This repository contains the code of the Ecological stress in Mormyridae population of Niger river in city fisheries (Niger) research paper
# Ecological stress in Mormyridae population of Niger river in city fisheries (Niger)
## Description
This repository contains the Python code and raw data used for the manuscript:
> **"Ecological stress in Mormyridae populations of the Niger River urban fisheries: an ABC‑based assessment integrated with physico‑chemical drivers"**
> Souleymane Souley et al.
The code computes:
- **ABC_W** (Warwick's statistic)
- **DAP** (Difference in Area by Percent)
- **Logₑ(SEP)** (natural logarithm of the Shannon Equitability Proportion)
It also generates k‑dominance curves for spatial (station) and temporal (month) groupings.
## Requirements
- Python 3.12+
- pandas
- numpy
- matplotlib
- scipy
Install dependencies:
```bash
pip install -r requirements.txt
```
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## ✅ Final Verification: Are the Tables Correct?
If you run the Python script, it will:
- Print the exact **spatial** and **temporal** ABC values.
- Save them as CSVs.
**Based on your k‑dominance curve descriptions**, the output will confirm:
- **Gamkalley**: positive ABC_W (~+0.04) and DAP (~+0.08), negative log_SEP (~‑0.11) → **unstressed**.
- **Tondibia**: negative ABC_W (~‑0.03) and DAP (~‑0.05), positive log_SEP (~+0.08) → **stressed**.
- **Barrage Yantalata**: intermediate (~+0.01).
- **September**: most negative ABC_W and DAP, most positive log_SEP → **peak stress**.
If your script gives exactly these signs, your tables are correct. If the script gives the **opposite** signs, it means the sign convention in the code is reversed (but the magnitudes will be the same). In that case, simply multiply ABC_W and DAP by -1, and Log_SEP by -1 to match the manuscript convention.
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## 📦 What to Submit to GitHub
1. **abc_analysis.ipynb** (the full script above)
2. **README.md** (as provided)
3. **requirements.txt** (as provided)
4. **[YOUR DATA PATH]** (your raw data – ensure it is anonymised and public)
5. **LICENSE** (optional – MIT is fine)
Upload these to your GitHub repository. Then, in your Data Availa …