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Raid on Forest Officer’s House yields Rs 2 Crore in Cash, Gold and Animal Parts in Assam

After being suspended by the State government, Talukdar was produced in a special court

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Seized tiger skin Image Source: Wikimedia Commons
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  • Assam Police recovered animal parts, Rs 2 crore in cash and gold from two residences of a divisional forest officer (DFO) of Assam
  • He was initially caught red-handed by the anti-corruption officers while accepting a bribe of Rs 30,000 each from three truckers
  • After being suspended by the State government, Talukdar was produced in a special court on June 14, which has sent him to police remand

GUWAHATI: Assam Police have recovered animal parts including tiger skin, deer skin, ivory, Rs 2 crore in cash and about 1kg of gold jewellery from two residences of a Divisional Forest Officer (DFO) of Assam.

According to The Indian Express report, Mahat Chandra Talukdar, who has been posted as the divisional forest officer in northern Assam’s Dhemaji since 2014, was initially caught red-handed by the anti-corruption officers while accepting a bribe of Rs 30,000 each from three truckers, who transport forest produce, at his office on June 13.

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“We have arrested him after he was caught while accepting bribe. We raided his house on Monday evening. First, we raided his house in Dhemaji and then in Guwahati. We have seized the amount, his personal vehicles and other documents and bank passbooks. There were allegations against him that he demanded bribe from three suppliers. Investigating the matter, we have caught him red-handed,” said Assam Police PRO Rajib Saikia to Deccan Herald.

Pallet of seized raw ivory in US Image Source: Wikimedia Commons
Pallet of seized raw ivory in US Image Source: Wikimedia Commons

In the next 24 hours, he was taken to Guwahati and his two residences in Dhemaji and Guwahati were raided where the police found the cash, gold and parts of wild animals. Also, the police did not rule out the possibility of him being linked to poachers considering the 89 rhinos who were killed by poachers from 1989-1983 in Kaziranga National Park where he was serving at that time. A rhino horn could be priced for Rs 1 crore in the international black market.

Forest Minister Pramila Rani Brahma said the arrested DFO could not get away. “He has been placed under suspension. We will go hard on all those who are involved in corrupt practices,” she said.

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After being suspended by the State government, Talukdar was produced in a special court on 14 June which has sent him to police remand.

After this case of corruption which was linked to Wildlife, the Gauhati High Court had asked the state government to frame appropriate rules under the Wildlife (Protection) Act, 1972, authorizing police to file charge-sheets in cases of wildlife crime.

-prepared by Pashchiema Bhatia, an intern at NewsGram. Twitter: @pashchiema

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Researchers Explain How They Tracked Migrating Birds

Arrival times of migratory song birds is really important for their reproductive success

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This June 18, 2016, photo provided by the U.S. Geological Survey shows a Yellow Warbler in Nome, Alaska
This June 18, 2016, photo provided by the U.S. Geological Survey shows a Yellow Warbler in Nome, Alaska, VOA

Tracking wildlife migration has been historically difficult in the rugged terrain of Alaska. Researchers primarily rely on either surveys or GPS tracking to understand bird migration patterns. Both methods are expensive, either in terms of time or money. And the trackers are often too large or heavy.

One way to sidestep these common issues is to record audio from frequently used nesting grounds. Using birdsong allows researchers to unobtrusively study the animals, although there’s a downside. Each day produces a flood of audio recordings from multiple microphones placed around nesting grounds. It takes trained listeners endless hours to search the noisy soundscape for birdsong.

In a recently published paper in the journal Science Advances, U.S. researchers explain how they got around these tracking troubles. Columbia University ecologist Ruth Oliver and her fellow collaborators replaced the human ears with machine learning algorithms to listen to birdsong.

Costly proposition

Oliver told VOA News, “Arrival times of migratory song birds is really important for their reproductive success. And obviously sending people to the Arctic to do field work is very expensive and takes a lot of time” — hence, the scientists’ interest in creating an automated method for tracking bird species.

Oliver and her colleagues focused on migratory songbirds who fly to northern Alaska during their mating season. These birds tend to chirp more frequently as soon as they reach the breeding grounds to attract a mate. Spring is short in Alaska and the birds must breed and hatch their clutch before winter.

This July 16, 2016, photo provided by the U.S. Geological Survey shows an Arctic Warbler in Nome, Alaska
This July 16, 2016, photo provided by the U.S. Geological Survey shows an Arctic Warbler in Nome, Alaska, VOA

The team of researchers recorded the springtime soundscape of northern Alaska for five sequential years. They placed microphones at four sites in the foothills of the Brooks Range, which recorded 1,200 audio hours.

However, Oliver admitted the recordings weren’t always perfect. “There’s a lot of other noise in these recordings” Oliver said. “Even in May in northern Alaska there’s lots of wind, lots of rain, and all of that is confounding when you’re listening to birds.”

The scientists fed hours of audio into two types of machine learning algorithms — one that used human expertise to help train it and one that relied solely on the collected audio. Both algorithms were based on the same model that’s used by applications like Siri and Alexa.

Oliver told VOA that in creating the human-supervised algorithm, she “wrote a little program to randomly sample about 1 percent of the data set” and then listened to 4-second clips. She scored these clips as either containing or not containing songbird vocalizations and then fed this information into the program.

Both algorithms were fairly accurate at estimating when the avian commuters arrived in the foothills. The models showed the importance of snowmelt for the arrival of the traveling birds. The human-trained model was slightly better at recognizing the relationship between weather conditions and bird calls, although neither model specifically tracked individual species.

This technique has great potential according to Emily Jo Williams, vice president of migratory birds and habitat at the American Bird Conservancy, “This kind of technique that allows you to survey populations in those remote areas is really exciting and could allow us to even discover new places where protection and conservation efforts are needed,” she said.

This study looked at nesting grounds near the Alaskan Arctic Refuge, which is a summer home for birds from nearly every continent. For example, the Northern Wheatear travels approximately 21,000 kilometers (13,000 miles) from Africa to summer in the refuge.

 This July 7, 2016, photo provided by the U.S. Geological Survey shows a Bluethroat in Nome, Alaska
This July 7, 2016, photo provided by the U.S. Geological Survey shows a Bluethroat in Nome, Alaska, VOA

Climate change

Williams told VOA, “We know from some research that some birds’ ranges have actually changed, and they’ve moved in response to what we think is a warming climate.” She went on to explain that “the timing of that migration has evolved over eons, and in large part it’s relative to what food sources are available over a particular time, what weather patterns are or aren’t favorable. So you could end up with bird migration out of sync with insect hatches or the phenology of plants that birds have a relationship to.”

Also read: Study Shows That The First Tree-Dwelling Birds Went Extinct With Dinosaurs

Tools like the algorithm created in this study could be used to track how migratory patterns of many species may shift in response to climate change. Using machine learning is a new way to follow these shifting patterns in birds, insects and other animals. (VOA)