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Microtargeting for conservation.

Alexander L Metcalf1, Conor N Phelan1, Cassandra Pallai2

  • 1W.A. Franke College of Forestry and Conservation, University of Montana, 440 CHCB, 32 Campus Drive, Missoula, MT, 59812, U.S.A.

Conservation Biology : the Journal of the Society for Conservation Biology
|March 20, 2019
PubMed
Summary
This summary is machine-generated.

Microtargeting, using big data analytics, significantly boosts conservation outreach efficiency. This approach identifies private landowners most likely to restore riparian areas, doubling restoration likelihood compared to traditional methods.

Keywords:
保护营销学asignación de recursosconservation marketingland-use planningmercadotecnia de la conservaciónplaneación del uso de sueloplaneación espacialplaneación sistemática de la conservaciónprivate landsprotocolo de intervenciónresource allocationretorno de la inversiónreturn on investmentspatial planningsystematic conservation planningtierras privadastriage优先等级分类土地利用规划投资收益私有土地空间规划系统保护规划资源分配

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Area of Science:

  • Conservation science
  • Human dimensions of conservation
  • Environmental management

Background:

  • Achieving conservation goals requires widespread human behavior change and efficient outreach.
  • Current conservation planning often overlooks human dimensions, focusing primarily on biophysical attributes.
  • Microtargeting, a technique using big data analytics, is underutilized in conservation but widely used in marketing and political campaigns.

Purpose of the Study:

  • To investigate the effectiveness of microtargeting for improving conservation outreach efficiency.
  • To develop and evaluate a propensity model predicting restoration behavior among private landowners.
  • To assess the added value of microtargeting beyond traditional geospatial prioritization.

Main Methods:

  • Developed a propensity model to predict restoration behavior in 203,645 private landowners in the Chesapeake Bay Watershed.
  • Analyzed high-resolution land-cover and cadastral data to identify private owners of riparian areas needing restoration.
  • Evaluated the model using a database of past restoration actions (n=4978) and validated it on a parallel database (n=4989).

Main Results:

  • The microtargeting propensity model identified landowners with the highest likelihood of conducting restoration.
  • Landowners in the top decile of the propensity model were over twice as likely (135% increase) to have conducted restoration compared to average landowners.
  • Microtargeting significantly enhanced the efficiency and efficacy of conservation programs beyond biophysical prioritizations.

Conclusions:

  • Microtargeting techniques can dramatically increase the efficiency and efficacy of conservation programs.
  • This approach offers advantages over traditional biophysical prioritization methods alone.
  • Microtargeting facilitates more robust research into social-ecological systems and behavior change for conservation.