STLPR0%

Conservation detection dogs investigate poaching, monitor wildlife in Missouri 4%

By Will Firra0%

6/25/2026, 10:00:00 AM

BS Summary: This article contains 21 faulty reasoning types, including Anecdotal, Appeal to Authority, and Optimism Bias, with Halo Effect as the most egregious example at 15.2% saturation with 146 hits. Analysis detected 649 faulty-reasoning hits from 958 analyzed words, generating a BS Score of 18.2% and a BS Rank of 4% (20,389 of 21,198 articles). This article is better (less manipulative) than 96.20% of the article peer group.

On a windy January day in Moniteau County, Corporal Matt Wheaton stood on a dirt road at the edge of an empty field. 
Wheaton and his partner were on the hunt. 
“Big, open fields like this are places that people regularly drive around looking for deer to shoot off the road,” Wheaton said. 
Wheaton is a conservation agent. 
The Missouri Department of Conservation, or MDC, assigns an agent to investigate crimes against people, wildlife and department property in each county. 
Wheaton is responsible for Morgan County, not Moniteau, but agents have statewide jurisdiction, allowing them to support investigations across the state. 
“An agent got a call that somebody was shining a light in this field last night and they heard a couple gunshots,” Wheaton said. 
Spotlighting and shooting from a roadway are considered poaching by the MDC, but Wheaton’s investigation was based on scant information. 
That’s where Wheaton’s partner comes in. 
“This is Chuck,”Wheaton said. 
“He is a five-year-old German shorthaired pointer.” 
Chuck is a conservation detection dog. 
He can detect a variety of types of evidence that might be important to an investigation. 
“The dog could find us a bloodspot that’s out in the field, where a deer may have been killed,” Wheaton said. 
“The dog could find the shell casings. 
Sometimes the shell casings come out of the vehicle, sometimes they don’t.” 
“The dog is very good at finding puzzle pieces for us to then connect back to a person,” Wheaton said. 
He stressed that Chuck and dogs like him don’t replace traditional detective work. 
The investigation still hinges on the work of a conservation agent. 
“The dog finds the piece, not, the dog makes the case.” 
Sniffing out evidence 
Wheaton and Chuck are one of just nine conservation K-9 units working for the MDC. 
Based in Morgan County, Wheaton mostly assists in central Missouri. 
Chuck worked in a search pattern up and down a stretch of the dirt road. 
Eventually, he found a puzzle piece. 
The sound of his panting and sniffing grew louder as he trapped a shell casing between his paws.Wheaton narrated for me as his partner scooted back and forth, almost losing the shell casing in his excitement before recovering it. 
Wheaton confirmed Chuck had found a piece of evidence, then rewarded him with praise and some time with a toy fashioned from a rubber hose. 
On another day a conservation agent might have continued the investigation by interviewing witnesses and gathering more evidence. 
If Wheaton is assisting an investigation outside his own county, the investigation will stay in the hands of the local agent. 
But on the day I rode along with Wheaton and Chuck, they headed to the next training exercise instead. 
Rather than discovering evidence of actual poaching, Chuck had sniffed out the shell casings fired by an MDC employee hours before the exercise. 
“We did a mockup of what 90% of our calls are going to be,”Wheaton said. 
MDC’s K-9 units were deployed 183 times in 2025, according to numbers provided by the department. 
Additionally, the department’s nine K-9 units attended 328 outreach programs. 
Wheaton started working with dogs recreationally, hunting with them across the state and even traveling for competitions. 
He wasn’t accepted into MDC’s K-9 program on his first try. 
But when he was accepted, he was paired with Chuck for training in Indiana. 
Chuck was born in Hungary but responds to commands in English and German. 
From training to working 
Chuck’s skillset is rare. 
“About 1 in a thousand dogs is actually a good fit for this line of work,” said Kayla Pratt, co-founder of K-9 Conservationists. 
Pratt runs a nonprofit that works with conservation detection dogs. 
Rather than being used for enforcement, the dogs assist in scientific research. 
Dogs can assist in a variety of ways, but they essentially do the same kind of work that Chuck does, searching for evidence more effectively than humans could. 
Pratt said there are no barriers to entry in her field. 
That’s helped drive growth in the field, but it creates challenges. 
Without certifications or licenses, establishing credibility is hard. 
If training exercises are designed poorly, there’s a risk dogs can become accustomed to cues they won’t receive in the world. 
“I think the gap between the science community and the training community is lessening,” said Clara Wilson, a researcher at the Penn Vet Working Dog Center. 
The Penn Vet Working Dog Center runs trainings and conferences that bring together researchers, veterinarians and dog handlers to help share expertise within the field. 
For Chuck’s next training exercise, Wheaton had an MDC employee hide targets before he and Chuck arrived on scene. 
Chuck successfully located jars of wild turkey parts and sturgeon eggs. 
He also found a decommissioned firearm hidden in a tree, and even a can Wheaton had me throw out the window of a truck as we drove down a road. 
“We do law enforcement off the beaten path,”Wheaton said. 
And that isn’t restricted to conservation crimes. 
Agents deal with everything from drug-related crimes to searches for people with active warrants. 
There’s room for lighter aspects of law enforcement, too. 
Throughout the day, Wheaton stopped to chat with boaters and hunters. 
He answered questions about Missouri law and asked his own about conditions on public land. 
Sometimes, Wheaton hands out cards with pictures of Chuck’s face on it, like to a group of kids who were getting ready for a day on the water. 
Just like Wheaton, Chuck wears a badge to work. 
At the end of the day, they go home together. 
“He’s one of those dogs that, very much when we’re working, it's all business,”Wheaton said. 
“And when we’re not, you’ll understand what I’m talking about.” 
Article reasoning-pattern comparisonThis article: 1.1%Will Firra: 0.3%Belleville News-Democrat: 2.4%Confirmation Bias1.1%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 1.2%Anchoring Bias0.0%This article: 2.0%Will Firra: 0.9%Belleville News-Democrat: 2.7%Availability Heuristic2.0%This article: 1.6%Will Firra: 0.4%Belleville News-Democrat: 0.8%Representativeness Heuristic1.6%This article: 0.6%Will Firra: 0.2%Belleville News-Democrat: 0.5%Hindsight Bias0.6%This article: 1.7%Will Firra: 2.1%Belleville News-Democrat: 1.4%Overconfidence Bias1.7%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 7.3%Framing Effect0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 1.1%Loss Aversion0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 1.0%Status Quo Bias0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.3%Sunk Cost Effect0.0%This article: 5.0%Will Firra: 1.9%Belleville News-Democrat: 4.8%Optimism Bias5.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 1.9%Pessimism Bias0.0%This article: 2.1%Will Firra: 0.5%Belleville News-Democrat: 7.1%Negativity Bias2.1%This article: 2.6%Will Firra: 0.7%Belleville News-Democrat: 3.1%Self-Serving Bias2.6%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.9%Fundamental Attribution Error0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.2%Actor-Observer Bias0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 2.2%In-Group Bias0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.5%Out-Group Homogeneity Bias0.0%This article: 15.2%Will Firra: 3.8%Belleville News-Democrat: 2.4%Halo Effect15.2%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.1%Horn Effect0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 1.0%Recency Bias0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.3%Primacy Effect0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.1%Blind-Spot Bias0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.5%Ad Hominem0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.4%Straw Man0.0%This article: 7.7%Will Firra: 1.9%Belleville News-Democrat: 3.6%Appeal to Authority7.7%This article: 3.2%Will Firra: 0.8%Belleville News-Democrat: 1.3%False Dilemma3.2%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.9%Slippery Slope0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.2%Circular Reasoning0.0%This article: 1.6%Will Firra: 3.0%Belleville News-Democrat: 3.7%Hasty Generalization1.6%This article: 0.6%Will Firra: 0.2%Belleville News-Democrat: 0.3%Red Herring0.6%This article: 1.1%Will Firra: 0.3%Belleville News-Democrat: 0.7%Bandwagon1.1%This article: 4.0%Will Firra: 1.0%Belleville News-Democrat: 6.6%Appeal to Emotion4.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.8%Begging the Question0.0%This article: 1.5%Will Firra: 0.4%Belleville News-Democrat: 2.1%Post Hoc (False Cause)1.5%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.1%Tu Quoque0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.4%Burden of Proof0.0%This article: 1.4%Will Firra: 0.3%Belleville News-Democrat: 0.2%Appeal to Nature1.4%This article: 1.5%Will Firra: 0.4%Belleville News-Democrat: 0.2%Composition/Division1.5%This article: 9.8%Will Firra: 2.5%Belleville News-Democrat: 2.6%Anecdotal9.8%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.1%No True Scotsman0.0%This article: 0.9%Will Firra: 0.2%Belleville News-Democrat: 1.3%Ambiguity (Equivocation)0.9%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.1%Middle Ground0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.1%Personal Incredulity0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.2%Special Pleading0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.2%Genetic Fallacy0.0%This article: 2.5%Will Firra: 0.6%Belleville News-Democrat: 0.8%Unattributed Quote2.5%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.9%Quote-first Misdirection0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 1.8%Biased Writer Voice0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 1.1%Indoctrination0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.8%Politically Left Leaning Bias0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.2%Politically Right Leaning Bias0.0%This article: 0.0%Will Firra: 0.0%Belleville News-Democrat: 0.7%Attempt to Sell a Product or S…0.0%

958 words analyzed.

Speakers

5speakers37%attributed speech600writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 9 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageMatt Wheaton • 22 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageMissouri Department of Conservation • 22 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageMatt Wheaton • 24 words • 100.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageMatt Wheaton • 4 words • 0.0% coverageMatt Wheaton • 7 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageMatt Wheaton • 21 words • 0.0% coverageMatt Wheaton • 7 words • 0.0% coverageMatt Wheaton • 12 words • 0.0% coverageMatt Wheaton • 20 words • 0.0% coverageMatt Wheaton • 13 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageMatt Wheaton • 11 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 39 words • 0.0% coverageMatt Wheaton • 25 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageMatt Wheaton • 15 words • 0.0% coverageMissouri Department of Conservation • 16 words • 0.0% coverageMissouri Department of Conservation • 10 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageKayla Pratt • 23 words • 0.0% coverageKayla Pratt • 10 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageKayla Pratt • 11 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageClara Wilson • 26 words • 0.0% coveragePenn Vet Working Dog Center • 25 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageMatt Wheaton • 9 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageMatt Wheaton • 15 words • 0.0% coverageMatt Wheaton • 10 words • 0.0% coverage
Selected voice

Kayla Pratt

77%flagged-word coverage
44 attributed words12% of attributed speech46% writer coverage

No manipulation-pattern hits were found in this speaker's attributed words or the writer's voice.

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.