Marine Conservation Network Effectiveness Indicators

library(MarConsNetAnalysis)
library(MarConsNetData)

bioregion <- data_bioregion()

areas <- data_CPCAD_areas(bioregion,zones=FALSE) |> 
  dplyr::mutate(area=sf::st_area(geoms))

areas_zones <- data_CPCAD_areas(bioregion,zones=TRUE)

kba <- data_KBA(bioregion)
ebsa <- data_EBSA(bioregion)
sarch <- data_SAR_CH(bioregion)

Global Biodoversity Framework - Target 3

Coverage of protected areas and OECMs

totalarea <- as.numeric(sf::st_area(bioregion))
mpacoverage <- areas_zones |> 
  dplyr::rowwise() |> 
  dplyr::mutate(cover=ind_coverage(geoms,bioregion,intersection=TRUE,proportion=FALSE),
                mgmt=dplyr::if_else(MGMT_E=="Fisheries And Oceans Canada",
                                    "DFO",
                                    "Other")) |> 
  as.data.frame() |> 
  dplyr::group_by(TYPE_E,mgmt) |> 
  dplyr::summarise(`Bioregion Coverage (%)`=sum(cover))
totalcoverage <- sum(mpacoverage$`Bioregion Coverage (%)`)
#The Scotian Shelf Bioregion is `r round(totalcoverage,2)`% covered by protected areas and OECMs.

ggplot2::ggplot(mpacoverage)+
  ggplot2::geom_col(ggplot2::aes(x=TYPE_E,y=`Bioregion Coverage (%)`))+
  ggplot2::scale_x_discrete(guide = ggplot2::guide_axis(angle = 30))+
  ggplot2::facet_wrap(~mgmt, scales="free")+
  ggplot2::theme_classic()+
  ggplot2::theme(axis.title.x = ggplot2::element_blank())

Protected area coverage of Key Biodiversity Areas

kbacoverage <- ind_coverage(areas,kba,intersection = TRUE)

The protected areas and OECMs cover round(kbacoverage,2)% of the EBSAs in the bioregion

ebsacoverage <- ind_coverage(areas,ebsa,intersection = TRUE)

The protected areas and OECMs cover round(ebsacoverage,2)% of the EBSAs in the bioregion

Protected Area Management Effectiveness (PAME)

We would need to pick a method and do a whole process with MPC, but for now unquantified!

ProtConn

distkm <- calc_in_sea_distance(cellsize=100000,bioregion,areas)

PC <- data.frame()
for(dkm in c(10,20,50,100,200)){
  PC <- dplyr::bind_rows(PC,
                         data.frame(dkm,
                                    ProtConn = ind_ProtConn(distkm,dkm,bioregion)
                         ))
}

ggplot2::ggplot(PC |> dplyr::mutate(`Median Dispersal Distances (km)`=factor(dkm,levels=unique(dkm))))+
  ggplot2::geom_col(ggplot2::aes(x=`Median Dispersal Distances (km)`,y=ProtConn))+
  ggplot2::geom_hline(yintercept=sum(mpacoverage$cover),linetype = "dashed")+
  ggplot2::theme_classic()

Protected Area Connectedness Index (PARC-Connectedness)

Terrestrial only!

Red List of Ecosystems Connectivity Indicator (in development)

In development…

The number of protected areas that have completed a site-level assessment of governance and equity (SAGE)

Species Protection Index

SPI <- ind_coverage(areas,sarch,intersection = TRUE)

The Species Protection Index is round(SPI,2)%