How to analyse animal arrival times to food I have created spatial foraging models with populations of 5 animal species that search for food. Each model allows to species to vary in certain parameters e.g. speed. 
And each model run has multiple food patches. There are 100 runs of a given model. 
For instance, each model run will have: 116 individuals of species 1, 8 species 2, 80 species 3, 65 species 4 and 42 species 5. 
I have used R to give me the number of times each species arrives first to a food patch. So I have species 1 arriving first to food 324 times, species 2 arrives first 177 times, 1420 for species 3, 50 for species 4 and 25 for species 5. 
My question is how do I determine if this observed arrival order is different than expected? Is it simply a case of setting up something like a chi squared test? 
            observed   expected
species1    324        1996/5 = 399.2
species2    177        399.2
species3    1420       399.2
species4    50         399.2
species5    25         399.2
TOTAL       1996       1996

I feel like I'm missing something here because I'm not taking into account the original population sizes. Thanks.
EDIT: Just reflecting on it there. The expected values should be proportions of the total population right?
 A: If species membership had no effect on the chances of first arriving at a food patch, you would expect the distribution of species that have arrived first to a food patch to reflect the prior species distribution. 
The total number of animals: 116+8+80+65+42 = 311.
The proportions multiplied by the total number of observations (1996) would give you the expected counts.
          observed    expected
species1   324         (116/311) * 1996 = 744.5
species2   177         (8/311 ) * 1996   =  51.3
species3   1420        (80/311) * 1996  = 513.4
species4   50          (65/311) * 1996  = 417.1
species5   25          (42/311) * 1996  = 269.5

Now you can run a chi-squared test of independence as you have suggested. 
> freqs = c(324,177,1420,50,25,
+           744.5, 51.3, 513.4, 417.1, 269.5)
> data = matrix(freqs, nrow=5)
> data
     [,1]  [,2]
[1,]  324 744.5
[2,]  177  51.3
[3,] 1420 513.4
[4,]   50 417.1
[5,]   25 269.5
> result <- chisq.test(data)
> result

    Pearson's Chi-squared test

data:  data
X-squared = 1151.3, df = 4, p-value < 2.2e-16

The p value is smaller than the prespecified level , so you reject the null hypothesis that the first arrivals are identically distributed across all species. 
To complement, just looking at the contingency table, species 3 seems to have very good survival skills. Species 4 and 5 are definitely under-performers.
