### Data and software

This analysis used two main data sources: (1) annual (through 2020) summaries of landings by species and by region provided by the Atlantic Coastal Cooperative Statistics Program (ACCSP), and (2) vessel-tracking data provided by Global Fishing Watch. The ACCSP is a cooperative state-federal program of U.S. states and the District of Columbia; it was established in 1995 to be the principal source of fisheries-dependent information on the Atlantic Coast of the United States. For the ACCSP data, I obtained annual landings by species for the North Atlantic region, Mid Atlantic region, and South Atlantic region (excluding landings from the Gulf of Mexico). The weekly cumulative landings data was obtained from the NOAA Fisheries Greater Atlantic Quota Monitoring website. Global Fishing Watch is an organization that provides access to information on commercial fishing activities, in particular information on the identity and location of fishing vessels^{34}. Many large vessels use a system known as the Automatic Identification System (AIS) to avoid collisions at sea, broadcast their location to port authorities and other vessels, and to view other vessels in their vicinity. Vessels fitted with AIS transceivers can be observed by AIS base stations and by satellites fitted with AIS receivers. The US Coast Guard requires all vessels larger than 65 feet to have an AIS receiver onboard. Global Fishing Watch obtains AIS data for fishing vessels and enables users with Internet access to monitor fishing activity globally, and to view individual vessel tracks. They also partner with academic researchers to provide more fine-scale data.

To obtain the vessel-tracking data for the relevant fisheries, I reviewed NOAA databases of squid and mackerel permits (2019 version; vessels with squid permits are automatically issued a Butterfish permit), and the Atlantic tuna permits (2020 version) and matched each permitted vessel to its unique Maritime Mobile Service Identity (MMSI) number, which is associated with Global Fishing Watch tracking information. I was able to identify 84% (187/224) of squid/butterfish permitted vessels (I focused on the SMB1A (Tier 1) permit category associated with the vast majority ( approx. 99%) of squid catch^{35}), 100% of Tier 1 and Tier 2 mackerel-permitted vessels (56/56 vessels), and 74% of active tuna longline vessels (100/135 vessels). “Active” is defined as having reported successfully setting pelagic longline gear at least once between 2006 and 2012^{36}. This translates to a total of 17.55 million observations on fishing vessel locations for all three fisheries. I drop any observations that are missing either a latitude or longitude entry. For the squid and mackerel fisheries, I drop any observations with unusual longitudes ((ge 0^{circ }) and (<-,90^{circ })) and latitudes ((le 30^{circ })). For the tuna longline fishery, I drop any observations with a longitude less than (-,82^{circ }) to exclude fishing within the Gulf of Mexico as well as a vessel that takes a trip through the Panama canal. GFW uses a machine learning algorithm to classify each observed vessel location as either engaged in fishing or not engaged in fishing (for example in transit)^{37}. I use the same classification and define all recorded fishing locations as fishing activity and classify all recorded locations (fishing and non-fishing) as vessel activity.

An important question for the GFW data is how extensive is the coverage of the vessels identified. In Fig. 14, I plot a series of histograms showing the percentage of days in a calendar year that a vessel is observed at least once. For 2021, the calendar year ends on April 30th. It can be seen that coverage has improved considerably since 2012 and is now close to 50% for most fisheries. This mean that the location of a vessel is observed, on average, at least once every 2 days. Is this adequate coverage to get a clear picture of vessel activity? It is difficult to answer this question with a high degree of certainty but the answer appears to be yes. For example, in 2018 there was a total of 5623 sets made by vessels in the pelagic longline fishery for Atlantic tuna and swordfish^{38}. Considering that there are around 135 active vessels, this corresponds to 42 sets per vessel per year. Therefore a lower bound on days at sea would be around 84 and an upper bound would be around 126. A similar conclusion is reached by looking at information on average trips per year (10) and average days per trip (10): 100 days at sea per year^{39}. Since the mean number of days observed in the tuna longline fishery has been above 146 since 2018, this suggests very good coverage of vessel activity. It is more challenging to estimate the degree of daily coverage for squid and mackerel vessels since these vessels are often permitted to operate in multiple fisheries and may operate year-round targeting different species at different times. I do note that the seasons for both species are relatively short (the majority of landings occur within a 5 months window) and mean coverage has been above 40% since 2017 (146 days, just under 5 months).

Finally, the bathymetry data displayed in Fig. 1 was obtained from the GEBCO Compilation Group (2020) GEBCO 2020 Grid (doi:10.5285/a29c5465-b138-234d-e053-6c86abc040b9). All analyses were performed in R version 4.1.1 and RStudio 1.4.1717^{40}. All the code necessary to replicate the figures, tables, and analysis in this paper is provided here: https://github.com/lynham/atlantic_monument.

### Blue paradox tests

Near the monument is defined as occurring within a box with bounding coordinates of ((40.6^{circ }), (-,68.268^{circ })) and ((38.865^{circ }), (-,65.943^{circ })). These coordinates correspond to the most northern, most western, most southern, and most eastern coordinates within the monument itself (coordinates provided by NOAA).

### Distance traveled

To calculate distance traveled, I group vessel locations by vessel and then order them in chronological order. I use the Haversine formula to calculate the distance between all chronological locations. I do not calculate a distance between two locations if the elapsed time between locations is greater than 48 h. The second location is coded as missing for the distance variable (this is typically less than 0.5% of all observations for each fishery). I then sum distance traveled by month by vessel.

### Regression specifications

In Column (1) of Table 2, I estimate a regression of the following form:

$$begin{aligned} {text{Obs}}_{i,t}=beta _{0} + beta _{1}{text{Inside}}_{i} + beta _{2}{text{After}}_{t} + beta _{3}{text{Inside}}_{i}*{text{After}}_{t} + u_{i,t} end{aligned}$$

(1)

where the outcome variable (({text{Obs}}_{i,t})) is the total number of observed vessel locations in area *i* on day *t*. ({text{Inside}}_{i}) is an indicator variable for whether the observed vessel locations are inside the monument, ({text{After}}_{t}) is a time-dependent indicator variable for after the announcement of the monument but before its actual closure (September 15, 2016–November 14, 2016), and ({text{Inside}}_{i}*{text{After}}_{t}) is the two indicator variables interacted with (i.e. multiplied by) each other. (u_{i,t}) is an unobserved error term. The primary coefficient of interest is (beta _{3}), which can be interpreted as the increase in vessel activity inside the monument relative to nearby areas, following the announcement but prior to actual closure. In other words, the Blue Paradox effect.

In Column (2) of Table 2, I estimate a regression of the following form:

$$begin{aligned} {text{Inside}}_{i,t}=beta _{0} + beta _{1}{text{After}}_{t} + {{mathbf{v}_{mathbf{i}}}{boldsymbol{‘phi }}} + u_{i,t} end{aligned}$$

(2)

where the outcome variable (({text{Inside}}_{i,t})) is an indicator variable for whether vessel *i* is inside the monument at time *t*. ({text{After}}_{t}) is a time-dependent indicator variable for days after the announcement of the monument but before its actual closure (September 15, 2016–November 14, 2016), **v**_{i} is a vector of individual vessel dummies to account for vessel-specific differences in fishing preferences, and (u_{i,t}) is an unobserved error term. This regression serves as a simple test of whether vessels were more likely to be in the monument after the announcement but prior to its closure.

The main regression equation I estimate in Table 3 is the following:

$$begin{aligned} y_{i,t}=beta _{0} + beta _{1}{text{MON}}_{t} + beta _{2}{text{Region}}_{i} + beta _{3}{text{Region}}_{i}*{text{MON}}_{t} + u_{i,t} end{aligned}$$

(3)

where the primary outcome variable ((y_{i,t})) is the log of landings for region *i* in year *t*. ({text{MON}}_{t}) is a dummy variable for the closure of the monument, ({text{Region}}_{i}) is a dummy variable indicating whether the landings are for the impacted region, and ({text{Region}}_{i}*{text{MON}}_{t}) is the two dummy variables interacted with (i.e. multiplied by) each other. (u_{i,t}) is an unobserved error term. The primary coefficient of interest is (beta _{3}), which can be interpreted as the effect of the monument closure on landings in the impacted region. Equation 3 is a difference-in-differences equation^{41,42,43}, which implicitly controls for changes in factors such as market forces or fishery management rules that affect both regions. The setup is equivalent to a Before-After-Control-Impact (BACI) design in ecological research using observational data^{44,45}.

In Table 4 I estimate regressions of the following form:

$$begin{aligned} y_{i,t}=beta _{0} + beta _{1}{text{MON}}_{t} + {{mathbf{v}}_{{mathbf{i}}}{boldsymbol{‘phi }}} + u_{i,t} end{aligned}$$

(4)

where (y_{i,t}) is the outcome variable of interest (monthly distance traveled) for vessel *i* in month *t*. (beta _{0}) is the standard intercept term and (beta _{1}) is the main slope parameter of interest. ({text{MON}}_{t}) is a dummy variable that takes the value of 0 for all dates when the monument is open to fishing and 1 for all dates when the monument is closed to fishing. **v**_{i} is a vector of individual vessel dummies. The reason for including the vessel dummies is that not all vessels show up in the GFW database prior to the closure of the monument. Thus, the interpretation of (beta _{1}) is the average effect of the monument on monthly distance traveled, after controlling for the fact that different vessels tend to have higher or lower monthly averages to begin with.

Source: Ecology - nature.com