Analytical guidelines to increase the value of citizen science data: using eBird data to estimate species occurrence

Abstract

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, variation in effort, and variation in observer skill.To demonstrate 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 three widely applied metrics for describing species distributions: encounter rate, occupancy probability, and relative abundance. For each method, we outline approaches for data processing and modelling that are suitable for using citizen science data for estimating species distributions.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 two key processes 1) the use of complete checklists rather than presence-only data, and 2) the use of covariates describing variation in effort and detectability for each checklist. Including these covariates accounted for heterogeneity in detectability and reporting, and resulted in substantial differences in predicted distributions. The data processing and analytical steps we outlined led to improved model performance across a range of sample sizes.When using citizen science data it is imperative to carefully consider the appropriate data processing and analytical procedures required to address the bias and variation. Here, we describe the consequences and utility of applying our suggested approach to semi-structured citizen science data to estimate species distributions. 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.

Modeling avian full annual cycle distribution and population trends with citizen science data

“…Finally, several of the approaches used here to control for biases in citizen science data can be applied more generally. Johnston et al (2019) discuss how information describing participant search effort along with complete checklists can be used to account for the bias of imperfect detection and how spatiotemporal sampling can be used to balance the data used to train distribution and abundance models.…”

“…, Johnston et al. ). There has also been work developing methods to tackle the challenges associated with estimating distributions and abundance across very large spatial extents throughout the year, including the variation in data density across broad spatial and temporal extents (Fink et al.…”

Comparing abundance distributions and range maps in spatial conservation planning for migratory species

“…Citizen science data can be highly variable, although analytical methods can account for much of this variation (Johnston et al. , , Kelling et al. ).…”

Seasonal and directional dispersal behavior in an ongoing dove invasion

“…I limited all analyses to data from 2007 and later because the eBird interface did not enable species-specific field notes until June 2006 (C. L. Wood, unpubl.). Prior to analysis, I discarded observations that failed to meet eBird data quality criteria, and I condensed multi-observer reports to single reports (Johnston et al 2019). I conducted all data filtering and analysis in R (R Core Team).…”