A Statistical and Machine Learning Based Study of Ecological Impacts in Lamprey

Abstract

The aim of this study is to investigate the effects of changing sex ratios on ecosystems, with Lamprey as the subject of the study. It focuses on the impacts of changing sex ratios of Lamprey populations on their population size and ecological roles, and analyses their position in the food chain. Factors such as population size, sex ratio and growth rate of Lamprey were modelled and predicted through the development of the Lamprey Dynamic Model (LDM), incorporating relevant data analysis methods and mathematical models. Statistical and machine learning methods such as correlation analysis, Hermite interpolation algorithm, and Fourier fitting were used. The results showed that the sex ratio of Lamprey was closely related to environmental factors such as water temperature, oxygen content and water hardness. Through LDM modelling, the patterns of sex ratio effects on Lamprey population size and growth rate, as well as changes in its position in the food chain, were revealed. Positive and negative impacts of Lamprey populations on ecosystems were derived, including advantages such as adaptive sex ratio changes and reproductive migration strategies, as well as negative impacts of over-regulation of sex ratio and invasiveness on ecological balance. These findings provide new insights into understanding population dynamics and species interactions in freshwater ecosystems.