This summer the Cornell Lab of Ornithology is specifically looking for data from more obscure and under-birded areas.  This data will help scientists understand better how birds are affected by forces like climate change and habitat loss.  The project runs until September. They are partnering with the New York Times to get the word out. The following is an explanation of the project.

If a bird is not in a forest and there is no one to see that it is not there, is it really not there?

That, in essence, is the conundrum that the Cornell Lab of Ornithology is confronting. For more than two decades, the lab has run eBird, a project that collects observations from amateur bird watchers. It is a successful project: Nearly 900,000 participants around the world have submitted some 18 million lists a year of what they have spotted during their bird-watching sessions. And the number of lists has been growing at a pace of some 20 percent a year.

That has proved to be a trove for scientists to study changes in populations and behavior of birds, revealing “complex relationships between people and birds in ways that we couldn’t have before,” said Tom Auer, who leads the geospatial data science team at the Cornell lab.

For example, the voluminous eBird data has established how the bright lights of big cities draw in migratory birds, especially young ones. And cities, with their canyons of concrete and asphalt, are generally poor habitats for birds. Cornell scientists are now studying whether the diversion leads to exhaustion and starvation, and whether fewer birds survive the migratory journey.

But, as the project relies on the efforts of volunteers, the data does not cover all places equally. “You can imagine obvious places where there aren’t data,” Mr. Auer said. “Mostly because people are drawn to places where they can see the most birds.”

Neglected areas include farmland and industrial tracts. The sparsity of data affects the ability to answer questions like whether a change in farming practices helps or hurts birds. “It helps if people can spread out and can cover wider habitats,” Mr. Auer said.

So in the next months, please consider submitting eBird checklists from the following places:

    • Non-recreational open spaces. Is that sidewalk tree a popular spot for house sparrows? Is that wetland behind a nearby Walmart teeming with life? Try there!
    • Areas away from roads. Most birding checklists occur close to roads. The farther you can get from them, the better.
    • Farms and fields. Rural agricultural areas are some of the least-birded habitats. Submit checklists from public roads adjacent to crop fields, livestock grazing lands and other cultivated areas.
    • Areas between eBird “Hotspots.” Use the “Explore” tab in the eBird app to find nearby “Hotspots” — shared locations where other birders have submitted observations. Go birding in areas between or far from those Hotspots.
    • Areas with few observations of a particular species. What’s a common bird species in your area? Look up past reports of that species on the eBird Species Map and zoom in on your city. Then, visit areas without any previous observations of that species, and file a checklist.

When you submit a checklist in eBird, be sure to add #NYT in the comments section. This will let us know that your observation was part of this project.

For scientists, knowing where birds are not is as important as knowing where they are. That can reveal declining populations, shifting habitats or changes in migration.

That is a tall ask, though — a social experiment in asking people to go out of the way to places where there are probably fewer birds to spot.

Mr. Auer also said that the lab would like to recruit not just experienced bird-watchers but also those who are just learning to identify various species. “Having that variety of skill levels actually improves the quality of research we do,” he said.

The newcomers will generally be less observant and make more mistakes, but a lot of errors are caught when Cornell reviews the data, and new watchers can provide a useful comparison to the more experienced observers.

“If we didn’t have beginning birders to compare to expert birders, we wouldn’t really know how good the expert birders were at detecting birds,” Mr. Auer said. “We’ve done tests with our models, where we remove beginning birders, and when we do that, the models perform more poorly than if we included the beginners.”