Amirianmatlob, Mohammad2025-08-152025-08-152025-08-14https://hdl.handle.net/10222/85345Phytoplankton are central to global biogeochemical cycles and marine ecosystems, yet accurately modeling their growth and photosynthetic responses under dynamic environmental conditions remains a major challenge. This thesis addresses fundamental questions in this domain by introducing original models that explain microbial growth dynamics under abrupt nutrient shifts using minimal state variables, and by developing a parsimonious framework for interpreting photosynthesis–irradiance responses. These ideas are grounded in advanced mathematical concepts, including fractional calculus and set theory, yet practical and easy to apply to real data. Their validity is rigorously tested using a suite of statistical methods—including non-linear optimization, linear and non-linear regression, generalized additive mixed models, and machine learning methods—applied to extensive laboratory and open-ocean datasets. Chapter 1 introduces the concept of physiological memory into microbial growth model through the Monod-memory model. Grounded in robust mathematical theory, this model quantifies memory as a biologically meaningful parameter measurable in laboratory settings. By integrating the computational efficiency of the classical Monod model with the physiological depth of the Droop model, it adequately captures population dynamics under nutrient-limited and starvation conditions. In addition to nutrients, light availability is another key driver of phytoplankton growth, typically characterized by the photosynthesis–irradiance (P–I) curve. Chapter 2 focuses on this relationship, introducing a novel P–I model that accurately captures the plateau part of the curve and decline in photosynthesis rate associated with photoinhibition—an area where traditional models have struggled for over a century. This formulation enhances both the fit to empirical data and the interpretability of P–I parameters, marking a significant advancement in light-response modeling. Chapter 3 extends this work by introducing a hybrid statistical–physiological framework that links P–I parameters to environmental drivers, enabling large-scale, mechanistic interpretations of phytoplankton photosynthesis. Chapter 4 presents two independent open-source R packages developed as part of this thesis, promoting transparency, reproducibility, and broader adoption of the models. Collectively, these contributions bridge theoretical innovation with empirical rigor, significantly advancing the modeling of phytoplankton growth and function in dynamic environmental contexts.enMODELLING PHYTOPLANKTON GROWTH RATES UNDER CHANGING RESOURCE CONDITIONS