Inconsistent grid resolutions for overlap (single telemetry object and fit list)

[Joseph Kolowski]

Hi All,

I know there have already been a few threads on this, but I believe this is a somewhat unique issue. 

I am attempting to get overlap values for ADKEs, and am getting the dreaded:

"Error in grid.intersection(UD) : Inconsistent grid resolutions." 

Unlike in previous threads though, all individuals are from the same telemetry object, and akde() is being run on a single list of fitted models for these individuals. This is my standard approach and I've not run into issues previously. The only tweak is that I am running this in parallel on RStudio Server, but I can't see how that would impact things. 

I guess my question is whether there are other issues that can cause one to have inconsistent grid resolutions, even when all animals are in the same telemetry object, and all the fitted models are based on that telemetry object. Code example is below, and I encounter no other errors. Objects all summarize and plot fine. I initially thought this might be caused by having two groups of animals that were separated by a large distance. But this example below includes only individuals living in the same area. And I'm actually having this same issue with another dataset now. I"m starting to wonder if I need to force the grid as suggested by Allie Anderson (Nov 19, 2025) as a general rule whenever I''m aiming to estimate overlap?

I'm OK to try to that, but did not anticipate needing to add that to my workflow since I haven't needed to do it in the past at all. Thanks for any suggestions!

Joe

"tapir_tel_n" is a single telemetry object with 6 individuals. 

tapir_fits_n <-
  mclapply(seq_along(tapir_tel_n),
           function(i) {
             
             my_tel <- tapir_tel_n[[i]]
             
             my_guess <- ctmm.guess(my_tel,
                                    CTMM = ctmm(error = TRUE),
                                    interactive = FALSE)
             
             ctmm.select(my_tel,
                         my_guess,
                         verbose = FALSE)
           },
           mc.cores = 6,
           mc.preschedule = FALSE)

tapir_akde_n <- mclapply(seq_along(tapir_tel_n),
                       function(i) {
                         
                         akde(tapir_tel_n[[i]],
                              CTMM = tapir_fits_n[[i]],
                              weights = TRUE)},
                       mc.cores = 6,
                       mc.preschedule = FALSE)

north_overlap <- overlap(tapir_akde_n,
                       level = 0.95,
                       debias = TRUE)

Error in grid.intersection(UD) : Inconsistent grid resolutions.