Hi all,

I am trying to get started on modeling some seed dispersal data, but am having a hard time getting started and looking for some help.

I have a dataset that contains information about seeds caught in a seedtrap. Basically a container was placed under X number of seeds on an individual shrub and, over N time steps, the number of (1) predated seeds (2) whole seeds, and (3) seeds still in the fruit that were found in the container were recorded. The data look like this:

TotalSeedsLeft,date,predatedSeeds,wholeSeeds,SeedsInFruit

100,t,9,1,2

88,t+1,7,2,2

77,t+3,5,1,1

70,t+4,8,2,3

The total seeds were not observed until all were gone, and the timesteps were not uniform. I am basically interested in (a) how many seeds did not fall into the trap (%), and of those that fell into the trap (b) how many were predated (%), whole seeds (without fruit) (%) and whole fruit (%).

In my thinking, this can be thought of as either a binomial test (successes and failures), but also has some elements of survival analysis (though I am not really interested in time to failure (or dispersal).

Ultimately, I have thought about collapsing the data, but this seems to be losing some information. Also, the observer made observational errors, so this could likely only be included if we keep each timestep.

Any help on getting started would be appreciated. Sorry for the lack of code, but if I get pointed in the right direction, I can provide code in the next step.

Have a great weekend.