Northeast Wildlife

Northeast Wildlife Monitoring Network

[Northeast Wildlife @NEWildNet]

Interested in learning more about the sounds that reindeer make? Check out this awesome @TEDx talk by Michael Palace from @UofNH https://t.co/9slHhhr9ao

[Northeast Wildlife @NEWildNet]

Large effort led by @mefishwildlife, @UofNH and @USGSCoopUnits

[Northeast Wildlife @NEWildNet]

New research from NEWMN network: https://t.co/UpKqM9nzHI. We evaluated factors influencing the ability of observers to identify demographic characteristics of moose. Large number of #undergraduate researchers from @UMassAmherst on this project.

[Roland Kays @RolandKays]

Lewis & Clark wrote of bison so thick "the whole face of the country was covered." Yellowstone's Lamar Valley still looks like that. Thousands of bison, + wolves & lions. We checked trail cameras to document the recovery & diversity
https://t.co/QgZYLhPUhD

[Roland Kays @RolandKays]

Post Doc Job open with us to work with biologists to build AI models to analyze camera trap data.  The specific focus will be to study animal coloration and camouflage, but there will be opportunities to work on other related projects.
https://t.co/38qBIn3wDo

[Northeast Wildlife @NEWildNet]

Fresh out in @Ecol_Evol, we developed SiMPL magnets (remote camera design) to detect all creatures great and small. 🙂 https://t.co/qaDjQgO0Ti. Excellent work @greentree_jai and others! Data from SiMPL magnets were critical for AI dev in DeepFaune NE https://t.co/MCRS9gl4NT

[Remington Moll @remington_moll]

So proud to be a part of this amazing work

Wolves, coyotes, bobcats, foxes, and fishers are all dispersing all kinds of fungi across the landscape

[USGSCoopUnits 🐾 🎣 @USGSCoopUnits]

The Idaho CRU is using trail cameras to evaluate relationships among black bear, bobcat, coyote, mountain lion, and gray wolf – and it’s not all about avoiding rivals! Some predators share space, likely due to shared food sources.

https://t.co/nfn7xdlZap

[Northeast Wildlife @NEWildNet]

This effort was led by @LarryNatureGuy who has been instrumental on the development of practical #AI models for wildlife conservation and management. Check out his other paper that benchmarks human vs AI classifications: https://t.co/u3KskHHHOS

[Northeast Wildlife @NEWildNet]

New research from @Ecol_Evol using data from NEWMN and beyond to develop an improved species classification #AI model of #wildlife captured on remote #cameras. Check it out here! https://t.co/MCRS9gl4NT

[Northeast Wildlife @NEWildNet]

This uses data from a wide variety of camera setups, including the SiMPL magnets (to be published shortly), snow stake design https://t.co/j5qNtzcPyk, and from other camera designs. The classification approach had very high accuracy for species in New England and beyond!

[Northeast Wildlife @NEWildNet]

Can you predict microhabitat selection of animals using drones? Yes, you can! Check out this new article from @WildlifeBiol that used traditional and 3D forest metrics (derived from drones) to predict snowshoe hare microhabitat selection. https://t.co/8p9gzUVtKM

[Northeast Wildlife @NEWildNet]

Interesting new paper in @WildlifeBiol on ways to use ARUs for estimating #grouse abundance. Cool! Here it is: https://t.co/FeMcabqyUq

[Spectrum News 1 Worcester @SpecNews1Worc]

MOOSE MONTH: As the leaves start to change and temperatures cool, @MassDFG is reminding drivers to keep an eye out for moose. Fall is moose breeding season, which means #moose activity will likely pick up across Central and Western Massachusetts.

https://t.co/l4vvaJXkJn

[Remington Moll @remington_moll]

📢New paper 📢

MS student Clara Dawson's 1st chapter is out!

Does increased habitat connectivity always increase wildlife-vehicle collision rates?

No!

We found nonlinear effects of connectivity on wildlife-vehicle collisions

Free access: https://t.co/ghGmXycByu

[Mike Hallworth @MTHallworth]

We used cone and beech nut observations to model the probability of a masting event #rstats - check it our predictions👇

[AIM Network @AIMcameratrap]

Moose populations in New York and New England face an uncertain future due to climate change, increasing deer populations, and parasite impacts. @AIMcameratrap and @NEWildNet collaborators are working hard to collect monitoring data across this landscape. @sunyesf @Roosevelt_ESF

[AIM Network @AIMcameratrap]

Some great highlights of our work over the past few years covered by Rose Schneider @timesunion
@sunyesf @Roosevelt_ESF @PSCNatSci @NEWildNet

[AIM Network @AIMcameratrap]

Great meeting with many of the @NEWildNet cooperators at the Silvio Conte NWR recently. Thank you to the @USFWSNortheast for hosting us! Just under 1,000 #cameratraps currently deployed across the Northeast! @Roosevelt_ESF @sunyesf @alexejpksiren

[Northeast Wildlife @NEWildNet]

“Examination of ML-performance is critical to assessing the efficacy of ML-assisted tagging”

[Northeast Wildlife @NEWildNet]

Check out new article from @LarryNatureGuy using camera data from NEWMN network, including sites and coauthors from @mefishwildlife and @VTFishWildlife, @gmfl_nfs! Also includes undergrad coauthors from @UVM_RSENR! https://t.co/u3KskHH9Zk

[Northeast Wildlife @NEWildNet]

“Correct ML predictions sped up image tagging while incorrect predictions slowed it”