Effects of ignoring survey design information for data reuse. Issue 6 (29th June 2021)

• Record Type: Journal Article
• Authors: Foster, Scott D.; Vanhatalo, Jarno; Trenkel, Verena M.; Schulz, Torsti; Lawrence, Emma; Przeslawski, Rachel; Hosack, Geoffrey R.
• Is Part Of: Ecological applications. Volume 31:Issue 6(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-29
• Subjects: bias -- data -- database -- findable -- accessible -- interoperable -- reusable data -- Horvitz‐Thompson estimator -- inclusion probability -- model -- population density estimate -- reuse -- survey design; Ecology -- Periodicals; Environmental protection -- Periodicals; Biology, Economic -- Periodicals; 577.05
• DOI: 10.1002/eap.2360
• Languages: English
• ISSNs: 1051-0761

Abstract

Abstract: Data are currently being used, and reused, in ecological research at an unprecedented rate. To ensure appropriate reuse however, we need to ask the question: "Are aggregated databases currently providing the right information to enable effective and unbiased reuse?" We investigate this question, with a focus on designs that purposefully favor the selection of sampling locations (upweighting the probability of selection of some locations). These designs are common and examples are those designs that have uneven inclusion probabilities or are stratified. We perform a simulation experiment by creating data sets with progressively more uneven inclusion probabilities and examine the resulting estimates of the average number of individuals per unit area (density). The effect of ignoring the survey design can be profound, with biases of up to 250% in density estimates when naive analytical methods are used. This density estimation bias is not reduced by adding more data. Fortunately, the estimation bias can be mitigated by using an appropriate estimator or an appropriate model that incorporates the design information. These are only available however, when essential information about the survey design is available: the sample location selection process (e.g., inclusion probabilities), and/or covariates used in their specification. The results suggest that such information must be stored and served with the data to support meaningful inference and data reuse.