One of the huge advantages of having sensors (Celium in our case) on traps is the ability to identify trap vandalism early and to have a specific date/time when the damage occured to help identify the perpetrator.
One of the key ‘patterns’ that identifies potential vandalism is when we see a series of daytime triggers along a trapline. Whilst that pattern can be caused by trap maintenance, that’s easy to understand if the pattern is brought to our attention.
Identifying the patterns manually is challenging and very time consuming. But it’s the sort of thing that could be easily done at the database level… and if it was done using machine learning, that would also unlock richer analysis of other kill patterns.
Only Trap.NZ can do this effectively. If we approach Encounter Solutions, their already challenged database would need to be complicated by adding line information. Worse, a different solution would be needed for Zip’s sensors… it needs to be a Trap.NZ initiative.
Thanks for listening!
David