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I was wondering if there are any spatial ecologists out there who can provide some insight on the trouble I'm having with my Masters research.

I am looking at the association a bat species to high density aggregations of a flowering plant. The bats were tracked throughout the night with a point taken at approximately 10 minute intervals. The bats were tracked over consecutive nights until an asymptote in their space use was reached. So now I have point patterns for each bat that pretty much covers the entirety of their space use at that period.

When the data is displayed visually there is a clear pattern of the bats that were tracked during the flowering period moving into the aggregations of the plant however they also spend a lot of time foraging around their roost sites.

I have simulated the point pattern of each bat 999 times by randomly shifting and rotating the points within the study window (the internal structure of the observed pattern was maintained in the simulations to account for autocorrelation). I then did randomisation tests to see if the average distance to the nearest plant aggregation for the observed data is within the lower 5% of the distribution of the averages for the simulated point patterns. However because the majority of the points collected are highly associated with the roosts the average distance is primarily effected by the position of the roost in relation to any of the plant aggregations.

I've been thinking of ways to disentangle the association with roosts and the association with the plant.

First I was thought about putting a buffer around the roosts and removing the points falling within these buffers from the analysis. The problem is deciding on a buffer. In the other areas I have been using a buffer equal to the circular error probable of the radio tracking error but placing this around the roost will only incorporate the points where the bat was actually inside the roost not foraging around it. All the bats have different foraging patterns around their roosts and I can't see how to assign a buffer distance that is not completely arbitrary.

The second method we have been looking into is taking out the points that are more closely associated (closer in distance) with the roost than the nearest plant aggregation and then running the randomisation to look at the association of the remaining points to the plant. I want to get an idea of whether or not removing the association with the roosts in this way will potentially bias the analysis.

Any thoughts or further suggestions?!

Thanks in advance!

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Very interesting question. However, I would suggest to migrate this to CrossValidated, as probably the folks there will give more appropriate answers. –  nico Sep 19 '12 at 7:20
    
Yes, but I think the idea of comparing the data without plants with data that include plants is the right idea. Have you only measurements where plants are in the vicinity? Then only a realistic simulation of movement in the absence of plants can be a good model for comparison. But maybe someone else has already created a good simulation? –  rwst Sep 19 '12 at 8:43
    
@nico thanks I'll do that now! –  Georgia Sep 20 '12 at 2:42
    
@ rwst Unfortunately there has been very little spatial work done on these bats. We tracked four bats before the plant came into flower an four during. That's probably not a large enough sample size to make any serious conclusions with so that's why I went down the road of simulating the bat locations but I'll look into it thanks! –  Georgia Sep 20 '12 at 2:49
    
@Georgia: I think the moderators can do that directly. I will flag for their attention. –  nico Sep 20 '12 at 6:45
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closed as off topic by Rory M Sep 20 '12 at 16:09

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