eBird

Citizen science data are valuable for addressing a wide range of ecological research questions, and there has been a rapid increase in the scope and volume of data available. However, data from large-scale citizen science projects typically present a number of challenges that can inhibit robust ecological inferences. These challenges include: species bias, spatial bias, and variation in effort.To demonstrate addressing key challenges in analysing citizen science data, we use the example of estimating species distributions with data from eBird, a large semi-structured citizen science project. We estimate two widely applied metrics of species distributions: encounter rate and occupancy probability. For each metric, we assess the impact of data processing steps that either degrade or refine the data used in the analyses. We also test whether differences in model performance are maintained at different sample sizes.Model performance improved when data processing and analytical methods addressed the challenges arising from citizen science data. The largest gains in model performance were achieved with: 1) the use of complete checklists (where observers report all the species they detect and identify); and 2) the use of covariates describing variation in effort and detectability for each checklist. Occupancy models were more robust to a lack of complete checklists and effort variables. Improvements in model performance with data refinement were more evident with larger sample sizes.Here, we describe processes to refine semi-structured citizen science data to estimate species distributions. We demonstrate the value of complete checklists, which can inform the design and adaptation of citizen science projects. We also demonstrate the value of information on effort. The methods we have outlined are also likely to improve other forms of inference, and will enable researchers to conduct robust analyses and harness the vast ecological knowledge that exists within citizen science data.

In this paper we describe eBird, a highly successful citizen science project. With over 150,000 participants worldwide and an accumulation of over 140,000,000 bird observations globally in the last decade, eBird has evolved into a major tool for scientific investigations in diverse fields such as ornithology, computer science, statistics, ecology and climate change. eBird's impact in scientific research is grounded in careful data curation practices that pay attention to all stages of the data lifecycle, and attend to the needs of stakeholders engaged in that data lifecycle. We describe the important aspects of eBird, paying particular attention to the mechanisms to improve data quality; describe the data products that are available to the global community; investigate some aspects of the downloading community; and demonstrate significant results that derive from the use of openly-available eBird data.