Computational Analysis of Monterrey Sardine (Sardinops sagax) Responses to Sea Surface Temperature (SST) and Chlorophyll-a (Chla) Variability in the Gulf of California, 1998-2015

Abstract

This study employs advanced computational techniques to conduct a spatio temporal analysis of monthly sea surface temperature (SST) and chlorophyll-a concentration (Chla) in the Gulf of California (GC) from 1998 to 2015. The analysis uses satellite imagery with 4 km resolution from AVHRR, MODIS-Aqua, and SeaWiFS sensors, which were processed through Idrisi Terrset 2020’s geospatial modeling tools. Key computational methods include raster image processing, reclassification for precise geographic delineation, and statistical modeling using the Theil-Sen estimator and multiple linear regression techniques within the Earth Trends Modeler module. These method senabled the generation of robust spatio temporal models, correlations, and trend analyses of environmental variables like SST and Chla. Data on Monterey sardine (Sardinops sagax) catches, categorized byfishing zones, were also analyzed, revealing a declining trend, with sardine catches decreasing by 16.5 tonsper month on average and dropping from over 60% of total small pelagic catches in 2007-2010 to just 2% in 2014-2015. The region of large islands (zones III, IV, and V) was identified as the most productive, contributing 44% to total sardine production. A significant negative correlation (r = −0.81) was found between SSTand Chla, indicating that higher SSTs result in lower Chla concentrations. Monterey sardine catches also responded to environmental changes with a two-monthlag, showing correlations of r = −0.52 with SST and r = 0.57 with Chla. The comprehensive computational approach, which included linear and nonlinear modeling, provided critical insights in to the dynamics between environmental variables and sardine population trends, emphasizing the importance of on going monitoring and adaptive fisheries management in the face of climate change.