Speaker
Description
Ecological community data are inherently multivariate, and beta diversity is typically explored through unsupervised ordination (e.g., PCoA, NMDS). While these methods excel at revealing gradients of variance, they ignore a priori hypotheses about group structure. This talk introduces constrained ordination, specifically Canonical Analysis of Principal coordinates (CAP) and Linear Discriminant Analysis (LDA) as a supervised alternative for beta diversity. This shifting in the component orientations better aligns with showing which space is unique occupied but a class, the most common use case for beta diversity. I demonstrate how to project dissimilarity matrices onto axes that maximize separation among pre-defined groups (e.g., treatment types, habitats), thereby directly testing ecological hypotheses. This approach shifts the perspective from ordering components by descending variance to descending class distinction. All code and examples will be available in a GitHub Repo.
If you used AI tools or services to support the preparation of this submission, please state the name and reason for using each of them.
Yes, Gemini and DeepSeek V3 extensively before heavy edits and review. Especially for getting to first draft/boilerplate of code, abstract, methodology research, sound boarding, learning about material licensing.
| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | Ecology, Ordination, Beta Diversity, Data visualization, Dimension Reduction |
|---|---|
| Virtual Option | This submission is for onsite presentation only |
| Material License | MIT |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |