Maitreyi Sur

• Boise State University

Abstract Sophisticated animal‐borne sensor systems are increasingly providing novel insight into how animals behave and move. Despite their widespread use in ecology, the diversity and expanding quality and quantity of data they produce have created a need for robust analytical methods for biological interpretation. Machine learning tools are often...

• A central theme for conservation is understanding how animals differentially use, and are affected by change in, the landscapes they inhabit. However, it has been challenging to develop conservation schemes for habitat-specific behaviors. • Here we use behavioral change point analysis to identify behavioral states of golden eagles (Aquila chrysae...

An animal's movement is expected to be governed by an interplay between goals determined by its internal state and energetic costs associated with navigating through the external environment. Understanding this ecological process is challenging when an animal moves in two dimensions and even more difficult for birds that move in a third dimension....

Wind energy development is rapidly expanding in North America, often accompanied by requirements to survey potential facility locations for existing wildlife. Within the USA, golden eagles (Aquila chrysaetos) are among the most high-profile species of birds that are at risk from wind turbines. To minimize golden eagle fatalities in areas proposed f...

Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and...

Confusion matrix of RF classification predictions. (PDF)

Definition of statistics calculated from confusion matrices generated by models for classification of accelerometer data. (PDF)

Amount of video collected from a trained golden eagle outfitted with an accelerometer. (PDF)

Box plot of Overall Dynamic Body Acceleration (ODBA). (PDF)

Supplemental methods. (PDF)

Descriptive statistics for the 28 variables calculated for the segments defined by the change point model. (PDF)

Confusion matrix of KNN classification predictions. (PDF)