README: This folder contains all the code and data necessary to run the analysis for our manuscript, The role of biodiversity in mediating global levels of plant invasions.

README: This folder contains all the code and data necessary to run the analysis for our manuscript, The role of biodiversity in mediating global levels of plant invasions.

This code takes the NRI values for each of the countries (for each of the plants) and examines the relationship between NRI and invader presence in the transect. The analysis includes simple models and models that account for spatial autocorrelation at the country level.To run this, you need the NRI values that we calculated in the folders labeled Part 2 and Part 3. These values are saved in this folder! You do NOT need to run all the code in Part 2 and Part 3 to run this script.

This code takes the NRI values for each of the countries (for each of the plants) and examines the relationship between NRI and invader presence in the transect. The analysis includes simple models and models that account for spatial autocorrelation at the country level.To run this, you need the NRI values that we calculated in the folders labeled Part 2 and Part 3. These values are saved in this folder! You do NOT need to run all the code in Part 2 and Part 3 to run this script.

GBIF data is necessary to run this code. See Part 2 folder, as it is all the same info. This script is the same as NRI-PT2-022825, except for one primary difference. This includes the “add.inv.sp” function, which includes the invasive species in NRI calculation, even if it was not originally in the transect list. This way we are seeing relatedness of the invasive species to the rest of the community.

GBIF data is necessary to run this code. See Part 2 folder, as it is all the same info. This script is the same as NRI-PT2-022825, except for one primary difference. This includes the “add.inv.sp” function, which includes the invasive species in NRI calculation, even if it was not originally in the transect list. This way we are seeing relatedness of the invasive species to the rest of the community.

WARNING: This code takes a very, very long time to run (~20 minutes- 1 hour per country). Running this code is NOT necessary to check the code in PTS-2-3-Analysis-022825.The R script to calculate NRI is NRI-PT2-022825For six globally distributed invasive plants, we determined which countries the plant was invasive in using GRIIS (link: GRIIS.org/). We then went to GBIF and looked at all the global observations and went country by country and drew a rectangle over 70%+ of the country's observations (which we refer to as the area most densely invaded). We downloaded those observations, along with all the plant observations in the same space, with appropriate restrictions (year, quality, human observations, etc). The GBIF data are available in figshare, as are the citations. We then randomly chose transects within each country and made a list of all the plants in each transect. I then followed this tutorial (https://pedrohbraga.github.io/CommunityPhylogenetics-Workshop/CommunityPhylogenetics-Workshop.html) to calculate the NRI of each of the transects.b. Ultimately, the result of this file is a series of csvs (uploaded to figshare) that include the NRI values for each transect (across all plants).
To run this code, you need the GBIF data used. The GBIF citations include a DOI where you can access/download the data. The GBIF citations can be found in figshare (labeled GBIF Citations), but also in the supplementary materials.NOTE: Many of the GBIF files are too large to directly upload. In the figshare folder, in addition to the citations where you can access ALL the data by downloading each one individually, I have uploaded a sample of csvs that are ready for use. In other words, access to all the GBIF data is in the GBIF Citations doc, but I have also put in a couple of example GBIF csvs as well.I am more than happy to send the csvs (along with their GBIF citations) directly to people, however, it would need to be via a site that allows the transfer of large files.

WARNING: This code takes a very, very long time to run (~20 minutes- 1 hour per country). Running this code is NOT necessary to check the code in PTS-2-3-Analysis-022825.The R script to calculate NRI is NRI-PT2-022825For six globally distributed invasive plants, we determined which countries the plant was invasive in using GRIIS (link: GRIIS.org/). We then went to GBIF and looked at all the global observations and went country by country and drew a rectangle over 70%+ of the country's observations (which we refer to as the area most densely invaded). We downloaded those observations, along with all the plant observations in the same space, with appropriate restrictions (year, quality, human observations, etc). The GBIF data are available in figshare, as are the citations. We then randomly chose transects within each country and made a list of all the plants in each transect. I then followed this tutorial (https://pedrohbraga.github.io/CommunityPhylogenetics-Workshop/CommunityPhylogenetics-Workshop.html) to calculate the NRI of each of the transects.b. Ultimately, the result of this file is a series of csvs (uploaded to figshare) that include the NRI values for each transect (across all plants).
To run this code, you need the GBIF data used. The GBIF citations include a DOI where you can access/download the data. The GBIF citations can be found in figshare (labeled GBIF Citations), but also in the supplementary materials.NOTE: Many of the GBIF files are too large to directly upload. In the figshare folder, in addition to the citations where you can access ALL the data by downloading each one individually, I have uploaded a sample of csvs that are ready for use. In other words, access to all the GBIF data is in the GBIF Citations doc, but I have also put in a couple of example GBIF csvs as well.I am more than happy to send the csvs (along with their GBIF citations) directly to people, however, it would need to be via a site that allows the transfer of large files.

PT1-Analysis-022825: This file takes a dataframe containing climatic, economic, and species richness data for the 149 countries included in this study and uses structural equation modeling to explore the relationships between these variables. The script includes data cleaning, log transformations, detecting spatial autocorrelation, AIC model selection, SEM analysis, and ultimately visualization (making the figures in this manuscript).To run this script, the following pieces of data are needed: a. final.comps.country.table.csv: This includes the following column names: Country (the name), Population, area (in km^2), island (whether the country is island or mainland. Countries that have islands were categorized as mainland-- the US, for example), plantNum (which is the total number of native plants, which was collected from literature on country level floral surveys or convention on biological diversity reports), region (North Africa, Eastern Europe, etc), climate (tropics vs temperate), nias (number of invasive species according to GRIIS*), and naliens (number of alien species according to GRIIS*)b. KOFGI_2021.csv: This is a file that has the KOF indices for each country from 2021. This was sourced from https://kof.ethz.ch/en/forecasts-and indicators/indicators/kof-globalisation-index.htmlc. per capita gdp 2021- correct.csv: This is a file that has the per capita GDP values for each country from 2021. This was sourced from https://data.worldbank.org/indicator/NY.GDP.PCAP.CDd. *GRIIS csv files for 149 countries, sourced from griis.org: NOTE: You do NOT need this to run the code. We used the GRIIS files to find the number of alien and invasive species in each country. This data has been added to the final.comps.country.table.csv (see above). However, the GRIIS files are still uploaded to figshare, and the code that counts the number of alien and invasive plants in each country IS available in this R script. To bypass the species counts (so you do not have to upload all 149 csv files), simply go to the section that says “START” and make sure you have your working directory set correctly, and the country table downloaded and read in.

PT1-Analysis-022825: This file takes a dataframe containing climatic, economic, and species richness data for the 149 countries included in this study and uses structural equation modeling to explore the relationships between these variables. The script includes data cleaning, log transformations, detecting spatial autocorrelation, AIC model selection, SEM analysis, and ultimately visualization (making the figures in this manuscript).To run this script, the following pieces of data are needed: a. final.comps.country.table.csv: This includes the following column names: Country (the name), Population, area (in km^2), island (whether the country is island or mainland. Countries that have islands were categorized as mainland-- the US, for example), plantNum (which is the total number of native plants, which was collected from literature on country level floral surveys or convention on biological diversity reports), region (North Africa, Eastern Europe, etc), climate (tropics vs temperate), nias (number of invasive species according to GRIIS*), and naliens (number of alien species according to GRIIS*)b. KOFGI_2021.csv: This is a file that has the KOF indices for each country from 2021. This was sourced from https://kof.ethz.ch/en/forecasts-and indicators/indicators/kof-globalisation-index.htmlc. per capita gdp 2021- correct.csv: This is a file that has the per capita GDP values for each country from 2021. This was sourced from https://data.worldbank.org/indicator/NY.GDP.PCAP.CDd. *GRIIS csv files for 149 countries, sourced from griis.org: NOTE: You do NOT need this to run the code. We used the GRIIS files to find the number of alien and invasive species in each country. This data has been added to the final.comps.country.table.csv (see above). However, the GRIIS files are still uploaded to figshare, and the code that counts the number of alien and invasive plants in each country IS available in this R script. To bypass the species counts (so you do not have to upload all 149 csv files), simply go to the section that says “START” and make sure you have your working directory set correctly, and the country table downloaded and read in.