record the quantity of bird species they encounter. Further details, such as the number of observers participating in the trip, the hours spent birding, and a geotagged timestamp, enrich the dataset. This geolocation data enables us to identify birding trips within specific protected areas, and the trip duration and number of observers data allow us to control for survey effort.

Contrary to our other analyses, our unit of observation in this case is the individual birding trip. This choice is informed by our adoption of the main specification from a recent study that examined the relationship between air pollution regulation and bird abundances using eBird data [59]. Replicating this established specification limits our researcher degrees of freedom. In this specification, the dependent variable is the log total number of birds observed on a trip, and the control variables are duration of the birding trip in hours, the number of observers in the birding party, and fixed effects for hour of day, protected area, and calendar year. We also follow Ref. [59] in dropping the top 1% of bird abundance observations in order to remove outliers. We imputed missing values of birding trip duration and number of observers using their respective means calculated from non-missing values within our dataset, which encompasses eBird observations inside our protected areas of interest. We add as control variables three indicators for whether birding trip duration, number of observers, or hour of day were missing, which allows us to retain these observations in our regression. This regression employs 145,200 observations drawn from 20 treatment group protected areas and 106 control group areas between 1998 and 2022. Despite differences in the unit of observation and control variables, this regression applies the same dynamic difference-in-differences estimator as in our other regressions [49]. Table S7 displays cohort-specific ATTs. Four cohorts have negative ATTs; however, these cohorts contribute only 11% of treatment observations.

Figure S2 exhibits a decreasing trend in bird abundances in the years prior to transference. In the absence of transference to AP management, bird abundances may have continued to decrease. In this scenario, our ATT of -0.318 would underestimate the true increase in bird abundances due to AP management. We use the R package HonestDiD to implement the approach described in Section 2.4.3 of Ref. [98] in order to quantitatively assess this bias. Under the scenario that the pre-trends would have continued in the absence of transference to AP management, the resulting 95% confidence interval is (1.350, 1.730), indicative of a larger increase in bird abundances than our ATT. Thus, the pre-trends in Figure S2 do not invalidate our inference that AP management increases bird abundances.

When we restrict the eBird data to complete checklists only, the number of observations falls to 92,574. We obtain similar results in this case (ATT = 0.306 with a standard error of 0.075, compared to our main specification ATT of 0.318 with a standard error of 0.072).

# **A.2.1 Robustness to Changes in Birder Locations (Figure S3)**

We consider three threats to our interpretation that the post-transference increase in bird abundance per trip represents a true increase in the population of birds because of AP management. First, if AP shifts where birding trips occur within its protected areas toward locations with greater bird abundance, we would overestimate the increase in 4