The paper’s highlights:

• We highlight the smuggling of Galápagos iguanas in Africa, Asia, Europe, and the Pacific region.
• Wild caught Galapagos iguanas are laundered into international trade declared as captive-bred. The offspring of such animals receive CITES permits in violation of CITES regulations.
• Since 1975 all four iguana species have been listed on Appendix II of CITES.
• We notice inaccuracies documented in the CITES trade database.
• We urge CITES Parties to transfer all four species of Galápagos iguanas from CITES Appendix II to Appendix I.

To read the full manuscript use this link or the link in the reference section.

Cyclura carinata is endemic to the Turks and Caicos Islands (TCI), a small and beautiful archipelago located north of Hispaniola Island (Haiti and Dominican Republic), in the Caribbean Sea. A species is defined endemic when it is native to a specific region and when it naturally can be found only in that particular area. Turks and Caicos Rock Iguanas is, in fact, native to the TCI and can only be found in this archipelago. The only exception is represented by a small population of TCI Rock Iguanas found on Booby Cay, The Bahamas. Genetic studies have recently demonstrated that the Booby Cay population is the outcome of a past introduction event (Welch et al., 2017). Once occupying almost every single island in the TCI, the distribution range of C carinata dramatically decreased and they are considered extirpated from ca. 95% of their former range (Gerber, 2007).

The progressive reduction of their range, largely due to the intense development and construction activity on many islands, and the presence of introduced feral mammals (cats, dogs, rats, donkeys, pigs), caused, in 2004, the listing of this species as Critically Endangered (CR) in the International Union for the Conservation of Nature (IUCN) Red-List of Threatened Species (Gerber, 2004). This means that there is a concrete risk of extinction if no management actions are pursued.

A group of international researchers and volunteers, collaborating with local institutions like the Department of Environment and Coastal Resources (DECR) and the Turks and Caicos National Trust (TCINT), has been working for many years to ensure that this species could persist and thrive in its natural habitat. Thanks to their work, the implementation of different conservation strategies, and the involvement of local communities, the species has recently been down-listed from CR to Endangered (EN) in the IUCN Red-List of Threatened Species (Gerber et al., 2020). Although this represents very good news, it does not mean that conservation efforts can come to a stop. On the contrary, as the species persistence is still challenged by new development and the presence of introduced feral mammals, the level of guard and attention should not dim.

Among different conservation strategies, individuals translocation has proven an effective measure for TCI iguanas. A translocation of individuals entails the collection of adults (or young adults) from a source population to be introduced in a recipient area, usually identified within the historical range of a species. If no historical areas are available or known, a new location can also be selected as recipient area, as long as it presents the appropriate characteristics for the species to thrive.

A first translocation program was conducted in 2002 and 2003 (Gerber et al., 2004; Gerber, 2007). This year, following up on an intensive project to eradicate invasive mammals from some of the islands in the vicinity of Providenciales, another translocation was performed. Between July and August 2023 we moved 74 adults from Little Water Cay to Grouper Cay. The project authorized by the Scientific Research Permit No. 2023-06-30-32 issued by DECR, was funded through a Darwin grant and leaded by senior research scientist Dr. Glenn Gerber (San Diego Zoo Wildlife Alliance). We received support from many local political institutions as well from local tour operators.

Once captured, individuals are measured and sexed to make sure that an equal ratio of males and females are moved. Every individual is also assigned a unique identifier (Passive Integrated Trasponder-PIT), very much like the transponders that are used for house cats and dogs. These identifiers will ensure that, if recaptured in the future, that individual will be recognized as one of the animals already sampled. This is a more permanent solution to the application of colored beads. As beads are exposed, they can, eventually, fall from the animal following a fight between individual or the mating between males and females or simply because the wire used for attaching them to the nucal crest breaks.

In future years it will be necessary to monitor the recipient island of Grouper Cay to make sure that the translocated animals have successfully acclimated to the new island. The best way to monitor the success of a translocation is to document the reproduction of the translocated individuals. In the next few years we will monitor for the presence of hatchlings or yearlings. Stay tuned.

Did you like this post? Drop a comment down here or let me know by e-mail what you think of it or if you have questions or curiosities. Till next time.

Cheers

Not too long ago it was possible to download historical weather data relatively easily from a number of different websites. Unfortunately this option is no longer available and there really is only a handful of sites from which it is possible to download historical data for free.

One of them is here, hosted on the Iowa State University website. The user interface is pretty intuitive and it is possible to select from a pretty amazing variety of network of data from all over the world.

Another very cool thing about this portal is that it offers a few line of codes, in R and Python, to automate the download of the data of interest. I have tried the R script, but I couldn’t make it work “as is”. I did a few changes to the original code to make it do what it was supposed to do, and since I did it for my students I thought it would be nice to make a blog post. I here provide a full rundown on how to use the script. The description includes the installation of a few necessary packages to download historical weather data. The worked out example shows how to download data for the Italian peninsula, but with a few tweaks and changes it is possible to adapt it to download all data of interest.

Buckle up and lets begin. First of all, let me give you an overview of the OS and R version I am working with. After downloading the appropriate packages the code should work for all platforms. Regardless, I will start by sharing my session information.

sessionInfo()

R version 4.6.1 (2026-06-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Europe/Rome
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] htmlwidgets_1.6.4 compiler_4.6.1    fastmap_1.2.0     cli_3.6.6        
 [5] tools_4.6.1       htmltools_0.5.9   otel_0.2.0        yaml_2.3.12      
 [9] rmarkdown_2.31    knitr_1.51        jsonlite_2.0.0    xfun_0.57        
[13] digest_0.6.39     rlang_1.3.0       evaluate_1.0.5   

So, I pretty much just opened R and didn’t attach any package but the ones that automatically are loaded when you open a new working session.

To accomplish this task we do need to install and load a few packages that will allow R to interact with web pages and read/download information from the specified website. The code below will install on your machine the necessary packages. Of course, you will not need this step if the packages are already installed.

# Installing the appropriate packages
install.packages(c("jsonlite", "RCurl", "lubridate", "dplyr", "ggplot2"))

Once installed we can load the packages in the current working session.

# Load packages in the working session
x <- c("jsonlite", "RCurl", "lubridate", "dplyr", "ggplot2")
lapply(x, require, character.only = T)

Loading required package: jsonlite

Loading required package: RCurl

Loading required package: lubridate

Attaching package: 'lubridate'

The following objects are masked from 'package:base':

    date, intersect, setdiff, union

Loading required package: dplyr

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Loading required package: ggplot2

[[1]]
[1] TRUE

[[2]]
[1] TRUE

[[3]]
[1] TRUE

[[4]]
[1] TRUE

[[5]]
[1] TRUE

Perfect. If you have a list with all TRUE values it means that everything worked fine, and we can move on to the next steps. We can now specify 2 important variables that will be used in the automated script. We need a start and an end date that will be used to identify the time window of interest for which we would like to download the data of interest.

# specify the date boundaries of our dataset
date1 <- ISOdate(1990,1,1)   # start date in year, month, day format
date2 <- ISOdate(2020,12,31) # end date in year, month, day format

The second step is to set 2 other variables containing the data network infrastructure from which we want to download the data and the specific country/state of interest.

user.network <- c("ASOS") # data network
user.state <- c("IT")     # state

If we were to do this procedure manually directly on the ISU web page at this point we will be selecting the appropriate country/state and network from the dedicated drop down menu, as shown in Figure 1.

One of the advantages of the automated procedure through R, though, is that once we have perfected the procedure for a specified the country/state and data network we can repeat the same procedure over and over with different states and or location of interest. Anyway, lets keep going with our procedure. From Figure 1 it is possible to see that, after selecting the Italian ASOS network, a list of of available weather stations appears in the left box right underneath the Select Widget for IT_ASOS Network claim. What the web page is telling us is that of all the weather stations available for the ASOS network in Italy we can pick and choose the ones we want to download the data from. Unless we really know what we are doing and unless we know exactly the precise station we want data from we need to do a little more work to figure out how many stations are available, where they are and what is their specific ID.

So, using a little information taken directly from the URL bar of our browsers we can tell R to open a connection to the desired web page to download the information we need. We do this with the following few lines of code.

# Create and open a connection
network <- paste(user.state, user.network, sep = "__")
uri <- paste("https://mesonet.agron.iastate.edu/geojson/network/", network, ".geojson", sep="")
data <- url(uri)
jdict <- fromJSON(data)
class(jdict)

[1] "list"

names(jdict)

[1] "type"         "features"     "generated_at" "count"       

We have created and opened a connection to a .geojson file from the specified webpage containing all the necessary information. All the data are then stored in an object that we have called jdict. This is a list with four elements named as indicated above. Based on the names of the list elements we can infer that the second element of the list has the features of interest to us. We can look the first 5 rows of the data frame contained in the second element of the jdict list.

head(jdict[[2]])

     type   id properties.elevation properties.sname properties.time_domain
1 Feature EQYS               23.000        Sigonella             (2022-Now)
2 Feature LIBA               60.000         Amendola             (1944-Now)
3 Feature LIBC              161.000          Crotone             (1957-Now)
4 Feature LIBD               44.000             Bari             (1943-Now)
5 Feature LIBE              834.111  Monte S. Angelo            (2003-2025)
6 Feature LIBF               80.000           Foggia             (1943-Now)
  properties.archive_begin properties.archive_end properties.state
1               2022-04-12                   <NA>                 
2               1944-01-31                   <NA>             <NA>
3               1957-06-30                   <NA>             <NA>
4               1943-12-07                   <NA>             <NA>
5               2003-08-01             2025-03-04             <NA>
6               1943-09-30                   <NA>             <NA>
  properties.country properties.climate_site properties.wfo properties.tzname
1                 IT                      NA             NA       Europe/Rome
2                 IT                      NA             NA       Europe/Rome
3                 IT                      NA             NA       Europe/Rome
4                 IT                      NA             NA       Europe/Rome
5                 IT                      NA             NA       Europe/Rome
6                 IT                      NA             NA       Europe/Rome
  properties.ncdc81 properties.ncei91 properties.ugc_county properties.ugc_zone
1                NA                NA                    NA                  NA
2                NA                NA                    NA                  NA
3                NA                NA                    NA                  NA
4                NA                NA                    NA                  NA
5                NA                NA                    NA                  NA
6                NA                NA                    NA                  NA
  properties.county properties.sid properties.network properties.online
1                NA           EQYS           IT__ASOS              TRUE
2                NA           LIBA           IT__ASOS              TRUE
3                NA           LIBC           IT__ASOS              TRUE
4                NA           LIBD           IT__ASOS              TRUE
5                NA           LIBE           IT__ASOS             FALSE
6                NA           LIBF           IT__ASOS              TRUE
  properties.synop properties.attributes.METAR_RESET_MINUTE
1               NA                                       46
2            99999                                       55
3            99999                                       50
4            99999                                       50
5             9999                                        0
6               NA                                       50
  properties.attributes.HAS_PHOUR properties.attributes.GHCNH_ID geometry.type
1                               1                           <NA>         Point
2                               1                           <NA>         Point
3                               1                           <NA>         Point
4                               1                           <NA>         Point
5                            <NA>                           <NA>         Point
6                               1                           <NA>         Point
  geometry.coordinates
1         14.92, 37.40
2     15.7139, 41.5381
3     17.0792, 38.9967
4     16.7650, 41.1381
5     15.9476, 41.7084
6     15.5350, 41.4329

As anticipated, this element contains all the information we need. There are 108 stations in Italy from where we can download data. Of the many variables for each location what is more important to us is the id of each station, its properties.sname, the properties.time_domain and the geometry.coordinates of each station. In fact, as a simple exercise we can make a plot to see how and where are the stations distributed.

stationsPlot <- matrix(unlist(jdict$features$geometry$coordinates), nrow = 2)
plot(stationsPlot[1,], stationsPlot[2,], asp = 1)

It is interesting to see that there are so many stations that they can pretty much reproduce the outline and profile of the iconic Italian boot (Figure 2)! This is all nice and good. Now lets get to the fun part. Let us say that we want to download data from ALL the stations available. Using the web interface from Figure 1 we would be selecting all the stations from the left box, move them to the right box. Then go to the right side of the page (Figure 3) and specify a number of things like the kind of desired output (CSV rather than TXT rather than DAT), the appropriate dates for the time window of interest and what kind of weather variable we would like data for (temperature, rain, wind, etc. etc. etc.).

Now we create a string that contains an URL with the start date and the end date. This is the URL that would appear every time we would download manually the information from the website. At this time we still do not have any information re which station we request the data from.

service <- "https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py?"
service <- paste(service, "data=all&tz=Etc/UTC&format=comma&latlon=yes&", sep="")
service <- paste(service, "year1=", year(date1), "&month1=", month(date1), "&day1=", mday(date1), "&", sep="")
service <- paste(service, "year2=", year(date2), "&month2=", month(date2), "&day2=", mday(date2), "&", sep="")
service

[1] "https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py?data=all&tz=Etc/UTC&format=comma&latlon=yes&year1=1990&month1=1&day1=1&year2=2020&month2=12&day2=31&"

Now we set the working directory to a dedicated folder. Note that the data could be large several GB, and it may be convenient to store them on an external hard drive.

setwd("path/to/your/folder/of/choice")

OK. We are almost there. The final part of this tutorial consists in creating a loop that will take the part of the URL that is constant every time and add, recursively until the very last one, the ID of the weather station we want the data from. Once again, when you run the script make sure that you have enough space on your dedicated folder and that you have enough time and a good internet connection to download all the data. Needless to say that if you first want to try the script on only a few stations you can subset the stations ID and select only 1 or 2 for a quick test.

it_stations_id <- jdict$features$id
it_stations_nm <- jdict$features$properties$sname

for (i in 1:length(it_stations_id[1:2])){
  uri <- paste(service, "station=", it_stations_id[i], sep = "")
  print(paste("Network:", network, 
              "Downloading:", it_stations_nm[i], it_stations_id[i], sep=" "))
  data <- url(uri)
  datestring1 <- format(date1, "%Y%m%d")
  datestring2 <- format(date2, "%Y%m%d")
  outfn <- paste(network, "_", it_stations_id[i], "_", datestring1, "_to_", datestring2, sep="")
  download.file(uri, paste(outfn, ".csv"), "auto")
}

As the loop proceeds you should see information on what is being downloaded printed on your R prompt.

Once the procedure is completed you will have all your data ready to be analysed in your folder. In the next post we will try to look at some of the downloaded data and make a few basic analysis.

Did you like this post? Drop a comment down here or let me know by e-mail what you think of it or if you have questions or curiosities. Till next time.

Cheers