Can information from citizen science data be used to predict biodiversity in stormwater ponds?

Richard Svanbäck,Jani Heino,Frank Johansson,Luis Mauricio Bini,Paul Coiffard,Jacob Wester

doi:10.1038/s41598-020-66306-0

Richard Svanbäck, Jani Heino + Show 4 more

Open Access

https://doi.org/10.1038/s41598-020-66306-0

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Abstract

Citizen science data (CSD) have the potential to be a powerful scientific approach to assess, monitor and predict biodiversity. Here, we ask whether CSD could be used to predict biodiversity of recently constructed man-made habitats. Biodiversity data on adult dragonfly abundance from all kinds of aquatic habitats collected by citizen scientists (volunteers) were retrieved from the Swedish Species Observation System and were compared with dragonfly abundance in man-made stormwater ponds. The abundance data of dragonflies in the stormwater ponds were collected with a scientific, standardized design. Our results showed that the citizen science datasets differed significantly from datasets collected scientifically in stormwater ponds. Hence, we could not predict biodiversity in stormwater ponds from the data collected by citizen scientists. Using CSD from past versus recent years or from small versus large areas surrounding the stormwater ponds did not change the outcome of our tests. However, we found that biodiversity patterns obtained with CSD were similar to those from stormwater ponds when we restricted our analyses to rare species. We also found a higher beta diversity for the CSD compared to the stormwater dataset. Our results suggest that if CSD are to be used for estimating or predicting biodiversity, we need to develop methods that take into account or correct for the under-reporting of common species in CSD.

Highlights

Citizen science data (CSD) have the potential to be a powerful scientific approach to assess, monitor and predict biodiversity
We found a strong negative relationship between the difference in occupancy given by the datasets and the mean abundance in the stormwater ponds (Fig. 3; r = −0.88; P < 0.0001 with all species and r = −0.87; P < 0.0001 after removing C. virgo and C. splendens)
After splitting the dataset according to species abundance, we found that the differences between citizen science datasets and the 2018 dataset were remained only when common species were considered in the analyses (Table 2; Fig. 6)

Summary

Introduction

Citizen science data (CSD) have the potential to be a powerful scientific approach to assess, monitor and predict biodiversity. In the face of increasing human pressure on biodiversity[1], scientists need additional approaches to respond to the demand for information to guide environmental management, conservation planning and policymaking[2] One such approach consists of the involvement of citizens (volunteers) to gather data for scientific purposes (see reviews in Dickinson et al.[3,4]). Snäll et al.[9] listed several of these drawbacks, such as: (1) population records only include presence, but not absence, (2) sampling effort varies over space and time, (3) spatial coverage might vary, (4) methods of collection might vary, (5) records of rare and common species might be biased towards rare or common species, and (6) detectability of species varies among volunteers Because of these drawbacks, more studies are needed to examine how well CSD can be used to predict, for example, colonization of new habitats. We expected that community patterns based on data from rare species would be more similar to the patterns based on our dataset (stormwater ponds) because these species are more actively sought by citizen scientists than common species[3,20,21]

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