by Andrew Mayne
I pack my bags at the motel, hop on the interstate, and decide I can deal with Juniper’s car later.
Eight miles later I pass a sign that says I’ve left the county.
A quarter mile ahead I spot a motel.
The stubborn part of me, the part that got me fired, makes me click on my turn signal and pull into the parking lot.
CHAPTER TWENTY-TWO
THE GRAPH
This is insane. I toss my motel room key on the dresser and fall down on the bed. I should be working on my research. I have enough field samples. The smart thing is to drive back to Austin and finish what I can before the semester starts.
That’s the rational, logical thing. Or is it?
When Juniper’s body was found, the hunters went out to find her killer—the brave men of the tribe ventured out to defend their own. They may have never met her, but she was still part of the human race.
No other animal draws boundaries as far out as we do when it comes to protecting other members of our group.
My instinct tells me Juniper was killed by a man—or a woman, not to be presumptive.
It’s what fits the facts.
Then why do the people who are experts on this kind of thing not see it?
What do I know that they don’t?
Their medical examiner is competent enough, it would seem. Richards and Kendall know more about bears than I ever will. And Detective Glenn is no fool. After the animal was tracked down, he was still on the case.
If this was the first act of a movie, I’d be pointing the finger at him. I’m not very good at reading people, but in all my interactions with him, his suspicions were always directed at me.
I can’t rule anything out. Except one thing: I’m not the kind of person that could talk to someone for an hour and have any idea one way or another if they’re guilty.
All of those people collectively know more than me. Yet, here I am, staring at the ceiling, convinced Juniper’s killer walked on two feet.
Why?
What do I know that they don’t?
It’s not any one thing. My expertise isn’t deep in any field. My papers, my research, my life have been about drawing connections from very different fields. My domain is how things are related.
I trace life cycles. I look at gene flows. I build computer models and search for real-world analogues.
I seek out systems and circuits. Whether it’s the nitrogen in our bodies that came from fertilizer plants or it’s our genes for coding specific proteins that evolved a billion years ago.
Systems can go laterally through space. Others move linearly through time.
I get up from the bed, pull some maps out of my backpack, and tack them to the wall with glue dots.
I’m not a detective. I’m not a forensic specialist.
I’m a biologist and a computer programmer. These are my areas of expertise.
I stick a red circle where Juniper was found. Next to it I place a green one to represent that she’d physically been there. I place another on the car repair shop and another on her motel.
These are places where we know Juniper had been alive. It’s part of her graph. I place another on where she was working on her postgrad at Florida State and another where she lived in North Carolina. The final dot I place on Austin, where she was in my class.
These are points in her life graph. In a computer I can create a version that shows this over time. But right now it’s simple enough to see.
This is Juniper’s story.
Her life started in a delivery room in Raleigh. It ended in a forest in Montana.
What brought her to that point?
Life is decided by thousands of external and internal forces.
Her death could have been a random event, initiated by someone catching a glimpse of her as they passed her walking on the highway.
It could be someone she’s known for years, all the way back to North Carolina.
Maybe some FBI profiler could look at her wounds and tell you if it was personal or not. I wouldn’t know. And since the experts think it was a bear, I don’t know what credibility I’d give them right now.
I place a black circle next to the two by her body. This is her killer. We know at one point he was in the same place as her.
I place another where Bart was killed. Our killer was in that area at some point as well.
To be precise, I don’t know that the killer was there. It could have been an accomplice. Graphs don’t always measure actual locations of organisms. Sometimes they just map their influence. For now, I’ll just use black circles for the killer’s graph of influence.
The killer’s graph . . .
I sit back and take it in. I only have two data points, but that’s a start.
In my field, a graph can be just as illuminating as an actual animal or its DNA. Sometimes more so, because it can tell you how it lived and not just the color of its pelt or the arrangement of its genes. Sometimes less so, because a graph can be misleading. Too many unrelated data points leave you looking at chaos.
Sorting through chaos is why I developed MAAT. She’s the software I use to sort through thousands of points of information and find patterns.
MAAT is based on how I think, but much more advanced.
I built it using source code from a research project designed to find genes that contribute to longevity. It’s AI that builds better algorithms with each iteration. Each time becoming more and more complex.
I couldn’t tell you how the current version of MAAT works, just that she does. Sometimes.
When the researchers who developed the core AI behind MAAT asked it to figure out what gave a strain of fruit flies its longevity, it pointed to the genes that regulated resveratrol—the same chemical in red wine that has been tied to human longevity. When they tried to figure out why the software singled that out, the answer was a string of data that no human could understand.
What MAAT could tell you right now from the data points on my map is what is already obvious.
She’s really useful when you give her thousands or millions of points.
Points I don’t have. The killer is just two black dots in time and space. But . . . in the absence of firm data, the other trick is to give her assumptions.
If we were looking at mating cycles, and Juniper and her killer were two mountain lions, I could tell MAAT the frequency that a female is in estrus and an estimate of the male’s range. That information would give me an estimate about when they would encounter each other again. If a male mountain lion had multiple females it bred with and they had specific ranges, I might be able to predict where else he would show up.
And if there were general rules about the kinds of places they reproduced, I might be able to narrow down candidate spots based on available geographic information.
From all of this, MAAT could give me a dozen or so places where I could plant wildlife cameras and reasonably expect to catch the two large cats doing it, even over an area of dozens of miles—all of that based on three data points and general information not specific to an animal.
The problem is I don’t have any more data to put into MAAT.
I know nothing about the killer.
He was born at some point. He met Juniper. At some point after that, years or minutes, he killed her. His last appearance was getting her blood on Bart. Then he vanished from his graph.
I need more data than what’s on my map.
From where?
If I don’t have data, then I have to use the next best thing . . . which is also the worst best thing.
Assumptions.
I need to make guesses.
On a real graph these wouldn’t be black circles. They’d be half black, half white. They’re maybes.
Sometimes they lead you somewhere interesting. Other times they derail you for months . . . or years.
Our war on cancer has been filled with countless maybes. Billions of dollars and millions of human hours have been spent chasing a
fter a pattern we can’t even begin to guess at.
Even still, we’ve made some progress. Many of those maybes have panned out. People live longer than before because not all that effort was wasted. And for every maybe that turns out to be a no, we still move forward.
I need some maybes and assumptions about the killer.
I can’t be worried if they’re wrong. I just need a starting point.
Let’s make some . . .
Juniper’s killer was clever because he got away with it. That’s a hard thing to do. He was either very lucky or experienced.
Okay . . . let’s go with experienced.
Oh, shit. Sometimes one assumption makes something else automatically true.
An experienced killer implies that he’s done this before . . .
I open up my laptop and do a search for bear attacks in the United States and Canada.
I’m not sure what I was expecting, but there’s only been a handful in the last ten years.
The Fish and Wildlife Service has detailed reports. Most of them are in deep woods. I look for any within a few miles of a highway.
There are two. In the first, three years ago, a self-proclaimed grizzly expert was killed. I’d personally rule that a suicide.
The other was six years ago. A woman was found bleeding to death on a road. She died on the way to the hospital.
Experts decided that she’d also been killed by a grizzly. The report shows diagrams of wounds and a photo of a tissue sample. There’s even a hair. But no DNA analysis was done.
The bear they caught was identified by the victim’s blood on its pelt.
That sounds familiar—just like Juniper.
The hair on the back of my neck raises. It’s my own animal sense telling me I’m looking at something dangerous.
I put a red and a black circle where the other victim was found and a black one where the accused bear was trapped.
It’s fifty miles away in a different county, making Detective Glenn and the others seem less suspicious to me.
This has happened before, somewhere else.
But two red dots don’t make a pattern. Not yet.
I need more data.
CHAPTER TWENTY-THREE
THE HUMAN CIRCUIT
A wider search of bear attacks is a dead end for me. They’re supported by finding human remains in the animal’s scat. This doesn’t mean the killer couldn’t have left the victim to be scavenged by bears. Apparently bears are not very picky eaters. It just means that these look exactly like bear attacks. There’s nothing suspicious to them, unlike Juniper Parsons or the other woman, Rhea Simmons.
I pull up an article on Rhea. She was twenty-two and apparently hitchhiking her way across the country. Born in Alabama, her family had no idea she was in Montana.
Scanning through a few more articles, it seems like they’d been estranged for a few years. The first they heard about her whereabouts was when the police called.
What a horrible phone call to receive.
Rhea was a loner. A photo of her shows a hippie chick. The kind I’d seen around campus, struggling to figure out their place in the world. For Rhea, it was trekking out on her own.
Her case is promising, but there just isn’t a pattern yet, other than both she and Juniper were independent young women. Our killer may have a type, but there doesn’t appear to be enough alleged bear attacks to support a pattern.
Alleged . . . alleged implies someone to make the allegation . . .
If a bear kills you in the forest and nobody finds the body, is it a bear attack?
No.
It’s a disappearance.
Hikers said they heard Juniper’s cries. Rhea made it to the road.
What if nobody had heard Juniper? Would the killer have left her in the open?
Or would he have buried her?
The same for Rhea. If she’d never made it to the road, would we be looking at a missing-persons case?
I get a chill. If Rhea’s killer had managed to hide her body, she’d never have been a missing-persons case. At least not for months or years. Probably not in Montana.
Her parents didn’t even know where she was—or seem that concerned.
We’re used to the high-profile cases on cable news shows. The kind where a wife or husband vanishes under suspicious circumstances. Or when a daughter is last seen leaving somewhere and never checks in.
All of them have one thing in common: tight family structures.
What about the loners? What about people living on the fringe?
If the toothless woman who panhandled outside the 7-Eleven went missing one day, who would report it?
People drop out all the time. Drugs, psychiatric problems . . . there are a multitude of reasons.
More than once I’ve received concerned phone calls from parents worried because their child hasn’t called home in weeks.
It’s usually just a phase. Sometimes it’s not. People—especially young people—can begin to disconnect bit by bit, then fall away entirely, if only for a time.
I remember the story of a twenty-three-year-old California girl found dead in her car in a Walmart parking lot. Not only had nobody reported her missing, but she had been dead for three months. She killed herself, then rotted away in a heavily tinted car as people walked back and forth just a few feet away.
I Google missing-persons information and come across the webpage for the FBI’s National Crime Information Center. They have a listing for missing persons—a list of people who have vanished under suspicious circumstances. According to this, there are eighty-four thousand missing people right now in the United States.
Holy shit, that’s a lot of people.
To be sure, many of these are people with drug problems or other issues that made them easy to drop out.
But eighty-four thousand people? That’s like the city of Boulder, Colorado, disappearing.
And these are just the people where someone picked up a phone and told the police they were worried. Who knows how many more are unattached to a family group?
How many go missing and nobody knows?
You could have scores of serial killers out there and nobody would notice. My skin goes cold. We probably do.
What about Juniper’s killer? Is he responsible for more than her and Rhea?
How could I possibly know?
I look up some more data points and make a creepy discovery.
Montana and Wyoming have more missing persons per hundred thousand people than any other states except Alaska, Oregon, and Arizona. What the hell?
This could have to do with how the data are collected. One extra check box on a form can skew things out of proportion.
But still . . .
I click on the link to the Montana Missing Persons Clearinghouse.
The first things that appear are the photos of two smiling young girls. Below them is a Native American couple and their child.
There are a lot of young women on the list. The same for Wyoming.
I count at least a dozen women who fit Juniper and Rhea’s age range. Most, if not all, are probably runaways, many no doubt fleeing bad situations. Or, worse, leaving with men with ill intentions.
But I also have no reason to assume the killer limits himself to women.
There’s usually a strange thrill when I encounter a new data set. I can’t quite describe it. This time I feel guilty when I look at the faces of the missing.
I pull a box of colored thumbtacks from my luggage and push an orange one into my map for every missing woman over the age of eighteen in the surrounding states.
I do a new search to narrow it down by city. It’s depressing how little attention these missing-persons reports get. The data are scant.
The even more depressing thought is that the current state of their investigation is probably limited to having their name on a list and a report collecting dust in a filing cabinet.
Unless the police have clear evidence of foul play and a suspect, many
of these women may never be found.
After a few minutes of pinning data points, my map begins to fill with orange thumbtacks. I find myself reluctant to shove them in; they feel like nails in a coffin.
I notice something odd but don’t want to jump to conclusions.
This is getting too complex for my map. Fortunately I have a portable video projector. I connect my laptop and use my bio-geo mapping software to create a virtual map I can project on the wall.
I still like to stand next to things when I look at them.
All my orange dots pop up. I use a shader control to color counties by population. This helps me see whether the orange dots are correlated to population density.
There’s no way for me to know what’s good data and what’s bad, let alone what’s missing. But to paraphrase the Supreme Court’s statement about obscenity, when it comes to patterns, I know them when I see them.
I plug all the variables into MAAT, comparing missing-persons reports with population data. I also find some statistics on the percentage of reports proven to be runaways who are safely returned. This filters things a bit.
MAAT draws a wispy, dark-purple loop around my map. It goes off the frame and then returns to curve around.
It’s a graph showing a connection between missing persons that lie outside what you’d expect from a given population size. It also follows certain interstate highways, but not others.
In biology you become accustomed to different ways data can represent itself. Salmon returning upstream and herd animals have very linear patterns. Birds follow loops.
I’m looking at another pattern.
One that’s very familiar to me.
It’s a predator’s circuit.
I furiously type away, searching for the pattern imprinted on my memory.
I find it. It’s not the same shape, but it has similar symmetry. I could write a formula for a fractal that would generate patterns just like these.
But it’s not just a pattern, it’s a behavior.
The behavior generating the pattern on my wall, the one where Juniper’s killer is hiding, matches this other behavior quite clearly.
The creator of this other pattern is an efficient killer that has remained unchanged for millions of years. It’s developed a sophisticated system for hunting predicated upon always staying on the move, allowing it to return to the same points again and again without its prey being any wiser.