Tellico4x4,
For years I always said observation data was NOT very good for tracking actual changes in local population. And that was because, year to year, observation data didn't match actual numbers generated from photo censuses. The observation data would show wild swings from year to year while the population didn't change much. However, while playing around with some data I found that by using some simple statistical techniques intended to "smooth out" highly variable data from year to year, observation data suddenly showed a very strong match to actual population data. This statistical process is called a "Running Mean." And a running mean can be for any number of years (although it graphs out best if it is an odd number of years, such as a 3-year or 5-year running mean). In a 3-year running mean, each year's data point is the average of that year, the year before and the year after. Basically a 3-year span. A 5-year mean would be the average for that year, the two years before and the two years after. So in a 3-year running mean, the data point for 2019 would be the average of the three years 2018-20. The 2020 data point would be the average of the years 2019-21, etc.
The below graph is an example of how observation rates track population once both data sets are graphed as running means. This graph tracks the population of older bucks (2 1/2+) picked up by photo census during the month of November versus hunter observation rates of older bucks during the month of November. And again, both data sets are 3-year running means. The match isn't perfect, but pretty darn close. I would have no problem using that running mean observation data to make management decisions.
