conversion Efficiency, Opacity, and Maintenance Costs.

The Deconstruct Plant component deconstructs a plant into its constituent parts.

3.8.7 Deconstruct Plant Library

Like a Plant, a Plant Library can be broken into its individual parts. Use the Deconstruct Plant Library component to output the library Name and the list of contained Plant Names.

The example shows a Plant Library after it has been deconstructed using the Deconstruct Plant Library component.

3.8.8 Deconstruct Soil Set

Use the Deconstruct Soil Set component to break a given Soil Set into the soil types it contains.

The output is a list of soil types accepted by a specific Plant.

3.8.9 Import Plant Library

Use the Import Plant Library component to import a custom Plant Library.

Import of a Plant Library.

3.8.10 Save Plant Library

Use the Save Plant Library component to save your custom Plant Library to a file.

Save your custom Plant Library.

3.8.11 Species Library Inputs

The table provides information about the Type, Access, and Units for each input in the Species Library tab.

3.8.12 Species Library Outputs

The table summarizes the Type, Access, and Units for each output in the Species Library tab.

💡 Tip: To repeat this exercise using your own setup, you can use the Tutorial template 🦗 Grasshopper file Rhino.Ecologic_Species_Library.gh and the 🦏 Rhino 3D model example file Rhino.Ecologic_Example_Wood_Cabins.3dm, which are available by hovering over the Rhino.Ecologic tab at the top of the Grasshopper menu.

3.9 Troubleshooting

This section covers the most common problems you may encounter when installing and running Rhino.Ecologic, along with their likely causes and fixes. Most issues fall into one of four areas: the plugin not loading, the .NET runtime Rhino 8 is using, missing native dependencies, and files blocked by Windows. If a fix below does not resolve your problem, see 3.9.5 – Diagnosing further for how to gather more detail.

ℹ️ Note: Rhino.Ecologic requires Rhino 8 or later on Windows and runs only in .NET Core — it will not load in the legacy .NET Framework 4.8 runtime. See 3.9.2 if you are unsure which runtime Rhino is using.

3.9.1 Installation and component loading

3.9.2 .NET runtime (Rhino 8)

Rhino 8 can start in two runtime modes on Windows, and Rhino.Ecologic runs only in .NET Core. If the plugin fails to load, by far the most common cause is that Rhino started in .NET Framework 4.8 mode instead.

• .NET Core (/netcore) — required for Rhino.Ecologic, and the default in Rhino 8. Rhino 8.20 and later default to .NET 8; Rhino 8.19 and earlier default to .NET 7. Either .NET Core version works; Rhino 8 does not support .NET 9 or later.
• .NET Framework 4.8 (/netfx) — a legacy mode kept for older plugins and hosts such as Rhino.Inside Revit. Rhino.Ecologic will not load in this mode.

Use the table below to confirm and, if needed, switch the runtime.

💡 Tip: If you also use plugins that require .NET Framework, keep a dedicated desktop shortcut to Rhino.exe ending in /netcore and use it whenever you work with Rhino.Ecologic.

3.9.3 Missing native dependencies (DLL NOT FOUND)

Rhino.Ecologic relies on native (unmanaged) libraries — including BiophiliaAPI.dll and plants.dll — in addition to its managed .gha. A DLL NOT FOUND (or Unable to load DLL / FileNotFoundException) message means one of these could not be located or loaded.

If the message persists after these checks, run Rhino as Administrator once, temporarily disable antivirus to rule out quarantine, and inspect the Windows Event Viewer for the specific load failure.

3.9.4 Blocked or network-location files

Windows marks files downloaded from the internet as blocked, and .NET restricts loading assemblies from network drives. Either can prevent the .gha or its DLLs from loading, often with an error such as Could not load file or assembly … Operation is not supported (HRESULT: 0x80131515).

• Unblock each file: right-click the .gha and every accompanying DLL → Properties → tick Unblock → OK. To unblock a whole folder at once, run in PowerShell: Get-ChildItem -Path "<folder>" -Recurse | Unblock-File.
• Avoid network locations: install the plugin on a local drive rather than a mapped network share or cloud-synced folder, which are the usual triggers for the assembly-sandboxing error above.

3.9.5 Diagnosing further

If none of the above resolves the issue, gather more detail before reporting it:

• In Rhino, press F2 to open the command history and review the plugin-load report — it lists which components loaded and which failed.
• Read the runtime messages on the failing Grasshopper component (hover the warning/error bubble) for the exact exception text.
• Check the Windows Event Viewer (Windows Logs → Application) for .NET Runtime entries logged at the time of the failure.
• Note your Rhino version and runtime from SystemInfo.

When contacting support, include the Rhino version and runtime, the plugin version, the exact error text, and — where possible — the Grasshopper definition and a minimal Rhino model that reproduces the problem.

💡 Tip: Most loading problems come down to two things — an unblocked install on a local drive, and running Rhino in .NET Core (3.9.2). Check those first before digging deeper.

Chapter 4: The Ecological Core — The Joschinski Model

4.1 Conceptual foundation

Ecological systems are governed by interacting biotic and abiotic processes that operate across multiple spatial and temporal scales. Plant performance depends simultaneously on resource availability (e.g., light), environmental constraints (e.g., temperature, soil chemistry), and interactions with other organisms. These factors do not act independently; rather, they form tightly coupled feedback structures. Such cross-scale interactions render ecological community dynamics inherently nonlinear and difficult to infer from static correlations alone. Mechanistic modeling therefore becomes essential for representing the processes that generate observable vegetation patterns.

At the core of Rhino.Ecologic lies the Joschinski Model (Joschinski et al. 2024), a spatially explicit, individual-based ecological simulation model that governs the dynamic behavior of plant communities within the voxelized design space. The model forms the computational backbone of the ecological analysis pipeline. It is neither a statistical black box nor a correlative predictor; rather, it is a mechanistic, rule-based system that simulates ecological processes directly on the geometry-derived spatial domain.

The Joschinski Model belongs to the class of individual-based community models (IBMs). In this modeling paradigm, each plant is represented as an autonomous individual characterized by species-specific functional traits. Community-level patterns are not imposed externally; instead, they emerge from localized interactions among individuals competing for limited resources under defined environmental constraints. Ecological dynamics unfold across discrete annual time steps, during which germination, growth, biomass allocation, reproduction, dispersal, and mortality are simulated sequentially.

Unlike classical Species Distribution Models (SDMs), which estimate potential species occurrence based on statistical correlations between environmental variables and observation data, the Joschinski Model simulates the ecological processes that generate these patterns. Species dominance, coexistence, and decline arise from competition, trait differences, and spatial structure rather than from fitted probability surfaces.

4.2 Spatial representation and environmental integration

The spatial domain of the simulation is defined by the voxel graph generated from the project geometry. During initialization, the 3D model is discretized into a structured volumetric grid, where each voxel stores geometric and environmental attributes derived from both design input and geographic location. These include soil depth, soil type, soil volume, solar radiation, and precipitation.

The voxel structure is not merely a storage mechanism; it defines the topology of ecological interaction. Voxels form a connected spatial graph in which plants compete for light and space. Vertical subdivision into substrata increases the resolution of light competition and enables shading to be calculated with spatial fidelity. This discretized representation allows the model to resolve three-dimensional competition while maintaining computational efficiency.

Environmental parameters influence plant performance locally. Solar input is computed per voxel and intercepted according to crown geometry and shading relationships. Soil constraints regulate establishment and survival based on rooting depth and soil compatibility. In this way, environmental data derived from the design model directly influence ecological trajectories.

4.3 Plant representation and trait-based dynamics

Each plant individual is defined through a trait-based parametrization. These traits include life span, growth form, maximum height, crown geometry, rooting depth, shade tolerance, biomass allocation strategy, and energetic conversion efficiency. Rather than representing vegetation abstractly, the model assigns each plant an explicit geometric envelope that determines how it intercepts light and casts shade on neighboring individuals.

Growth follows species-specific trajectories under ideal conditions. However, actual development is contingent on resource availability. Light functions as the primary limiting factor in the system. Intercepted energy is converted into biomass according to species-specific efficiencies, after which maintenance costs are subtracted. Remaining resources are allocated to structural growth and reproduction. Under competitive shading, biomass production declines, growth slows, and mortality risk increases.

Competition is therefore not imposed as a ranking mechanism but emerges from spatial overlap and energy limitation. As crowns expand and overlap, light distribution becomes heterogeneous. Taller or more shade-tolerant individuals may gain competitive advantage, while suppressed individuals may stagnate or die. Community structure thus develops dynamically from local interactions.

The “Joschinski Model”, a spatial temporal individual-based community model. This model tracks multiple individuals from multiple species through their life cycles. ©McNeel Europe 2025.

4.4 Succession, stochasticity, and emergence

Successional dynamics arise from differences in life-history strategies. Fast-growing, short-lived species may dominate early stages when light is abundant, while slower-growing but taller or more shade-tolerant species gradually shape canopy structure over time. Soil constraints and dispersal processes further regulate species composition.

The model incorporates stochasticity in processes such as seed dispersal, germination success, and competitive light allocation. As a result, repeated simulation runs with identical inputs may yield numerically different outcomes. This variability reflects ecological realism rather than computational instability. Outputs should therefore be interpreted as probabilistic trajectories or pattern tendencies rather than deterministic predictions. For reproducibility and debugging, a fixed random seed can be activated.

Plant growth in the model is influenced by several key factors:

GH graph	Description
	The graph displays growth in height over time. The curve is species-specific and remains fixed—it does not respond to environmental conditions. Each species follows its own predefined growth trajectory.
	The graph displays biomass accumulation, specifically green leaf growth. Under ideal conditions, this growth follows a curve similar to that of height development.

However, if a plant is shaded by neighboring individuals, its ability to collect energy is reduced. As a result, biomass production slows, the plant falls behind in development, and in extreme cases, may die due to resource limitations.

4.5 Core logic and theory

This section summarises the concepts of the model on a high level only.

💡 Tip: For more detailed information, refer to the xx [insert link to ODD]

The principle agents of the simulation are seeds and plant individuals.The seeds have the following attributes:

• they need space: the soil can accomodate a potentially large, but not infinite number of seeds.
• they live: they can die, or they can be converted into individuals
• they can form seed banks: Seeds produced in autumn may germinate the next year, or they can stay dormant and be buried in the soil for many years.

The individual has the following attributes:

• it needs a place to live: Soil (space) is a limiting resource, and soil determines whether a plant can grow at all.
• it lives: it grows, reproduces and dies.
• it is photoautotrophic: it converts light into a (carbohydrate) resource; it lives from these resources and produces green (photosynthetically active) biomass and seeds.
• it has a shape: with its geometric representation, it is able to cast a shades.
• it interacts with the environment: Stressors can affect each of the above properties, i.e. cause mortality, deplete resources or affect the soil [not yet implemented].

In summary, the model simulates germination and seed dispersal, growth, resource allocation, shading, soil suitability and environmental stress. Plants must be hit by light to collect energy resources. The amount of intercepted light depends on 1) the amount of solar energy hitting each voxel, and 2) the amount of light that is intercepted by other plants or plant parts. For simplicity, the model currently calculates the plant material growing above each target voxel and reduces the solar energy proportionately. Future iterations of the model will incorporate angular or diffuse light as well.

The table below summarizes the inputs, data types, access modes, units, and descriptions of parameters that are internally calibrated to ensure computational stability and ecological plausibility.

4.6 Scale, calibration, and intended use

The Joschinski Model is calibrated for small to medium spatial scales, typically between 100 and 10,000 m². It is particularly suited to urban design contexts such as green roofs, courtyards, façades, parks, and neighborhood-scale interventions where fine-grained spatial interactions meaningfully affect biodiversity and biomass development. While advanced parameters are exposed for expert use, most ecological constants are internally calibrated to ensure stability and ecological plausibility. Designers are encouraged to vary simulation duration and energy input, while structural competition parameters should be adjusted only with ecological expertise. Significant alteration of density or competition settings can change emergent system behavior in non-intuitive ways.

4.7 From geometry to ecology

By embedding the Joschinski Model within the Rhino and Grasshopper environment, ecological processes become part of the parametric design loop. Architectural geometry defines the spatial and environmental boundary conditions under which plant communities assemble. The simulation then reveals how vegetation evolves within these constraints over time.

The model therefore transforms ecological evaluation from a static post-processing step into a dynamic, generative system embedded in computational design. Rather than asking whether a finished design supports biodiversity, designers can explore how biodiversity emerges as a function of geometry, soil configuration, and environmental exposure.

💡 Tip: For more detailed information, refer to the separate guide The Joschinski Model Guide, which provides an in-depth explanation of the Ecological Model. Read the full Plant Model specification

Chapter 5: Rhino.Ecologic tutorials

5.1 Tutorial 1: title and core workflow

Easy to start Video_1

💡 Tip: To repeat this exercise using your own setup, you can use the Tutorial template 🦗 Grasshopper files xx.gh, and the 🦏 Rhino 3D model example file Rhino.Ecologic_Example_Wood_Cabins.3dm, which are available by hovering over the Rhino.Ecologic tab at the top of the Grasshopper menu.

5.2 Tutorial 2: `Title – Advanced or Extended Workflow´

Builds on Tutorial 1 conceptually, not technically Video_2

💡 Tip: To repeat this exercise using your own setup, you can use the Tutorial template 🦗 Grasshopper files xx.gh, and the 🦏 Rhino 3D model example file Rhino.Ecologic_Example_Wood_Cabins.3dm, which are available by hovering over the Rhino.Ecologic tab at the top of the Grasshopper menu.

Chapter 6: Rhino.Ecologic case studies

The following case studies illustrate how Rhino.Ecologic is applied in both professional practice and academic research contexts, demonstrating its capacity to support data-driven ecological decision-making across scales — from large urban developments to experimental design studios.

6.1 Henning Larsen – Fælledby, Copenhagen

Henning Larsen Architects is an internationally recognized architectural practice headquartered in Copenhagen, known for its research-driven approach to sustainable and performance-oriented design. The office operates at the intersection of architecture, landscape, urbanism, and environmental systems, integrating measurable sustainability goals into large-scale urban developments.

As part of the early development of Rhino.Ecologic, Henning Larsen provided the urban development project Fælledby in Copenhagen as a real-world case study to run the first large-scale simulation tests. Fælledby is a new residential district planned adjacent to protected natural landscapes, with a strong emphasis on biodiversity, low-impact development, and integration with surrounding ecosystems.

The project offered an ideal testing ground for Rhino.Ecologic: a complex urban geometry, varied soil and landscape conditions, and explicit biodiversity ambitions. Using the voxel-based spatial model and the embedded individual-based ecological simulation, we were able to evaluate biomass development, species distribution, and long-term vegetation dynamics within different design scenarios.

This collaboration allowed us to validate the ecological core of Rhino.Ecologic against a realistic, high-resolution urban context. It also helped refine workflows for integrating ecological simulation into professional design environments, ensuring that the tool responds to real project constraints, performance targets, and decision-making processes. Fælledby marked one of the first applied tests of Rhino.Ecologic within a professional architectural practice and demonstrated how ecological intelligence can move from abstract sustainability ambitions to quantifiable, scenario-based design evaluation.

Pilot test with international landscape architecture practice Henning Larsen Architects: Fælledby, Copenhagen‘s first all- timber neighborhood. Designed by Henning Larsen Architects.

6.2 Emergent Technologies & Design (EmTech), AA School London

The Emergent Technologies & Design (EmTech) programme at the Architectural Association (AA) School of Architecture in London is a postgraduate MSc programme that investigates the intersection of computational design, ecological systems, material research, and advanced fabrication. The programme is directed by Milad Showkatbakhsh, and focuses on developing innovative design methodologies grounded in biological principles, environmental intelligence, and systemic thinking.

EmTech students work at the convergence of architecture, ecology, and computation. Projects typically explore topics such as adaptive material systems, bio-integrated design strategies, environmental simulation, multi-agent systems, and data-driven spatial organization. Rather than treating sustainability as an afterthought, the programme frames ecological processes as generative design drivers.

For the past two consecutive years, Rhino.Ecologic has been integrated into the EmTech curriculum as part of advanced computational design studios. Students apply the tool to simulate biodiversity potential, biomass development, and long-term vegetation dynamics within speculative and real-world urban design scenarios. In this academic context, Rhino.Ecologic is not only used as an evaluation tool, but as a research instrument to explore how ecological simulation can fundamentally reshape parametric design methodologies. This collaboration positions Rhino.Ecologic within an experimental academic environment where ecological modeling, computational design, and systemic thinking converge, allowing students to prototype new forms of nature-inclusive architecture and urban systems.

6.2.1 Use Case A: Subterranean Currents, Master of Science Dissertation at AA (EmTech)

Authors: Sherine Elabd (MSc), Maria Aranzales (MArch), Orfeas Rachiotis (MArch).

“A Rhino.Ecologic plugin was utilized as an ecological simulation framework within the Rhino and Grasshopper environment to integrate environmental performance data directly into the design of the settlement’s agricultural units. The tool was specifically employed to calculate and predict the projected biomass of agricultural plots over a five-year period, focusing on native species such as date palms and wheat. By processing parameters including solar exposure, site topography, and growth patterns, the software generated a Flora Density Index, a single metric quantifying the biomass potential of each configuration. This index served as a primary objective in the project’s multi-objective optimization process using Wallacei generative engine, enabling the design team to scientifically evaluate different morphologies and determine the optimal spatial distribution of vegetation to maximize ecological productivity.”

Link to dissertation: https://issuu.com/aaemtech/docs/subterranean_currents_msc_

Link to AA EmTech: https://emtech.aaschool.ac.uk/

Subterranean Currents. Image © Sherine Elabd, Maria Aranzales, Orfeas Rachiotis, AA EmTech Programme.

Variations for Subterranean Currents project using Rhino.Ecologic. Image © Sherine Elabd, Maria Aranzales, Orfeas Rachiotis, AA EmTech Programme.

6.2.2 Use Case B: Post-Mining Palimpsest, Master of Science Dissertation at AA (EmTech])

Authors: Mauli Patel (Msc), Sung-Soo Park (Msc), Ajinkya Randive (MArch), Luis Castro Aguilar (MArch)

“Rhino Ecologic was utilized as a computational simulation tool to quantify and visualize the potential ecological recovery of the degraded mining landscape over time. By integrating site-specific environmental data, such as terrain geometry, solar radiation, and inclination, with detailed biological profiles of native plant species (including growth rates, rooting depths, and canopy expansion), the plugin simulated the progressive accumulation of plant volume and biomass. These simulations, conducted at a 2x2 meter voxel resolution over projected intervals of 2, 5, 7, and 9 years, provided critical data-driven validation for the project’s phytoremediation strategy, demonstrating how specific plant guilds would successfully establish themselves to improve soil conditions and generate organic matter within the stabilization corridor.”

Link to dissertation: https://issuu.com/aaemtech/docs/post-mining_palimpsest_msc_

Link to AA EmTech: https://emtech.aaschool.ac.uk/

Post-Mining Palimpsest. Image © Mauli Patel, Sung-Soo Park, Ajinkya Randive, Luis Castro Aguilar, AA EmTech Programme.

Variations for Post-Mining Palimpsest using Rhino.Ecologic. Image © Mauli Patel, Sung-Soo Park, Ajinkya Randive, Luis Castro Aguilar, AA EmTech Programme.

Chapter 7: Interoperability with other software solutions

Rhino.Ecologic is designed for interoperability within the Rhino and Grasshopper ecosystem, enabling structured data exchange and integration with external tools used in computational design, urban planning, landscape architecture, and BIM- and GIS-based workflows. Because Rhino.Ecologic operates natively in Grasshopper, its environmental and ecological outputs can be directly coupled with optimization frameworks such as Wallacei and with parametric landscape BIM tools such as RhinoLands, which provides GIS-compatible attribute tables and structured spatial data models. This allows ecological simulation results to inform optimization, parameter studies, and large-scale landscape modeling workflows in a consistent and data-driven manner.

7.1 Coupling Rhino.Ecologic with Wallacei

Wallacei is a multi-objective evolutionary optimization framework for Rhino and Grasshopper that enables designers to explore large parametric design spaces through genetic algorithms and Pareto-based solution ranking. When combined with Rhino.Ecologic, Wallacei transforms ecological simulation from an evaluative tool into an optimization driver within computational workflows.

7.1.1 Conceptual integration

In a standard Rhino.Ecologic workflow, ecological performance indicators such as biomass, biodiversity (species abundance), soil performance, or solar exposure are computed for a given design state. These indicators can be extracted using components such as: Get Biodiversity Summaries,Get Soil Summaries, Get Solar Summaries, and Get Voxel Summaries. These outputs can be directly connected to Wallacei as objective functions. Instead of manually adjusting geometry and rerunning simulations, Wallacei systematically varies parametric inputs, for instance building massing, terrace and roof Geometry, Soil Depth distribution, Voxel Size, Biophilic Area allocation, and Plant Library composition

Each generated design iteration is evaluated through Rhino.Ecologic, and the resulting ecological metrics are passed to Wallacei as fitness criteria. This establishes a closed performance-driven loop: Parametric Geometry → Ecological Simulation → Performance Metrics → Evolutionary Optimization

7.1.2 Multi-Objective ecological optimization

Ecological design problems are inherently multi-objective. Increasing biomass may reduce species diversity. Maximizing soil depth may conflict with structural constraints. Increasing solar exposure may alter understorey viability.

Wallacei allows simultaneous optimization of competing objectives such as maximizing biodiversity (species richness or abundance), maximizing biomass accumulation, minimizing artificial soil volume, balancing solar exposure across voxel types, and maximizing the ratio of biophilic voxels.

Rather than returning a single “best” solution, Wallacei generates a Pareto front—a set of non-dominated solutions representing different trade-offs between objectives. Designers can then select solutions based on project priorities rather than purely numerical ranking.

7.1.3 Technical data exchange

The interoperability between Rhino.Ecologic and Wallacei operates entirely within Grasshopper’s data structure:

1. Rhino.Ecologic computes spatio-temporal ecological outputs.
2. Summary statistics or filtered voxel metrics are extracted.
3. These values are converted into scalar fitness inputs.
4. Wallacei evaluates populations and evolves new parameter sets.
5. Updated geometry is re-fed into Rhino.Ecologic for the next generation.
   

Because Rhino.Ecologic produces structured, accessible data—not just visual output—it integrates seamlessly into evolutionary workflows without requiring custom scripting.

7.1.4 Use Cases

Coupling Rhino.Ecologic with Wallacei enables the ptimization of green roof configurations for maximum biodiversity, massing studies balancing density and ecological performance, soil allocation strategies for urban courtyards, parametric façade greening systems, and performance-driven urban block design.

In this setup, ecological simulation becomes an active generative constraint rather than a passive validation layer.

‘add image’ ‘add text’

💡 Tip: To repeat this exercise using your own setup, you can use the Tutorial template 🦗 Grasshopper files xx.gh, and the 🦏 Rhino 3D model example file Rhino.Ecologic_Example_Wood_Cabins.3dm, which are available by hovering over the Rhino.Ecologic tab at the top of the Grasshopper menu.

7.1.5 From evaluation to generative ecology

The combination of Rhino.Ecologic and Wallacei represents a methodological shift. Instead of asking “Does this design perform ecologically well?” the workflow asks “Which design configurations produce the most desirable ecological outcomes under defined constraints?” By embedding ecological simulation into evolutionary optimization, biodiversity and biomass become drivers of form generation within computational design systems rather than post-design metrics.

7.2 Coupling Rhino.Ecologic with Rhino.Lands

RhinoLands is a BIM tool for the parametric modeling of landscape and urban planning, in Rhino enviornment. It includes Grasshopper components for parametric design and structured representation, processing, and synthesis of landscape and territorial data. Within RhinoLands workflows, users can integrate spatio-temporal ecological data—including planting schemes generated with Rhino.Ecologic to instantiate 3D plant assets as information-rich BIM elements.

7.2.1 Conceptual integration

While Rhino.Ecologic simulates plant community dynamics, biomass development, and biodiversity over time, RhinoLands provides structured landscape elements with embedded semantic information. This includes terrain layers, soil stratigraphy, planting zones, and landscape components enriched with attribute data.

The integration allows ecological simulation outputs—such as species distribution, biomass accumulation, or soil performance—to be mapped onto parametric landscape elements. In this way, ecological performance becomes part of the landscape information model rather than an isolated analytical layer.

This creates a bidirectional workflow: Landscape Geometry → Ecological Simulation → Performance Data → Parametric Landscape Model

Design changes in RhinoLands (e.g., modified terrain, soil depth adjustments, planting zones) can be fed back into Rhino.Ecologic for re-simulation, enabling iterative refinement of ecological outcomes.

7.2.2 Structured data and attribute exchange

Both Rhino.Ecologic and RhinoLands operate within Grasshopper’s structured data environment. This shared infrastructure enables:

1. Transfer of terrain and soil geometries from RhinoLands into Rhino.Ecologic.
2. Simulation of ecological dynamics within the defined spatial domain.
3. Extraction of ecological performance metrics per voxel or zone.
4. Mapping of results back to RhinoLands attribute tables.

Because RhinoLands supports GIS-compatible attribute structures, ecological outputs can be organized, classified, and exported in formats suitable for planning documentation, BIM coordination, or further GIS analysis.

This structured exchange ensures that ecological simulation data remains embedded within spatially explicit landscape models.

7.2.3 Applications in landscape and urban projects

The coupling of Rhino.Ecologic and RhinoLands enables the evaluation of soil layer configurations in relation to biodiversity outcomes, parametric testing of planting strategies across terrain variations, biomass estimation across large landscape developments, scenario comparison for biodiversity-sensitive urban districts, and the integration of ecological metrics into BIM-based landscape documentation.

By linking dynamic ecological simulation with structured landscape modeling, designers can move from abstract sustainability targets toward quantifiable, spatially embedded ecological performance.

7.2.4 From simulation to landscape information modeling (LIM)

The integration of Rhino.Ecologic with RhinoLands extends ecological modeling into the realm of landscape information modeling (LIM). Instead of treating vegetation performance as a separate analysis step, ecological intelligence becomes embedded within the parametric landscape model itself.

This approach supports long-term, data-informed landscape planning in which geometry, soil configuration, planting strategy, and ecological dynamics are evaluated as interconnected systems rather than isolated components.

Planting Strategy from Rhino.Ecologic visualized in RhinoLands.

💡 Tip: To repeat this exercise using your own setup, you can use the Tutorial template 🦗 Grasshopper files xx.gh, and the 🦏 Rhino 3D model example file Rhino.Ecologic_Example_Wood_Cabins.3dm, which are available by hovering over the Rhino.Ecologic tab at the top of the Grasshopper menu.

Glossary

This glossary defines key terms used in Rhino.Ecologic workflows.

References

Acknowledgements

Development of Rhino.Ecologic has been supported by McNeel Europe and Robert McNeel & Associates. Early conceptual research informing aspects of this work was supported by the European Commission under Horizon 2020 (Grant Agreement No. 964414).