Manually assigning weights to observations

[Annika Dean]

Hello! 

I am currently using ctmm to construct home ranges based on opportunistic survey data. I am really appreciating the ease of including hard boundaries into the estimate but hoping to also include uneven sampling effort in this model like done in the literature noted below. Is there a way to incorporate manual weighting in this package? I see previously studies have used ArcMap or the R spatialEco package to assign manual weights but I was enjoying the ctmm package. 

Horne, J. S., Garton, E. O., & Sager-Fradkin, K. A. (2007). Correcting Home-Range Models for Observation Bias. The Journal of Wildlife Management, 71(3), 996–1001. https://doi.org/10.2193/2005-678

Bennington, S., Guerra, M., Johnston, D., Currey, R., Brough, T., Corne, C., Johnson, D.,
Henderson, S., Slooten, E., Dawson, S., & Rayment, W. (2023). Decadal stability in the distribution of bottlenose dolphins in Dusky Sound/Tamatea, New Zealand. New Zealand Journal of Marine and Freshwater Research, 57(3), 411–424. https://doi.org/10.1080/00288330.2022.2038214

Thank you!

Annika 

[Christen Fleming]

Hi Annika,

You should be able to feed a vector of manual weights into the weights argument of akde().

Best,

Chris

[Annika Dean]

Hi Chris, 

Thank you for the speedy response! I just ran a test on one individual where each observation was weighted by the survey effort in the 100m^2 area the observation fell in (function call for akde below). I compared the output of akde home range estimates with weighted observations to unweighted observations and there was no difference in the area estimates for these two methods. How are the weights incorporated in the estimates? 

kde_weighted <-
  akde(
    telem_individual_a,
    SP = land_sp,
   
    # Locations expected to be outside of the polygon:
   
    SP.in = FALSE,
   
    # Don't include auto-correlation:
   
    CTMM = individual_a_iid_fit_list,
   
    # Include manual weights:
   
    weights = points_for_telem %>%
      filter(id == "individual_a") %>%
      pluck("weights_standardized")
  )

Thanks again!

Annika