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Area of Science:

  • Ecology
  • Environmental Science
  • Statistical Modeling

Background:

  • Biodiversity monitoring relies on sample data, which can be unrepresentative, leading to biased inferences.
  • Unrepresentative samples occur when sampled locations differ from nonsampled ones in key variables.
  • Auxiliary variables, common causes of sample inclusion and the variable of interest, can help adjust samples.

Purpose of the Study:

  • To evaluate the effectiveness of six survey sample adjustment methods for unrepresentative biodiversity data.
  • To estimate mean occupancy and trends for Calluna vulgaris in Great Britain using citizen science data.
  • To assess the accuracy of adjusted estimates compared to unadjusted ones.

Main Methods:

  • Applied six adjustment techniques: subsampling, quasirandomization, poststratification, superpopulation modeling, doubly robust procedure, and multilevel regression and poststratification.
  • Utilized a large, unrepresentative citizen science dataset for Calluna vulgaris occupancy in Great Britain.
  • Estimated mean occupancy for 1987-1999 and 2010-2019, and the trend between these periods.

Main Results:

  • Most adjustment methods resulted in more accurate estimates of mean occupancy and trends compared to unadjusted data.
  • Standard uncertainty intervals for adjusted estimates generally did not encompass the true values.
  • The effectiveness of adjustments depended on the careful selection of auxiliary variables.

Conclusions:

  • Sample adjustment techniques can significantly reduce bias in biodiversity monitoring from unrepresentative datasets.
  • Complete unbiased inference is unattainable without knowledge of all relevant auxiliary variables.
  • Acknowledging and reporting potential residual bias is crucial when using adjusted samples.