FN Archimer Export Format
PT J
TI Effects of Ignoring Survey Design Information for Data Reuse
BT 
AF Foster, Scott D.
   Vanhatalo, Jarno
   Trenkel, Verena
   Schulz, Torsti
   Lawrence, Emma
   Przeslawski, Rachel
   Hosack, Geoffrey R.
AS 1:1;2:2,3;3:4;4:3;5:5;6:6;7:1;
FF 1:;2:;3:PDG-RBE-EMH;4:;5:;6:;7:;
C1 Data61 ,CSIRO ,Hobart Tasmania ,Australia
   Department of Mathematics and Statistics, University of Helsinki, Helsinki ,Finland
   Organismal and Evolutionary Biology Research Program, University of Helsinki ,Helsinki, Finland
   Ifremer, Nantes, France
   Data61 ,CSIRO ,Brisbane Queensland, Australia
   Geoscience Australia, Canberra ACT, Australia
C2 CSIRO, AUSTRALIA
   UNIV HELSINKI, FINLAND
   UNIV HELSINKI, FINLAND
   IFREMER, FRANCE
   CSIRO, AUSTRALIA
   GEOSCIENCE AUSTRALIA, AUSTRALIA
SI NANTES
SE PDG-RBE-EMH
IN WOS Ifremer UPR
   copubli-europe
   copubli-int-hors-europe
IF 6.105
TC 7
UR https://archimer.ifremer.fr/doc/00691/80339/83422.pdf
   https://archimer.ifremer.fr/doc/00691/80339/83423.pdf
   https://archimer.ifremer.fr/doc/00691/80339/83424.zip
LA English
DT Article
DE ;bias;data;database;findable;accessible;interoperable;reusable data;Horvitz-Thompson estimator;inclusion probability;model;population density estimate;reuse;survey design
AB 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. 
PY 2021
PD SEP
SO Ecological Applications
SN 1051-0761
PU Wiley
VL 31
IS 6
UT 000667599600001
DI 10.1002/eap.2360
ID 80339
ER

EF