The Antarctic Penguin Biogeography Project is an effort to collate all
known information about the distribution and abundance of Antarctic
penguins through time and to make such data available to the scientific
and management community. The core data product involves a series of
structured tables with information on known breeding sites and surveys
conducted at those sites from the earliest days of Antarctic exploration
through to the present. This database, which is continuously updated as
new information becomes available, provides a unified and comprehensive
repository of information on Antarctic penguin biogeography that
contributes to a growing suite of applications of value to the Antarctic
community (Che-Castaldo et al., 2023a). One such application is the
Mapping Application for Antarctic Penguins and Projected Dynamics
(MAPPPD; www.penguinmap.com), a browser-based search and visualization
tool designed specifically for the management community and other
non-specialists (Humphries et al., 2017), and as a Darwin Core archive
distributed through the Ocean Biodiversity Information System (OBIS) and
Global Biodiversity Information Facility (GBIF) clearing houses
(Che-Castaldo et al., 2023b). The mapppdr R package makes this
database more easily available to the scientific community by providing
its tables as data frames (one with simple features), along with the
BibTeX citation data as a bibentry object and the function penmap for
visualizing penguin breeding colony locations using leaflet. The
Antarctic Penguin Biogeography Project has been funded by the National
Aeronautics and Space Administration (NASA), the Pew Fellowship for
Marine Conservation, and the Institute for Advanced Computational
Science at Stony Brook University.
The package contains 12 data frames, 1 bibentry object, and 1
function:
penguin_obs- data frame containing all publicly available Pygoscelis, emperor, king and macaroni penguin counts and presence-absence data included in the APBP database at breeding sites south of 60 degrees S latitude in the APBP databasecitations- data frame containing bibliographic identifiers for all citations in the APBP databasesites- data frame containing the name and location of all known Pygoscelis, emperor, king and macaroni penguin breeding sites south of 60 degrees S latitude and all Antarctic Site Inventory (ASI) penguin/seabird census sites included in the APBP databasesites_sf-sitesdata frame with locations stored as simple features POINT geometriesspecies- data frame containing the names of all penguin species included in the APBP databasesite_species- data frame linking Pygoscelis, emperor, king and macaroni penguin species to their breeding sites in the APBP databasearticles- data frame containing citation information specific to journal articles whose data are included in the APBP databasecollections- data frame containing citation information specific to books or conference proceedings containing data included in the APBP databaseincollections- data frame containing citation information specific to book chapters or conference papers whose data are included in the APBP databaseunpublished- data frame containing citation information specific to unpublished data included in the APBP databasepersons- data frame containing bibliographic identifiers for all people who contributed data to the APBP databasecitation_persons- data frame linking contributors to their citations in the APBP databasemapppd_bib-BibEntryobject containing bibliographic information for all citations in the APBP databasepenmap- function that creates a leaflet map of all known Pygoscelis, emperor, king and macaroni penguin breeding sites south of 60 degrees S latitude included in the APBP database
This database is licensed under a Creative Commons Attribution 4.0 International License.
All code is licensed under a The GNU General Public License v3.0.
You can install the latest version (2.4) from Github with:
install.packages('devtools')
devtools::install_github('CCheCastaldo/mapppdr', build_vignettes = TRUE)Interacting with mapppdr is easily done using the following R packages
/ collections of packages. The first three (tidyverse, RefManageR,
and sf) are for manipulating the APBP data, while htmlwidgets is
useful for exporting penguin map leaflets (described below).
library(tidyverse)
library(RefManageR)
library(sf)
library(htmlwidgets)All counts and presence/absence data can be found in the penguin_obs
data frame. The site_id field references site information from the
sites or sites_sf data frames (the latter being just a simple
features version of the former). The species_id field references
species information from the species data frame. The citekey field
references bibliographic data that can be found in the citations data
frame and several different additional bibliographic data frames
(depending on the type of citation). The citekey also references the
cite key in the mapppd_bib bibentry object.
data(penguin_obs)
head(penguin_obs)## site_id species_id citekey month day doy date year season
## 1 ACUN ADPE coria2011laurie NA NA NA <NA> 1993 1993
## 2 ACUN ADPE woehler1997status NA NA NA <NA> 1994 1994
## 3 ACUN ADPE coria2011laurie NA NA NA <NA> 2004 2004
## 4 ACUN ADPE lynch2014global 2 25 56 2011-02-25 2011 2010
## 5 ACUN CHPE poncet1985survey 12 28 362 1983-12-28 1983 1983
## 6 ACUN CHPE coria2011laurie NA NA NA <NA> 2004 2004
## type presence count accuracy vantage
## 1 nests 1 2008 1 ground
## 2 nests 1 1920 1 <NA>
## 3 nests 1 1880 1 ground
## 4 nests 1 3079 5 vhr
## 5 nests 1 4000 4 ground
## 6 nests 1 7716 1 ground
It is easy to subset the data in various ways using the tidyverse. For example, the following code returns all the chinstrap nest counts in CCAMLR Sub area 48.2 from 1990 - 2000:
chinstrap_counts <- penguin_obs %>%
dplyr::filter(type == "nests" & year %in% c(2000:2010) & species_id == "CHPE") %>%
left_join(sites, by = "site_id") %>%
dplyr::filter(ccamlr_id == "48.2")
head(chinstrap_counts)## site_id species_id citekey month day doy date year season type
## 1 ACUN CHPE coria2011laurie NA NA NA <NA> 2004 2004 nests
## 2 FERG CHPE coria2011laurie NA NA NA <NA> 2004 2004 nests
## 3 GEDD CHPE coria2011laurie NA NA NA <NA> 2004 2004 nests
## 4 LOLA CHPE coria2011laurie NA NA NA <NA> 2004 2004 nests
## 5 MILL CHPE coria2011laurie NA NA NA <NA> 2004 2004 nests
## 6 PAND CHPE woehlerpersonal NA NA NA <NA> 2009 2009 nests
## presence count accuracy vantage site_name region
## 1 1 7716 1 ground Acuna Island South Orkney Islands
## 2 1 7268 2 ground Ferguslie Peninsula South Orkney Islands
## 3 1 7116 2 ground Cape Geddes South Orkney Islands
## 4 1 855 1 ground Lola Point South Orkney Islands
## 5 1 886 1 ground Mill Cove South Orkney Islands
## 6 1 2385 1 ground Pandemonium Point South Orkney Islands
## ccamlr_id latitude longitude
## 1 48.2 -60.76120 -44.63700
## 2 48.2 -60.69300 -44.56700
## 3 48.2 -60.68500 -44.59000
## 4 48.2 -60.72385 -44.74222
## 5 48.2 -60.75300 -44.61400
## 6 48.2 -60.73600 -45.64800
You can also use spatial information to subset count data. This is quite
easy using the sf package
tools. For
instance, the following code selects counts from the 22 sites whose
great circle distances are within 10 km of ACUN (including ACUN itself):
ACUN_area_counts <- sites_sf %>%
mutate(ACUN_distance = as.numeric(sf::st_distance(sites_sf, sites_sf %>%
dplyr::filter(site_id == "ACUN")))) %>%
dplyr::filter(ACUN_distance <= 10000) %>%
dplyr::select(site_id) %>%
inner_join(penguin_obs, by = "site_id")
st_geometry(ACUN_area_counts) <- NULL
glimpse(ACUN_area_counts)## Rows: 56
## Columns: 14
## $ site_id <chr> "ACUN", "ACUN", "ACUN", "ACUN", "ACUN", "ACUN", "ACUN", "AI…
## $ species_id <chr> "ADPE", "ADPE", "ADPE", "ADPE", "CHPE", "CHPE", "MCPE", "CH…
## $ citekey <chr> "coria2011laurie", "woehler1997status", "coria2011laurie", …
## $ month <int> NA, NA, NA, 2, 12, NA, NA, 12, 2, 12, 1, 12, 1, 12, NA, NA,…
## $ day <int> NA, NA, NA, 25, 28, NA, NA, 28, 10, 28, 22, 28, 22, 28, NA,…
## $ doy <int> NA, NA, NA, 56, 362, NA, NA, 362, 41, 362, 22, 362, 22, 362…
## $ date <date> NA, NA, NA, 2011-02-25, 1983-12-28, NA, NA, 1983-12-28, 20…
## $ year <int> 1993, 1994, 2004, 2011, 1983, 2004, 1993, 1983, 2020, 1983,…
## $ season <int> 1993, 1994, 2004, 2010, 1983, 2004, 1993, 1983, 2019, 1983,…
## $ type <chr> "nests", "nests", "nests", "nests", "nests", "nests", "nest…
## $ presence <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,…
## $ count <int> 2008, 1920, 1880, 3079, 4000, 7716, 2, 6000, 3000, 3300, 30…
## $ accuracy <int> 1, 1, 1, 5, 4, 1, 1, 4, 5, 4, 5, 3, 5, 4, 2, 5, 1, 1, 4, 1,…
## $ vantage <chr> "ground", NA, "ground", "vhr", "ground", "ground", "ground"…
A core APBP project goal is to reliably report the observation metadata,
and in this regard we work very hard to make sure all data in the
database is properly cited and original data sources made easily
available to end-users. One challenge with the APBP database
bibliography is that fact that many of citations contain special
characters (accents, umlauts, etc.). To accomplish this we allow the
bibliographic data to retain accents through UTF-8 special.
Bibliographic data in mapppdr is stored in a set of data frames that
allow for easy subsetting of the actual observation data using the
citekey field. For instance. the following code returns all journal
articles containing penguin counts where Néstor R. Coria is an author,
before subsetting penguin_obs for the counts themselves.
coria_citations <- persons %>%
dplyr::filter(family == "Coria" & given == "Néstor R.") %>%
inner_join(citation_persons, by = "person_id") %>%
dplyr::select(citekey) %>%
inner_join(articles, by = "citekey") %>%
dplyr::select(citekey, journal, doi, title)
glimpse(coria_citations)## Rows: 9
## Columns: 4
## $ citekey <chr> "carlini2009responses", "coria1995breeding", "coria2011laurie"…
## $ journal <chr> "Polar Biology", "Marine Ornithology", "Marine Ornithology", "…
## $ doi <chr> "10.1007/s00300-009-0637-y", NA, NA, NA, NA, "10.3402/polar.v3…
## $ title <chr> "Responses of Pygoscelis adeliae and P. papua populations to e…
coria_counts <- penguin_obs %>%
inner_join(coria_citations %>% dplyr::select(citekey), by = "citekey")
head(coria_counts)## site_id species_id citekey month day doy date
## 1 ACUN ADPE coria2011laurie NA NA NA <NA>
## 2 ACUN ADPE coria2011laurie NA NA NA <NA>
## 3 ACUN CHPE coria2011laurie NA NA NA <NA>
## 4 ALCO CHPE favero2000status 1 5 5 1998-01-05
## 5 ALCO CHPE gonzalez-zevallos2013abundance 2 9 40 2011-02-09
## 6 CHPT CHPE favero2000status 1 5 5 1998-01-05
## year season type presence count accuracy vantage
## 1 1993 1993 nests 1 2008 1 ground
## 2 2004 2004 nests 1 1880 1 ground
## 3 2004 2004 nests 1 7716 1 ground
## 4 1998 1997 nests 1 605 2 ground
## 5 2011 2010 nests 1 1100 1 ground
## 6 1998 1997 nests 1 13 2 ground
We could also easily print the full bibliographic information for these
particular counts, as well as export them as a .bibTeX file. This file
could be used in LaTeX, Overleaf, or imported into other reference
management software, such as Zotero.
out <- mapppd_bib[mapppd_bib$key %in% coria_citations$citekey]
RefManageR::WriteBib(out, file = "~/Desktop/Coria_citations.bib")## Writing 9 Bibtex entries ... OK
## Results written to file '~/Desktop/Coria_citations.bib'
print(out, .opts = list(bib.style = "authoryear", style = "markdown"))## Writing 9 Bibtex entries ... OK
## Results written to file '~/Desktop/Coria_citations.bib'
Carlini, A. R., N. R. Coria, M. M. Santos, et al. (2009). “Responses of Pygoscelis adeliae and P. papua populations to environmental changes at Isla 25 de Mayo (King George Island)”. In: Polar Biology 32.10, pp. 1427-1433. DOI: 10.1007/s00300-009-0637-y.
Coria, N. R., M. Favero, M. P. Silva, et al. (1995). “Breeding birds at Duthoit Point, Nelson Island, South Shetland Islands, Antarctica”. In: Marine Ornithology 23.1, pp. 61-64.
Coria, N. R., D. Montalti, E. F. Rombolá, et al. (2011). “Birds at Laurie Island, South Orkney Islands, Antarctica: Breeding species and their distribution”. In: Marine Ornithology 39.2, pp. 207-213.
Esponda, C. G., N. R. Coria, and D. Montalti (2000). “Breeding birds at Halfmoon Island, South Shetland Islands, Antarctica, 1995/96”. In: Marine Ornithology 28.1, pp. 59-62.
Favero, M., N. R. Coria, and M. P. Beron (2000). “The status of breeding birds at Cierva Point and surroundings, Danco Coast, Antarctic Peninsula”. In: Polish Polar Research 21.3-4, pp. 181-187.
González-Zevallos, D., M. M. Santos, E. F. Rombolá, et al. (2013). “Abundance and breeding distribution of seabirds in the northern part of the Danco Coast, Antarctic Peninsula”. In: Polar Research 32.0, pp. 1-7. DOI: 10.3402/polar.v32i0.11133.
Juáres, M. A., R. J. Casaux, J. Negrete, et al. (2020). “Update of the population size and breeding performance of gentoo penguins (Pygoscelis papua) at Stranger Point/Cabo Funes, South Shetland Islands”. In: Polar Biology 43.2, pp. 123-129. DOI: 10.1007/s00300-019-02614-0.
Juáres, M. A., M. M. Santos, J. Negrete, et al. (2015). “Adélie penguin population changes at Stranger Point: 19 years of monitoring”. In: Antarctic Science 27.05, pp. 455-461. DOI: 10.1017/S0954102015000152.
Santos, M. M., J. T. Hinke, N. R. Coria, et al. (2018). “Abundance
estimation of Adélie penguins at the Esperanza/Hope Bay mega colony”.
In: Polar Biology 41.11, pp. 2337-2342. DOI:
10.1007/s00300-018-2373-7.
The function penmap allows for quick visualizations of penguin
breeding sites in Antarctica using leaflet. The function takes a single
argument, species_map, which is just the list of species (using their
species_id codes) whose sites you wish to display on the map. These
species are listed as a character vector, whose default is all four
species. Colonies are clustered but as you zoom in (either by direct
zooming or by clicking on colony clusters), individual sites appear as
blue circles. Clicking on circles will reveal the site code, name, and
what penguin species are breeding there. You can click on the globe
button in the top left (directly below the zoom buttons) to zoom back
and and recenter the map. Sometimes, sites are so close to another that
resolving a cluster will set the zoom to maximum, potentially obscuring
other colonies nearby. To prevent this you can lock and unlock the
clusters by clicking on the button below the globe button. Locking
clusters will cause sites to fan out in a circle around the cluster but
at the current zoom level when clicked. You can disable this behavior by
clicking on this button again. The map provides several helpful layers.
These include the CCAMLR Statistical Areas and Divisions and Marine
Protected Areas, the Antarctic Coastline (all exported from the
CCCAMLRGIS R package) and the Landsat Image Mosaic Of Antarctica
(LIMA) (Bindschadler et al. 2008) WMS tiles hosted by the Polar
Geospatial Center.
The following code will create an Adélie only leaflet, which you can then open in a browser by clicking on the Show in New Window Viewer button (located to the right of the broomstick icon).
penmap("ADPE")
You can also export and share leaflets quite easily. For instance, the
following code will save the Adélie only leaflet to your Desktop. You
can then share this html file, which can be opened directly in a web
browser.
htmlwidgets::saveWidget(penmap("ADPE"), file = "~/Desktop/ADPE_map.html")Bindschadler, R., P. Vornberger, A. Fleming, A. Fox, J. Mullins, D. Binnie, S.J. Paulsen, B. Granneman, and D. Gorodetzky. (2008). “The Landsat image mosaic of Antarctica”. Remote Sensing of Environment 112(12): 4214-4226, https://doi.org/10.1016/j.rse.2008.07.006
Che-Castaldo, C., G.R.W. Humphries, and H.J. Lynch. (2023a). Antarctic Penguin Biogeography Project: Database of abundance and distribution for the Adélie, chinstrap, gentoo, emperor, macaroni and king penguin south of 60 S. Biodiversity Data Journal 11: e101476, https://doi.org/10.3897/BDJ.11.e101476
Che-Castaldo, C., G.R.W. Humphries, H. J.Lynch H and A. Van de Putte. (2023b). Antarctic Penguin Biogeography Project: Database of abundance and distribution for the Adélie, chinstrap, gentoo, emperor, macaroni, and king penguin south of 60 S. Version 2.3. SCAR - AntOBIS. Samplingevent dataset, https://doi.org/10.48361/zftxkr
Humphries, G.R.W., R. Naveen, M. Schwaller, C. Che-Castaldo, P. McDowall, M. Schrimpf and H.J. Lynch. (2017). “Mapping Application for Penguin Populations and Projected Dynamics (MAPPPD): Data and tools for dynamic management and decision support”. Polar Record 53(2): 160-166, https://doi.org/10.1017/S0032247417000055
McLean, M. W. (2017). “RefManageR: Import and Manage BibTeX and BibLaTeX References in R.” The Journal of Open Source Software, https://doi.org/10.21105/joss.00338
Pebesma, E. (2018). Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10(1): 439-446, https://doi.org/10.32614/RJ-2018-009
Thanassekos, S., K. Reid and L. Robinson (2020). CCAMLRGIS: Antarctic Spatial Data Manipulation. R package version 3.0.6. https://CRAN.R-project.org/package=CCAMLRGIS
Vaidyanathan, R., Y. Xie, J.J. Allaire, J. Cheng and K. Russell (2019). htmlwidgets: HTML Widgets for R. R package version 1.5.1. https://CRAN.R-project.org/package=htmlwidgets
Wickham et al. (2019). Welcome to the tidyverse. Journal of Open Source Software 4(43): 1686, https://doi.org/10.21105/joss.01686