Microtargeting for conservation

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.