AP operates [117] implies AP is more likely to shape local conflict dynamics than regional conflict dynamics. Absent a high degree of centralization, it seems implausible that one contingent of an armed group operating near an AP site could recuperate lost revenue resulting from AP’s activities by coordinating with another contingent to increase the looting of civilians elsewhere.

We test how the relationship between AP and conflict changes when we alter the buffer area used to define our unit of observation (Figure S18). Removing the buffer area altogether largely produces similar results to our main analysis: transference to AP is not significantly related to the probability of protests and riots, increases the probability of civilian targeting (now, at the ten-percent level), and increases the probability of battles (now, at the five-percent level). For the probability of battles and the probability of protests and riots, increasing the buffer radius to 50 kilometers or 75 kilometers produces similar results to our main analysis. However, the effect of transference to AP on the probability of civilian targeting becomes statistically indistinguishable from zero when we increase the buffer radius to 50 kilometers, and then becomes negatively signed and statistically significant when we increase the buffer radius to 75 kilometers. For the reasons described above, we caution against attributing the 75 kilometer decrease in civilian targeting to AP management. The downward shift in these estimates may reflect how larger buffers include in our analysis urban areas where violence against civilians in some African countries is more likely, all else equal [118].

# **A.7 Mechanisms and Management Effectiveness Tracking Tool (METT) data (Table 1)**

METT data contain 3,999 self-assessments of management effectiveness for 2,577 protected areas between 1999 and 2016 [74]. Six protected areas currently managed by AP have METT data for multiple years (treatment group), as do 27 protected areas identified by AP as anchor areas meeting their criteria for future management (control group). The six treatment group protected areas with multiple observations are Pendjari National Park (Benin), W National Park (Benin), Odzala-Kokoua National Park (Republic of Congo), Garamba National Park (Democratic Republic of the Congo), Bangweulu (Zambia), and Kafue National Park (Zambia). The data are so sparse that applying the dynamic difference-in-differences estimator we use for all other analyses results in an error. Consequently, we estimate a two-way fixed effects differences-indifferences regression instead (equivalently, a before-after-control-impact analysis). For a measure of management effectiveness _Y_ in protected area _i_ in calendar year _t_ , we estimate the following equation with ordinary least squares regression: where _APit_ equals 1 if protected area _i_ was managed by AP on or after year _t_ , _γi_ are protected area fixed effects, _δt_ are year fixed effects, _Xit_ is a matrix of other control variables, and _εit_ is the error term. The protected area fixed effects control for all timeinvariant characteristics of each protected area, such as physical geography, while the calendar year fixed effects account for time-varying factors that affect all protected 14