Christopher L. Wood

eBird: Engaging Birders in Science and Conservation

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.

BirdVis: Visualizing and Understanding Bird Populations

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.

Can Observation Skills of Citizen Scientists Be Estimated Using Species Accumulation Curves

Volunteers are increasingly being recruited into citizen science projects to collect observations for scientific studies. An additional goal of these projects is to engage and educate these volunteers. Thus, there are few barriers to participation resulting in volunteer observers with varying ability to complete the project’s tasks. To improve the quality of a citizen science project’s outcomes it would be useful to account for inter-observer variation, and to assess the rarely tested presumption that participating in a citizen science projects results in volunteers becoming better observers. Here we present a method for indexing observer variability based on the data routinely submitted by observers participating in the citizen science project eBird, a broad-scale monitoring project in which observers collect and submit lists of the bird species observed while birding. Our method for indexing observer variability uses species accumulation curves, lines that describe how the total number of species reported increase with increasing time spent in collecting observations. We find that differences in species accumulation curves among observers equates to higher rates of species accumulation, particularly for harder-to-identify species, and reveals increased species accumulation rates with continued participation. We suggest that these properties of our analysis provide a measure of observer skill, and that the potential to derive post-hoc data-derived measurements of participant ability should be more widely explored by analysts of data from citizen science projects. We see the potential for inferential results from analyses of citizen science data to be improved by accounting for observer skill.

Documenting stewardship responsibilities across the annual cycle for birds on U.S. public lands.

In the face of global environmental change, the importance of protected areas in biological management and conservation is expected to grow. Birds have played an important role as biological indicators of the effectiveness of protected areas, but with little consideration given to where species occur outside the breeding season. We estimated weekly probability of occurrence for 308 bird species throughout the year within protected areas in the western contiguous USA using eBird occurrence data for the combined period 2004 to 2011. We classified species based on their annual patterns of occurrence on lands having intermediate conservation mandates (GAP status 2 and 3) administered by the Bureau of Land Management (BLM) and the United States Forest Service (USFS). We identified species having consistent annual association with one agency, and species whose associations across the annual cycle switched between agencies. BLM and USFS GAP status 2 and 3 lands contained low to moderate proportions of species occurrences, with proportions highest for species that occurred year-round or only during the summer. We identified two groups of species whose annual movements resulted in changes in stewardship responsibilities: (1) year-round species that occurred on USFS lands during the breeding season and BLM lands during the nonbreeding season; and (2) summer species that occurred on USFS lands during the breeding season and BLM lands during spring and autumn migration. Species that switched agencies had broad distributions, bred on high-elevation USFS lands, were not more likely to be identified as species of special management concern, and migrated short (year-round species) to long distances (summer species). Our findings suggest cooperative efforts that address the requirements of short-distance migratory species on GAP status 2 lands (n = 20 species) and GAP status 3 lands (n = 24) and long-distance migratory species on GAP status 2 lands (n = 9) would likely benefit their populations. Such efforts may prove especially relevant for species whose seasonal movements result in associations with different environments containing contrasting global change processes and management mandates.

Behavior Identification in Two-Stage Games for Incentivizing Citizen Science Exploration

We consider two-stage games in which a leader seeks to direct the activities of independent agents by offering incentives. A good leader’s strategy requires an understanding of the agents’ utilities and the ability to predict agent behavior. Moreover, the optimization of outcomes requires an agent behavior model that can be efficiently incorporated into the leader’s model. Here we address the agent behavior modeling problem and show how it can be used to reduce bias in a challenging citizen science application. Adapting ideas from Discrete Choice Modeling in behavioral economics, we develop a probabilistic behavioral model that takes into account variable patterns of human behavior and suboptimal actions. By modeling deviations from baseline behavior we are able to accurately predict future behavior based on limited, sparse data. We provide a novel scheme to fold the agent model into a bi-level optimization as a single Mixed Integer Program, and scale up our approach by adding redundant constraints, based on novel insights of an easy-hard-easy phase transition phenomenon. We apply our methodology to a game called Avicaching, in collaboration with eBird, a well-established citizen science program that collects bird observations for conservation. Field results show that our behavioral model performs well and that the incentives are remarkably effective at steering citizen scientists’ efforts to reduce bias by exploring under-sampled areas. Moreover, the data collected from Avicaching improves the performance of species distribution models.