Invasive Plants on the Tree of Life

ISEF Category: Plant Sciences

Subcategory: Systematics and Evolution  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

Some invasive plants spread fast, but random chance does not always pick the winners. If invasive species cluster on certain branches of the plant tree of life, that hints at shared traits or evolutionary history. You can test that pattern with public data instead of growing dozens of species yourself.

What Is It?

Phylogenetic-signal analysis asks whether related species tend to share a trait more than you would expect by chance. Here, the trait is invasiveness. Think of the plant tree of life like a family tree, and invasiveness like a family habit. If distant relatives are just as likely to become invasive as close relatives, the pattern looks random. If certain lineages show up again and again, that points to evolutionary relatedness, shared traits, or both.

You can study this with public records from GBIF, invasive-species databases, and published phylogenetic trees. GBIF gives you occurrence records, which show where species have been observed. Invasive-species lists tell you which plants count as invasive in a region. Public phylogenies place those plants on an evolutionary tree. Your job is to compare the invasive species to a background set and ask whether invasives are more clustered than expected.

Why This Is a Good Topic

This project works well because you can test a real evolutionary question with public data. You do not need a greenhouse or months of plant growth. You need careful data cleaning, smart comparisons, and solid statistics. The project connects directly to biodiversity loss, ecosystem management, and invasion biology, so the results matter outside the fair.

Research Questions

• How does phylogenetic relatedness among invasive plants compare with a random set of noninvasive plants from the same region? ?
• What is the effect of sampling invasive species from different continents on the strength of phylogenetic clustering? ?
• Does phylogenetic signal change when you compare invasive species across plant families versus within a single family? ?
• To what extent do invasive species from a region share closer common ancestry than expected by chance? ?
• Which plant clades show the strongest association with invasiveness in public invasion lists? ?
• How does using different background species pools change the result of a phylogenetic signal test? ?

Basic Materials

• Laptop with internet access.
• Spreadsheet software such as Google Sheets or Excel.
• R or Python installed on your computer.
• Access to GBIF occurrence records.
• Public invasive-species list from a government or nonprofit source.
• Public plant phylogeny or tree file from a journal supplement or open database.
• External storage for downloaded data files.

Advanced Materials

• Access to a university computing cluster or powerful laptop.
• R with packages for phylogenetics and community ecology.
• Python with scientific libraries for data cleaning and plotting.
• Original or curated plant phylogeny with branch lengths.
• GIS software for mapping invasive occurrences.
• Database access for taxonomic name resolution and synonym checking.
• Version control system such as Git for tracking analysis changes.

Software & Tools

• R: Runs phylogenetic signal tests, null models, and visualizations for invasion patterns.
• Python: Cleans species names, merges datasets, and automates repeatable data workflows.
• GBIF occurrence download tools: Pulls plant observation records for filtering and mapping.
• OpenRefine: Helps match messy species names and remove duplicate records.
• FigTree: Lets you inspect and annotate phylogenetic trees before analysis.

Experiment Steps

1. Define the invasion question and choose a clear geographic scope, such as one country, one continent, or one biome.
2. Build a species list by combining invasive-species records with a matched noninvasive background set.
3. Match each species to a phylogeny and clean taxonomic names so the same plant is not counted twice under different names.
4. Choose the exact phylogenetic test you will use, then plan how you will generate a fair null model for comparison.
5. Check whether the result changes when you swap background species pools, regions, or clade-level filters.
6. Plan figures that show the tree pattern, the null distribution, and the invasive versus noninvasive comparison clearly.

Common Pitfalls

• Using mismatched species names, which breaks the link between invasive lists, GBIF records, and the phylogeny.
• Treating every GBIF record as equally reliable, which can leave in false occurrences, misidentifications, or cultivated plants.
• Choosing a background species pool that does not match the invaded region, which makes the null model unfair.
• Mixing invasive status from different authorities without standardizing definitions, which can flip species between native, introduced, and invasive categories.
• Reading phylogenetic clustering as proof of cause, when the pattern may reflect sampling bias, shared habitat, or taxonomic unevenness.

What Makes This Competitive

A stronger project goes beyond a single clustering test. You can compare several null models, several regions, or several ways of defining the background pool. You can also test whether the pattern holds at different taxonomic depths, like family, genus, or full tree. Clear code, careful data cleaning, and honest uncertainty analysis can turn this from a simple pattern check into a serious evolutionary study.

Project Variations

• Focus on invasive aquatic plants instead of terrestrial plants, and compare the tree signal across freshwater habitats.
• Compare invasive plants in one country with a global invasive list to see whether regional filters change the phylogenetic pattern.
• Test whether invasive species cluster more tightly in certain clades, then map those clades onto traits such as growth form or seed dispersal mode.

Learn More

• GBIF: Search the Global Biodiversity Information Facility for occurrence records, species pages, and data download tools.