mapppdr

MAPPPD in R

The Mapping Application for Penguin Populations and Projected Dynamics (MAPPPD) is a web-based, open access, decision-support tool designed to assist scientists, non-governmental organizations and policy-makers working to meet the management objectives as set forth by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) and other components of the Antarctic Treaty System (ATS). The MAPPPD database, described in Humphries et al. (2017), includes all publicly available published and unpublished count data on emperor, gentoo, Adélie and chinstrap penguins in Antarctica. A front end web interface located at www.penguinmap.com provides free and ready access to count data alongside population models that infer abundance in missing data and allow for straightforward aggregation across multiple populations (see Che-Castaldo et al. 2017). The mapppdr package makes the MAPPPD database tables available as data frames (one with simple features), the MAPPPD BibTeX citation data, and the function penmap for visualizing breeding colony locations using leaflet. The mapppdr package will always be updated before changes are reflected on the web interface at www.penguinmap.com and should be considered the most up-to-date database available on the distribution and abundance of Antarctic penguins.

R packages

mapppdr has been designed as a lightweight interface using well tested and easily available packages for databases and mapping. The first three (tidyverse, RefManageR, and sf) are for manipulating the MAPPPD database, while htmlwidgets is useful for exporting penguin map leaflets (described below).

library(tidyverse)
library(RefManageR)
library(sf)
library(htmlwidgets)

Penguin Count Data

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 using tidyverse functions. 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  morenopersonal    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 using the sf package tools. For example, 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: 55
#> Columns: 14
#> $ site_id    <chr> "ACUN", "ACUN", "ACUN", "ACUN", "ACUN", "ACUN", "AILS", "A…
#> $ species_id <chr> "ADPE", "ADPE", "ADPE", "ADPE", "CHPE", "CHPE", "CHPE", "C…
#> $ citekey    <chr> "coria2011laurie", "woehler1997status", "coria2011laurie",…
#> $ month      <int> NA, NA, NA, 2, 12, NA, 12, 2, 12, 1, 12, 1, 12, NA, NA, NA…
#> $ day        <int> NA, NA, NA, 25, 28, NA, 28, 10, 28, 22, 28, 22, 28, NA, NA…
#> $ doy        <int> NA, NA, NA, 56, 362, NA, 362, 41, 362, 22, 362, 22, 362, N…
#> $ date       <date> NA, NA, NA, 2011-02-25, 1983-12-28, NA, 1983-12-28, 2020-…
#> $ year       <int> 1993, 1994, 2004, 2011, 1983, 2004, 1983, 2020, 1983, 2019…
#> $ season     <int> 1993, 1994, 2004, 2010, 1983, 2004, 1983, 2019, 1983, 2018…
#> $ type       <chr> "nests", "nests", "nests", "nests", "nests", "nests", "nes…
#> $ presence   <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1…
#> $ count      <int> 2008, 1920, 1880, 3079, 4000, 7716, 6000, 3000, 3300, 3000…
#> $ accuracy   <int> 1, 1, 1, 5, 4, 1, 4, 5, 4, 5, 3, 5, 4, 2, 5, 1, 1, 4, 1, 4…
#> $ vantage    <chr> "ground", NA, "ground", "vhr", "ground", "ground", "ground…

Bibliographic Data

A core MAPPPD project goal is to reliably report the observation metadata, and in this regard we work very hard to make sure all data in MAPPPD is properly cited and original data sources made easily available to end-users. One challenge with the MAPPPD bibliography is that fact that many of citations contain special characters (accents, umlauts, etc.). People who contribute data to MAPPPD deserve to have their names spelled correctly. 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: 11
#> Columns: 4
#> $ citekey <chr> "carlini2005effect", "carlini2009responses", "coria1995adelie…
#> $ journal <chr> "Folia Zoologica", "Polar Biology", "Polar Biology", "Marine …
#> $ doi     <chr> NA, "10.1007/s00300-009-0637-y", "10.1007/BF00239717", NA, NA…
#> $ title   <chr> "The effect of Chinstrap penguins on the breeding performance…

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 = "Coria_citations.bib")
#> Writing 11 Bibtex entries ... OK
#> Results written to file 'Coria_citations.bib'
print(out, .opts = list(bib.style = "authoryear", style = "markdown"))

#> Writing 11 Bibtex entries ... OK
#> Results written to file 'Coria_citations.bib'

Carlini, A. R, N. R. Coria, M. M. Santos, et al. (2005). “The effect of Chinstrap penguins on the breeding performance of Adélie penguins”. In: Folia Zoologica 54.1-2, pp. 147-148.

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, P. G. Blendinger, and D. Montalti (1996). “The breeding birds of Cape Geddes, Laurie Island, South Orkney Islands, Antarctica”. In: Marine Ornithology 24.1-2, pp. 43-44.

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.

Coria, N. R, H. Spairani, S. Vivequin, et al. (1995). “Diet of Adélie penguins Pygoscelis adeliae during the post-hatching period at Esperanza Bay, Antarctica, 1987/88”. In: Polar Biology 15.6, pp. 415-418. DOI: 10.1007/BF00239717.

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, 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.

Penguin Map Leaflet

The function penmap allows for quick visualizations of the emperor, gentoo, Adélie and chinstrap penguins 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. Zoomed out, colony clusters are identified. Zooming in (either by direct zooming or by clicking on colony clusters) reveals individual sites as blue circles. Clicking on circles will reveal the site code, name, and the species breeding at that site. Clicking the globe button in the top left (directly below the zoom buttons) will reset the zoom to the pan-Antarctic scale 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 CCAMLRGIS 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 map, 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 leaflet maps quite easily. For instance, the following code will save the Adélie-only leaflet map 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")

Citations

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., S. Jenouvrier, C. Youngflesh, K. Shoemaker, G. Humphries, P. McDowall, L. Landrum, M. Holland, Y. Li, R. Ji, and H.J. Lynch. (2017). “Pan-Antarctic analysis aggregating spatial estimates of Adélie penguin abundance reveals robust dynamics despite stochastic noise”. Nature Communications 8: 832, https://doi.org/10.1038/s41467-017-00890-0

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