BirdVis: Visualizing and Understanding Bird Populations

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TitleBirdVis: Visualizing and Understanding Bird Populations
Publication TypeJournal Article
Year of Publication2011
AuthorsFerreira, N, Lins, L, Fink, D, Kelling, S, Wood, C, Freire, J, Silva, C
JournalIEEE Transactions on Visualization and Computer Graphics
Pagination2374 - 2383
Date PublishedJan-12-2011
KeywordsAnimal Models, Animals, Artificial Intelligence, Biological Population, Biological system modeling, biotic processes, bird observation records, bird population understanding, bird population visualization, Birds, BirdVis, climate, computer graphics, Computer Simulation, conservation biologists, crowdsourcing, Data Visualisation, Data visualization, Databases, Dynamics, eBird project, ecological well being, ecology, environmental science computing, Factual Ecosystem Flight, habitat, interactive visualization system, land managers, local scale environmental covariates, multiscale analysis, multiscale spatial temporal patterns, ornithologists, Ornithology, Predictive models, Software, Songbirds, spatial data, Spatial databases, spatio temporal bird distribution models, Species, species distribution models, species-habitat associations, Specificity User-Computer Interface, statistical analysis, statistical framework, Tag clouds, temporal data, vegetation phenology, zoology

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 their 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 multiscale spatialtemporal patterns. 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 coordinating conservation and extracting important ecological information. To address these limitations, as part of a collaboration among computer scientists, statisticians, biologists and ornithologists, 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 s- udies that show how BirdVis has helped scientists obtain new evidence for existing hypotheses, as well as formulate new hypotheses in their domain.

Short TitleIEEE Trans. Visual. Comput. Graphics
Refereed DesignationUnknown