Ellipsoid-Based Virtual Niches and Visualization

Provides a robust set of tools for researchers and modelers to construct and define virtual ecological niches using ellipsoid geometries. It enables the identification and extraction of suitable environmental areas, simulation of species occurrence points with various sampling strategies, and visualization of niche boundaries and simulated occurrences in both environmental and geographic space. Inspired by methodologies in 'NicheA' and the 'virtualspecies' R package, 'nicheR' aims to streamline the process of niche conceptualization and data generation for ecological studies. Methodological and theoretical foundations are described in Peterson et al. (2011, ISBN:9780691136882), Etherington et al. (2009) , Qiao et al. (2015) , Nunez-Penichet et al. (2021) , Cobos and Peterson (2022) , Alkishe et al. (2022) , and Leroy et al. (2015) .

Ellipsoid-Based Virtual Niches and Visualization

Mariana Castaneda-Guzman, Connor Hughes, Paanwaris Paansri, Marlon E. Cobos

• Background
• Package description
• Installing the package
• Workflow in nicheR
  • Building an ellipsoid
  • Predictions
  • Preparing and applying bias layers
  • Generating virtual occurrences
  • Simulating virtual communities
  • Visualization
• Checking the vignettes
• Note on AI usage
• Contributing

Background

The field of distributional ecology is evolving rapidly, with new algorithms, parameterization strategies, and hypotheses emerging at a fast pace. Evaluating these ideas requires software that can isolate mechanisms, account for bias, and clearly link ecological theory to model behavior.

Virtual niche simulations help researchers test ideas by creating controlled datasets that make model behavior easier to interpret. In practice, building ellipsoid-based virtual niches in R has typically required combining several packages to define niches, map them to geography, and visualize results, making analyses harder to reproduce and build on.

nicheR is an R package for building and visualizing ellipsoid-based ecological niches using environmental data.

Inspired by the conceptual foundations of NicheA and the flexibility of the virtualspecies package, nicheR provides a reproducible framework that connects niche construction, prediction, sampling, and visualization in one integrated workflow.

Package description

nicheR operates across two complementary spaces:

• E-space (Environmental Space): Ellipsoids represent multivariate environmental tolerances. This is where niches are defined, visualized, and compared.
• G-space (Geographic Space): Predictions are projected across raster layers to map suitable geographic regions and generate virtual occurrence data.

This dual-space structure allows explicit separation between niche definition, projection, and sampling processes, making it straightforward to test ecological hypotheses under controlled, reproducible conditions. The figure below shows a schematic view of how the package works.

Installing the package

Note: Internet connection is required to install the package.

The development version of nicheR can be installed using the code below.

# Installing and loading packages
if (!require("devtools")) install.packages("devtools")

# To install the package use
devtools::install_github("castanedaM/nicheR")

# To install the package and its vignettes use (if needed use: force = TRUE)
devtools::install_github("castanedaM/nicheR", build_vignettes = TRUE)

Having problems?

If you have any problems during installation, restart your R session, close other RStudio sessions you may have open, and try again. If during the installation you are asked to update packages, do so if you do not need a specific version of one or more of the packages. If any package gives an error when updating, install it alone using install.packages(), then try installing nicheR again.

To load the package use:

library(nicheR)

Workflow in nicheR

A typical nicheR workflow follows these steps:

1. Build an ellipsoid — define the niche from environmental ranges
2. Predict suitability — project the ellipsoid onto raster or data frame inputs
3. Prepare bias layers (optional) — define and apply sampling biases
4. Generate occurrences — draw virtual occurrence records using various strategies
5. Simulate communities (optional) — generate multi-species virtual communities

A brief description of each step is presented below. For complete demonstrations, see the package vignettes listed in Checking the vignettes.

Building an ellipsoid

The ellipsoid is the core object in nicheR. It represents the virtual species’ niche as a multivariate ellipse in environmental space, defined by a centroid and a covariance matrix that determine its position, size, and orientation. Ellipsoids are built from a range data frame specifying the minimum and maximum for each environmental variable. nicheR computes the centroid, covariance matrix, semi-axes lengths, and niche volume automatically.

nicheR includes base-R plotting functions for visualizing ellipsoids in E-space, both for individual dimension pairs and all pairwise combinations at once. Adding the environmental background contextualizes where the niche sits relative to available conditions.

For details on ellipsoid construction, covariance-based rotation, niche volume, and visualization options, see the Building ellipsoids vignette.

Predictions

Once the ellipsoid is built, predict() projects it onto environmental data to produce Mahalanobis distance and suitability surfaces. Suitability is highest at the niche centroid and decreases toward the boundary. Truncated outputs set values to zero (suitability) or NA (Mahalanobis) outside the ellipsoid, which is useful for enforcing strict niche-boundary sampling downstream. Both SpatRaster and data.frame inputs are supported and return matching output types.

For a full guide to prediction outputs, truncation logic, raster vs. data frame workflows, and E-space vs. G-space visualization, see the Predicting suitability and Mahalanobis distance vignette.

Preparing and applying bias layers

Bias layers represent external factors that influence sampling detectability or effort, independent of the species’ actual niche. Each layer is assigned a direction: "direct" means higher values increase sampling probability, while "inverse" means higher values decrease it. prepare_bias() standardizes all layers to [0, 1], applies the directional transformations, and combines them into a single composite bias surface. apply_bias() then weights the suitability prediction by this surface to simulate non-random, biased occurrence sampling.

For details on bias surface construction, directional effects, and how bias interacts with predictions and sampling, see the Sampling bias data vignette.

Generating virtual occurrences

nicheR provides two sampling functions: sample_data() for unbiased sampling directly from suitability or Mahalanobis surfaces, and sample_biased_data() for sampling from a bias-weighted prediction surface. Three sampling strategies are available: "centroid" samples preferentially near the niche center, "edge" samples near the boundary, and "random" samples uniformly across the suitable area. Two weighting methods control sampling probability: "suitability" weights by suitability score and "mahalanobis" weights by distance from the centroid.

For a full guide to sampling strategies, methods, strict vs. non-strict modes, and biased sampling workflows, see the Sampling occurrence data vignette.

Simulating virtual communities

nicheR includes functions for simulating multi-species virtual communities in environmental space. Three community types are available: random_ellipses() generates species with niches distributed randomly across E-space, nested_ellipses() generates nested communities where each species niche is contained within the previous one, and conserved_ellipses() generates communities where all species niches remain similar to a reference niche, representing niche conservatism. Community predictions produce presence-absence matrices and species richness maps that can be used to explore macroecological patterns.

For a full demonstration of community simulation, prediction, and richness mapping, see the Virtual community simulation vignette.

Visualization

nicheR includes a set of plotting functions for visualizing ellipsoids, predictions, and occurrence data in E-space. plot_ellipsoid() opens a new E-space plot, add_data() overlays points colored by a continuous variable, add_ellipsoid() overlays an ellipsoid boundary, and plot_ellipsoid_pairs() produces multi-panel pairwise projections with globally consistent axis limits.

For a full guide to the plotting functions and all display options, see the Visualizing ellipsoids in environmental space vignette.

Checking the vignettes

Users can check nicheR vignettes for a full explanation of the package functionality. The vignettes can be checked online at the nicheR site under the menu Articles.

To build the vignettes when installing the package from GitHub, make sure to use the argument build_vignettes = TRUE.

Check each of the vignettes with the code below:

# Guide to building ellipsoid-based niches
vignette("creating_ellipsoid_based_niches")

# Guide to predicting suitability and Mahalanobis distance
vignette("predict")

# Guide to generate occurrence data from virtual niches
vignette("generate_occurrence")

# Guide to preparing and applying sampling bias
vignette("bias")

# Guide to simulating virtual communities
vignette("virtual_communities")

# Guide to visualizing ellipsoids in environmental space
vignette("plotting_vignette")

Note on AI usage

To maintain high standards of code quality and documentation, we have used AI LLM tools in this package. We used these tools for grammatical polishing and exploring technical implementation strategies for specialized functions. We manually checked and tested all code and documentation refined with these tools.

Contributing

We welcome contributions to improve nicheR. To maintain the integrity and performance of the package, we follow a few core principles:

• Quality over quantity: we prioritize well-thought-out, stable improvements over frequent, minor changes. Please ensure your code is well-documented and follows the existing style of the package.
• Minimal dependencies: one of the goals of nicheR is to remain efficient. We prefer solutions that use base R or existing dependencies. Proposals that introduce new package dependencies will be evaluated for their necessity.
• AI-assisted code: if you use AI tools to generate code alternatives or improvements, please manually verify the logic and accuracy of the output and demonstrate the benefit in your Pull Request.
• Testing: new features should include examples, and tests should be performed to ensure they work as intended and do not break existing workflows.

If you have an idea for a major change, please open an Issue first to discuss it with the maintainers.