A lot or a little? Wolves discriminate quantities better than dogs

Being able to mentally consider quantities makes sense for any social species. This skill is important during the search for food, for example, or to determine whether an opponent group outnumbers one's own. Scientists from the Messerli Research Institute at the Vetmeduni Vienna studied how well dogs can discriminate between different quantities and discovered that wolves perform better than dogs at such tasks. Possibly dogs lost this skill, or a predisposition for it, during domestication.

The results were published in the journal Frontiers in Psychology.

People and animals have been shown to discriminate between quantities. Lions, chimpanzees and hyenas, for example, will only approach a group of attackers if their own group outnumbers that of the intruders. These animals use numerical information to make decisions about their social life.

Testing numerical competence

In 2012 (To the article "Quantity discrimination in wolves (Canis lupus)) Friederike Range and Zsofia Virányi from the Messerli Research Institute at the University of Veterinary Medicine Vienna showed that wolves are capable of discriminating between different food quantities. In their present study, they asked whether dogs also possess this skill or if this form of numerical competence was lost through domestication.

For the study, Range and her colleagues from the Department of Comparative Cognitive Research tested 13 crossbreed dogs raised at the Wolf Science Center in Ernstbrunn. The animals are living there together in different packs. The researchers tested the dogs for their quantity discrimination skills by presenting pieces of cheese. Those pieces were sequentially placed into two opaque tubes -- one on the left and another on the right side. Eventually, the dogs had to decide which tube contained more cheese pieces than the other. By pressing the correct buzzer, the dogs were rewarded with cheese from the respective tube. Furthermore, the dogs did not see the person placing the cheese into the tubes, which excludes the human influence as a factor.

"We deliberately performed the test in such a way that the dogs never saw the full quantity of food at once. We showed them the pieces sequentially. This allows us to exclude the possibility that the dogs were basing their decisions on simple factors such as overall volume. The dogs had to mentally represent the number of pieces in a tube," explains first author Range.

Dogs performed worse than wolves

Range and her colleagues compared the results of the wolf test with those from the dog test. The comparison showed that dogs were unable to discriminate between difficult comparisons such as two pieces of food versus three or three pieces versus four. The wolves, in comparison, fared much better. "Dogs are better able to discriminate the quantities of food when they can see them in their entirety," says Range. "But this requires no mental representation."

Numerical competence lost with domestication

Range and her team are now investigating why the dogs performed so poorly in these tests. Is it because they have difficulties processing numerical information or is it their lacking ability for mental representation? It is possible that one of these skills was lost over the course of domestication. Human beings could be to blame. "Compared to wolves, domestic dogs no longer have to search for food on their own. They have a secure place to sleep and even mating decisions are made by people. Dogs are thus excluded from natural selection," Range explains.

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Conservation targeting tigers pushes leopards to change

A leopard may not be able to change its spots, but new research from a World Heritage site in Nepal indicates that leopards do change their activity patterns in response to tigers and humans -- but in different ways.

The study is the first of its kind to look at how leopards respond to the presence of both tigers and humans simultaneously. Its findings suggest that leopards in and around Nepal's Chitwan National Park avoid tigers by seeking out different locations to live and hunt. Since tigers -- the socially dominant feline -- prefer areas less disturbed by people, leopards are displaced closer to humans. Though they may share some of the same spaces, leopards avoid people on foot and vehicles by shifting their activity to the night.

A scientific paper based on the study, led by Neil Carter, postdoctoral fellow at the National Socio-Environmental Synthesis Center (SESYNC), was published this week in the journal Global Ecology and Conservation. In addition to Carter, the co-authors are Micah Jasny of Duke University, Bhim Gurung of the Nepal Tiger Trust in Chitwan, and Jianguo "Jack" Liu of Michigan State University.

"This study shows the complexity of coupled human and natural systems," said Liu, director of the Michigan State University Center for Systems Integration and Sustainability. "It also demonstrates the challenge of conserving multiple endangered species simultaneously."

Most areas where leopards and tigers co-exist are human-dominated. Accounting for the multi-layered interactions between leopards, tigers, and people is therefore key to understanding the ripple effects of human activities such as conservation actions, the researchers say.

The study has important implications in light of the Global Tiger Recovery Program, which is committed to doubling the worldwide tiger population by 2022. As tiger populations -- and the territories they occupy -- grow, leopards are increasingly likely to be pushed into areas where people live. The jostling of wildlife occupancy may open the door to more conflicts between people and leopards that could include leopard attacks on both people and livestock, as well as retaliatory killings of leopards.

The researchers' findings underscore how successful conservation efforts need science that takes into account the complex feedbacks between humans and nature.

"We want to see increased tiger numbers -- that's a great outcome from a conservation perspective. But we also need to anticipate reverberations throughout other parts of the coupled human and natural systems in which tigers are moving into," said Carter, "such as the ways leopards respond to their new cohabitants, and in turn how humans respond to their new cohabitants."

While working on his doctoral degree at Michigan State, Carter spent two seasons setting motion-detecting camera traps for leopards, tigers, their prey, and the people who walk the roads and trails of Chitwan, both in and around the park. Chitwan, nestled in a valley along the lowlands of the Himalayas, is home to high numbers of leopards and tigers. People live on the park's borders, but rely on the forests for ecosystem services such as wood and grasses. They venture in on dirt roads and narrow footpaths to be 'snared' on Carter's digital memory cards. The roads also are used by military patrols to thwart would-be poachers.

Analyses of the thousands of camera trap images begin to tell the story of who is using which spaces and when they're using them. Sometimes, though, 'seeing' isn't enough.

"People who use camera traps and other kinds of related monitoring tools realize there's a possibility that the animal is there, but you just didn't detect it," said Carter. "For example, your area of interest may be too large to set up cameras everywhere. Or, it's harder to detect animals in certain forest types if there are a lot of leafy trees blocking the camera's field of view -- even if the animal is right there."

Because traditional field-based research can be logistically restrictive, time-intensive, and expensive, the researchers used cutting-edge computational models to fill in data gaps and statistically estimate the location and timing of leopard-tiger-human activity.

"The computational component of this research is essential since it allows us to make strong inferences about leopard behavior in Chitwan based on a small sample," said Jasny, who spent an internship at CSIS working on the leopard-tiger-human data with Carter.

Carter says that while there are many models that look quantitatively at the relationships amongst ecological components of an ecosystem, those models rarely consider humans. Integrating human activity adds a layer of real-world complexity that is more representative of the ecosystem as a whole -- providing insights that can help researchers better understand how people and wildlife mutually influence one another.

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