Evaluating the Fitness for Use of Citizen Science Data for Wildlife Monitoring

Elizabeth A. Wentz,Leah R. Gerber,Heather A. Fischer

doi:10.3389/fevo.2021.620850

Elizabeth A. Wentz, Leah R. Gerber + Show 1 more

Open Access

https://doi.org/10.3389/fevo.2021.620850

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Abstract

Contributory citizen science programs focused on ecological monitoring can produce fine-grained and expansive data sets across spatial and temporal scales. With this data collection potential, citizen scientists can significantly impact the ability to monitor ecological patterns. However, scientists still harbor skepticism about using citizen science data in their work, generally due to doubts about data quality. Numerous peer-reviewed articles have addressed data quality in citizen science. Yet, many of these methods are not useable by third-party scientists (scientists who are not directly involved in the citizen science program). In addition, these methods generally capture internal data quality rather than a dataset’s potential to be used for a specific purpose. Assessing data fitness for use represents a promising approach to evaluating data accuracy and quality for different applications and contexts. In this article, we employ a Spatial, Temporal, Aptness, and Application (STAAq) assessment approach to assess data fitness for use of citizen science datasets. We tested the STAAq assessment approach through a case study examining the distribution of caribou in Denali National Park and Preserve. Three different datasets were used in the test, Map of Life data (a global scale citizen science mobile application for recording species observations), Ride Observe and Record data (a program sponsored by the park staff where incentivized volunteers observe species in the park), and conventionally collected radio collar data. The STAAq assessment showed that the Map of Life and Ride Observe and Record program data are fit for monitoring caribou distribution in the park. This data fitness for use approach is a promising way to assess the external quality of a dataset and its fitness to address particular research or monitoring questions. This type of assessment may help citizen science skeptics see the value and potential of citizen science collected data and encourage the use of citizen science data by more scientists.

Highlights

Contributory citizen science programs focused on ecological monitoring are generally initiated by scientists, researchers, or resource managers
We focused on adapting a method to work with the data collected by a contributory style citizen science program focused on ecological monitoring
Mobile technology creates opportunities for citizen science programs to collect more ecological data covering more temporal and spatial extents (Jepson and Ladle, 2015). These data can be vital for ecological monitoring; without adequate data quality assessments, these data may go unused by scientists (Coleman et al, 2009; Boulos et al, 2011; Dickinson et al, 2012; Hart et al, 2012; Roy et al, 2012; Devisch and Veestraeten, 2013; Starr et al, 2014)

Summary

Introduction

Contributory citizen science programs focused on ecological monitoring are generally initiated by scientists, researchers, or resource managers. Fitness of Citizen Science Data programs can collectively produce finer grained and more expansive data sets over regional and global scales and collect data more frequently, covering long temporal extents (Theobald et al, 2015). With these data collection abilities, citizen scientists can significantly impact the ability to monitor ecological patterns (Dickinson et al, 2010; Magurran et al, 2010; Andelman, 2011; Jetz et al, 2012; Ballard et al, 2017; Kress et al, 2018). They are concerned that individuals from the public lack the necessary skills to identify species or collect data in a rigorous manner (Burgess et al, 2017)

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