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Effects of ignoring survey design information for data reuse
Author(s) -
Foster Scott D.,
Vanhatalo Jarno,
Trenkel Verena M.,
Schulz Torsti,
Lawrence Emma,
Przeslawski Rachel,
Hosack Geoffrey R.
Publication year - 2021
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1002/eap.2360
Subject(s) - computer science , covariate , estimator , reuse , inference , sampling design , survey data collection , selection (genetic algorithm) , sampling (signal processing) , data mining , sampling bias , sample size determination , statistical inference , statistics , machine learning , filter (signal processing) , mathematics , ecology , artificial intelligence , population , demography , sociology , computer vision , biology
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.