Effects of Ignoring Survey Design Information for Data Reuse

Type Article
Date 2021-09
Language English
Author(s) Foster Scott D.ORCID1, Vanhatalo Jarno2, 3, Trenkel VerenaORCID4, Schulz Torsti3, Lawrence Emma5, Przeslawski Rachel6, Hosack Geoffrey R.1
Affiliation(s) 1 : Data61 ,CSIRO ,Hobart Tasmania ,Australia
2 : Department of Mathematics and Statistics, University of Helsinki, Helsinki ,Finland
3 : Organismal and Evolutionary Biology Research Program, University of Helsinki ,Helsinki, Finland
4 : Ifremer, Nantes, France
5 : Data61 ,CSIRO ,Brisbane Queensland, Australia
6 : Geoscience Australia, Canberra ACT, Australia
Source Ecological Applications (1051-0761) (Wiley), 2021-09 , Vol. 31 , N. 6 , P. e02360 (8p.)
DOI 10.1002/eap.2360
WOS© Times Cited 3
Keyword(s) bias, data, database, findable, accessible, interoperable, reusable data, Horvitz-Thompson estimator, inclusion probability, model, population density estimate, reuse, survey design

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 favour 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 datasets 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.

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How to cite 

Foster Scott D., Vanhatalo Jarno, Trenkel Verena, Schulz Torsti, Lawrence Emma, Przeslawski Rachel, Hosack Geoffrey R. (2021). Effects of Ignoring Survey Design Information for Data Reuse. Ecological Applications, 31(6), e02360 (8p.). Publisher's official version : https://doi.org/10.1002/eap.2360 , Open Access version : https://archimer.ifremer.fr/doc/00691/80339/