To know where the birds are going, researchers turn to citizen science and machine learning
Computer scientists at the University of Massachusetts Amherst, in collaboration with biologists at the Cornell Lab of Ornithology, recently announced in the journal Methods in Ecology and Evolution a new, predictive model ...
"Humans have been trying to figure out bird migration for a really long time," says Dan Sheldon, professor of information and computer sciences at UMass Amherst, the paper's senior author and a passionate amateur birder. "But," adds Miguel Fuentes, the paper's lead author and graduate student in computer science at UMass Amherst, "it's incredibly difficult to get precise, real-time information on which birds are where, let alone where, exactly, they are going."
There have been many efforts, both previous and ongoing, to tag and track individual birds, which have yielded invaluable insights. But it's difficult to physically tag birds in large enough numbers—not to mention the expense of such an undertaking—to form a complete enough picture to predict bird movements. "It's really hard to understand how an entire species moves across the continent with tracking approaches," says Sheldon, "because they tell you the routes that some birds caught in specific locations followed, but not how birds in completely different locations might move."
(a) Simulated spring migration trajectories. (b) Timing of spring migration departure and (c) arrival derived from simulated trajectories. (d) Migratory connectivity: square cells show breeding origins of individuals in the northwest (orange) and northeast (blue) parts of the breeding range. Filled density contours show the predicted wintering distributions of individuals breeding in those respective regions. Credit: Fuentes et al., 10.1111/2041-210X.14052
Observed movements of GPS-tracked woodcocks (single thick path) and simulated trajectories (thin paths) for 2500 simulated birds originating at the same starting location as observed birds. Credit: Fuentes et al., 10.1111/2041-210X.14052
Each heatmap shows the predicted movement distribution of a GPS-tracked individual originating within the circle at the base of the arrow. Darker colors indicate a higher predicted likelihood of movement to that area. The point of the arrow shows the observed ending location. Shown are examples of 3-week (h), 6-week (i), and 12-week (j) conditional forecasts. Credit: Fuentes et al., 10.1111/2041-210X.14052