anchor areas are privately managed, so we removed these from our control group. To determine which areas were privately managed, we first reviewed the literature for mention of “delegated” or “collaborative” management models. We then exhaustively reviewed the websites of the following major conservation organizations that support protected area management in Africa: Wildlife Conservation Society, World Wildlife Fund, Frankfurt Zoological Society, Zoological Society of London, Peace Parks Foundation, Born Free Foundation, and African Wildlife Foundation. We then searched the web one anchor area at a time to look for any language that suggested the area might be privately managed.

We exclude two protected areas from the control group because they are in AP’s incubator program. These areas are being managed by a different NGO, but AP is providing advice and training. We display the full list of control and treatment protected areas in Table S5. We also exclude three areas that were briefly managed by AP before their withdrawal to reallocate resources elsewhere, at least temporarily. Our final control group includes 123 protected areas.

The validity of our estimates rely most importantly on the “parallel trends” assumption: the change in outcome in control protected areas represents the change in outcome that would have happened in treatment areas if management of those areas was not transferred to AP [91]. We consider the validity of the parallel trends assumption for each outcome separately based on the levels and trend in the relative period estimates in the years prior to transference. When trends in pre-period coefficients exist, we use context-specific knowledge to characterize the likely direction of the bias [97, 98]. Related to the parallel trends assumption is the “no anticipation” assumption, that AP management has no causal effects on outcomes prior to transference [91]. We believe the no anticipation assumption is likely to be satisfied because negotiations between AP and country governments prior to transference are not publicly disclosed.

Unless otherwise noted, the data in all regressions are at the level of protected area-year, and the control variables are protected area fixed effects, calendar year fixed effects, and functions of temperature and precipitation. Controlling for weather may improve the precision of estimated effects on outcomes that depend on weather, as well as avoid omitted variables bias if AP management incidence or transference timing depends on contemporaneous weather. We cluster standard errors at the protected area level because that is the level at which treatment is assigned [99].

The temperature and precipitation control variables originate from the ERA5-Land dataset [100]. The temporal resolution of the dataset is hourly, and the spatial resolution is approximately 9 by 9 km grid cells. We calculate non-linear transformations of temperature and precipitation at the original resolution of the data before aggregating to the level of protected area-year. Specifically, we calculate squared and cubed precipitation in meters, and degree hours in 3 C bins. For example, an observation with a temperature of 13 C would have a value of 2 in the 11-to-14 C bin (because 13 minus 11 is 2) and a value of 0 in all other bins. We convert from degree hours to degree days, and consolidate some of the sparse degree day bins. The weather control variables we include in our regressions are a third-order polynomial in precipitation in m, and the following 12 degree day bins: -19 to 5 C, 5 to 8 C, 8 to 11 C, 11 to 14 C, 14 to 17 C, 17 to 20 C, 20 to 23 C, 23 to 26 C, 26 to 29 C, 29 to 32 C, 32 to 35 16