Searching efficiency of egg parasitoid enhanced by sesame flowers

Regulatory ecosystem services are important in keeping pest populations below damaging levels.  In rice ecosystems, one group of arthropods that performs this function well is the egg parasitoids.

These are tiny hymenopterans that have long ovipositors that can pierce through the plugs protecting the eggs that the planthopper females insert into the stem tissues.  Being highly susceptible to insecticides these parasitoids are often depleted in intensively sprayed fields.

In Vietnam, ecological engineering fields with nectar flowers grown on the bunds had higher egg parasitism rates.  In China, ecological engineering fields with sesame on the bunds had higher densities of parasitoids.  These fields were featured in the cover of the journal Annals of Applied Biology March 2011.  The prospect of using sesame to improve parasitism in rice fields was reviewed by Gurr et al 2011.


Experimental set up. A control B With sesame flower (arrowed)

A laboratory setup as the experimental arena was used to investigate if the searching efficiency of the egg parasitoid, Anagrus nilaparvate, was significantly improved when caged with the sesame flower.

An inverted plastic cup with a hole in the bottom was placed over a rice plant grown in a plastic pot.

Planthopper females at densities of 2, 4, 6, 8, 10 and 16 respectively were introduced into each cup to deposit eggs into the rice stems. Two days later, the planthoppers were removed and a fresh sesame flower and a pair of newly emerged parasitoids introduced.

The same arena without the sesame flower was maintained as the control. The treatment was replicated 64 times and the control 78 times.

At 24 hours after introduction the pair of parasitoids was removed and the number of planthopper eggs, both parasitized and non-parasitized, were counted using a disserting microscope.  The experiments were maintained at 26+ 1 oC and 12:12h light in the growth chambers.


Functional response curves of number of eggs attacked by Anagrus with increasing egg densities.

To quantify searching efficiency, we fitted the data, number parasitized eggs (Na) and total number of eggs available (N) in each setup, to the Roger’s Random Parasitoid Equation (RPE)

 Na = N [1-exp (-aT / (1 + aThN))]

where a is the searching efficiency and Th the handling time.

 The Random Parasitoid Equation developed by Royama (1971) and Rogers (1972) is a density-dependent model to describe parasitoid attacks on preys available.

 The two parameters, a and Th may be estimated using a nonlinear curve the fitting technique available in DPS(Data Processing System) developed by Professor Tang Qiyi of Zhejiang University.

Searching efficiency, a, was higher when sesame flowers were placed in the cage compared with the control; 0.3547 + 0.0825 and 0.3337+ 0.0718, respectively.

Similarly the handling time, Th, which is the time the parasitoid spends not searching for eggs is reduced when sesame flowers were present; 0.0217 compared with 0.0127, respectively.

The reciprocal of Th is the asymptotic number of eggs that the parasitoid would attack in 24 hours and was much higher when sesame flowers were present; 44.05 and 78.74.

These results imply that the egg parasitoid, Anagrus, has higher searching efficiency when sesame flowers were present, probably because of the availability of nectar food resources from the flowers. Thus fields with sesame grown on the bunds can effectively improve egg parasitization of planthopper eggs.

The functional response analysis though elegant and easy to conduct in the laboratory has its limitations. The experimental arena tends to confine the searching of the parasitoid to a smaller space than in nature and thus overestimates parasitism rates.

Such lab methods are usually best used to compare 2 or more treatments using the same experimental arena and under the same conditions.


Rogers, D. 1972. Random search and insect population models. Journal of Animal Ecology 41:369–383.

Royama, T. 1971. A comparative study of models for predation and parasitism. Researches on Population Ecology, S1:1–90.

Tang, Q. 2010. DPS – Data processing System – Experimental Design, Statistical Analysis and Data Mining.  Science Press, Beijing, China.

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