A combination of statistical mechanics and elements of genetic population theory has succeeded in reproducing features of ageing processes [1]. At equilibrium one assumes a constant population pop as result of a balance between number of deaths and born individuals. However, if the inherited harmful mutations rate M is high, or intense hunting hunt takes place, all this may lead to extinction when pop(t) goes to zero after some time t.
We focus on such critical features and simulate the time evolution within the bit-string Penna model [2]. A computer word represents a genome and each bit corresponds to presence (1) or lack (0) of harmful mutation which is activated at age defined by the bit position. With each evolution step the age increases by one and next bit is disclosed bringing up a risk of another harmful mutation which may be terminal if the total number of these bad mutations reaches a threshold value T. Then the individual dies. It also may die for other reasons, hunting ,among them. If it survives, it gives birth to offsprings that compensates the losses in population. Each baby takes on the bit-string after parents.
We used parameters as in [1] to recover results for the hunt = 0 and the mutation rate M = 1. Then we were scanning pop(hunt, M) after long time t in order to determine a critical line on the (hunt, M) plane such that population do not survive for larger hunt or M. In [3] we concluded that too many mutations lead to a phase transition for pop with suggestion of the critical exponent 1/2. In this work we observe second order phase transitions and hunting seems to make this transition smoother with a bigger exponent. Our findings do not support the results reported in [1] on overfishing when rather a rapid decrease in population indicates the opposite tendency, perhaps of the first order type of transition.