life table ppt.

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WEL-COME

USE OF LIFE TABLES IN INSECT PEST MANAGEMENT:

USE OF LIFE TABLES IN INSECT PEST MANAGEMENT

DEFINITION:

DEFINITION Deevey(1947) “is a concise summary of certain vital statistics of a population, whose members start life together. Or A ‘life table’ is a kind of “bookkeeping system” that ecologists often use to keep track of stage specific mortality in the populations they study.

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A table describes for the successive age intervals the number of deaths, remaining survivors, rate of mortality and expectation of further life. To create life table an ecologist follow the life history of many individuals in a population. Keeping a track of how many offspring each female produces, when each one dies and what caused its death. After amassing data from different populations, different years and different environment conditions, the ecologist summarizes this data by calculating average mortality within each developmental stages .

ROLE OF LIFE TABLES:

ROLE OF LIFE TABLES Study of factors to determine the need to the need to modify the life system of pest with a view to reducing its number below ETL. Study of applied ecology which involves application of biological knowledge for achieving desired populations. Determining the key mortality factor and predicting the appearance in future. Dividing pest control procedures that suits the available technology compatible with the economic and environment quality requirement . ETL gives for timing application of need based application of insecticides. Estimation of intensity of pest population.

Importance of life tables:

Importance of life tables Life tables describes for the successive age intervals the deaths, remaining survivor, rate of mortality and expectation of further life. Life table provides an important tool in understanding their life cycles. By this technique we can determine the mechanical relationship of various environmental factors and find out the key factor that accounts for large part of the change in population size. Especially useful approach in entomology where developmental stage are discrete and mortality rates may vary widely from one life stage to other. To know when population suffers high mortality, this is usually the time when it is most vulnerable.

Life tables are two types a) age-specific LT or horizontal LT : It is based on the fate of a real cohort, the individuals belonging to a single generation. The population may be stationary or fluctuating. b) time-specific LT or vertical LT: It is based on the fate of a imaginary cohort found by determining the age structure, where there is a considerable overlapping generations. Age determination is a pre-requisite for time-specific LT. :

Life tables are two types a) age-specific LT or horizontal LT : It is based on the fate of a real cohort, the individuals belonging to a single generation. The population may be stationary or fluctuating. b) time-specific LT or vertical LT : It is based on the fate of a imaginary cohort found by determining the age structure, where there is a considerable overlapping generations. Age determination is a pre-requisite for time-specific LT.

For preparing a life table for the natural population of an insect, the following columns are required B = age interval at which the sample was taken in units of time (days, weeks etc) lx = Number surviving at the beginning of age class ‘x’ (usually out of 1000 born dx = Number dying during at age interval stated in the ‘x’ column. Dfx =The mortality factor responsible for ‘dx’. 100qx = percentage mortality. Sx = survival rate within the age interval ‘x’. :

For preparing a life table for the natural population of an insect, the following columns are required B = age interval at which the sample was taken in units of time (days, weeks etc) lx = Number surviving at the beginning of age class ‘x’ (usually out of 1000 born dx = Number dying during at age interval stated in the ‘x’ column. Dfx =The mortality factor responsible for ‘dx’. 100qx = percentage mortality. Sx = survival rate within the age interval ‘x’.

Ex : In a hypothetical insect population, an average female will lay 200 eggs before she dies. Half of the eggs (on average) will be consumed by predators, 90% of the larvae will die from parasitisation and 3/5th of the pupae will freeze to death in winter. :

Ex : In a hypothetical insect population, an average female will lay 200 eggs before she dies. Half of the eggs (on average) will be consumed by predators, 90% of the larvae will die from parasitisation and 3/5 th of the pupae will freeze to death in winter. Development stage Number alive Mortality factor Number dying % mortality Egg 200 predation 100 50 Larvae 100 Parasitisation 90 90 pupa 10 freezing 6 60

CONSTRUCTING A LIFE TABLE:

CONSTRUCTING A LIFE TABLE Data collection and compilation Number entering insect stages for use in life tables can be estimated in a variety of ways. Single sample by insect density may for some system be a measure of total number entering a stage. Estimation of mortality from a single sample do not usually adequately asses the total number of dying in the stage from the factor. More recently stage frequency analysis have been used to estimate numbers dying from specific agents.

GROWTH RATE ANALYSIS The approach was first applied to the cabbage aphid (Brevicorne brassicae). An assumption of the method is that a stable age distribution has been attained when the population is studied. Carter et al. criticized this assumption and stated that instar distribution in the field should not be used to calculate growth roots. Hutchinson and Hogg used eaged cohorts of the pea aphid to determine the survival and fecundity schedules and from these estimated the population growth rate. :

GROWTH RATE ANALYSIS The approach was first applied to the cabbage aphid ( Brevicorne brassicae ). An assumption of the method is that a stable age distribution has been attained when the population is studied. Carter et al. criticized this assumption and stated that instar distribution in the field should not be used to calculate growth roots. Hutchinson and Hogg used eaged cohorts of the pea aphid to determine the survival and fecundity schedules and from these estimated the population growth rate .

DEATH RATE ANALYSIS Another method is to calculate marginal attack rates from the death rates of individuals at interval through out the study. This method focuses on interval specific losses rather than stage-specific losses and consequently does not require estimates of number entering individuals. :

DEATH RATE ANALYSIS Another method is to calculate marginal attack rates from the death rates of individuals at interval through out the study. This method focuses on interval specific losses rather than stage-specific losses and consequently does not require estimates of number entering individuals.

2.ANALYSIS OF SERIES OF LIFE TABLES When sets of life tables are available, they may be analyzed to determine the ecological role that natural enemies play in the host-natural enemies system result of manipulation designed to produce with particular characters. Techniques for assessing regulation One can analyze population of LT data to detect stability and regulation using 2 approaches. i). addresses general questions of population stability with reference to density counts or censuses in successive generations. ii). Concerned with density relatedness of a specific mortality factor in LT. :

2.ANALYSIS OF SERIES OF LIFE TABLES When sets of life tables are available, they may be analyzed to determine the ecological role that natural enemies play in the host-natural enemies system result of manipulation designed to produce with particular characters. Techniques for assessing regulation One can analyze population of LT data to detect stability and regulation using 2 approaches. i). addresses general questions of population stability with reference to density counts or censuses in successive generations. ii). Concerned with density relatedness of a specific mortality factor in LT .

KEY FACTOR ANALYSIS:

KEY FACTOR ANALYSIS Is a procedure to identify the mortality factors that are most responsible for change in population density between generations. Varley and Gradwell’s method involved a graphic techniques in which total mortality (k) from a set of LT were plotted for a set of conservative generations along with each of the component stage specific k-values.

NUMIRICAL CHANGES IN INSECT POPULATION If the size of an insect population in an area is estimated at frequent intervals by sampling, a graph can be drawn showing the variation of numbers with time, such graphs are usually called population curves. More detailed population curves show estimates of the total density as it varies within generation such curves usually reflect the influence of redial conditions on population numbers. When the population curves for different stages of insect are compared it is found that are only a few basic patterns of short changes. :

NUMIRICAL CHANGES IN INSECT POPULATION If the size of an insect population in an area is estimated at frequent intervals by sampling, a graph can be drawn showing the variation of numbers with time, such graphs are usually called population curves. More detailed population curves show estimates of the total density as it varies within generation such curves usually reflect the influence of redial conditions on population numbers. When the population curves for different stages of insect are compared it is found that are only a few basic patterns of short changes.

The annual cycle of an insect species may consist of a single generation since numerical increases from an insect species can result only from egg laying. The pattern of numerical increases depends on the duration of the oviposition period relative to the era of the life cycle when oviposition is very long, no marked peak of numbers occurs on smooth population curve is obtained.:

The annual cycle of an insect species may consist of a single generation since numerical increases from an insect species can result only from egg laying. The pattern of numerical increases depends on the duration of the oviposition period relative to the era of the life cycle when oviposition is very long, no marked peak of numbers occurs on smooth population curve is obtained.

Patterns of numerical changes when the duration of the oviposition period changes relatively to the whole life cycle (op= oviposition period) :

Patterns of numerical changes when the duration of the oviposition period changes relatively to the whole life cycle (op= oviposition period)

Normal variation of development of individual increases with the time, the whole population spends in successive nymphal instars and increases the overlap between instars. :

Normal variation of development of individual increases with the time, the whole population spends in successive nymphal instars and increases the overlap between instars.

Showing the lengthening of the oviposition period in successive generations caused by normal variation of development times of individual. :

Showing the lengthening of the oviposition period in successive generations caused by normal variation of development times of individual.

When the season is favorable for development is long, two or more generations of the species may occur each year. Successive generation often show different patterns of numerical change. A well defined break of diapause at the end of the winter or after a period of summer drought is often followed by a short period of concentrated oviposition. The mode of decline from the peak of insect number after oviposition can be expected to vary with the mortality to which a population is subjected. If the mortality effects only one development stage of insect ie. marked as age specific that will be clearly indicated of the population. :

When the season is favorable for development is long, two or more generations of the species may occur each year. Successive generation often show different patterns of numerical change. A well defined break of diapause at the end of the winter or after a period of summer drought is often followed by a short period of concentrated oviposition. The mode of decline from the peak of insect number after oviposition can be expected to vary with the mortality to which a population is subjected. If the mortality effects only one development stage of insect ie. marked as age specific that will be clearly indicated of the population.

PowerPoint Presentation:

Population curves of the cabbage aphid ( Brevicoryne brassicae) in which overlapping generation cause numerical trends longer than the average duration of one generation.

Generalized description of numerical change:

Generalized description of numerical change Change in the shape of population curve over only length of time depends only on changing relations between births and deaths. N t = N o e (b-d)t Where, t = is short interval of time. N t = number of insects after time ‘t’ N o = number of insects at the beginning of the interval b = birth rate reducing time ‘t’. d = death rate reducing time ‘t’. e = is constant which for convenience is taken as the base of Napieran logarithm

Influences affecting the birth rate:

Influences affecting the birth rate 1. The average fecundity of the female . The potential number of young, they could produce in the time. Tsetse fly ( Glosina palpalis ) is able to produce about 12 young per female. The potential reproductive ability of the spp. is of primary importance in the study of population dynamics. 2. The average fertility of the female The realized production of offspring resulting from the modification of fecundity either by physiological or by fertilization success where that is necessary.

3. The sex ratio. Is nothing but the proportion of male to female of the progeny produced by single female. Ex : Tsetse fly approximately half of the individuals, are female. 4. Other influences. The birth rate of population is affected by the presence of non reproducing female. In the determination of the relative number of reproducing and non-reproducing of a multiplicity of influences and process become involved in the variation of the birth rate. :

3. The sex ratio . Is nothing but the proportion of male to female of the progeny produced by single female. Ex : Tsetse fly approximately half of the individuals, are female. 4. Other influences. The birth rate of population is affected by the presence of non reproducing female. In the determination of the relative number of reproducing and non-reproducing of a multiplicity of influences and process become involved in the variation of the birth rate.

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Finally environmental influences can affect the proportion of reproducing female directly by causing age-specific mortality immediately before oviposition begins.

Causes of mortality :

Causes of mortality Low vitality (by envi. Factors ). Ageing. Accidents. Physiochemical conditions (temperature). Natural enemies. (predators, parasitoids, pathogens) Shortage of food. Shelter.

Analysis of LT’s:

Analysis of LT’s Survivorship curves – a. The graphical representation of the fall- off numbers with time. b. calculate the number living (lx) at a given age (x) and plot graph of (lx) against (x). The life table and life expectancy. Life and fertility tables and the net reproductive rate.

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Type I : mortality acts more heavily on the old individuals. Seen in Drosophila and higher vertebrates. Type II : a constant number die per unit of time with low mortality rates. This type of curve is applied to birds, bacteria etc. Type III : a constant number of die per unit area with high mortality rates. Ex: many parasitoids. Type IV : mortality acts heavily on young stage.

Applications of life tables:

Applications of life tables The weakest link in the life cycle can be determined and made use of to control pests. The effect of most effective biotic factors causing death of the pest can found out and used effectively. Field life table studies on natural enemies can help in determining the cause of their failure in field information can help to find out the best release techniques. Field life table studies predator can help in identifying their natural enemies which can be manipulated to exploit maximum potential of predators. Based on survivorship curves one can operate control measures when the mortality factors operating slow and thus can obtain economic results.

Applications (conti…):

Applications (conti…) It provides useful information on their mortality factors, information thus obtained can be incorporated in mass production techniques and make it more efficient. Studies on pest and natural enemies may provide exact time of release of predator and parasitoid and utilize them to their best potential. Life table studies on the weeds can yield information on the most effective natural enemies which in turn can help to make decision on import, colonization of weed insects or weed pathogen.

Conclusion :

Conclusion A ll the ‘lx’ and ‘dx’ values in the life table indicate the density of individuals per unit of the host or habitat. The role of various factors in bringing about numerical changes in the successive age intervals is determined from a series of life tables for a number of generations of the spp. Life tables have to be prepared for different populations of the same spp throughout its range of occurrence to have a representative view. Measurements have to be made on density, mortality and associated factors in different parts of the habitat.

References :

References Applied animal ecology – A.S. Atwal and S.S. bains Integrated pest management concepts and approaches – G.S. Dhaliwal and Ramesh Arora Ecological methods – Chapman and hall, southwood, T.R.E. 1978, U.K. Morris R.F. (1989), sampling insect population, An review entomology 243-64 (173) Morris R.F. (1995), contemporaneous mortality factors in population dynamics , can.ent.97 (167)

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