arthropods_visualization

Repository for code and resources for an interactive visualization of CAP LTER ground-dwelling arthropod data

The app is built with Streamlit and Plotly, and uses NLCD-derived land cover to classify sites into Urban, Desert, Agricultural (plus Other/Unknown).

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Repository Structure

Key files:

• streamlit_app.py — main Streamlit dashboard
• 41_core_arthropods.csv — core arthropod data
• arthros_temporal.csv — site metadata (including coordinates)
• site_landuse_from_nlcd.csv — site → landuse mapping derived from NLCD
• requirements.txt — Python dependencies

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Data Overview

1. 41_core_arthropods.csv

• site_code — CAP LTER site identifier
• sample_date — date of sampling
• display_name — taxon name (used as Taxa)
• trap_name — trap type / ID
• count — abundance / count per record
• (plus observer, comments, flags, etc.)

2. arthros_temporal.csv

Contains temporal and spatial metadata for sites, including:

• site_code
• lat, long (or similar latitude / longitude columns)

These are used to map points on the site map.

3. site_landuse_from_nlcd.csv

Generated in R using FedData::get_nlcd() + terra::extract().

• site_code
• landuse — collapsed NLCD class: Urban, Desert, Agricultural, Other

The dashboard uses landuse as region3.

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Dashboard Features

The Streamlit app provides:

Filters (sidebar)

• Sites — multi-select site_code
• Taxa — multi-select (top 100 taxa by total count)
• Year — multi-select years present in the data

All visualizations respond to these filters.

Visualizations

1. Community composition (top 10 taxa × site)

   • Stacked bar chart
   • X: Site
   • Y: Total count
   • Colors: Taxa (top 10 per current filter, others grouped as "Other")

2. Quarterly abundance by region

   • Grouped bar chart
   • X: Region (Urban, Desert, Agricultural, Other)
   • Bars: Quarters Q1–Q4
   • Y: Total arthropod counts

3. Site map (bubble size = total counts)

   • Mapbox scatter plot
   • Points: Site lat/long
   • Size: Total counts
   • Hover: Site ID, total counts

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Running Locally

1. Clone or Download the repository

  https://github.com/CAPLTER/arthropods_visualization.git

2. Create and activate a virtual environment

python -m venv .venv
.venv\Scripts\activate      # on Windows
source .venv/bin/activate     # on macOS / Linux

3. Install dependencies

pip install -r requirements.txt

4. Run the Streamlit app

streamlit run streamlit_app.py

Streamlit will open a browser tab at : http://localhost:8501

Deploying on Streamlit

1. Push this repo to GitHub
2. Go to Streamlit Community Cloud
3. Click "New app" and select:
   • Repo: your GitHub repo
   • Branch: main (or whichever)
   • File: streamlit_app.py
4. Deploy

You’ll get a URL that can be embedded in WordPress using iframe.