Hello, first you may want to define your hypothesis, if in fact there is one. If the project is just to look at infestation rate, simple counts of presence/absence by species may suffice, or if you are quantifying parasite load, than you would need count per animal and possible the size/stage of the total parasites (total parasite mass or lengths). If on the other hand you are looking at the diversity of the parasitic infection there are a number of indices that could be used(Berger-Parker, Shannon-Weiner, Simpson, etc), each with strengths/weaknesses.  So really it depends on what you are trying to accomplish with your research. There may be more appropriate indices, but more information is needed to provide alternate suggestions. Good Luck!

You list 3 habitats by name and imply at least two more. This probably means that some of the habitats will be represented in your sample by fewer than six individuals. Your error will be quite high with this small a sample size. If biodiversity is an issue then you will miss rare parasites or parasites that are only present for part of the year because they are on the alternate host the rest of the time.

The way you present the problem implies that your set of 30 snails is composed of two or more different species of snail. I would assume that the parasite prefers one (or a few) species over the others. That will affect your results.

As Mark pointed out there are too many directions that this could go. Are you interested in distribution through space, time, species? Are you interested in abundance, or biodiversity? Have you collected other data (temperature, humidity, alternate host abundance, elevation, distance from stream/pond, water quality, vegetation biodiversity, etc... Are these herbivorous snails or predatory?

However, as an undergraduate research project, I would suggest not worrying about finding the "right" or "best" ecological index. If you can clearly define the question, then you can try and apply all of the suggested methods for analyzing the data. Do they all give the same result? If so, choose the simplest approach. If you don't get the same answer with the different methods, then why?

If this is an observational experiment, then I think you should get more observations if at all possible. Thirty snails just doesn't seem like enough data when spread between 5 or more habitats and (presumably) multiple species of snail. If this is all you can get, then you could try simplifying the data (e.g. terrestrial versus aquatic).

thank you so much Mark and Timothy. 

actually, i'm still quite confused. here is my objectives of my project.

i think my project is just to check the infestation rate in each species and check which species that carry the high infestation rate. what can u suggest?

To understand your problem, you need to play with a few numbers. I will try to lead you through the problem and provide a way that you can check your answer.

You have 30 snails. There is an infection rate p (you need to try several values of p). The probability of a snail being infected is independent of the probability of any other snail being infected.

Simple answer: Calculate the probability that you will not collect any infected snails is just (1-p) raised to the 30th power.  If the infection rate is 9% then (1-0.09)^30 = 0.059052973. One minus this value is roughly 0.94. So it is very likely that you will collect at least one infected snail in a sample of 30 individuals given that 9% of the individuals in the population carry the parasite.

One must take the example a bit further to see that this is the correct answer, and to be able to answer questions like: what is the probability that I will get two or more infected snails, or exactly two infected snails --- or three of 15, or any number of your choice.

An immutable law of probability is that probability goes from zero to one, and includes the values of zero and 1. If probability is zero an even will never happen. If it is 1 it will always happen.

Factorial is denoted with an exclamation point. Two factorial is written 2!. 5! = 5*4*3*2*1 while 8! = 8*7*6*5*4*3*2*1.

The sample size is n. The number of infected individuals is r.

The general formula is then (p^r)*((1-p)^(n-r)*(n!/(r!*(n-r)!)

If you apply this formula to all possible outcomes of zero infected individuals in your sample of 30 to 30 infected individuals you will find that the sum of all these values equals 1 to the limit of your precision in doing the calculations.

The attached worksheet shows that the sum of all outcomes from 0 to 30 infected individuals is one. That results is a good indicator that this is the correct answer, and therefore all the other answers within the table are also correct.

Use the attached worksheet with infection rates from 0.000005 through 0.042505 and change these values to suit yourself. The bottom line is that if you combine all 30 snails into a single sample, you will still have a 27% chance of not collecting a single infected individual if the infection rate in the population is 4%. If your sample size of 30 includes several species and several locations, then you might have a sample size for one species x location of say 5. Work through the attached spreadsheet to figure out what infection rate you would need to have a 50-50 chance of collecting at least one infected individual in a sample size of 5. You should get an answer of roughly 0.12.

It is now your turn. Play with the table and enter various numbers for sample size and infection rate. Now look at your data. You have 30 snails. You found no indication of any parasite in your sample. What does that mean?

You will want a program called pooledinfrate written as an excel macro by Dr. Biggerstaff. Even if you do not use pooled samples, this is a useful program because it calculates infection rates and confidence intervals from this kind of data. If you are doing biochemical tests for parasite infection (e.g. PCR), then one could grind up tissue from 5 snails and process it as a single sample. This is now a pooled sample and there are special methods for the analysis of this kind of data. You know less about the status of a single individual but can greatly increase your sample size. It is a very powerful technique for dealing with rare events, but it requires some care in its use.