Incorporating circuit theory into a dynamic model for crowd-sourced observations of migratory birds

TL;DR

This paper develops a novel hidden Markov model incorporating circuit theory to analyze crowd-sourced bird observations, revealing insights into migratory patterns despite data limitations.

Contribution

It introduces a new transition structure for hidden Markov models based on circuit theory, applied to large-scale crowd-sourced bird data.

Findings

Model effectively captures migratory patterns of Baltimore orioles and yellow-rumped warblers.

Circuit theory-based transition structure improves modeling of spatial movement.

Provides insights into species distribution dynamics over time.

Abstract

While the overarching pattern of biannual avian migration is well understood, there are significant questions pertaining to this phenomenon that invite further study. Necessary to any analysis of these questions is an understanding of how a given species' spatial distribution evolves in time. While studies of animal movement are often conducted using telemetry data, the collection of such data can be time- and resource-intensive, frequently resulting in small sample sizes. Ecological surveys of animal populations are also indicative of species distribution trends, but may be constrained to a limited spatial domain. Within this article we utilize crowd-sourced observations from the eBird database to model the abundance of migratory bird species in space and time. While crowd-sourced observations are individually less reliable than those produced by experts, the sheer size and spatial coverage of the eBird database make it attractive for use in this setting. We introduce a hidden Markov model for observed bird counts utilizing a novel transition structure developed using principles from circuit theory. After illustrating model properties we fit it to observations of Baltimore orioles and yellow-rumped warblers within the eastern United States and discuss insight it provides into the migratory patterns for these species.