Past, current, and future trends of red spiny lobster based on PCA with MaxEnt model in Galapagos Islands, Ecuador

A novel approach integrating principal component analysis (PCA) with maximum entropy (MaxEnt) modeling significantly enhances the accuracy and predictive power for assessing the potential distribution of red spiny lobster in the Galápagos Islands. The study demonstrates that using PCA-transformed environmental variables as inputs for MaxEnt models outperforms raw data, achieving better area under the curve (AUC) and partial receiver operating characteristic (pROC) metrics. This method was applied to historical data from 1997 to 2015 and projected future scenarios up to 2070, revealing critical insights into habitat suitability. Future climate change scenarios suggest potential shifts in suitable habitats, underscoring the need for adaptive management strategies to conserve this valuable species. The findings highlight the effectiveness of PCA-MaxEnt integration as a robust tool for ecological modeling and conservation planning.

Moya, W., Jacome, G., & Yoo, C. (2017). Past, current, and future trends of red spiny lobster based on PCA with MaxEnt model in Galapagos Islands, Ecuador. Ecology and Evolution, 7(13), 4881–4890. Portico. https://doi.org/10.1002/ece3.3054