The population dynamics of pest insects is a subject of interest to farmers, agricultural economists, ecologists, and those concerned with animal welfare.
Factors affecting populations
Density-independent: Affect a population equally regardless of its density. Examples: A winter freeze may kill a constant fraction of potato leafhoppers in a peanut field regardless of the total number of leafhoppers. Japanese beetle larvae survive well with lots of summer rain. Temperature, humidity, fires, storms, dissolved oxygen for aquatic species. Density-dependent: Affect a population more or less as the population is bigger. Examples: A bigger population may be more vulnerable to diseases and parasites. A bigger population may have more intraspecific competition, while a smaller population may have more interspecific competition. Emigration from the population may increase as it becomes more crowded.
Life tables
A life table shows how and how many insects die as they mature from eggs to adults. It helps with pest control by identifying at what life stage pest insects are most vulnerable and how mortality can be increased. A cohort life table tracks organisms through the stages of life, while a static life table shows the distribution of life stages among the population at a single point in time. Following is an example of a cohort life table based on field data from Vargas and Nishida (1980). The overall mortality rate was 94.8%, but this is probably an underestimate because the study collected the pupae in cups, and these may have protected them from birds, mice, harsh weather, and so on.
Life expectancy From a life table we can calculate life expectancy as follows. Assume the stages x {\displaystyle x} are uniformly spaced. The average proportion L x {\displaystyle L_{x}} of organisms alive at stage x {\displaystyle x} between beginning and end is
L x = l x + l x + 1 2 {\displaystyle L_{x}={\frac {l_{x}+l_{x+1}}{2}}} . The total number T x {\displaystyle T_{x}} of future stages to be lived by individuals at age x {\displaystyle x} and older is
T x = L x + L x + 1 + L x + 2 + . . . {\displaystyle T_{x}=L_{x}+L_{x+1}+L_{x+2}+...} . Then the life expectancy e x {\displaystyle e_{x}} at age x {\displaystyle x} is
e x = T x l x {\displaystyle e_{x}={\frac {T_{x}}{l_{x}}}} . We could have done the same computation with raw numbers of individuals rather than proportions.
Basic reproductive rate If we further know the number F x {\displaystyle F_{x}} of eggs produced (fecundity) at age x {\displaystyle x} , we can calculate the eggs produced per surviving individual m x {\displaystyle m_{x}} as
m x = F x a x {\displaystyle m_{x}={\frac {F_{x}}{a_{x}}}} , where a x {\displaystyle a_{x}} is the number of individuals alive at that stage. The basic reproductive rate R 0 {\displaystyle R_{0}} , also known as the replacement rate of a population, is the ratio of daughters to mothers. If it's greater than 1, the population is increasing. In a stable population the replacement rate should hover close to 1. We can calculate it from life-table data as
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