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A response to estimating hybridization in the wild using community science data: A path forward.

Nicholas M Justyn1, Corey T Callaghan2, Geoffrey E Hill1

  • 1Department of Biological Sciences, Auburn University, Auburn, Alabama, 36849.

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|October 27, 2021
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Summary

Filtering citizen science data, like eBird observations, can bias hybridization rate studies. Limited filtering is best for broad questions on bird hybridization across species.

Keywords:
Avescitizen scienceeBirdhybridizationspeciation

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Area of Science:

  • Ornithology
  • Ecology
  • Bioinformatics

Background:

  • Citizen science databases like eBird offer vast datasets for ecological research.
  • Analyzing large datasets requires careful consideration of data processing methods, including filtering and subsampling.
  • Previous studies have not fully addressed the potential biases introduced by specific data filtering techniques.

Purpose of the Study:

  • To examine the potential biases and assumptions associated with various filtering and subsampling methods applied to citizen science bird observation data.
  • To evaluate how different filtering strategies impact the estimation of hybridization rates in bird populations.
  • To recommend optimal data handling approaches for broad ecological questions using citizen science data.

Main Methods:

  • The study discusses theoretical implications of data filtering on hybridization rate calculations.
  • It analyzes the assumptions underlying common filtering techniques in ornithological research.
  • Comparative analysis of potential biases introduced by restricting data by species, time, or location.

Main Results:

  • Filtering eBird data by species known to hybridize, specific times, or locations can significantly inflate calculated hybridization rates.
  • Assumptions about a species' complete hybridization capacity across its range are often unfounded and can lead to biased results.
  • The study highlights that overly restrictive filtering can misrepresent the true prevalence of hybridization.

Conclusions:

  • A limited filtering approach is recommended when using citizen science databases for broad ecological questions, such as determining the per-individual hybridization rate across numerous bird species.
  • Researchers should be cautious about the assumptions embedded in data filtering methods.
  • Careful consideration of data processing is crucial for accurate ecological inference from citizen science data.