Data preparation ================ Species occurrence data ----------------------- Importing occurrence data into *R* is easy. But collecting, georeferencing, and cross-checking coordinate data is tedious. Discussions about species distribution modeling often focus on comparing modeling methods, but if you are dealing with species with few and uncertain records, your focus probably ought to be on improving the quality of the occurrence data (Lobo, 2008). All methods do better if your occurrence data is unbiased and free of error (Graham *et al.*, 2007) and you have a relatively large number of records (Wisz *et al.*, 2008). Importing occurrence data ------------------------- In most cases you will have a file with point locality data representing the known distribution of a species. Below is an example of using ``read.table`` to read records that are stored in a text file. We are using an example file that is installed with the ``predicts`` package, and for that reason we use a complex way to construct the filename, but you can replace that with your own filename. (remember to use forward slashes in the path of filenames!). ``system.file`` inserts the file path to where the ``predicts`` package is installed. .. code:: r library(terra) ## terra 1.9.22 library(predicts) filename <- file.path(system.file(package="predicts"), "ex/bradypus.csv") # this is the file we will use: basename(filename) ## [1] "bradypus.csv" Now read the file and inspect the values. .. code:: r bradypus <- read.csv(filename) # first rows head(bradypus) ## species lon lat ## 1 Bradypus variegatus -65.4000 -10.3833 ## 2 Bradypus variegatus -65.3833 -10.3833 ## 3 Bradypus variegatus -65.1333 -16.8000 ## 4 Bradypus variegatus -63.6667 -17.4500 ## 5 Bradypus variegatus -63.8500 -17.4000 ## 6 Bradypus variegatus -64.4167 -16.0000 # we only need columns 2 and 3: bradypus <- bradypus[,2:3] head(bradypus) ## lon lat ## 1 -65.4000 -10.3833 ## 2 -65.3833 -10.3833 ## 3 -65.1333 -16.8000 ## 4 -63.6667 -17.4500 ## 5 -63.8500 -17.4000 ## 6 -64.4167 -16.0000 You can also read such data from Excel files with the ``readxl`` package. No matter how you do it, the objective is to get a matrix (or a ``data.frame``) with at least 2 columns that hold the coordinates of the locations where a species was observed. Coordinates are typically expressed as longitude and latitude (i.e. angular), but they could also be Easting and Northing in UTM or another planar coordinate reference system (map projection). The convention used here is to organize the coordinates columns so that longitude is the first and latitude the second column (think x and y axes in a plot; longitude is x, latitude is y); they often are in the reverse order, leading to undesired results. In many cases you will have additional columns, e.g., a column to indicate the species if you are modeling multiple species; and a column to indicate whether this is a ‘presence’ or an ‘absence’ record (a much used convention is to code presence with a 1 and absence with a 0). If you do not have any species distribution data you can get started by downloading data from the `Global Biodiversity Inventory Facility (GBIF) `__. In the ``geodata`` package there is a function ``sp_occurrence`` that you can use for this. The data used below were downloaded, and saved to a permanent data set for use in this chapter, using the ``sp_occurrence`` function like this: .. code:: r acaule <- geodata::sp_occurrence("solanum", "acaule", geo=FALSE) ## Loading required namespace: jsonlite ## 6974 records found ## 0-300-600-900-1200-1500-1800-2100-2400-2700-3000-3300-3600-3900-4200-4500-4800-5100-5400-5700- ## 6000-6300-6600-6900-6974 If you want to understand the order of the arguments given here to ``sp_occurrence`` or find out what other arguments you can use with this function, check out the help file (remember you can’t access help files if the library, in this case ``geodata``, is not loaded), by typing: ``?sp_occurrence`` or ``help(sp_occurrence)``. Many occurrence records may not have geographic coordinates. In this case, out of the 1366 records that GBIF returned (in January 2013), there were 1082 records with coordinates, .. code:: r # load the saved S. acaule data acfile <- file.path(system.file(package="predicts"), "ex/acaule.csv") acaule <- read.csv(acfile) # how many rows and colums? dim(acaule) ## [1] 1366 25 #select the records that have longitude and latitude data colnames(acaule) ## [1] "species" "continent" "country" ## [4] "adm1" "adm2" "locality" ## [7] "lat" "lon" "coordUncertaintyM" ## [10] "alt" "institution" "collection" ## [13] "catalogNumber" "basisOfRecord" "collector" ## [16] "earliestDateCollected" "latestDateCollected" "gbifNotes" ## [19] "downloadDate" "maxElevationM" "minElevationM" ## [22] "maxDepthM" "minDepthM" "ISO2" ## [25] "cloc" acgeo <- subset(acaule, !is.na(lon) & !is.na(lat)) dim(acgeo) ## [1] 1082 25 # show some values acgeo[1:5, c(1:5,7:10)] ## species continent country adm1 adm2 ## 1 Solanum acaule Bitter South America Argentina Jujuy Santa Catalina ## 2 Solanum acaule Bitter South America Peru Cusco Canchis ## 3 Solanum acaule f. acaule Argentina ## 4 Solanum acaule f. acaule Bolivia ## 5 Solanum acaule f. acaule Bolivia ## lat lon coordUncertaintyM alt ## 1 -21.9000 -66.1000 NA NA ## 2 -13.5000 -71.0000 NA 4500 ## 3 -22.2666 -65.1333 NA 3800 ## 4 -18.6333 -66.9500 NA 3700 ## 5 -18.8000 -65.8833 NA 4080 Below is a simple way to make a map of the occurrence localities of *Solanum acaule*. It is important to make such maps to assure that the points are, at least roughly, in the right location. .. code:: r library(geodata) wrld <- world(path=".") plot(wrld, xlim=c(-110,90), ylim=c(-80,40), col="light yellow", border="light gray") # add the points points(acgeo$lon, acgeo$lat, col='red', pch=20) |image1| The ``wrld`` dataset contains rough country outlines. You can use other datasets of polygons (or lines or points) as well. For example, you can download higher resolution data country and subnational administrative boundaries data with the ``gadm`` function of the ``geodata`` package. You can also read your own shapefile data using the ``vect`` function in the ``terra`` package. Data cleaning ------------- Data ‘cleaning’ is particularly important for data sourced from species distribution data warehouses such as GBIF. Such efforts do not specifically gather data for the purpose of species distribution modeling, so you need to understand the data and clean them appropriately, for your application. Here we provide an example. ``Solanum acaule`` is a species that occurs in the higher parts of the Andes mountains of southern Peru, Bolivia and northern Argentina. Do you see any errors on the map? There are a few records that map in the ocean just south of Pakistan. Any idea why that may have happened? It is a common mistake, missing minus signs. The coordinates are around (65.4, 23.4) but they should in Northern Argentina, around (-65.4, -23.4) (you can use the “click” function to query the coordintates on the map). There are two records (rows 303 and 885) that map to the same spot in Antarctica (-76.3, -76.3). The locality description says that is should be in Huarochiri, near Lima, Peru. So the longitude is probably correct, and erroneously copied to the latitude. Interestingly the record occurs twice. The orignal source is the International Potato Center, and a copy is provided by “SINGER” that along the way appears to have “corrected” the country to Antarctica: .. code:: r acgeo[c(303,885),1:10] ## species continent country adm1 adm2 ## 328 solanum acaule acaule Bolivia ## 1169 Solanum acaule subsp. acaule South America Bolivia La Paz Pacajes ## locality lat lon coordUncertaintyM ## 328 -17.08300 -68.417 NA ## 1169 Pacajes. Caquiaviri, Kalla Centro. -17.01667 -68.600 NA ## alt ## 328 NA ## 1169 3950 The point in Brazil (record acaule[98,]) should be in soutern Bolivia, so this is probably due to a typo in the longitude. Likewise, there are also three records that have plausible latitudes, but longitudes that are clearly wrong, as they are in the Atlantic Ocean, south of West Africa. It looks like they have a longitude that is zero. In many data-bases you will find values that are ‘zero’ where ‘no data’ was intended. The ``gbif`` function (when using the default arguments) sets coordinates that are (0, 0) to ``NA``, but not if one of the coordinates is zero. Let’s see if we find them by searching for records with longitudes of zero. Let’s have a look at these records: .. code:: r lonzero <- subset(acgeo, lon==0) # show all records, only the first 13 columns lonzero[, 1:13] ## species continent country adm1 adm2 ## 1159 Solanum acaule Bitter subsp. acaule Argentina ## 1160 Solanum acaule Bitter subsp. acaule Bolivia ## 1161 Solanum acaule Bitter subsp. acaule Peru ## 1162 Solanum acaule Bitter subsp. acaule Peru ## 1163 Solanum acaule Bitter subsp. acaule Argentina ## 1164 Solanum acaule Bitter subsp. acaule Bolivia ## locality lat lon ## 1159 between Quelbrada del Chorro and Laguna Colorada -23.716667 0 ## 1160 Llave -16.083334 0 ## 1161 km 205 between Puno and Cuzco -6.983333 0 ## 1162 km 205 between Puno and Cuzco -6.983333 0 ## 1163 between Quelbrada del Chorro and Laguna Colorada -23.716667 0 ## 1164 Llave -16.083334 0 ## coordUncertaintyM alt institution collection catalogNumber ## 1159 NA 3400 IPK GB WKS 30027 ## 1160 NA 3900 IPK GB WKS 30050 ## 1161 NA 4250 IPK WKS 30048 304709 ## 1162 NA 4250 IPK GB WKS 30048 ## 1163 NA 3400 IPK WKS 30027 304688 ## 1164 NA 3900 IPK WKS 30050 304711 The records are from Bolivia, Peru and Argentina, confirming that coordinates are in error. Alternatively, it could have been that the coordinates were correct, perhaps referring to a location in the Atlantic Ocean where a fish was caught rather than a place where *S. acaule* was collected). Records with the wrong species name can be among the hardest to correct (e.g., distinguishing between brown bears and sasquatch, Lozier *et al.*, 2009). The one record in Ecuador is like that, there is some debate whether that is actually a specimen of ``S. albicans`` or an anomalous hexaploid variety of *S. acaule*. Duplicate records ----------------- Interestingly, another data quality issue is revealed above: each record in ‘lonzero’ occurs twice. This could happen because plant samples are often split and send to multiple herbariums. But in this case it seems that the IPK (The Leibniz Institute of Plant Genetics and Crop Plant Research) provided these data twice to the GBIF database (perhaps from seperate databases at IPK?). The function ‘duplicated’ can sometimes be used to remove duplicates. .. code:: r # which records are duplicates (only for the first 10 columns)? dups <- duplicated(lonzero[, 1:10]) # remove duplicates lonzero <- lonzero[dups, ] lonzero[,1:13] ## species continent country adm1 adm2 ## 1162 Solanum acaule Bitter subsp. acaule Peru ## 1163 Solanum acaule Bitter subsp. acaule Argentina ## 1164 Solanum acaule Bitter subsp. acaule Bolivia ## locality lat lon ## 1162 km 205 between Puno and Cuzco -6.983333 0 ## 1163 between Quelbrada del Chorro and Laguna Colorada -23.716667 0 ## 1164 Llave -16.083334 0 ## coordUncertaintyM alt institution collection catalogNumber ## 1162 NA 4250 IPK GB WKS 30048 ## 1163 NA 3400 IPK WKS 30027 304688 ## 1164 NA 3900 IPK WKS 30050 304711 Another approach might be to detect duplicates for the same species and some coordinates in the data, even if the records were from collections by different people or in different years. (in our case, using species is redundant as we have data for only one species) .. code:: r # differentiating by (sub) species # dups2 <- duplicated(acgeo[, c('species', 'lon', 'lat')]) # ignoring (sub) species and other naming variation dups2 <- duplicated(acgeo[, c('lon', 'lat')]) # number of duplicates sum(dups2) ## [1] 483 # keep the records that are _not_ duplicated acg <- acgeo[!dups2, ] Let’s repatriate the records near Pakistan to Argentina, and remove the records in Brazil, Antarctica, and with longitude=0 .. code:: r i <- acg$lon > 0 & acg$lat > 0 acg$lon[i] <- -1 * acg$lon[i] acg$lat[i] <- -1 * acg$lat[i] acg <- acg[acg$lon < -60 & acg$lat > -50, ] Cross-checking -------------- It is important to cross-check coordinates by visual and other means. One approach is to compare the country (and lower level administrative subdivisions) of the site as specified by the records, with the country implied by the coordinates (Hijmans *et al.*, 1999). We first make a ``SpatVector`` .. code:: r library(terra) acv <- vect(acg, geom=c("lon", "lat"), crs="+proj=longlat +datum=WGS84") class(acv) ## [1] "SpatVector" ## attr(,"package") ## [1] "terra" We can now use do a spatial query of the polygons in ``wrld`` .. code:: r ovr <- extract(wrld, acv) Object ‘ovr’ has, for each point, the matching record from ``wrld``. We need the variable ‘NAME_0’ in the data.frame of ``wrld``. .. code:: r head(ovr) ## id.y GID_0 NAME_0 ## 1 1 ARG Argentina ## 2 2 PER Peru ## 3 3 ARG Argentina ## 4 4 BOL Bolivia ## 5 5 BOL Bolivia ## 6 6 BOL Bolivia cntr <- ovr$NAME_0 We should ask these two questions: (1) Which points (identified by their record numbers) do not match any country (that is, they are in an ocean)? (There are none (because we already removed the points that mapped in the ocean)). (2) Which points have coordinates that are in a different country than listed in the ‘country’ field of the GBIF record .. code:: r i <- which(is.na(cntr)) i ## integer(0) j <- which(cntr != acv$country) # for the mismatches, bind the country names of the polygons and points m <- cbind(cntr[j], acg$country[j]) colnames(m) <- c("polygons", "acaule") m ## polygons acaule ## [1,] "Bolivia" "Argentina" ## [2,] "Peru" "Bolivia" In this case the mismatch is probably because ``wrld`` is not very precise as the records map to locations very close to the border between Bolivia and its neighbors. .. code:: r plot(acv) lines(wrld, col='blue', lwd=2) points(acv[j, ], col='red', pch=20, cex=2) |image2| The wrld polygons that we used in the example above are not very precise, and they probably should not be used in a real analysis. See `GADM `__ for more detailed administrative division files, or use functions from the ``predicts`` package (e.g. ``predicts::gadm(country='BOL', level=0)`` to get the national borders of Bolivia; and \`?predicts::world to get more precise boundaries for all countries). Georeferencing -------------- If you have records with locality descriptions but no coordinates, you should consider georeferencing these. Not all the records can be georeferenced. Sometimes even the country is unknown (country==“UNK”). Here we select only records that do not have coordinates, but that do have a locality description. .. code:: r georef <- subset(acaule, (is.na(lon) | is.na(lat)) & ! is.na(locality) ) dim(georef) ## [1] 131 25 georef[1:3,1:13] ## species continent country adm1 adm2 ## 606 solanum acaule acaule BITTER Bolivia ## 607 solanum acaule acaule BITTER Peru ## 618 solanum acaule acaule BITTER Peru ## locality lat ## 606 La Paz P. Franz Tamayo Viscachani 3 km from Huaylapuquio to Pelechuco NA ## 607 Puno P. San Roman Near Tinco Palca NA ## 618 Puno P. Lampa Saraccocha NA ## lon coordUncertaintyM alt institution collection ## 606 NA NA 4000 PER001 CIP - Potato collection ## 607 NA NA 4000 PER001 CIP - Potato collection ## 618 NA NA 4100 PER001 CIP - Potato collection ## catalogNumber ## 606 CIP-762165 ## 607 CIP-761962 ## 618 CIP-762376 For georeferencing, you can try to use the predicts package function ``geocode`` that sends requests to the Google API (this used to be simple, but these days you need to first get an “API_KEY” from Google). We demonstrate below, but its use is generally not recommended because for accurate georeferencing you need a detailed map interface, and ideally one that allows you to capture the uncertainty associated with each georeference (Wieczorek *et al.*, 2004). Here is an example for one of the records with longitude = 0, using Google’s geocoding service. We put the function into a ‘try’ function, to assure elegant error handling if the computer is not connected to the Internet. Note that we use the “cloc” (concatenated locality) field. .. code:: r georef$cloc[4] #b <- geocode(georef$cloc[4], geo_key="abcdef" ) #b Before using the geocode function it is best to write the records to a table and “clean” them in a spreadsheet. Cleaning involves translation, expanding abbreviations, correcting misspellings, and making duplicates exactly the same so that they can be georeferenced only once. Then read the the table back into *R*, and create unique localities, georeference these and merge them with the original data. Sampling bias ------------- Sampling bias is frequently present in occurrence records (Hijmans *et al.*, 2001). One can attempt to remove some of the bias by subsampling records, and this is illustrated below. However, subsampling reduces the number of records, and it cannot correct the data for areas that have not been sampled at all. It also suffers from the problem that locally dense records might in fact be a true reflection of the relative suitable of habitat. As in many steps in SDM, you need to understand something about your data and species to implement them well. See Phillips *et al.* (2009) for an approach with MaxEnt to deal with bias in occurrence records for a group of species. .. code:: r # create a SpatRaster with the extent of acgeo r <- rast(acv) # set the resolution of the cells to (for example) 1 degree res(r) <- 1 # extend (expand) the extent of the SpatRaster a little r <- extend(r, ext(r)+1, snap = "out") # sample: set.seed(13) acsel <- spatSample(acv, size=1, "random", strata=r) # to illustrate the method and show the result p <- as.polygons(r) plot(p, border='gray') points(acv) # selected points in red points(acsel, cex=1, col='red', pch='x') |image3| Note that with the “chess” argument in ``terra::spatSample`` function you can also do ‘chess-board’ sampling. This can be useful to split the data in ‘training’ and ‘testing’ sets (see the model evaluation chapter). At this point, it could be useful to save the cleaned data set. For example, you can use ``as.data.frame(acsel)`` and then ``write.csv``. Or you can use ``pack`` and ``saveRDS`` so that we can use them later. We did that, and the saved file is available from the ``predicts`` package and can be read like this: .. code:: r file <- paste(system.file(package="predicts"), '/ex/acaule.rds', sep='') acsel <- readRDS(file) In a real research project you would want to spend much more time on this first data-cleaning and completion step, partly with *R*, but also with other programs. 2.8 Exercises ------------- 1. Use the ``sp_occurrence`` function to download records for the African elephant (or another species of your preference, try to get one with between 10 and 100 records). Use option “geo=FALSE” to also get records with no (numerical) georeference. 2. Summarize the data: how many records are there, how many have coordinates, how many records without coordinates have a textual georeference (locality description)? 3. Use the ‘geocode’ function to georeference up to 10 records without coordinates 4. Make a simple map of all the records, using a color and symbol to distinguish between the coordinates from gbif and the ones returned by Google (via the geocode function). Use ‘gmap’ to create a basemap. 5. Do you think the observations are a reasonable representation of the distribution (and ecological niche) of the species? More advanced: 6. Use the ‘rasterize’ function to create a raster of the number of observations and make a map. Use “world” from the geodata package for country boundaries. 7. Map the uncertainty associated with the georeferences. Some records in data returned by gbif have that. You can also extract it from the data returned by the geocode function. .. |image1| image:: figures/sdm3-1.png .. |image2| image:: figures/sdm6e-1.png .. |image3| image:: figures/sdm100A-1.png