Spatiotemporal exploratory models for broad‐scale survey data

Fink, Daniel; Hochachka, Wesley M.; Zuckerberg, Benjamin; Winkler, David W.; Shaby, Ben; Munson, M. Arthur; Hooker, Giles; Riedewald, Mirek; Sheldon, Daniel; Kelling, Steve

doi:10.1890/09-1340.1

Cited by 220 publications

(266 citation statements)

References 63 publications

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“…Datasets that have been gathered for a specific purpose will often result in unexpected phenomena or patterns emerging, that will then promote further more focussed studies. Many studies are available in scientific literature where data mining and model construction have resulted in the discovery of new patterns and processes being found in ecological systems (e.g., [42][43][44]). Howard and Davis [45,46] have published a number of peer-reviewed papers on data predominantly collected by citizen scientists, gathering useable scientific data on autumn migration flyways of monarch butterflies (Danaus plexippus).…”

Section: The Citizen Science Landscapementioning

confidence: 99%

The Citizen Science Landscape: From Volunteers to Citizen Sensors and Beyond

Catlin-Groves

2012

International Journal of Zoology

105

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Within conservation and ecology, volunteer participation has always been an important component of research. Within the past two decades, this use of volunteers in research has proliferated and evolved into "citizen science." Technologies are evolving rapidly. Mobile phone technologies and the emergence and uptake of high-speed Web-capable smart phones with GPS and data upload capabilities can allow instant collection and transmission of data. This is frequently used within everyday life particularly on social networking sites. Embedded sensors allow researchers to validate GPS and image data and are now affordable and regularly used by citizens. With the "perfect storm" of technology, data upload, and social networks, citizen science represents a powerful tool. This paper establishes the current state of citizen science within scientific literature, examines underlying themes, explores further possibilities for utilising citizen science within ecology, biodiversity, and biology, and identifies possible directions for further research. The paper highlights (1) lack of trust in the scientific community about the reliability of citizen science data, (2) the move from standardised data collection methods to data mining available datasets, and (3) the blurring of the line between citizen science and citizen sensors and the need to further explore online social networks for data collection.

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Section: The Citizen Science Landscapementioning

confidence: 99%

The Citizen Science Landscape: From Volunteers to Citizen Sensors and Beyond

Catlin-Groves

2012

International Journal of Zoology

105

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“…Link et al (2006) used the CBC data to model population change of American Black Duck (Anas rubripes) and found similar results to models performed with the Midwinter Waterfowl Inventory. Fink et al (2010) used eBird data to model seasonal changes in distribution of Tree Swallows (Tachycineta bicolor) and Northern Cardinals (Cardinalis cardinalis) within their range.…”

Section: Spatial Modeling and Mappingmentioning

confidence: 99%

A Habitat-based Wind-Wildlife Collision Model with Application to the Upper Great Plains Region

Forcey¹

2012

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Executive SummaryWind energy is a rapidly growing and relatively clean source of renewable energy, which can ultimately meet growing demands for electricity in the US (Manville 2005). Despite the large potential for wind energy production in the US (AWEA 2009) and the minimal carbon emissions that occur from producing this power, the proliferation of wind energy has some drawbacks, including land disturbance caused by the installation of a wind facility, potential decline in aesthetics of a landscape, difficulty in attaining public acceptance, and threats to aerial and terrestrial wildlife (Kunz et al. 2007a;Kuvlesky et al. 2007). Wind project facilities pose two possible hazards to wildlife: the potential for mortality (i.e., collisions with wind turbines and associated power lines) and the potential for habitat effects (Kuvlesky et al. 2007). While both collisions and habitat effects can impact wildlife, this study examined collision hazards only; other studies currently being funded by the US Department of Energy (DOE) are examining habitat displacement effects.Most previous studies on collision impacts at wind facilities have taken place at the site-specific level and have only examined small-scale influences on mortality. In this study, we examine landscape-level influences using a hierarchical spatial model combined with existing datasets and life history knowledge for six bird and three bat species: Horned Lark, Red-eyed Vireo, Mallard, American Avocet, Golden Eagle, Whooping Crane, red bat, silver-haired bat, and hoary bat. These species were modeled in the central United States within Bird Conservation Regions 11, 17, 18, and 19 (NABCI 2012). For the bird species, we modeled bird abundance from existing datasets as a function of habitat variables known to be preferred by each species to develop a relative abundance prediction for each species. For bats, there are no existing abundance datasets so we identified preferred habitat in the landscape for each species and assumed that greater amounts of preferred habitat would equate to greater abundance of bats. The abundance predictions for bird and bats were modeled with additional exposure factors known to influence collisions such as visibility, wind, temperature, precipitation, topography, and behavior to form a final mapped output of predicted collision risk within the study region. Separate collision models were derived for each season given differences in habitat and behavior across seasons; season-specific models were combined to form a cumulative model representing collision risk throughout the year. To evaluate our collision predictions, we reviewed published mortality studies from wind farms in our study region and collected data on reported mortality of our focal species to compare to our modeled predictions. Because of the uncertainty with the degree to which abundance and exposure factors influence collision risk, we performed a sensitivity analysis evaluating model performance of 6 different scenarios where habitat and exposure factors were w...

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“…The migration trajectories of these two subspecies thus form an X, and Yellow Palm moves north earlier in spring and south later in fall than its western counterpart. STEM has provided the first data set showing the complete annual cycle for this species [12,32].…”

Section: Understanding the Migration Of Different Warbler Taxamentioning

confidence: 99%

“…In order to overcome these limitations and improve the understanding of the broad-scale dynamics of continent-scale bird migrations, scientists from the Cornell Lab of Ornithology (CLO) have devel- [12]. STEM uses bird observation data from the eBird project [10] to predict bird distribution in large spatial and temporal scales.…”

Section: Introductionmentioning

confidence: 99%

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BirdVis: Visualizing and Understanding Bird Populations

Ferreira

Lins

Fink

et al. 2011

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Tag cloud lenses in BirdVis are used as a tool to explore and understand relative habitat preferences suggested by a species distribution model over space, time, and across species. Here, we show occurrence maps for the Indigo Bunting. The lenses cover three different regions on three different dates of the breeding season of 2009: May 4, June 22, and August 24, 2009. They show important differences in habitat preferences across these regions as well as how the preferences change over time within a region. The ability to interact with these visualizations, by changing regions and dates, and comparing different bird species, provides an unprecedented tool for scientists to understand bird distribution and consequently obtain insights about the environment and how it changes over time. The supplemental video gives an overview of different features and visualizations provided by BirdVis.Abstract-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.

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Spatiotemporal exploratory models for broad‐scale survey data

Cited by 220 publications

References 63 publications

The Citizen Science Landscape: From Volunteers to Citizen Sensors and Beyond

The Citizen Science Landscape: From Volunteers to Citizen Sensors and Beyond

A Habitat-based Wind-Wildlife Collision Model with Application to the Upper Great Plains Region

BirdVis: Visualizing and Understanding Bird Populations

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