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Abstract Broad‐scale biodiversity monitoring relies, at least in part, on the efforts of citizen, or community, scientists. To ensure robust inferences from citizen science data, it is important to understand the spatial pattern of sampling effort by citizen scientists and how it deviates from an optimal pattern. Here, we develop a generalized workflow to estimate the optimal distribution of sampling effort for inference of species diversity (e.g., species richness, Shannon diversity, and Simpson's diversity) patterns using the relationship between species diversity and land cover. We used data from the eBird citizen science project that was collected across heterogeneous landscapes in Florida (USA) to illustrate this workflow across different grain sizes. We found that a relatively small number of samples are needed to meet 95% sampling completeness when diversity estimation is focused on dominant species: 43, 64, 96, 123, 172, and 176 for 5 × 5, 10 × 10, 15 × 15, 20 × 20, 25 × 25, and 30 × 30‐km2 grain sizes, respectively. In contrast, three to five times more samples are necessary to infer species diversity when estimation is focused on rare species. However, in both cases, the optimal distribution of effort was spatially heterogeneous, with more effort needed in regions of higher diversity. Our results highlight the potential of citizen science data to make informed comparisons of species diversity in space and time, as well as how sampling effort inherently depends on monitoring goals, such as whether dominant or rare species are targeted. Our general workflow allows for the quantification of sampling effort needed to estimate species diversity with citizen science data and can guide future adaptive sampling by citizen science participants. |
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