Quick Cartography: Mapping Species and Communities in a Flash

Distribution maps play a vital role in protecting and managing individual species, as well as identifying biodiversity hotspots and sensitive areas with imperiled species NatureServe. Utilizing remote sensing data, such maps are created to assess land surface cover for various purposes such as resource assessment, land management, and modeling (Aspinall, 2002). Incorporating species occurrence information into these maps is essential for providing spatial context for ecological understanding (Barkowsky & Freksa, 1997). However, this process involves prioritizing criteria due to medium constraints, with choices between uniform or selective adjustments (Hijmans & Elith, 2023). While computer-generated maps typically prioritize uniformity for systematic solutions, selective approaches offer tailored maps to meet specific ecological research needs (Austin, 2002; Barkowsky & Freksa, 1997; Hijmans & Elith, 2011; Sillett et al., 2012).

Keyanswers offers a function that enhances data visualization through maps generated using the etopo 1 model. The code for this function is accessible on the Keyanswers GitHub page, where all arguments are explained along with some basic examples. You can find the function code on the GitHub page of Keyanswers. The following maps have their labels in English, French, Quechua, Basque, Italian, Russian, and Spanish; Simply because I could do it.

Thanks for read, be happy.

Single map

d = data.frame(Lon = c(-118 , -108), Lat = c(22.5, 34))
d

##    Lon  Lat
## 1 -118 22.5
## 2 -108 34.0

QMap(d)

Fig. 1: Basic map obtained from a 2x2 data frame dimension

Maps with species data of Farfantepenaeus aztecus

Here is the data for Farfantepenaeus aztecus obtained from GBIF using the rgbif package. Later, it was converted into a data frame, and the column names were explored to select them for plotting occurrence or abundance values.

require('rgbif')
fpaz = occ_data(scientificName = 'Farfantepenaeus aztecus', limit = 1000)

Dfpaz = data.frame(fpaz$data)
data.frame(colnames(Dfpaz[,1:20]))

##    colnames.Dfpaz...1.20..
## 1                      key
## 2           scientificName
## 3          decimalLatitude
## 4         decimalLongitude
## 5                   issues
## 6               datasetKey
## 7         publishingOrgKey
## 8          installationKey
## 9   hostingOrganizationKey
## 10       publishingCountry
## 11                protocol
## 12             lastCrawled
## 13              lastParsed
## 14                 crawlId
## 15           basisOfRecord
## 16         individualCount
## 17        occurrenceStatus
## 18               lifeStage
## 19                taxonKey
## 20              kingdomKey

The result of the above steps is a data frame with three columns (decimalLatitude, decimalLongitude, IndividualCounts), which were reordered and renamed (Latitude, Longitude, Counts). The dimensions of the data frame were checked. Following this, NA records were removed, and the information of each column was reviewed using the summary function.

##   Longitude Latitude Counts
## 1   -97.324  27.7074      1
## 2   -97.324  27.7074      1
## 3   -97.324  27.7074      1
## 4   -97.324  27.7074      1
## 5   -97.324  27.7074      1
## 6   -97.324  27.7074      1

## [1] 1000    3

## [1] 164   3

##    Longitude         Latitude         Counts      
##  Min.   :-97.32   Min.   :18.35   Min.   :  1.00  
##  1st Qu.:-91.47   1st Qu.:27.71   1st Qu.:  1.00  
##  Median :-75.75   Median :36.71   Median :  2.00  
##  Mean   :-80.41   Mean   :32.79   Mean   : 10.29  
##  3rd Qu.:-75.47   3rd Qu.:37.78   3rd Qu.:  6.25  
##  Max.   :-72.30   Max.   :40.88   Max.   :541.00

Variable map

In this example the function through the sunits, vnames, and col1 allow to select the names of the variable represented in dots on maps, the units for the meters legend, and the colors of the dots. This map is in French.

QMap(DF, sunits = "Mètres", vnames = expression(paste("Occurrence - ", italic("Farfantepenaeus aztecus"))),col1='orange')

Fig. 2: Occurrence data for Farfantepenaeus aztecus

Variable and fonts

In the below map the text font was changed using ftext argument. This map is in Quechua. To present a different map of Farfantepenaeus aztecus was created a new data frame using a subset from the original data.

DF2 = DF[(DF$Longitude > -80 & DF$Latitude > 34),]
summary(DF2)

##    Longitude         Latitude         Counts      
##  Min.   :-76.52   Min.   :35.19   Min.   :  1.00  
##  1st Qu.:-75.83   1st Qu.:36.53   1st Qu.:  1.00  
##  Median :-75.57   Median :37.40   Median :  2.00  
##  Mean   :-75.42   Mean   :37.39   Mean   : 11.41  
##  3rd Qu.:-75.25   3rd Qu.:38.20   3rd Qu.:  7.00  
##  Max.   :-72.30   Max.   :40.88   Max.   :541.00

QMap(DF2, xlab = "Ñawi", ylab = "Wiraqocha", n = 3, vnames = expression(paste(" K'anchay - ", italic("Farfantepenaeus aztecus"))), col1 = 'yellow', sunits = "Pisqu - Llaqtap'isqu (m)", ftext = "Nirmala UI")

Fig. 3: Abundance data for Farfantepenaeus aztecus

Windrose and barscale

The option to relocate the windrose and barscale is available, such as is presented in this subset.

DF3 = DF[(DF$Longitude > -100 & DF$Longitude < -80) & (DF$Latitude < 35),]
summary(DF3)

##    Longitude         Latitude         Counts      
##  Min.   :-97.32   Min.   :18.35   Min.   : 1.000  
##  1st Qu.:-93.66   1st Qu.:18.49   1st Qu.: 1.000  
##  Median :-92.57   Median :18.66   Median : 1.000  
##  Mean   :-93.23   Mean   :20.99   Mean   : 7.413  
##  3rd Qu.:-91.70   3rd Qu.:25.48   3rd Qu.: 5.000  
##  Max.   :-91.25   Max.   :28.98   Max.   :83.000

QMap(DF3,  vnames = expression(paste("Aberastasuna - ", italic("Farfantepenaeus aztecus"))), col1 = "orange", n = 3, sunits =  "Metros (m)",  ftext = "Malgun Gothic" , xlab = "Longitudea", ylab = "Latitudea", Apos = "tr", Bpos = "bl")

Fig. 4: Farfantepenaeus aztecus Abundance in Basque

Maps with genus data of Enteromorpha

The records of the green algae Enteromorpha were obtained from GBIF using the rgbif package. The data was sorted by deleting all longitude values lower than zero. Consequently, two records, each with a count of one, were removed. Later, the abundance values of this genus were plotted.

asd = occ_data(scientificName = 'Enteromorpha', limit = 1000)

Dasd = data.frame(asd$data)

DFs = Dasd[c(4,3,16)]
colnames(DFs) = c("Longitude","Latitude", "Counts")
dim(DFs)

## [1] 1000    3

DFs = na.omit(DFs)
DFs = DFs[DFs$Longitude > 0,]
summary(DFs)

##    Longitude         Latitude         Counts      
##  Min.   : 1.721   Min.   :50.76   Min.   : 1.000  
##  1st Qu.: 7.503   1st Qu.:58.47   1st Qu.: 1.000  
##  Median : 8.056   Median :62.64   Median : 1.000  
##  Mean   : 9.185   Mean   :61.78   Mean   : 1.849  
##  3rd Qu.: 8.944   3rd Qu.:63.12   3rd Qu.: 1.000  
##  Max.   :19.071   Max.   :69.70   Max.   :70.000

Variables and labels

QMap(df = DFs, xlab = "Longitudine", ylab = "Latitudine", sunits = "Metri (m)", n = 3, col1 = "yellow", Bpos = "br", vnames = expression(paste('Abbondanza -', italic(' Enteromorpha'))))

Fig. 4: Occurence data for Enteromorpha genus

The last example using species distribution data was conducted with information on members of the Bythograea genus. This genus comprises small marine crabs found in hydrothermal vents, distributed in areas such as the southern East Pacific Rise and the Galapagos Rift (Guinot & Hurtado, 2003).

##    Longitude Latitude Counts
## 1 -108.85571 23.94162      1
## 2 -108.55900 23.35280      1
## 3 -108.54300 23.35760      2
## 4  -85.91183 -0.76969      1
## 5 -108.52800 23.38030      1
## 6 -108.52800 23.38030      1

## [1] 219   3

Variables, fonts, axis angle

QMap(DAc2,  vnames = expression("Встречаемость"), col1 = "black", n = 3, sunits =  "Метры (m)", 
     ftext = "Malgun Gothic" , xlab = "Долгота", ylab = "Широта", Apos = "tr", Bpos = "br", angle = 90)

Fig. 5: Ocurrence data for Bythograea genus

The function offers multiple options for quickly generating maps with data. However, it’s crucial to emphasize the importance of carefully positioning station names. The placement of station labels, as required when working with ggplot2, is crucial. Therefore, a bit of preprocessing could enhance the data representation, as demonstrated in the example below.

Dat = data.frame(Lon = runif(40,-17, -5), Lat = runif(40,36, 56))
Dat$Sta = stringi::stri_rand_strings(40,2,'[a-z]')
Dat = Dat[order(-Dat$Lon),]
head(Dat)

##          Lon      Lat Sta
## 34 -5.669603 48.88529  sh
## 9  -5.830071 37.68831  wk
## 36 -5.858242 50.62612  sy
## 7  -6.474031 40.16144  lv
## 38 -6.780251 47.87269  eh
## 13 -7.563808 37.99459  qq

Dat$var = c()
for(i in 1:40){
  if(Dat$Lon[i] < -10 & Dat$Lon[i] > -16.5 & Dat$Lat[i] < 55 & Dat$Lat[i] > 36.2 | Dat$Lat[i] >= 45 & Dat$Lat[i] <= 50.1 & Dat$Lon[i] < -6){
    Dat$var[i] = runif(40, 3600, 27000)
  } else {
    Dat$var[i] = NA
  }
}
summary(Dat)

##       Lon               Lat            Sta                 var       
##  Min.   :-16.865   Min.   :36.64   Length:40          Min.   : 3951  
##  1st Qu.:-13.300   1st Qu.:44.68   Class :character   1st Qu.: 8397  
##  Median :-11.216   Median :48.45   Mode  :character   Median :13706  
##  Mean   :-11.067   Mean   :47.63                      Mean   :14225  
##  3rd Qu.: -8.928   3rd Qu.:51.84                      3rd Qu.:19215  
##  Max.   : -5.670   Max.   :55.19                      Max.   :26020  
##                                                       NA's   :14

QMap(df = Dat, col1 = "yellow", Bpos = "br", n = 4, sta = 3, poy = 0.3, vnames = "Abundancia",sunits = "Metros (m)", xlab = "Longitud", ylab = "Latitud")

Fig. 6: Example to display station labels

In conclusion, while the function offers diverse options for swiftly generating maps, meticulous attention to station label placement is paramount. By prioritizing preprocessing methods, such as those demonstrated in the examples, data representation can be significantly enhanced, ensuring clear and informative visualizations.

References

Aspinall, R. J. (2002). Use of logistic regression for validation of maps of the spatial distribution of vegetation species derived from high spatial resolution hyperspectral remotely sensed data. Ecological Modelling, 157(2–3), 301–312. https://doi.org/10.1016/S0304-3800(02)00201-6

Austin, M. P. (2002). Spatial prediction of species distribution: An interface between ecological theory and statistical modelling. Ecological Modelling, 157(2–3), 101–118. https://doi.org/10.1016/S0304-3800(02)00205-3

Barkowsky, T., & Freksa, C. (1997). Cognitive requirements on making and interpreting maps. In S. C. Hirtle & A. U. Frank (Eds.), Spatial Information Theory A Theoretical Basis for GIS (Vol. 1329, pp. 347–361). Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-63623-4_60

Bauer, R. (2017). A Plotting Toolbox for 2D Oceanographic Data. Package ‘oceanmap’ (0.0.3) [R language].

Bauer, R. (2020). A Plotting Toolbox for 2D Oceanographic Data. Package ‘oceanmap’ (0.1.1) [R language].

Chang, W. (2016). Tools for using fonts. Package ‘extrafont’ (0.17) [R language].

Guinot, D., & Hurtado, L. A. (2003). Two new species of hydrothermal vent crabs of the genus Bythograea from the southern East Pacific Rise and from the Galapagos Rift (Crustacea Decapoda Brachyura Bythograeidae). Comptes Rendus. Biologies, 326(4), 423–439. https://doi.org/10.1016/S1631-0691(03)00126-4

Deckmyn, A. (2018). Draw Geographical Maps. Package ‘maps’ [R language].

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Hijmans, R., & Elith, J. (2011). Species distribution modelling with R (p. 87).

Hijmans, R. J., & Elith, J. (2023). Species Distribution Models.

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Sillett, T. S., Chandler, R. B., Royle, J. A., Kéry, M., & Morrison, S. A. (2012). Hierarchical distance-sampling models to estimate population size and habitat-specific abundance of an island endemic. Ecological Applications, 22(7), 1997–2006.

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