This data science project focuses on optimizing sportfishing for Pacific Bluefin Tuna off the coast of San Diego by analyzing seasonal patterns and oceanic conditions. The objective is to identify the most favorable time of the year to maximize Pacific Bluefin Tuna catches. The project integrates data from various sources to explore correlations between sportfishing catches, weather temperatures, and sea surface temperatures.
Sportfishing enthusiasts and commercial fishermen are often interested in maximizing their success rate when targeting specific species. In this project, we delve into Pacific Bluefin Tuna sportfishing specifically in order to uncover patterns that can enhance catch success. By analyzing data from the year 2020, we explore correlations between sportfishing catches and environmental factors such as weather and sea surface temperatures.
We begin by importing necessary modules and ensuring all dependencies are met. Datasets include Pacific Bluefin Tuna catches off the coast of California in 2020, San Diego daily weather temperatures, sea surface temperatures off the coast of San Diego, and the history of sea surface temperatures off the coast of San Diego going back to 1916. The data is processed and visualized using tools like calendar heatmaps, scatter plot graphs, and line graphs.
This comprehensive analysis sheds light on the most optimal conditions for Pacific Bluefin Tuna sportfishing. It is important to consider both weather temperatures for seasonality and sea surface temperatures for better Pacific Bluefin Tuna presence accuracy in order to aid fishermen with data that allows them to plan their expeditions strategically. As sea surface temperatures continue to rise off the coast of San Diego, we can assume that the season for high volume Pacific Bluefin Tuna catches will expand beyond July and August in the coming years.
- Python 3.12
pip install calplot