Resource Driven Population Dynamics and Modeling Population Growth

Populations and population dynamics can be understood by the combined interactions of resource supply, demographic pressures, and genetic interactions. In this work, a computational model integrating resource-based population growth and traditional Mendelian inheritance is presented. The modelization of population change over successive generations with a defined sex-based framework and scoring of genotypes (RR, Rr, and rr) is included, and stochastic mating and Poisson-distribution-based birth rate subject to resource interactions are also included in this Python-based computational model. Death rate is a stochastic function of resource use, factoring for age and intergenerational survival probabilities. We will examine the dynamics of the population under a wide spectrum of birth rates, overall resource availability, as well as overall resource utilization per capita under detailed simulations. The findings describe the different regions of collapse, growth, and stabilization phases of evolution along with the shifted high fertility boundary under strong resource conditions. For a scenario of abundant resources and a stable population, we have demonstrated that the genotype distribution tends to a Hardy-Weinberg equilibrium. Additional examples incorporating genetic disorders describe the different evolution patterns under recessive and dominant conditions along with the rapid elimination of dominant deleterious alleles. We also examine the population response to three categories of disasters: moderate, severe, and catastrophic, calculating minimum fertility values for recovery and the number of survivors for each case. Simplified models of single-locus genetics, without mutation or migration, allow for an integrated analytical approach to investigating the interplay of environmental pressure, genetic composition, and demographic processes in shaping population resilience. This research demonstrates the utility of computer simulations in bridging the gap between ecological theory and genetic models of population and also yields fresh approaches to understanding population survival over time, subject to fluctuating environmental pressures.