Mapping species richness using opportunistic samples:a case study on ground-floor bryophyte species richness in the Belgian province of Limburg

Neyens, Thomas and Diggle, Peter and Faes, C. and Beenaerts, Natalie and Artois, Tom and Giorgi, Emanuele (2019) Mapping species richness using opportunistic samples:a case study on ground-floor bryophyte species richness in the Belgian province of Limburg. Scientific Reports, 9. ISSN 2045-2322

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Abstract

In species richness studies, citizen-science surveys where participants make individual decisions regarding sampling strategies provide a cost-effective approach to collect a large amount of data. However, it is unclear to what extent the bias inherent to opportunistically collected samples may invalidate our inferences. Here, we compare spatial predictions of forest ground-floor bryophyte species richness in Limburg (Belgium), based on crowd- and expert-sourced data, where the latter are collected by adhering to a rigorous geographical randomisation and data collection protocol. We develop a log-Gaussian Cox process model to analyse the opportunistic sampling process of the crowd-sourced data and assess its sampling bias. We then fit two geostatistical Poisson models to both data-sets and compare the parameter estimates and species richness predictions. We find that the citizens had a higher propensity for locations that were close to their homes and environmentally more valuable. The estimated effects of ecological predictors and spatial species richness predictions differ strongly between the two geostatistical models. Unknown inconsistencies in the sampling process, such as unreported observer’s effort, and the lack of a hypothesis-driven study protocol can lead to the occurrence of multiple sources of sampling bias, making it difficult, if not impossible, to provide reliable inferences.

Item Type:
Journal Article
Journal or Publication Title:
Scientific Reports
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1000
Subjects:
ID Code:
139703
Deposited By:
Deposited On:
19 Dec 2019 15:35
Refereed?:
Yes
Published?:
Published
Last Modified:
23 Sep 2020 05:52