BSK
Well-Known Member
Some people can experience things and simply mentally put the pieces of a pattern together. I am not one of those people. My brain needs to see the world reduced to numbers before I can see the patterns. One of the insane levels of data collection I go to with trail cameras is to record the date and exact time I get all bucks on camera. I also record the bucks relative age (breaking them down into yearlings [1 1/2], middle-aged [2 1/2 and 3 1/2], and mature [4 1/2+], simply because those three groups of bucks seem to display similar behavior patterns). I also record location, what the camera is pointed at (scrape, trail, road, food plot, etc.), and a general behavior of the buck (scraping, passing through, feeding, chasing, etc.). Because I record the exact time the buck first shows up on camera (and I check the times on the cameras every time I visit to ensure the time is accurate to the minute), I can compare the times against a sunrise and sunset table for the date to see if that camera encounter occurred during legal hunting daylight. Over time, I can graph out the number of buck camera events I get per day across the entire hunting season. What has appeared when doing so is some very distinct patterns in when bucks are most active on my property through the fall months. I have used this data many times to decide on which days I absolutely need to be hunting. I never use the data to exclude a hunting trip, but I definitely use the data long in advance to plan what days I better to be in the field.
However, when looking at the data from year to year, I've noticed differences in the annual patterns based on what the seasonal conditions have been. The most prominent has been acorn crops and drought. I see a very different pattern in peak buck activity dates in a good acorn year versus a droughty acorn-failure year. I have also observed massive changes in peak dates following major habitat changes (such as large-scale timbering). For instance, in 2019 the remnants of a hurricane road north up Kentucky Lake and devastated the hardwood forest on either side. My property took a real hit. Immediately after, we brought in a logging crew to not only clean up the downed timber but while they were at it, conduct the biggest logging operation we had ever experienced. We cut the timber off 1/5 of the property. That large-cale logging operation completely changed the patterns of buck usage on the property. For that reason, I analyze data pre-2019 versus post-2019.
To get to the point, below are two graphs of the average daily number of older (2 1/2+ year-old) bucks we've picked up on camera, post 2019, during adequate acorn years versus drought acorn-failure years. The first graph displays the total number of buck events caught on camera, 24-hours per day. When looking at this data, there isn't a huge difference between good acorn years and drought acorn-failure years, with the exception of how the entire pattern is delayed about a week in drought acorn-failure years. However, the important difference occurs when I only graph those older buck camera events that occurred during legal daylight (second graph). When looking at only legal daylight data it becomes clear that peak daylight older buck activity peaks much later in the year. In essence, in good acorn years, hunters need to be in the woods right at the start of Muzzleloader season to take advantage of the peak that occurs right at the end of October and beginning of November. In addition, there is a secondary peak around November 17. However, in a drought acorn-failure year, peak daylight activity occurs much later, with peak dates being around November 21, and then again around December 1.
I realize this data only applies to one property in one area. Unfortunately, no one collects data at this level, so I have no other properties to compare against. However, for a given property, data like this can be invaluable.
However, when looking at the data from year to year, I've noticed differences in the annual patterns based on what the seasonal conditions have been. The most prominent has been acorn crops and drought. I see a very different pattern in peak buck activity dates in a good acorn year versus a droughty acorn-failure year. I have also observed massive changes in peak dates following major habitat changes (such as large-scale timbering). For instance, in 2019 the remnants of a hurricane road north up Kentucky Lake and devastated the hardwood forest on either side. My property took a real hit. Immediately after, we brought in a logging crew to not only clean up the downed timber but while they were at it, conduct the biggest logging operation we had ever experienced. We cut the timber off 1/5 of the property. That large-cale logging operation completely changed the patterns of buck usage on the property. For that reason, I analyze data pre-2019 versus post-2019.
To get to the point, below are two graphs of the average daily number of older (2 1/2+ year-old) bucks we've picked up on camera, post 2019, during adequate acorn years versus drought acorn-failure years. The first graph displays the total number of buck events caught on camera, 24-hours per day. When looking at this data, there isn't a huge difference between good acorn years and drought acorn-failure years, with the exception of how the entire pattern is delayed about a week in drought acorn-failure years. However, the important difference occurs when I only graph those older buck camera events that occurred during legal daylight (second graph). When looking at only legal daylight data it becomes clear that peak daylight older buck activity peaks much later in the year. In essence, in good acorn years, hunters need to be in the woods right at the start of Muzzleloader season to take advantage of the peak that occurs right at the end of October and beginning of November. In addition, there is a secondary peak around November 17. However, in a drought acorn-failure year, peak daylight activity occurs much later, with peak dates being around November 21, and then again around December 1.
I realize this data only applies to one property in one area. Unfortunately, no one collects data at this level, so I have no other properties to compare against. However, for a given property, data like this can be invaluable.