Realising the full potential of citizen science monitoring programs

Highlights

• We review the ability of volunteer-monitoring schemes to achieve management, ecological knowledge or social objectives.

• We use return-on-investment to determine the influence and cost-effectiveness of different citizen science programs.

• Cross-sectional schemes (Bird Atlases) and longitudinal schemes (Breeding Bird Surveys) achieve different objectives.

• Schemes with higher temporal and spatial resolution are more cost-effective and influential in the scientific literature.

• We outline ways to devise cost-effective citizen science programs capable of fulfilling multiple objectives.

Abstract

Citizen science is on the rise. Aided by the internet, the popularity and scope of citizen science appears almost limitless. For citizens the motivation is to contribute to “real” science, public information and conservation. For scientists, citizen science offers a way to collect information that would otherwise not be affordable. The longest running and largest of these citizen science programs are broad-scale bird monitoring projects. There are two basic types of protocols possible: (a) cross-sectional schemes such as Atlases – collections of surveys of many species contributed by volunteers over a set period of time, and (b) longitudinal schemes such as Breeding Bird Surveys (BBS) – on-going stratified monitoring of sites that require more coordination. We review recent applications of these citizen science programs to determine their influence in the scientific literature. We use return-on-investment thinking to identify the minimum investment needed for different citizen science programs, and the point at which investing more in citizen science programs has diminishing benefits. Atlas and BBS datasets are used to achieve different objectives, with more knowledge-focused applications for Atlases compared with more management applications for BBS. Estimates of volunteer investment in these datasets show that compared to cross-sectional schemes, longitudinal schemes are more cost-effective, with increased BBS investment correlated with more applications, which have higher impact in the scientific literature, as measured by citation rates. This is most likely because BBS focus on measuring change, allowing the impact of management and policy to be quantified. To ensure both types of data are used to their full potential we recommend the following: elements of BBS protocols (fixed sites, long-term monitoring) are incorporated into Atlases; regional coordinators are in place to maintain data quality; communication between researchers and the organisations coordinating volunteer monitoring is enhanced, with monitoring targeted to meet specific needs and objectives; application of data to under-explored objectives is encouraged, and data are made freely and easily accessible.

Introduction

Citizen science, the involvement of citizens from the non-scientific community in academic research, has become increasingly important in conservation science, as resources for monitoring fail to match the scale of the questions at hand. For citizens, the motivation is to contribute to scientific understanding and conservation decisions. For scientists, citizen science provides an opportunity to gather information that would be impossible to collect because of limitations in time and resources (Dickinson et al., 2010). The field of ornithology has the longest history of citizen science (Greenwood, 2007), with thousands of amateur and professional ornithologists worldwide. The National Audubon Society’s Christmas Bird Count in the United States, started in 1900, is the longest-running citizen science project with over 110 years of data collected so far. Advances in technology have led to new citizen science internet applications that use crowd-sourcing to invite large numbers of the public to monitor biodiversity over broad geographic regions, and allow volunteers to access and interpret the data they collect (Howe, 2006). This has resulted in datasets that are often very large and readily accessible. To respond to the many and varied needs of biodiversity management, many conservation agencies rely on these volunteer-compiled datasets to inform their management strategies. Citizen science is often the only practical way to achieve the geographic extent required to document ecological patterns and address ecological questions at scales relevant to species range shifts, migration patterns, disease spread, broad-scale population trends, changes in national and state policy, and impacts of environmental processes like climate change. The varied uses of these data mean that quality assurance and control is critical. At least one comparative analysis suggests that citizen science data can provide similar information to professionally collected and designed monitoring programs (Szabo et al., 2012). A century on since the first citizen science program, it is timely to examine what makes citizen science programs effective at achieving high quality datasets that are useful for answering pure and applied questions (Mackechnie et al., 2011).

The largest citizen science programs are broad-scale bird monitoring schemes that can be categorised as one of two protocols: cross-sectional surveying (e.g. Atlases) and longitudinal surveying (e.g. Breeding Bird Surveys). Atlases are collections of species occurrences contributed by volunteers over a set time period, with volunteers generally free to choose where they survey. As of 2012, more than 400 Bird Atlases have been developed. The spatial sampling units of these programs are variable (0.02–3092 km²), and the spatial extent can be anything from local areas (21 km²) to entire continents (10,390,000 km²). As much as 68% of Atlases are ‘repeat’ Atlases, covering the same areas as those covered by a previous Atlas (Dunn and Weston, 2008). The number and density of contributors is highly variable over space, and the data collected are often uneven through the year (Dunn and Weston, 2008, Gibbons et al., 2007). Due to the often unstructured or undirected nature of sampling in Atlases (with volunteers usually allowed to conduct surveys wherever and whenever they want), data quality issues caused by volunteer bias in survey effort (Botts et al., 2010, Dennis and Thomas, 2000, Reddy and Dávalos, 2003), survey inconsistencies over time (Szabo et al., 2010), errors in records (Cohn, 2008, Robertson et al., 2010), and issues of scale (Araujo et al., 2005), must be dealt with when using these data. In contrast, Breeding Bird Surveys are based on a network of sampling locations at which species occurrence and relative abundance are collected at given time steps to document temporal trends (Brotons et al., 2007, Robertson et al., 2010). These on-going programs are less common than purely cross-sectional schemes, as they generally require more institutional coordination of stratified surveys and provide datasets that are more representative and less biased by volunteer behaviour. Despite limitations, both types of programs have the potential for high volunteer involvement, and can provide numerous direct and indirect benefits to conservation.

We review recent applications in the scientific literature of citizen science bird-monitoring programs to explore lessons learned. In doing so, we examine whether we are making the most of the considerable efforts of the volunteers and data coordinators. Past reviews have highlighted various applications of citizen science monitoring programs, often related to learning about ecological systems and their management (Table 1; Donald and Fuller, 1998, Dunn and Weston, 2008; Gibbons et al., 2007, Pomeroy et al., 2008, Robertson et al., 2010, Underhill et al., 1991). A substantial focus of the literature has been on the methodology of volunteer-monitoring programs (usually Atlases or Breeding Bird Surveys), and the issues that need to be dealt with when using and analysing the data (e.g. Dickinson et al., 2010, Donald and Fuller, 1998, Dunn and Weston, 2008, Gibbons et al., 2007, Pomeroy et al., 2008, Robertson et al., 2010, Schmeller et al., 2012, Thomas, 1996, Underhill et al., 1991). This emphasis has potentially overshadowed the many benefits these datasets have to offer. There has been little discussion about which programs are best for achieving particular objectives or whether additional objectives could be met using these same datasets. We investigate the range of potential objectives of using volunteer monitoring data, and compare the ability of different volunteer-monitoring schemes to achieve them. We examine the level of stakeholder investment in these programs and ask if broad-scale citizen science bird monitoring has been a cost-effective investment, by relating the quality and quantity of inputs to the scientific outcomes. We aim to inform two investment questions: (a) What is the minimum amount of investment needed for different citizen science programs, and (b) At what point would spending more money on citizen science programs deliver little additional benefit? Finally, we explore how we can make volunteer-monitoring datasets more useful for informing research or management, to optimally use resources spent on supporting these projects.