Temporal isolation is between populations due to barriers related to time, such as differences in mating periods or differences in the time of day that individuals are most active. Geographic isolation between populations is due to physical barriers, not time. It wouldn't be both of them because only temporal isolation deals with time, versus geographic isolation is based on the physical barrier between populations such as mountains, rivers, or, for example, insects living on different trees in the jungle. Reproductive isolation is the inability to interbreed between species for various reasons like sterile offspring, physical incompatibility, or different mating rituals.

Genetic drift occurs when the frequency of alleles in a population change by random chance. Change in allele frequency based on biological fitness is natural selection, not genetic drift. The change in allele frequency due to members moving from one population to another describes migration. A brand new allele that did not exist in the parents is the result of mutation.

The Bottleneck effect happens when an event occurs that drastically reduces the population of a species. The remaining individuals most likely do not represent the genetics of the population before the catastrophic event, since it becomes a much smaller sample size. 

The bottleneck effect is the correct answer here. The effect is defined as a sharp reduction in a populations size due to an environmental effect. In this scenario, the asteroids were the environmental effect and it caused the Freg population to decrease significantly. Also, none of the other answers are real theories.

Genetic drift is a direct result of independent assortment. Since genes are not inherited by any organized mechanism, there are random fluctuations during which certain alleles experience an increase in frequency over others.

Genetic drift results in random changes in allele frequency; these changes are not a cause of genetic drift. In smaller populations and extreme cases, random changes can result in the loss of an allele entirely within the population. The results of genetic drift are more prominent in smaller populations due to their already reduced gene pool. Since genetic drift is random, both beneficial and harmful alleles can be promoted or eliminated.

Genetic drift cannot increase genetic diversity. The only way to increase genetic diversity is by the introduction of new traits and alleles. Genetic drift can reduce genetic diversity by eliminating alleles from a population, but is incapable of creating new traits. This can only be done through mutation.

First, determine the expected ratios using a Punnet Square. Given that red is dominant to white, the genotype of a true-breeding white flower can be denoted rr, and the genotype of a heterozygous flower can be denoted Rr. This cross will produce 50% Rr, 50% rr. Then, converting these percentages to decimals (50%= 0.50), and multiplying by the total population size of the observed population (0.50*100), gives expected values of 50 Rr (red flowers) and 50 rr (white flowers). These expected values and the observed values can then be plugged into the chi square equation . The equation will be . Degrees of freedom is n-1, so 2-1= 1. To determine p-value, use critical values table. The chi square value of 6.76 with 1 degrees of freedom will fall between a critical value corresponding with a p-value of 0.05 and 0.025. Thus, p>0.025

 A larger difference between observed and expected values will result in a larger chi square value. Using the critical values table, for a given degrees of freedom, as chi square value increases, p-value decreases.

When the p-value is equal to or below the significance level (alpha), the null hypothesis is rejected. For the p-value to be low, the chi square value would need to be large (large difference between observed and expected values). The null hypothesis would not be accepted (under no circumstance is a null hypothesis “accepted”; onlay rejected or failed to reject). The alternative hypothesis would be accepted if the p-value is equal to or below the significance level (alpha).

A larger difference between observed and expected values will result in a larger chi square value. Using the critical values table, for a given degrees of freedom, as chi square value increases, p-value decreases. A small p-value results in rejection of the null hypothesis.