The Positive Impact of Conservation Action

Governments recently adopted new global targets to halt and reverse the loss of biodiversity. It is therefore crucial to understand the outcomes of conservation actions. We conducted a global meta-analysis of 186 studies (including 665 trials) that measured biodiversity over time and compared outcomes under conservation action with a suitable counterfactual of no action. We find that in two-thirds of cases, conservation either improved the state of biodiversity or at least slowed declines. Specifically, we find that interventions targeted at species and ecosystems, such as invasive species control, habitat loss reduction and restoration, protected areas, and sustainable management, are highly effective and have large effect sizes. This provides the strongest evidence to date that conservation actions are successful but require transformational scaling up to meet global targets

Insights into accuracy of social scientists' forecasts of societal change

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. Following provision of historical trend data on the domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N=86 teams/359 forecasts), with an opportunity to update forecasts based on new data six months later (Tournament 2; N=120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than simple statistical models (historical means, random walk, or linear regressions) or the aggregate forecasts of a sample from the general public (N=802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models, and based predictions on prior data