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Computer and information scientists join forces with other fields to help solve societal and environmental challenges facing humanity, in pursuit of a sustainable future.

In the context of automatic speech recognition and acoustic event detection, an adaptive procedure named per-channel energy normalization (PCEN) has recently shown to outperform the pointwise logarithm of mel-frequency spectrogram (logmelspec) as an acoustic frontend. This letter investigates the adequacy of PCEN for spectrogram-based pattern recognition in far-field noisy recordings, both from theoretical and practical standpoints. First, we apply PCEN on various datasets of natural acoustic environments and find empirically that it Gaussianizes distributions of magnitudes while decorrelating frequency bands. Second, we describe the asymptotic regimes of each component in PCEN: temporal integration, gain control, and dynamic range compression. Third, we give practical advice for adapting PCEN parameters to the temporal properties of the noise to be mitigated, the signal to be enhanced, and the choice of time-frequency representation. As it converts a large class of real-world soundscapes into additive white Gaussian noise, PCEN is a computationally efficient frontend for robust detection and classification of acoustic events in heterogeneous environments.

Ensuring that conservation decisions are informed by the best available data is a fundamental challenge in the face of rapid global environmental change. Too often, new science is not easily or quickly translated into conservation action. Traditional approaches to data collection and science delivery may be both inefficient and insufficient, as conservation practitioners need access to salient, credible, and legitimate data to take action. Open access data could serve as a tool to help bridge the gap between science and action, by providing conservation practitioners with access to relevant data in near real time. Broad-scale citizen-science data represent a fast-growing resource for open access databases, providing relevant and appropriately scaled data on organisms, much in the way autonomous sensors do so on the environment. Several such datasets are now broadly available, yet documentation of their application to conservation is rare. Here we use eBird, a project where individuals around the world submit data on bird distribution and abundance, as an example of how citizen-science data can be used to achieve tangible conservation science and action at local, regional, and global scales. Our examination illustrates how these data can be strategically applied to improve our understanding of spatial and temporal distributions of birds, the impacts of anthropogenic change on ecological systems, and creative conservation solutions to complex problems. We raise awareness of the types of conservation action now happening with citizen-science data, and discuss the benefits, limitations, and caveats of this approach.

Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop, benchmark, and combine several machine listening techniques to improve the generalizability of SED models across heterogeneous acoustic environments. As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise. Starting from a CNN yielding state-of-the-art accuracy on this task, we introduce two noise adaptation techniques, respectively integrating short-term (60 milliseconds) and long-term (30 minutes) context. First, we apply per-channel energy normalization (PCEN) in the time-frequency domain, which applies short-term automatic gain control to every subband in the mel-frequency spectrogram. Secondly, we replace the last dense layer in the network by a context-adaptive neural network (CA-NN) layer. Combining them yields state-of-the-art results that are unmatched by artificial data augmentation alone. We release a pre-trained version of our best performing system under the name of BirdVoxDetect, a ready-to-use detector of avian flight calls in field recordings., 32 pages, in English. Submitted to PLOS ONE journal in February 2019; revised August 2019; published October 2019

While many observations of species are being collected by citizen science projects worldwide, it can be challenging to identify projects collecting data that effectively monitor biodiversity. Over the past several years the allure of taking a “Big Data” approach has provided the opportunity to gather massive quantities of observations via the Internet, too often with insufficient information to describe how the observations were made. Information about species populations — where and when they occur and how many of them are there — (i.e., the signal) can be lost because insufficient information is gathered to account for the inherent biases in data collection (i.e., the noise). Here we suggest that citizen science projects that have succeeded in motivating large numbers of participants, must consider factors that influence the ecological process that affect species populations as well as the observation process that determines how observations are made. Those citizen science projects that collect sufficient contextual information describing the observation process can be used to generate increasingly accurate information about the distribution and abundance of organisms. We illustrate this using eBird as a case study, describing how this citizen science platform is able to collect vital contextual information on the observation process while maintaining a broad global constituency of participants. We highlight how eBird provides information with which to generate biodiversity indicators — specifically distribution, abundance, and habitat associations — across the entire annual cycle, even for populations of long distance migratory birds, a highly challenging taxon.

This article addresses the automatic detection of vocal, nocturnally migrating birds from a network of acoustic sensors. Thus far, owing to the lack of annotated continuous recordings, existing methods had been benchmarked in a binary classification setting (presence vs. absence). Instead, with the aim of comparing them in event detection, we release BirdVox-full-night, a dataset of 62 hours of audio comprising 35402 flight calls of nocturnally migrating birds, as recorded from 6 sensors. We find a large performance gap between energy-based detection functions and data-driven machine listening. The best model is a deep convolutional neural network trained with data augmentation. We correlate recall with the density of flight calls over time and frequency and identify the main causes of false alarm.

Over the next 5 years major advances in the development and application of numerous technologies related to computing, mobile phones, artificial intelligence (AI), and augmented reality (AR) will have a dramatic impact in biodiversity monitoring and conservation. Over a 2-week period several of us had the opportunity to meet with multiple technology experts in the Silicon Valley, California, USA to discuss trends in technology innovation, and how they could be applied to conservation science and ecology research. Here we briefly highlight some of the key points of these meetings with respect to AI and Deep Learning. Computing: Investment and rapid growth in AI and Deep Learning technologies are transforming how machines can perceive the environment. Much of this change is due to increased processing speeds of Graphics Processing Units (GPUs), which is now a billion-dollar industry. Machine learning applications, such as convolutional neural networks (CNNs) run more efficiently on GPUs and are being applied to analyze visual imagery and sounds in real time. Rapid advances in CNNs that use both supervised and unsupervised learning to train the models is improving accuracy. By taking a Deep Learning approach where the base layers of the model are built upon datasets of known images and sounds (supervised learning) and later layers relying on unclassified images or sounds (unsupervised learning), dramatically improve the flexibility of CNNs in perceiving novel stimuli. The potential to have autonomous sensors gathering biodiversity data in the same way personal weather stations gather atmospheric information is close at hand. Mobile Phones: The phone is the most widely used information appliance in the world. No device is on the near horizon to challenge this platform, for several key reasons. First, network access is ubiquitous in many parts of the world. Second, batteries are improving by about 20% annually, allowing for more functionality. Third, app development is a growing industry with significant investment in specializing apps for machine-learning. While GPUs are already running on phones for video streaming, there is much optimism that reduced or approximate Deep Learning models will operate on phones. These models are already working in the lab, with the biggest hurdle being power consumption and developing energy efficient applications and algorithms to run complicated AI processes will be important. It is just a matter of time before industry will have AI functionality on phones. These rapid improvements in computing and mobile phone technologies have huge implications for biodiversity monitoring, conservation science, and understanding ecological systems. Computing: AI processing of video imagery or acoustic streams create the potential to deploy autonomous sensors in the environment that will be able to detect and classify organisms to species. Further, AI processing of Earth spectral imagery has the potential to provide finer grade classification of habitats, which is essential in developing fine scale models of species distributions over broad spatial and temporal extents. Mobile Phones: increased computing functionality and more efficient batteries will allow applications to be developed that will improve an individual’s perception of the world. Already AI functionality of Merlin improves a birder’s ability to accurately identify a bird. Linking this functionality to sensor devices like specialized glasses, binoculars, or listening devises will help an individual detect and classify objects in the environment. In conclusion, computing technology is advancing at a rapid rate and soon autonomous sensors placed strategically in the environment will augment the species occurrence data gathered by humans. The mobile phone in everyone’s pocket should be thought of strategically, in how to connect people to the environment and improve their ability to gather meaningful biodiversity information.

Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a ‘Big Data’ approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence–absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter- or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals., published version, peerReviewed

Data from well-designed experiments provide the strongest evidence of causation in biodiversity studies. However, for many species the collection of these data is not scalable to the spatial and temporal extents required to understand patterns at the population level. Only data collected from citizen science projects can gather sufficient quantities of data, but data collected from volunteers are inherently noisy and heterogeneous. Here we describe a ‘Big Data’ approach to improve the data quality in eBird, a global citizen science project that gathers bird observations. First, eBird’s data submission design ensures that all data meet high standards of completeness and accuracy. Second, we take a ‘sensor calibration’ approach to measure individual variation in eBird participant’s ability to detect and identify birds. Third, we use species distribution models to fill in data gaps. Finally, we provide examples of novel analyses exploring population-level patterns in bird distributions.

Citizen science, big data, and a habitat marketplace enable dynamic habitat for migratory birds in California’s Central Valley., In an era of unprecedented and rapid global change, dynamic conservation strategies that tailor the delivery of habitat to when and where it is most needed can be critical for the persistence of species, especially those with diverse and dispersed habitat requirements. We demonstrate the effectiveness of such a strategy for migratory waterbirds. We analyzed citizen science and satellite data to develop predictive models of bird populations and the availability of wetlands, which we used to determine temporal and spatial gaps in habitat during a vital stage of the annual migration. We then filled those gaps using a reverse auction marketplace to incent qualifying landowners to create temporary wetlands on their properties. This approach is a cost-effective way of adaptively meeting habitat needs for migratory species, optimizes conservation outcomes relative to investment, and can be applied broadly to other conservation challenges.

Automated classification of organisms to species based on their vocalizations would contribute tremendously to abilities to monitor biodiversity, with a wide range of applications in the field of ecology. In particular, automated classification of migrating birds' flight calls could yield new biological insights and conservation applications for birds that vocalize during migration. In this paper we explore state-of-the-art classification techniques for large-vocabulary bird species classification from flight calls. In particular, we contrast a “shallow learning” approach based on unsupervised dictionary learning with a deep convolutional neural network combined with data augmentation. We show that the two models perform comparably on a dataset of 5428 flight calls spanning 43 different species, with both significantly outperforming an MFCC baseline. Finally, we show that by combining the models using a simple late-fusion approach we can further improve the results, obtaining a state-of-the-art classification accuracy of 0.96.

Bird migration is a critical indicator of environmental health, biodiversity, and climate change. Existing techniques for monitoring bird migration are either expensive (e.g., satellite tracking), labor-intensive (e.g., moon watching), indirect and thus less accurate (e.g., weather radar), or intrusive (e.g., attaching geolocators on captured birds). In this paper, we present a vision-based system for detecting migrating birds in flight at night. Our system takes stereo videos of the night sky as inputs, detects multiple flying birds and estimates their orientations, speeds, and altitudes. The main challenge lies in detecting flying birds of unknown trajectories under high noise level due to the low-light environment. We address this problem by incorporating stereo constraints for rejecting physically implausible configurations and gathering evidence from two (or more) views. Specifically, we develop a robust stereo-based 3D line fitting algorithm for geometric verification and a deformable part response accumulation strategy for trajectory verification. We demonstrate the effectiveness of the proposed approach through quantitative evaluation of real videos of birds migrating at night collected with near-infrared cameras.

The scope and nature of biological and environmental research are evolving in response to environmental challenges such as global climate change, invasive species and emergent diseases. In particular, scientific studies are increasingly focusing on long-term, broad-scale, and complex questions that require massive amounts of diverse data collected by remote sensing platforms and embedded environmental sensor networks; collaborative, interdisciplinary science teams; and new approaches for managing, preserving, analyzing, and sharing data. Here, we describe the design of DataONE (Data Observation Network for Earth)—a cyberinfrastructure platform developed to support rapid data discovery and access across diverse data centers distributed worldwide and designed to provide scientists with an integrated set of familiar tools that support all elements of the data life cycle (e.g., from planning and acquisition through data integration, analysis and visualization). Ongoing evolution of the DataONE architecture is based on participatory, user-centered design processes including: (1) identification and prioritization of stakeholder communities; (2) developing an understanding of their perceptions, attitudes and user requirements; (3) usability analysis and assessment; and (4) engaging science teams in grand challenge exemplars such as understanding the broad-scale dynamics of bird migration. In combination, the four approaches engage the broad community in providing guidance on infrastructure design and implementation.

Conservation prioritization requires knowledge about organism distribution and density. This information is often inferred from models that estimate the probability of species occurrence rather than from models that estimate species abundance, because abundance data are harder to obtain and model. However, occurrence and abundance may not display similar patterns and therefore development of robust, scalable, abundance models is critical to ensuring that scarce conservation resources are applied where they can have the greatest benefits. Motivated by a dynamic land conservation program, we develop and assess a general method for modeling relative abundance using citizen science monitoring data. Weekly estimates of relative abundance and occurrence were compared for prioritizing times and locations of conservation actions for migratory waterbird species in California, USA. We found that abundance estimates consistently provided better rankings of observed counts than occurrence estimates. Additionally, the relationship between abundance and occurrence was nonlinear and varied by species and season. Across species, locations prioritized by occurrence models had only 10–58% overlap with locations prioritized by abundance models, highlighting that occurrence models will not typically identify the locations of highest abundance that are vital for conservation of populations.

Summary 1. Species monitoring is an essential component of assessing conservation status, predicting effects of habitat change and establishing management and conservation priorities. The pervasive access to the Internet has led to the development of several extensive monitoring projects that engage massive networks of volunteers who provide observations following relatively unstructured protocols. However, the value of these data is largely unknown. 2. We develop a novel cross-data validation method for measuring the value of survey data from one source (e.g. an Internet checklist program) relative to a second, benchmark data source. The method fits a model to the data of interest and validates the model using benchmark data, allowing us to isolate the training data's information content from its biases. We also define a data efficiency ratio to quantify the relative efficiency of the data sources. 3. We apply our cross-data validation method to quantify the value of data collected in eBird – a western hemisphere, year-round citizen science bird checklist project – relative to data from the highly standardized North American Breeding Bird Survey (BBS). The results show that eBird data contain information similar in quality to that in BBS data, while the information per BBS datum is higher. 4. We suggest that these methods have more general use in evaluating the suitability of sources of data for addressing specific questions for taxa of interest.

Managing digital data for long-term archival and disaster recovery is a key component of our collective responsibility in managing digital data and metadata. As more and more data are collected digitally and as the metadata for traditional museum collections becomes both digitized and more comprehensive, the need to ensure that these data are safe and accessible in the long term becomes essential. Unfortunately, disasters do occur and many irreplaceable datasets on biodiversity have been permanently lost. Maintaining a long-term archive and putting in place reliable disaster recovery processes can be prohibitively expensive, both in the cost of hardware and software as well as the costs of personnel to manage and maintain an archival system. Traditionally, storing digital data for the long term and ensuring the data are loss-less, safe and completely recoverable when a disaster occurs has been managed on-premises with a combination of on-site and off-site storage. This requires complex data workflows to ensure that all data are securely and redundantly stored in multiple highly dispersed locations to minimize the threat of data loss due to local or regional disasters. Files are often moved multiple times across operating systems and media types on their way to and from a deep archive, increasing the risk of file integrity issues. With the recent advent of an array of Cloud Services from organizations such as Amazon, Microsoft and Google to more focused offerings from Iron Mountain, Atempo and others, we have a number of options for long term archival of digital data. Deep archive solutions, storage where retrieval expected only in the case of a disaster, are offered by many of these organizations at a rate substantially less than their normal data storage fees. The most basic requirement for an archival system is storing multiple replicates of the data in geographically isolated locations with a mechanism for guaranteeing file integrity, usually using a checksum algorithm. Additional components that are integral to a robust archive include a simple metadata search and reliable retrieval. In this presentation, we’ll discuss the need for long term archive and disaster recovery capabilities, detail the current best practices of data archival systems and review a variety of archival options that have become available with Cloud Services.

New technologies are rapidly changing the way we collect, archive, analyze, and share scientific data. For example, over the next several years it is estimated that more than one billion autonomous sensors will be deployed over large spatial and temporal scales, and will gather vast quantities of data. Networks of human observers play a major role in gathering scientific data, and whether in astronomy, meteorology, or observations of nature, they continue to contribute significantly. In this paper we present an innovative use of the Internet and information technologies that better enhances the opportunity for citizens to contribute their observations to science and the conservation of bird populations. eBird is building a web-enabled community of bird watchers who collect, manage, and store their observations in a globally accessible unified database. Through its development as a tool that addresses the needs of the birding community, eBird sustains and grows participation. Birders, scientists, and conservationists are using eBird data worldwide to better understand avian biological patterns and the environmental and anthropogenic factors that influence them. Developing and shaping this network over time, eBird has created a near real-time avian data resource producing millions of observations per year.

The increasing availability of massive volumes of scientific data requires new synthetic analysis techniques to explore and identify interesting patterns that are otherwise not apparent. For biodiversity studies, a “data-driven” approach is necessary because of the complexity of ecological systems, particularly when viewed at large spatial and temporal scales. Data-intensive science organizes large volumes of data from multiple sources and fields and then analyzes them using techniques tailored to the discovery of complex patterns in high-dimensional data through visualizations, simulations, and various types of model building. Through interpreting and analyzing these models, truly novel and surprising patterns that are “born from the data” can be discovered. These patterns provide valuable insight for concrete hypotheses about the underlying ecological processes that created the observed data. Data-intensive science allows scientists to analyze bigger and more complex systems efficiently, and complements...

Ecological systems are inherently complex. The processes that affect the distributions of animals and plants operate at multiple spatial and temporal scales, presenting a unique challenge for the development and coordination of effective conservation strategies, particularly for wide-ranging species. In order to study ecological systems across scales, data must be collected at fine resolutions across broad spatial and temporal extents. Crowdsourcing has emerged as an efficient way to gather these data by engaging large numbers of people to record observations. However, data gathered by crowdsourced projects are often biased due to the opportunistic approach of data collection. In this article, we propose a general class of models called AdaSTEM, (for adaptive spatio-temporal exploratory models), that are designed to meet these challenges by adapting to multiple scales while exploiting variation in data density common with crowdsourced data. To illustrate the use of AdaSTEM, we produce intra-seasonal distribution estimates of long-distance migrations across the Western Hemisphere using data from eBird, a citizen science project that utilizes volunteers to collect observations of birds. Subsequently, model diagnostics are used to quantify and visualize the scale and quality of distribution estimates. This analysis shows how AdaSTEM can automatically adapt to complex spatiotemporal processes across a range of scales, thus providing essential information for full-life cycle conservation planning of broadly distributed species, communities, and ecosystems.

Complete guidance for mastering the tools and techniques of the digital revolution With the digital revolution opening up tremendous opportunities in many fields, there is a growing need for skilled professionals who can develop data-intensive systems and extract information and knowledge from them. This book frames for the first time a new systematic approach for tackling the challenges of data-intensive computing, providing decision makers and technical experts alike with practical tools for dealing with our exploding data collections. Emphasizing data-intensive thinking and interdisciplinary collaboration, The Data Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business examines the essential components of knowledge discovery, surveys many of the current research efforts worldwide, and points to new areas for innovation. Complete with a wealth of examples and DISPEL-based methods demonstrating how to gain more from data in real-world systems, the book: Outlines the concepts and rationale for implementing data-intensive computing in organizations Covers from the ground up problem-solving strategies for data analysis in a data-rich world Introduces techniques for data-intensive engineering using the Data-Intensive Systems Process Engineering Language DISPEL Features in-depth case studies in customer relations, environmental hazards, seismology, and more Showcases successful applications in areas ranging from astronomy and the humanities tomore » transport engineering Includes sample program snippets throughout the text as well as additional materials on a companion website The Data Bonanza is a must-have guide for information strategists, data analysts, and engineers in business, research, and government, and for anyone wishing to be on the cutting edge of data mining, machine learning, databases, distributed systems, or large-scale computing.« less

Although citizen science projects can engage a very large number of volunteers to collect volumes of data, they are susceptible to issues with data quality. Our experience with eBird, which is a broad-scale citizen science project to collect bird observations, has shown that a massive effort by volunteer experts is needed to screen data, identify outliers and flag them in the database. The increasing volume of data being collected by eBird places a huge burden on these volunteer experts and other automated approaches to improve data quality are needed. In this work, we describe a case study in which we evaluate an automated data quality filter that improves data quality by identifying outliers and categorizing these outliers as either unusual valid observations or mis-identified (invalid) observations. This automated data filter involves a two-step process: first, a data-driven method detects outliers (ie. observations that are unusual for a given region and date). Next, we use a data quality model based on an observer's predicted expertise to decide if an outlier should be flagged for review. We applied this automated data filter retrospectively to eBird data from Tompkins Co., NY and found that that this automated process significantly reduced the workload of reviewers by as much as 43% and identifies 52% more potentially invalid observations.

How do you successfully engage an audience in a citizen-science project? The processes developed by eBird (www.ebird.org), a fast-growing web-based tool that now gathers millions of bird observations per month, offers a model.

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.

Increasing public engagement in volunteer science1, either through data collection2 or processing3, is both raising public awareness of science and gathering useful information for scientists. While the payoffs of citizen science4 are potentially large, achieving them requires new approaches to data management and analysis that can only result from strong cross-disciplinary collaborations. This is especially true in ecology and conservation biology, where historically the understanding of species’ responses to environmental change has been constrained by the limited spatial5 or temporal scale6 of available data. Here we describe collaborative research in ecology, computer science, and statistics to generate essential information for conservation management of North American birds: accurate dynamic bird distributions models based on habitat associations across much of North America. Unique is our ability to describe the broad-scale dynamics of seasonal bird distributions and the associated seasonal patterns of habitat use. Our source of bird distribution data is eBird7, an online bird checklist program that currently gathers more than 74,000 checklists monthly from a large network of contributors. Our results were made possible through a data intensive scientific workflow8 that includes analytical methods merged from the fields of machine learning and statistics. We believe that this novel approach of data collection, synthesis, analysis, and visualization will serve as a hallmark for future research initiatives, with broad applicability across many scientific domains.

The Cornell Laboratory of Ornithology's mission is to interpret and conserve the earth's biological diversity through research, education, and citizen science focused on birds. Over the years, the Lab has accumulated one of the largest and longest-running collections of environmental data sets in existence. The data sets are not only large, but also have many attributes, contain many missing values, and potentially are very noisy. The ecologists are interested in identifying which features have the strongest effect on the distribution and abundance of bird species as well as describing the forms of these relationships. We show how data mining can be successfully applied, enabling the ecologists to discover unanticipated relationships. We compare a variety of methods for measuring attribute importance with respect to the probability of a bird being observed at a feeder and present initial results for the impact of important attributes on bird prevalence.

In this paper we describe eBird, a citizen-science project that takes advantage of the human observational capacity to identify birds to species, which is then used to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a Human-Computer Learning Network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both, and thereby continually improves the effectiveness of the network as a whole. In this paper we explore how Human-Computer Learning Networks can leverage the contributions of a broad recruitment of human observers and processes their contributed data with Artificial Intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts.