scallops

## Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUE

Gears

The fishing data were processed and passed through minimal QA/QC. All ports were assigned lat and lons (as best as possible), all landings were assigned to an atlantis group.

SCA is the atlantis group for scallops.

Gear Categories identified, in the data set, as scallop vessels were

Scallop Gear

These GEARCAT(s) were filtered to provide the following output

Landings (All species)

The landings of Scallop trips with associated bycatch

Total scallop landings compared to bycatch

Landings of Scallops by state

#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?

Landings of Scallops by port

Timing of Landings

What is the distribution of effort over the calendar year

sca |> 
  dplyr::select(TRIPID,MONTH,InsideDAS) |> 
  dplyr::distinct() |> 
  dplyr::mutate(MONTH = as.factor(MONTH)) |> 
  dplyr::group_by(MONTH) |> 
  dplyr::summarise(effort = sum(InsideDAS),
                   .groups = "drop") |> 
  ggplot2::ggplot()+
  ggplot2::geom_col(ggplot2::aes(x=MONTH,y=effort)) + 
  ggplot2::ylab("Days at Sea") +
  ggplot2::ggtitle(paste0(range(sca$Year),collapse="-"))

Magnitude of Landings

What is the distribution of landings over the calendar year

sca |> 
  dplyr::filter(Code %in% speciesCodes) |> 
  dplyr::select(MONTH,InsideLANDED) |> 
  dplyr::mutate(MONTH = as.factor(MONTH)) |> 
  dplyr::group_by(MONTH) |> 
  dplyr::summarise(landings = sum(InsideLANDED),
                   .groups = "drop") |> 
  ggplot2::ggplot()+
  ggplot2::geom_col(ggplot2::aes(x=MONTH,y=landings)) + 
  ggplot2::ylab("Metric Tons") +
  ggplot2::ggtitle(paste0(range(sca$Year),collapse="-"))

Select the main ports

The following ports three main ports are selected. Other smaller ports (listed under main port) are combined with main ports. hese ports comprise ~ 90% of total landings.

MASSACHUSETTS
- NEW BEDFORD (240403)
  - FAIRHAVEN (242203)
NEW JERSEY
- CAPE MAY (330309)
  - LONG BEACH (331627)
  - POINT PLEASANT (330127)
VIRGINIA
- NEWPORT NEWS (490910)
  - SEAFORD (490869)
  - HAMPTON (490118)

Landings by Atlantis Box

compared to model output from v2.2.0

Landings (data) by box spatially

#> Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
#> give correct results for longitude/latitude data
#> Warning: `position_dodge()` requires non-overlapping x
#> intervals.

Boxes that contribute 95% of landings 2, 3, 5, 6, 7, 8, 9, 12, 13, 14, 15

Landings (data) by box spatially for main ports

Aggregate remaining data as “other” fleet

The remaining ports (other than the ports listed above) make up ~10% of total landings. All trips associated with these ports will be combined to form one fleet which will fish in the footprint below. This maintains the communities at sea idea for the main ports but takes into account all remaining landings to satisfy model requirements

Total Landings over 1996-2021 = 5.5382^{4} metric tons

Time series of landings by box of other fleet

Effort

The effort variable is Days at sea (DAS) and is a crude metric. Only integer values and rounded up to the nearest whole day. It is the length of the trip. Note: For each record of a trip (multiple records based on the species caught) the DAS is replicated, so to calculate the true DAS we need to find distinct Trip-DAS

After aggregating the top ports based on the decisions found in this section {#sel} and combining all remaining ports into an “other fleet” we can look at the total effort by fleet in units: days at sea

#> Calculating trip effort for main ports
#> calculating landings for main ports
#> calculating effort for other ports
#> calculating landings for other ports
#> combining all data

Effort by box (Time series)

Partition effort by box.

Note: In atlantis effort is entered as as daily effort (days of effort per day). This equates to dividing the annual effort by 365 days. Here we show the effort by box for the top ports (by landings)