“A penguin is an angel that we forgot a little too long in the refrigerator.”

— Michel Laubu

Let’s go!

Information about data

Seabirds and the status of their populations can provide critical insights on the state of their surrounding marine environment, hence, they are often considered as indicator. The penguin species that breed in Antarctica and forage in the Southern Ocean, feed extensively on krill or are ice-obligates, and are exemplar species for studying the potential links between system fluctuations and predator populations or Antarctic marine ecosystem responses to environmental perturbations. We considered four species, the Adélie (Pygoscelis adeliae), Chinstrap (Pygoscelis antarcticus), Gentoo (Pygoscelis papua) and Emperor Penguins (Aptenodytes forsteri), which either have large proportions of their global populations breeding in Antarctica, or are endemic to Antarctica.

setting up

all libraries

library(readr)
library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)
library(ggOceanMapsData)
library(ggOceanMaps)
library(sf)


library(vembedr)

initialisation

df2 <- read_csv("C:/Users/herzb/Desktop/Data_Sheet_1_Marine Important Bird and Biodiversity Areas for Penguins in Antarctica, Targets for Conservation Action.CSV")

Maping antartica

First, I’m trying to see if geom_hex() and geom_spatial_point() can give us some useful information.

ggplot(data = df2, aes(x = longitude_epsg_4326, y = latitude_epsg_4326)) +
  geom_spatial_point(crs = 4326)

df2 %>% ggplot(aes(x=longitude_epsg_4326, y=latitude_epsg_4326)) + geom_hex()

As we can see, geom_hex() is giving us a bit more of information because we have one point sector that seems to have more count, at longitude -100 and latitude [-70;-60]. Nevertheless, it’s not necessary to try harder with those code, because it’s difficult to understand where we are exactly. Antarctica is at a place to specific (in a pole), that require better representation.

Now we are going to see those same points (representing penguin), with 2 differents maps.

world_map <- map_data("world")
ggplot(world_map, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill="lightgray", colour = "white") +
  geom_point(data = df2, aes(x = longitude_epsg_4326, y = latitude_epsg_4326), inherit.aes = FALSE)

basemap(limits = -60, glaciers = TRUE) +
geom_spatial_point(data = df2, aes(x = longitude_epsg_4326, y = latitude_epsg_4326), color = "blue")
## Assuming `crs = 4326` in stat_spatial_identity()

Both maps are interesting in they own ways. but something is missing. We have four species of penguin, it could be interesting to see how those species are located on the maps.

Now we have groups of penguin!

df2 <- df2 %>% mutate(common_name = replace(common_name, common_name == "ADP", "Adélie")) %>%
  mutate(common_name = replace(common_name, common_name == "CHP", "Chinstrap")) %>%
  mutate(common_name = replace(common_name, common_name == "GEP", "Gento")) %>%
  mutate(common_name = replace(common_name, common_name == "EMP", "Emperor")) %>%
  rename(Species = common_name)


world_map <- map_data("world")
ggplot(world_map, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill="lightgray", colour = "white") +
  geom_point(data = df2, aes(x = longitude_epsg_4326, y = latitude_epsg_4326, color=Species), inherit.aes = FALSE)

basemap(limits = -60, glaciers = TRUE) +
geom_spatial_point(data = df2, aes(x = longitude_epsg_4326, y = latitude_epsg_4326, color = Species))
## Assuming `crs = 4326` in stat_spatial_identity()

As you can see, these species are present all around the coasts. 2 other species are located only in a specific area!

Thank you!

Paper that gives the dataframe:

@ARTICLE{10.3389/fmars.2020.602972,

AUTHOR={Handley, Jonathan and Rouyer, Marie-Morgane and Pearmain, Elizabeth J. and Warwick-Evans, Victoria and Teschke, Katharina and Hinke, Jefferson T. and Lynch, Heather and Emmerson, Louise and Southwell, Colin and Griffith, Gary and Cárdenas, César A. and Franco, Aldina M. A. and Trathan, Phil and Dias, Maria P.},

TITLE={Marine Important Bird and Biodiversity Areas for Penguins in Antarctica, Targets for Conservation Action},

JOURNAL={Frontiers in Marine Science},

VOLUME={7},

YEAR={2021},

URL={https://www.frontiersin.org/article/10.3389/fmars.2020.602972},

DOI={10.3389/fmars.2020.602972},

ISSN={2296-7745},

ABSTRACT={Global targets for area-based conservation and management must move beyond threshold-based targets alone and must account for the quality of such areas. In the Southern Ocean around Antarctica, a region where key biodiversity faces unprecedented risks from climate change and where there is a growing demand to extract resources, a number of marine areas have been afforded enhanced conservation or management measures through two adopted marine protected areas (MPAs). However, evidence suggests that additional high quality areas could benefit from a proposed network of MPAs. Penguins offer a particular opportunity to identify high quality areas because these birds, as highly visible central-place foragers, are considered indicator species whose populations reflect the state of the surrounding marine environment. We compiled a comprehensive dataset of the location of penguin colonies and their associated abundance estimates in Antarctica. We then estimated the at-sea distribution of birds based on information derived from tracking data and through the application of a modified foraging radius approach with a density decay function to identify some of the most important marine areas for chick-rearing adult penguins throughout waters surrounding Antarctica following the Important Bird and Biodiversity Area (IBA) framework. Additionally, we assessed how marine IBAs overlapped with the currently adopted and proposed network of key management areas (primarily MPAs), and how the krill fishery likely overlapped with marine IBAs over the past five decades. We identified 63 marine IBAs throughout Antarctic waters and found that were the proposed MPAs to be adopted, the permanent conservation of high quality areas for penguin species would increase by between 49 and 100% depending on the species. Furthermore, our data show that, despite a generally contracting range of operation by the krill fishery in Antarctica over the past five decades, a consistently disproportionate amount of krill is being harvested within marine IBAs compared to the total area in which the fishery operates. Our results support the designation of the proposed MPA network and offer additional guidance as to where decision-makers should act before further perturbation occurs in the Antarctic marine ecosystem.}