Differences in bootstrap and block bootstrap I have to be somewhat vague regarding data and such for confidentiality purposes (I'm not allowed to share whole data). I have a dataset (X) that represents transects (representing line transects of animals in space).
  Birds        area      len       transect
  1            0.239310  0.478621  1
  2            0.238463  0.476927  1
  1            0.244382  0.488765  2
  4            0.236501  0.473002  2 
  0            0.245832  0.491665  3
  1            0.241026  0.482052  4 

When I calculate the mean density of the birds, e.g.:
meandensity <- sum(X$Birds)/sum(X$area)

in the dataset, I get a mean value of ~3.63.   
The problem is I've been given a MATLAB module written by someone else (and I can't read MATLAB code) who claims to be using a block bootstrap method to estimate mean and confidence intervals.  When I run that module, the mean density gets estimated at ~4.6.  
Now, I spoke to this person and have tried to replicate his bootstrap method and am getting a value of ~3.5.  
My questions:  Can the bootstrapped estimate of the mean really differ this much from the mean of the whole dataset? My understanding was that the bootstrap estimate shouldn't be super far from the mean, and differences between bootstrap methods would impact the CIs more than the estimated mean.  
 A: Can you find a way to assess the amount of spatial dependence in your data? If you find evidence for weak dependence, that should confirm that your understanding is correct. For that assessment, I would imagine you need some lat and long coordinates for the mid-spot of each area or something to that effect.  If you don't have access to these coordinates, you would have to blindly apply block bootstrapping, which is not necessarily ideal, as you may end up either under-estimating or over-estimating the block length.
By the way, for your computation of density, shouldn't you divide the sum of the bird counts to the sum of the areas? It seems like your formula is set backwards (unless I am missing something).
I guess another way to estimate the expected density (birds/km^2) is to use a Poisson regression where the bird count in each area is the outcome variable and the area size is treated as an offset. The model would include an intercept as well as a possibly nonlinear function of lat and possibly nonlinear function of lon, with the latter two aiming to capture spatial dependence among bird counts from areas that are close to each other. (See Can I use glm with Poisson family if counts data are treated as density?.) 
