Comparing avian species richness estimates from structured and semi-structured citizen science data (original) (raw)

Citizen science, including structured and semi-structured forms, has become a powerful tool to collect biodiversity data. However, semi-structured citizen science data have been criticized for higher variability in quality, including less information to adjust for imperfect detection and uneven duration that bias the estimates of species richness. Species richness estimators may quantify bias in estimates. Here, we test the effectiveness of Chao1 estimator in eBird (semi-structured) by comparing it to averaged species richness in Breeding Bird Survey Taiwan, BBS (structured) and quantifying bias. We then fit a power function to compare bias while controlling for differences in count duration. The Chao1 estimator increased the species richness estimates of eBird data from 56 to 69% of the average observed BBS and from 47 to 59% of the average estimated BBS. Effects of incomplete short duration samples and variability in detectability skills of observers can lead to biased estimates. Using the Chao1 estimator improved estimates of species richness from semi-structured and structured data, but the strong effect of singleton species on bias, especially in short duration counts, should be evaluated in advance to reduce the uncertainty of estimation processes. Biodiversity loss impacts ecosystem services and ecosystem functions worldwide 1. Most recently, the loss of biodiversity has been driven by climate change, habitat conversion and fragmentation, and introduction of invasive species 2-5. Under these impacts, it has become crucial for scientists to develop methods to monitor biodiversity across different temporal and spatial scales. Species richness, one of the most common measures of biodiversity, is defined as the number of species in a given area 6,7. Unfortunately, monitoring species richness is well known for being expensive and labor-intensive, and often beyond the means of modestly funded research studies. In contrast, citizen science has recently emerged as an alternative that provides a low-cost approach to collect species richness data. Citizen science projects invite volunteers to participate in and contribute observations for scientific purposes 8. One of the biggest advantages of citizen science projects is that it generates a large number of observations, which often involve documenting species richness and species composition. Citizen science can be grouped into three main categories: structured, semi-structured, and unstructured citizen science 9. Structured citizen science adheres to a rigorous data collection methodology and aims to produce higher quality data, by standardizing the quality of observations from volunteer training, survey duration, and choice of sampling locations 6. Despite the higher quality output from structured citizen science programs, such as Breeding Bird Survey Taiwan (BBS), acquiring such data is not timely in most cases, because some observations need to be organized and validated before release to public. On the other hand, semi-structured citizen science usually provides options for observers to collect information 9 and users can immediately access information. Semi-structured citizen science projects such as eBird usually allow observers to survey without time or location restrictions and data can be contributed by observers of all skill levels 6. Yet, the abundant observations distributed across large spatial and temporal scales may be a strength of semi-structured citizen-science data. Unstructured citizen science has the least restriction regarding data collection methodology, such as iNaturalist 9. However, unstructured citizen science often lacks