BirdVis

BirdVis: Visualizing and Understanding Bird Population

Birds are unrivaled windows into biotic processes at all levels and are proven indicators of ecological well-being. Understanding the determinants of species distributions and its dynamics is an important aspect of ecology and is critical for conservation and management. Through crowdsourcing, since 2002, the eBird project has been collecting bird observation records. These observations, together with local-scale environmental covariates such as climate, habitat, and vegetation phenology have been a valuable resource for a global community of educators, land managers, ornithologists, and conservation biologists. By associating environmental inputs with observed patterns of bird occurrence, predictive models have been developed that provide a statistical framework to harness available data for predicting species’ distributions and making inferences about species-habitat associations. Understanding these models, however, is challenging because they require scientists to quantify and compare multi-scale spatial-temporal patterns. To do so, a large series of coordinated or sequential plots must be generated, individually programmed, and manually composed for analysis. This hampers the exploration and is a barrier to making the cross-species comparisons that are essential for cordinating conservation and extracting important ecological information.

We have developed BirdVis, an interactive visualization system that supports the analysis of spatio-temporal bird distribution models. BirdVis leverages visualization techniques and uses them in a novel way to better assist users in the exploration of interdependencies among model parameters. Furthermore, the system allows for comparative visualization through coordinated views, providing an intuitive interface to identify relevant correlations and patterns. We justify our design decisions and present case studies that show how BirdVis has helped the scientists obtain new evidence for existing hypotheses, as well as formulate new hypotheses in their domain.

Project Members

Nivan Ferreira, SCI Institute, University of Utah Lauro Lins, SCI Institute, University of Utah Daniel Fink, Cornell Lab of Ornithology Juliana Freire, SCI Institute, University of Utah Claudio Silva, SCI Institute, University of Utah Steve Kelling, Cornell Lab of Ornithology Chris Wood, Cornell Lab of Ornithology