28 – What makes a successful citizen science program?^361volunteers, as well as for promoting recovery of the threatened species. For all
BirdLife projects, the overarching aim is generally driven by the need for recovery,
or prevention of decline, of the focal bird or group of birds. This chapter discusses
these four elements – data management and collection, participant engagement,
conservation actions and species recovery – in further detail.
Data collection and management
The purpose of many citizen science programs is to collect data that can be used to
assess the ongoing status of a species or population, and/or to inform decisions
regarding on-ground management actions. One of the perceived benefits of citizen
science programs is the volume of data that can be collected, and at spatial scales
generally not possible by individual research scientists (Dickinson et al. 2010;
Chapter 27). BirdLife’s experience is that there are some limitations on the types of
data that can be collected through citizen science projects, and with the types of
analyses that are appropriate for such data: a range of caveats need to be placed on
results generated from data collected by citizen scientists.
BirdLife has identified several potential biases in citizen collected datasets.
The most obvious biases relate to the variation in skills among observers: the
amount of experience an observer has will inf luence accuracy of identification,
detection and the ability to count birds accurately. Detection biases are difficult to
determine and ignoring non-detection may be better than trying to account for it
(Welsh et al. 2013). There are also inherent issues with count data (see Elphick
2008 for review), which, when using untrained (or minimally trained) volunteers,
can affect the level of confidence in the data collected. Binomial data is also not
without risk; incorrect species identification, false negatives (Tyre et al. 2003), and
detection biases can occur.
Other biases can occur in citizen science projects, particularly at large
landscape scales and where participants are free to select their own survey sites. In
some instances these biases ref lect true limitations. For example, the BirdLife
Australia Atlas (Birdata) records for budgerigars Melopsittacus undulates are
clustered along the network of roads through Australia. Moving far from roads in
much of arid Australia is difficult due to environmental and access constraints.
More generally, participants are likely to select sites that facilitate birdwatching and
are biased towards sites that promise greater bird diversity (Barrett et al. 2003).
With unstructured threatened species surveys (e.g. swift parrots), participants
preferentially survey locations where they think they have a greater chance of
finding the species. This means that sites with expected low occurrence are
surveyed less and when they are surveyed, null records are less likely to be
reported. This leads to data gaps.
At smaller spatial scales, and particularly with dedicated monitoring design in
place, these location biases may not be as problematic. More specific project