Hi Sevgan, Geostatistical methods can be one of the options. When you do spatial analysis (Geostatistics or SADIE), you first have to look whether data has the trend which is contributed by extrinsic factors (local soil condition, weather pattern, landscape variability etc.) in general. You can statistically test whether trend is statistically significant or not. If yes, you should proceed to remove the trend, and use the residuals in the analysis and the results (if you see still aggregation) is due to intrinsic factor. If you want a publication related to this, I can send you my recent one to you. Let me know and good luck.

Thanks Jhalendra, I would highly appreciation any publication related to this from you.

Here is the publication.

Article Spatial Distribution of Grape Root Borer (Lepidoptera: Sesii...

the attachment

Thanks Jhalendra for the publication.

A species aggregation characteristics can be quantified a number of ways, with Taylor's equation, which relates variance to mean density, being one of the easiest to use. 

A species' aggregation characteristics tend to decrease with increasing age (for most arthropods) due to increasing mortality and increasing dispersal. For the most part, species aggregation only increases with age for particular stages when an aggregation pheromone is produced as a larval defense behavior or leading up to adult mating.

Species aggregation is also a function of sample unit size. For example, if your sample unit is a mm^2 of plant leaf surface area, you will derive aggregation characteristics for a species than are quite different from what you would derive were you to increase your sample unit size larger, say to the size of a whole plant or even larger.

If your goal is to separate the effects of intrinsic and extrinsic variables, there are a number of experiments that you could conduct, each of which involves studying aggregation characteristics for a number of age classes and at a number of sample unit sizes. Depending on how detailed you wish to address this topic, you can also categorize species aggregation by the relationship between the probability of sample units containing 0, 1, 2, ... individuals of species, and mean population density. A number of probability distribution function have been developed for this purpose, some of which are more robust than others. I prefer to use the negative binomial probability distribution function with a dynamic k coefficient. However, pdfs do not allow you to determine spatial association from an arthropod behavior perspective, for the most part.

Lastly, you can use geo-statistics to explore two- and three-dimensional spatial association. However, detailed spatial distribution data for most species are extremely scarce and as a result the types of geo-statistics that you can use are quite abstracted from the biology of the species that is being studied. Nevertheless, a geo-statistic type analysis might be of use to you.

An area of research that I am currently pursuing is the use of two-dimensional arthropod simulation modeling, with the movement of herbivorous arthropods determined by preference for different hosts plants and by wind angle and speed. It will be a while before this research advances to the point of producing peer-reviewed papers.

As general reading on arthropod species aggregation characteristics, I suggest you consider reading the following:

Wilson, L. T. 1982. Development of an optimal monitoring program in cotton: Emphasis on spider mites and Heliothis spp. Entomophaga 27: 45-50.

Wilson, L. T. and P. M. Room. 1983. Clumping patterns of fruit and arthropods in cotton, with implications for binomial sampling. Environmental Entomology 12 (1): 50-54.

Wilson, L. T. 1985. Estimating the abundance and impact of arthropod natural enemies in IPM systems, pp. 303-322. In: M. A. Hoy, and D. C. Herzog (eds. ), Biological Control in Agricul¬tural Integrated Pest Management Systems. Academic Press, New York, New York.

Wilson, L. T., W. L. Sterling, D. R. Rummel, and J. E. DeVay. 1989. Quantitative sampling principles in cotton IPM, pp. 85-120. In: Frisbie, R. E. , K. M. El-Zik, and L. T. Wilson (eds. ). Integrated Pest Management Systems and Cotton Production. John Wiley and Sons, New York.

Wilson, L. T. 1994. Estimating abundance, impact, and interactions among arthropods in cotton agroecosystems. pp. 475-514. In: L. P. Pedigo, and G. D. Buntin. Handbook of Sampling Methods for Arthropods in Agriculture. CRC Press, Inc. Boca Raton, Florida.