TY - JOUR
T1 - Birdvis
T2 - Visualizing and understanding bird populations
AU - Ferreira, Nivan
AU - Lins, Lauro
AU - Fink, Daniel
AU - Kelling, Steve
AU - Wood, Chris
AU - Freire, Juliana
AU - Silva, Cláudio
N1 - Funding Information:
Another interesting direction we plan to explore is how to represent trends. The ability to visualize how habitat associations vary over time is key to discovering regions when and where these associations are stationary. Stationary regions are contiguous pieces of land where all habitat predictor importance throughout a year follow similar curves when compared to curves of neighboring areas. Ecologists studying climate change are now realizing how important it is to understand where and when these habitat-occurrence relationships are stationary. When modeling bird distributions, if a model developed for one area is transferred to another stationary area, the results become spurious extrapolations. With the tag cloud lenses in BirdVis, it is possible to get evidence of stationary regions: by moving the lenses in a near by region, if the tag relative sizes do not change much, we are able to identify a region as stationary. But while TCLs offer a means to continuously explore the spatial dimensions of the data, they only support exploration over discrete moments in the temporal dimension. We would like to investigate the use of techniques such as the ones proposed by Lee et al. [22] to represent trends in tag cloud lenses. Acknowledgments. We thank the eBird participants for providing the data used in our case studies and the staff at the Cornell Laboratory of Ornithology for managing these data. The bird pictures displayed in BirdVis were provided by Bill Schmoker (schmoker. org). This work has been supported by the Leon Levy Foundation; the Wolf Creek Foundation; the National Science Foundation through DataONE (0830944), IIS-0905385, IIS-0844546, IIS-0746500, CNS-0751152; the Institute for Computational Sustainability (0832782), research grant (1017793); TeraGrid computing resources provided under grant numbers TG-DEB100009 and DEB110008.
PY - 2011
Y1 - 2011
N2 - 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 studies that show how BirdVis has helped scientists obtain new evidence for existing hypotheses, as well as formulate new hypotheses in their domain.
AB - 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 studies that show how BirdVis has helped scientists obtain new evidence for existing hypotheses, as well as formulate new hypotheses in their domain.
KW - Ornithology
KW - multiscale analysis
KW - spatial data
KW - species distribution models
KW - temporal data
UR - http://www.scopus.com/inward/record.url?scp=80955157810&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80955157810&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2011.176
DO - 10.1109/TVCG.2011.176
M3 - Article
C2 - 22034358
AN - SCOPUS:80955157810
SN - 1077-2626
VL - 17
SP - 2374
EP - 2383
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 12
M1 - 6065004
ER -