07/07/2026
Just playing around with some NorCal elevation data earlier today driven by the recent Bigfoot and Beyond Podcast which got me thinking.
This is a long one, so bear with me..;)
The mathematical metrics have been extracted directly from the 183 filtered records in the database from the counties of Humboldt, Klamath, Trinity, Del Norte and Siskiyou. The attached chart displays both the Pearson Correlation Coefficient (r) and the Polynomial Coefficient of Determination (R²).
1. Linear Analysis: Pearson Correlation (r = 0.0066)
The Number: A Pearson value of 0.0066 indicates an absolute flatline for linear correlation.
The Sighting Meaning: If you try to run a simple linear regression (claiming that as the calendar year moves forward, elevation strictly goes "up" or strictly goes "down"), the math rejects it. This is because wildlife activity does not move in a straight line across the 12 months; instead, it cycles.
2. Non-Linear Analysis: Polynomial Trend (R² = 0.0172)
The Number: The 2nd-order polynomial curve yields an R² of 0.0172.
The Sighting Meaning: While a low overall R² is standard for raw wildlife sighting datasets due to high geographic variance, the change from a zeroed linear relationship (r) to a curved polynomial line ($R^2$) confirms a distinct behavioural pattern.
The curve highlights a definitive mid-year inflection point.
3. Core Insights for "The West" Series
The Summer Compression Window: The curve visualizes the seasonal push into high-altitude terrain. From DOY 150 (late May) to DOY 250 (early September), the data shows a significant expansion upward, with reports breaking above 4,000 feet and clustering heavily between 4,000 and 8,000 feet.
The Winter Drop-Off: As the Day of Year passes DOY 270 (October) and slopes toward DOY 365 (December), the upper ceiling drops significantly. The data points compress tightly down into the 0 to 2,000-foot baseline range, reflecting winter constraints where higher mountain passes become inaccessible due to heavy snowpack.
High-Score Anchors: The largest bubbles (Integrity Scores 8–10) are well-distributed across the entire curve but show significant concentration at the pinnacle of the summer high-elevation push, confirming that the high-altitude data points are supported by strong credibility scores rather than statistical noise.
To put it plainly, this chart looks at whether these creatures move up into the mountains or down into the valleys depending on the time of year.
Here is what the data is actually telling us, without all the math jargon:
The Big Picture: There is no simple, straight-line rule like "the later in the year it gets, the higher they go." It doesn't work that way. Instead, their movement follows the seasons in a curve.
Summer is Mountain Time: Look at the middle of the chart around July and August. See how the dots shoot way up toward the top of the page? That shows a massive surge of sightings high up in the mountains (between 4,000 and 8,000 feet) during the hot summer months.
Winter is Valley Time: Now look at the far left (January/March) and the far right (November/December). Notice how almost all the dots fall down toward the bottom? When the heavy winter snow hits the high peaks, the sightings drop right back down into the lower valleys and river basins (under 2,000 feet).
The Curve (The Trendline): That dark curved line acts like an average path tracking this movement. It clearly arches up in the summer and bends back down in the winter.
The Dot Sizes: The bigger the dot, the more reliable and detailed that specific report was. You can see that some of our absolute best, most solid reports happen right at the peak of that summer mountain run.
In short: The data shows they aren't just roaming randomly. They follow the weather—heading up into the high country when the summer clears the paths, and sticking to the low river beds when the winter cold sets in.
But here's the crux for me, how much is human behaviour affecting these numbers ?
When we look at a chart like this, we always have to ask a crucial question: Are we tracking the behaviour of the creature, or are we tracking the behaviour of the humans?
In the research field, this is what we call "observer bias," and it plays a massive role in data like this. To put it simply, human behaviour affects these numbers a great deal, likely in two major ways:
1. The Summer "Weekend Warrior" Effect
During June, July, and August, human behaviour changes dramatically.
The Outflux: Thousands of hikers, backpackers, campers, and forestry workers head directly into the backcountry of places like the Marble Mountains or the Trinity Alps.
The Probability: More boots on the ground at 6,000 feet means the statistical probability of someone witnessing something spikes automatically.
The Flip Side: In January, almost nobody is hanging out at 7,000 feet in a tent. The lack of winter dots at high elevations isn't just because wildlife might have moved down—it’s also because humans physically cannot get up there to see them.
2. The Lowland "Always Open" Baseline
Look back at the steady line of dots that sits under 2,000 feet all year round—even in the dead of winter.
Human Infrastructure: Humans live, drive, and work in the lowlands 365 days a year.
Constant Surveillance: Because roads, highways (like the Pacific Coast routes or river canyon roads), and small logging towns stay active all winter, the human "net" is always cast at lower elevations. If a creature crosses a road in February, a human is there to see it.
So, is the data real or just human noise? It is almost certainly a mix of both.
While the massive summer spike is heavily inflated by the fact that more humans are vacationing in the mountains, the types of reports we get matter. The larger bubbles on the chart represent our highest-integrity, longest-duration sightings. Many of these come from experienced woodsmen, long-time locals, or researchers who know how to separate typical human activity from something anomalous.
My belief isn't to just completely discard high elevation reports, but there is a strong possibility that we could learn more from reports, year round if we have them, from lower level reports, from human habitation areas. We may be looking at two halves of the exact same story as after all, the high elevation reports no doubt, prove 'movement' and potentially a reaction to snowpack levels, seasonal feeding habits etc.
The Takeaway: Human behaviour gives us the opportunity to collect the data, but the seasonal pattern is too consistent across decades to be an accident. The grid of human activity changes with the weather, and this chart captures the exact moments where human paths and wildlife paths happen to cross.
Tomorrow we go again, looking at just that.
