Data‐driven counterfactual evaluation of management outcomes to improve emergency conservation decisions

Abstract

Abstract Monitoring is needed to assess conservation success and improve management, but naïve or simplistic interpretation of monitoring data can lead to poor decisions. We illustrate how to counter this risk by combining decision‐support tools and quantitative counterfactual analysis. We analyzed 20 years of egg rescue for tara iti (Sternula nereis davisae) in Aotearoa New Zealand. Survival is lower for rescued eggs; however, only eggs perceived as imminently threatened by predators or weather are rescued, so concluding that rescue is ineffective would be biased. Equally, simply assuming all rescued eggs would have died if left in situ is likely to be simplistic. Instead, we used the monitoring data itself to estimate statistical support for a wide space of uncertain counterfactuals about decisions and fate of rescued eggs. Results suggest under past management, rescuing and leaving eggs would have led to approximately the same overall fledging rate, because of likely imperfect threat assessment and low survival of rescued eggs to fledging. Managers are currently working to improve both parameters. Our approach avoids both naïve interpretation of observed outcomes and simplistic assumptions that management is always justified, using the same data to obtain unbiased quantitative estimates of counterfactual support.

Keywords

• confusion matrix
• decision making
• decision tree
• endangered species
• hindsight bias
• impact evaluation