Open jmlondon opened 4 days ago
@emchuron and I had a discussion about two approaches for handling long time gaps between observed locations
The initial consideration was to focus on the first because it seemed easier to implement and might result in better predictions/pseudo tracks because the gap periods wouldn't have influence on the model fit. After some experimentation though, this approach leads to short segments that may not converge during the model fit. Imagine you might have a stretch of 8 days of no observations followed by 7-10 locations and then another 8 day gap. Fitting a model to just those 7-10 locations can be unreliable.
In most cases, the large time gaps are not resulting in poor model fits or convergence issues. Instead the problem comes on the prediction side when large correlated loops are generated that are unrealistic.
So, I think the second approach is the path worth pursuing and here's what's needed to accomplish that
sounds good to me! Let me know if I can help at all
For both ribbon and spotted seals, there are 'outlier' predictions showing up in the final movement dataset that need to be addressed.
For example, spotted seal predictions (
pl_predict_pts
) shows points well into the southern hemisphereAnd ribbon seals, while remaining in the northern hemisphere, have some relatively extreme smoothed projections
The likely culprits worth investigating: