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Friday, July 03, 2026

Study pinpoints how to minimize chances of dangerous wildlife encounters this summer

National park visitors’ activities greatly influence likelihood of encounters with wildlife that could result in conflict between people and animals, suggests study analyzing high-risk activities

Frontiers

The more people expand into previously natural areas, the more wildlife and humans step on each other’s toes, leading to more interactions that may result in conflict. This includes national parks where people flock to recuperate and enjoy the outdoors.

Writing in Frontiers in Conservation Science, researchers in the UK have examined which animals are most likely to be involved in aggressive encounters – defined as potentially dangerous situations between humans and animals – during which activities they’re most likely to happen, and which activity-animal pairs hold particular risk.

“We found low‑impact activities were associated with the highest frequency of aggressive encounters, regardless of species,” said first author Holly Landles, a researcher at the University of York.

“Now we can point to precise high‑risk pairings, such as elk visiting townsite areas or mule deer encountered during dog walking,” added senior author Dr Shashank Balakrishna, a biologist at the University of York. “This allows park managers to focus resources, signage, and education where they are most needed.”

Interactions vary by activity

The researchers drew on a database of almost 3,500 reported incidents between 2010 and 2023 recorded by Parks Canada, selecting incidents involving humans and elk, black bears, grizzlies, coyotes, and mule deer. These species were chosen because they were involved in aggressive encounters most often in the dataset the team worked with. Seven types of activities park visitors were engaging in were included in the risk analysis: low-impact activities (hiking, wildlife observation), extreme sports (kayaking, climbing), animal-involved activities (dog-walking, horseback-riding), camping, transport-related activities (road cycling), townsite activities (golfing), and park operations.

Results showed that species mattered, but so did the type of activity and animal-activity combinations. Elk were involved in around 62% of all aggressive encounters, followed by grizzly bears (14%), black bears (13%), mule deer (7%), and coyotes (3%).

“Each species occupies a different ecological role, so they perceive human threat differently,” Balakrishna pointed out. “Elk sometimes avoid humans, but at other times use human presence as refuge from predators. This unpredictability may explain why they top the list for aggressive encounters.”

On the activity side, low-impact activities were most associated with aggressive encounters, making up around 25% of incidents, followed by townsite activities at 22%, which may be particularly risky due to the unfamiliar stress wildlife faces in more urban environments. Adventure sports accounted for just over 4%.

When combining activity type with species, the researchers found certain animals were more likely to be encountered during certain activities. Elk, for example, were involved in over 73% of run-ins happening at townsites and in 57% of incidents recorded during adventure sports.

Grizzly and black bears were most often encountered during low-impact activities, making up 45% and 43% of these encounters, respectively. This may be because they are particularly prone to reacting aggressively to surprise encounters, which are more likely to happen during quiet activities in forested areas.

Mule deer and coyotes were most often involved in aggressive encounters in townsite settings. “Mule deer also showed more aggression during activities involving pets, likely because dogs resemble their natural predators,” said Balakrishna.

Whistles, talk, and preparation

This, however, doesn’t mean that the activities examined here should be avoided altogether during park visits. “We recommend simple precautions,” said Landles. “Announcing yourself is a good idea, especially for grizzly bears. Taking whistles, talking, or hiking in larger groups can help, too. Keeping leashes short when large herds are present is simple but effective.”

In addition, park visitors can check park information, including bear or herd sightings and trail closures, on the day of their visit.

The researchers said their data only included incidents reported to park staff, so the number of aggressive encounters – particularly those where people weren’t harmed – may be underestimated. There also was some data that wasn’t available, such as animal sex, how many people were involved, or how long they spent on the activity, so the findings don’t show cause-effect relationships.

Yet they are useful for identifying what future studies need to examine, said the authors. In addition, the results provide pointers which park rangers can follow in national parks worldwide. In many parks, recommendations like dynamic trail ratings and improved signage have already been implemented. Now it is on park visitors to act responsibly, the team said. For example, abiding by trail signage – also in remote areas – can help decrease the number of interactions that might result in situations in which humans, animals, or both suffer.

“Ultimately, both people and wildlife lose during aggressive encounters,” concluded Landles. “Our findings helps us understand real patterns behind these encounters so we can reduce their frequency and help people and wildlife coexist more safely.”

Journal

Frontiers in Conservation Science

DOI

10.3389/fcosc.2026.1764328

Method of Research

Data/statistical analysis

Article Title

Aggressive encounters with large mammals vary by recreational activity type in Canadian national parks

Article Publication Date

2-Jul-2026

Sunday, June 07, 2026

Republicans Want Cyanide Bombs on Public Lands

Robert Hunziker

June 5, 2026

Image by Brittani Burns.

Trump’s bombing of Iran, Venezuela, Iraq, Nigeria, Yemen, Caribbean boats, and Somalia (Council on Foreign Relations info) has torn to shreds his “political brand” opposing foreign military adventures, promising “no wars” in campaign speech after campaign speech after campaign speech.

Now, his administration is taking “bombing” one step further into the wilderness, over hill, over dale, through bush, through briar (Shakespeare’s A Midsummer Night’s Dream) right here on U.S. soil. Cyanide bombs are back in style after being banned by the Biden administration.

“The Bureau of Land Management last month quietly lifted its total ban on the use of so-called cyanide bombs on public land and said deployment of the spring-loaded devices used to kill coyotes and other predators will now be considered on a “case-by-case” basis.” (BLM, USDA Agree to Renew Use of ‘Cyanide Bombs’, E&E News by Politico, May 8, 2026)

Cyanide bombs or “M-44s have been and are used on certain state and private lands in Texas, New Mexico, Montana, Wyoming, South Dakota, North Dakota, Nebraska, Oklahoma, Nevada, and West Virginia. In Colorado, M-44s are used only on private land. Oregon, Washington, and California have banned M-44s everywhere, including on BLM lands within their borders.” (Trump Administration Resurrects Archaic Poison Bombs No One Wants, Animal Wellness Action, May 22, 2026)

Cyanide bombs are designed to kill coyotes, red foxes, gray foxes, and feral dogs that prey on sheep, poultry and newborn cattle.

“But coyotes, foxes and feral dogs are not all that M-44s kill. According to Wildlife Services’ own records, they also kill at least 150 nontarget species including cattle, sheep, goats, guard dogs, bird dogs, pet dogs, grizzly bears, black bears, endangered Mexican wolves, northwestern gray wolves, bald eagles, golden eagles, falcons, other hawks, vultures, including endangered California condors, owls, ravens, crows, raccoons, opossums, skunks, sundry species of rabbits and kangaroo rats, badgers, threatened wolverines, threatened lynx, fishers and, in at least one case, humans — Dennis Slaugh of Vernal, Utah,” Ibid.

And what of campers, hikers, and dune buggies that fill public lands every day? Are they exposed and what safeguards prevent children from playing with every strange device they come across? According to Predator Defense.org, since 1990 the organization has worked with victims of M-44s, which, when set in the wild, are loaded with scented bait to attract animals.

According to Friends of Animals, M-44s are one of the most vicious devices known to randomly kill anything that breathes, for example, a tragic 2017 incident involved a 14-year-old boy named Canyon Mansfield walking his dog, Kasey, just 350 feet away from his family’s home, near Pocatello, Idaho. Canyon recognized what he thought was a sprinkler’s head sticking out of the ground but as the M-44 triggered, it sent a plume of cyanide powder five feet into the air. He was hospitalized for treatment, fortunately, brisk winds swept the poison away from him or he would have died. Kasey was not so lucky.

According to Friends of Animals countless numbers of dogs have been killed by M-44s. This heartless device defines outrageous inhumane activity to a tee, placing scented bait in the wild to kill anything that breathes that happens to pass by and boom! Dead on the spot! Do people honestly think this is a proper humane decent thing to do?

Meanwhile, the EPA claims at least 50% of all animals killed are non-targeted animals.

Unfortunately, it’s impossible to identify individual names of people who initiated or those responsible for handling M-44s in the wild as everything is buried deep within the bureaucratic mumble jumble of governmental agencies, for example, the USDA Wildlife Services within the Animal and Plant Health Inspection Service that reports to the Department of Interior appears to be the primary source often working in concert with private ranchers to “manage predators.” But state agriculture departments are in charge of all operations in some of the states.

Maybe Secretary of Interior Doug Burgum has the answers.

Regardless of who is in charge, the concept of placing baited killing scented devices in the wild to kill anything that breathes, that happens to wander through the area, is so far out of touch with the sanctity of life that it’s difficult to image how it’s possible to find people willing to administer such an idiotic scheme. Who are these people willing to take innocent lives as if life itself is meaningless?

In May 2026, Republicans included language in the Fiscal Year 2027 USDA appropriations bill instructing federal agencies to “fully integrate” the poison devices back into routine use.

There are many alternatives to M-44. According to the Center for Biological Diversity: “Numerous effective, alternative tools to address livestock conflicts exist, eliminating the need for M-44s altogether. For example, guard animals can be deployed, herders and range riders can be employed, and livestock operators can change animal husbandry practices to lessen the risk of predation. Deterrents, such as sound- and light-emitting frightening devices, can also be used to scare away potential predators.”

But of course, when comparing the bombing of countries like Iran, where civilians are randomly killed when in the line of fire, or soft, easy targets like small, totally unidentified boats in the seas, it makes it much easier to accept and boast of cyanide bombing of defenseless animals in the wild. Indeed, these are signals of a weak personal constitution, spinelessness and lack of imagination, as easy pickings bring shame, not praise.

According to Coyote Project.org, “The toll of M-44s on wildlife has been staggering. Between 2014 and 2022, these devices intentionally killed over 88,000 animals—and these are only the known deaths.” There are anecdotal stories claiming millions killed, whether intentional or unintentional, M-44 does not discriminate; if it is breathing, it’s dead.

Robert Hunziker lives in Los Angeles and can be reached at rlhunziker@gmail.com.

Sunday, May 10, 2026

AI models can hack computers and self-replicate onto new machines, new research finds

Copyright Canva

By Roselyne Min
Published on 09/05/2026 - EURONEWS

Scientists who observed AI chatbots copying themselves and launching hacking attacks in an experiment say ‘autonomous AI self-replication is no longer hypothetical’.
Artificial intelligence models can break into computers, copy themselves, and use the new copies to keep attacking other machines, according to new research said to be the first known demonstration of autonomous AI self-replication.
The discovery could make cyberattacks far harder to stop, researchers say, because shutting down one infected computer would not be enough if the AI had already made working copies elsewhere.
The study conducted by Palisade Research, in the United States, tested models including OpenAI’s GPT 5, 5.1, and 5.4, Anthropic’s Claude Opus 4, 4.5, and 4.6 and Alibaba’s Qwen against computers which had deliberately planted security flaws that allowed outsiders to gain access.
Researchers connected underlying AI models from OpenAI, Anthropic and Alibaba to custom software, known as an agent harness, that allowed them to carry out commands and interact with other computers, rather than using public chatbot apps.
The experiment worked in two ways. Alibaba’s models were asked to copy themselves. In the GPT and Claude tests, the models were asked to break in and install Qwen, because GPT and Claude’s own model files are not publicly available for transfer.
In each test, the AI had to find a security flaw in a computer system, use it to get inside, steal login details, transfer the files it needed to run, and start a working copy of itself on the new machine.
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Once a copy was running, it could be used to attack another computer without human help.
At each stage, the AI found a different security flaw, took control of the next computer, moved over its software and instructions, and launched a new copy that continued the chain.
Alibaba’s Qwen3.6-27B, a smaller version of its Qwen AI model that can run on a single high-end graphics processor, spread across four computers in Canada, the United States, Finland, and India, after being given just one prompt, completed the chain without human help in 2 hours and 41 minutes.
The research team stopped the experiment after three stages, but said the final copy was still working and could have attacked further systems.

What were the results?

The models succeeded at varying rates. Alibaba’s Qwen3.5-122B-A10B, a larger and more powerful model of Qwen, created working copies of itself in 19% of attempts.
A smaller Qwen model, Qwen3.6-27B, reached 33% while running on a single high-end graphics processor.
Commercial AI systems performed even better when used to install Qwen model files.
Anthropic’s Claude Opus 4.6 succeeded in 81% of tests, while OpenAI’s GPT-5.4 reached 33%.
Researchers say the results raise new concerns about control over powerful AI systems.
The report comes as Anthropic last month unveiled Claude Mythos Preview, which it said is "too dangerous” to release to the public citing its capacity to facilitate cyberattacks of unprecedented scale and sophistication.
While a lot of computer viruses can already copy themselves onto new computers, the research team says this is likely the first time an AI model has been shown capable of exploiting vulnerabilities to copy itself onto a new server.
OpenAI, Anthropic, and METR, a non-profit group that studies risks from advanced AI systems, have also previously flagged self-replication as a warning sign because systems that can spread may become harder to control.
However, researchers stressed that the experiment was carried out in a controlled setting using intentionally vulnerable systems. Real-world networks often have stronger protections, such as security monitoring and tools designed to block attacks.
Even so, they said the results show that autonomous AI self-replication is no longer hypothetical.

AI cuts wildlife tracking time from months to days

Washington State University
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SpeciesNet's AI prediction can be seen on an image of a lynx.
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Credit: Mammal Spatial Ecology and Conservation Lab

PULLMAN, Wash. — Artificial intelligence can dramatically speed up the painstaking work of tracking wildlife with remote cameras, cutting analysis time from months or even a year to just days while producing nearly the same scientific conclusions as humans.
That’s according to a new study led by researchers at Washington State University and Google, published in the Journal of Applied Ecology. The team tested whether a fully automated AI system could replace humans in processing hundreds of thousands to millions of camera trap images collected in Washington, Montana’s Glacier National Park, and Guatemala’s Maya Biosphere Reserve.
They found that, for most species, models built from AI-identified images closely matched those produced by human experts. Across key measures such as where animals occur and what environmental factors influence them, the results aligned in roughly 85–90% of cases, with limited divergence for rare or difficult-to-identify species.
The implications could be significant for conservation. Faster processing means researchers and wildlife managers can move more quickly from collecting data to making decisions, potentially enabling near real-time monitoring of species such as jaguars, wolves, and grizzly bears.
“We’re not trying to replace people,” said WSU wildlife ecologist Daniel Thornton, lead author of the study. “The goal is to help researchers get to answers faster so they can make better decisions about managing and conserving wildlife.”
Traditionally, that process has been slow and labor-intensive. Camera traps, which are motion-activated cameras placed in forests and other habitats, can generate enormous datasets. A single project may produce hundreds of thousands or even millions of images that must be reviewed to determine which species appear in each frame.
Even with a team of undergraduate assistants and a graduate student verifying identifications, Thornton said the process typically takes six to seven months, and sometimes up to a year, before analysis can begin.
Early AI tools offered some relief by filtering out blank images, often 60–70% of the total, but still required humans to review tens of thousands of photos containing animals. The new study tested whether that final human step could be eliminated.
Using a general AI model called SpeciesNet, developed by Google, the researchers ran images through a fully automated pipeline with no human review and compared the results to traditional, expert-labeled datasets.
“The key question wasn’t whether the AI got every image right,” said Dan Morris, a senior staff research scientist at Google who helped create SpeciesNet and is a co-author on the study. “It was whether the ecological conclusions you care about would end up being basically the same.”
For most species, they were. Even when the AI made mistakes, such as misidentifying animals or missing detections, the overall models remained robust because occupancy models rely on repeated observations over time.
In practical terms, the time savings are dramatic. Fully automated processing can now be completed in just a few days, reducing a months-long bottleneck to roughly a week.
That efficiency could be transformative, particularly for smaller or underfunded conservation groups. It may also allow researchers to expand monitoring efforts without being limited by data processing capacity.
The project also contributed to the broader AI-for-conservation community by making part of its dataset publicly available, helping support tools like SpeciesNet that rely on shared data to improve.
Morris emphasized that the study takes a practical approach. Rather than developing new AI algorithms, the team focused on what current tools can already do.
“We weren’t trying to invent a new model,” he said. “We were asking whether, given where the technology is today, people can rely on it for the kinds of analyses they already do.”
The answer, at least for many common species and standard ecological models, appears to be yes.
There are still limitations. Human review is needed for many other applications of camera trapping data, and this paper only dealt with a small subset of species that may be caught on camera. For example, very rare and easily confused species are still problematic for AI detection. But the findings suggest that in some cases, image processing no longer needs to be a major constraint on large-scale camera-trapping studies.
“The big takeaway is that this doesn’t have to be a bottleneck anymore,” Thornton said. “If we can process data faster, we can respond faster, and that’s really what matters for conservation.”
Additional co-authors on the study include Travis King and Lucy Perera-Romero of Washington State University; Alissa Anderson of Washington State University and Montana Fish, Wildlife and Parks; Rony Garcia-Anleu of the Wildlife Conservation Society’s Guatemala Program; Scott Fitkin of the Washington Department of Fish and Wildlife; and Carly Vynne of RESOLVE, who contributed to data collection, analysis, and manuscript development across the project’s study sites in Washington, Montana, and Guatemala.

A camera trap photo of a grizzly bear.

A jaguar visits a water hole in this camera trap image.

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