Abstract
Estimates of biodiversity change are essential for the management and conservation of ecosystems. Accurate estimates rely on selecting representative sites, but monitoring often focuses on sites of special interest. How such site-selection biases influence estimates of biodiversity change is largely unknown. Site-selection bias potentially occurs across four major sources of biodiversity data, decreasing in likelihood from citizen science, museums, national park monitoring, and academic research. We defined site-selection bias as a preference for sites that are either densely populated (i.e., abundance bias) or species rich (i.e., richness bias). We simulated biodiversity change in a virtual landscape and tracked the observed biodiversity at a sampled site. The site was selected either randomly or with a site-selection bias. We used a simple spatially resolved, individual-based model to predict the movement or dispersal of individuals in and out of the chosen sampling site. Site-selection bias exaggerated estimates of biodiversity loss in sites selected with a bias by on average 300-400% compared with randomly selected sites. Based on our simulations, site-selection bias resulted in positive trends being estimated as negative trends: richness increase was estimated as 0.1 in randomly selected sites, whereas sites selected with a bias showed a richness change of -0.1 to -0.2 on average. Thus, site-selection bias may falsely indicate decreases in biodiversity. We varied sampling design and characteristics of the species and found that site-selection biases were strongest in short time series, for small grains, organisms with low dispersal ability, large regional species pools, and strong spatial aggregation. Based on these findings, to minimize site-selection bias, we recommend use of systematic site-selection schemes; maximizing sampling area; calculating biodiversity measures cumulatively across plots; and use of biodiversity measures that are less sensitive to rare species, such as the effective number of species. Awareness of the potential impact of site-selection bias is needed for biodiversity monitoring, the design of new studies on biodiversity change, and the interpretation of existing data.
Highlights
Species extinctions have increased as a result of human impacts (Pimm et al 2014; Ceballos et al 2015), and populations of many groups appear to be in rapid decline (Dirzo et al 2014; Díaz et al 2019)
Based on our literature review, citizen science data were more affected by site-selection bias than the other data sources
Common motivations for participating in citizen science programs are improving species identification skills and discovering new species (Richter et al 2018), which could lead to a site-selection bias
Summary
Species extinctions have increased as a result of human impacts (Pimm et al 2014; Ceballos et al 2015), and populations of many groups appear to be in rapid decline (Dirzo et al 2014; Díaz et al 2019). Species richness is declining in many locations (Murphy & Romanuk 2014; Newbold et al 2015), this is by no means universal, and several syntheses show considerable variation, with richness gains and losses being relatively equal (Vellend et al 2013; Dornelas et al 2014; Elahi et al 2015; Blowes et al 2019) These estimates can be confounded by sampling biases that influence data availability and analyses that are possible (Gonzalez et al 2016), including overor underrepresentation of geographic regions, land-use types, and taxonomic groups (Martin et al 2012; McRae et al 2017). Siteselection bias and potential solutions are well-known at the population level, less is known about how biases translate to community-level trends (Fournier et al 2019)
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