DOI: 10.5061/dryad.msbcc2g82
title: Anthropogenic activity and climate change exacerbate the spread of pathogenic bacteria in the environment

description: Climate change is profoundly impacting human health. Human pathogenic
 bacteria (HPB) infections mediated by the environment are considered a
 significant cause of global health losses. However, the biogeography of
 HPB and their response to climate change remain largely unknown. Here, we
 constructed and analyzed a global atlas of potential HPB using 1,066,584
 samples worldwide. HPB are widely present in the global environment, and
 their distribution follows a latitudinal diversity gradient. Climate and
 anthropogenic factors are identified as major drivers of the global
 distribution of HPB. Our predictions indicated that by the end of this
 century, the richness, abundance, and invasion risk of HPB will increase
 globally, with this upward trend becoming more pronounced as development
 sustainability declines. Therefore, the threat of environmentally mediated
 HPB infections to human health may be more severe in a world where
 anthropogenic activities are intensifying and the global climate is
 warming.
description: # Anthropogenic activity and climate change exacerbate the spread of
 pathogenic bacteria in the environment ## Introduction We have submitted
 our raw data and all R codes for machine learning and climate change
 analysis of human pathogenic bacteria (HPB). The files which are included
 in my submission contain all of the information for machine learning
 analysis. Please place these files in a folder named `pathogen` and set
 default working directory of R to the previous level of the `pathogen`.
 For example, if the path of `pathogen` is `a/b/c/pthogen`, execute the
 command `setwd(dir = "a/b/c/")` in R. ## Detailed description *
 `variables.xlsx` contains the definitions of variables in each dataset. *
 `dat.csv` is data used for global distribution of HPB richness
 and `dat_abun.csv` is data used for global distribution of HPB
 abundance. `df_412_terra_nohuman.csv` and `df0_add_nohuman.csv` are data
 used for climate change analysis of HPB richness and abundance. * Folder
 ending in `spatial` or `spatial_abun`: Split the training and testing sets
 that are spatially distinct using the `blockCV` package. * `map_spatial`
 and `map_spatial_abun` are used to predict the global distribution of HPB
 richness and abundance. There are six code
 files: `step1_select_sample.R` is used for data
 preprocessing; `step2_vif.R` is used to calculate variance inflation
 factor; `step3_feature_selection.R` is used to identify the optimal
 feature set; `step4_hyperparameter_tuning.R` is used to optimize the
 model's hyperparameters; `step5_test_model.R` is used to validate
 model on the test set; and `step6_modelling.R` is used for model
 construction. * `future_spatial` and `future_spatial_abun` are used to
 analyze the impact of climate change on HPB. There are seven code
 files: `step1_extract_variables.R` is used to extract climate variables
 corresponding to each location; `step2_data_process.R` is used for data
 preprocessing; `step3_MESS.R` is used to calculate multivariate
 environmental similarity surface; `step4_feature_selection.R` is used
 to identify the optimal feature set; `step5_modelling.R` is used for model
 construction; `step6_test_model.R` is used to validate model on the test
 set; and `step7_future_predict.R` is used to predict global distribution
 of HPB richness and abundance in the future. * Folder ending in
 `spatial_cv` or `spatial_cv_abun`: Apply spatial cross-validation through
 the `blockCV` package. * `map_spatial_cv` and `map_spatial_cv_abun` are
 used to predict the global distribution of HPB richness and
 abundance. There are five code files: `step1_vif.R` is used to calculate
 variance inflation factor; `step2_feature_selection.R` is used to identify
 the optimal feature set; `step3_hyperparameter_tuning.R` is used to
 optimize the model's hyperparameters; `step4_test_model.R` is used
 to validate model on the test set; and `step5_modelling.R` is used for
 model construction. * `future_spatial_cv` and `future_spatial_cv_abun` are
 used to analyze the impact of climate change on HPB. There are seven code
 files: `step1_extract_variables.R` is used to extract climate variables
 corresponding to each location; `step2_data_process.R` is used for data
 preprocessing; `step3_MESS.R` is used to calculate multivariate
 environmental similarity surface; `step4_feature_selection.R` is used
 to identify the optimal feature set; `step5_modelling.R` is used for model
 construction; `step6_test_model.R` is used to validate model on the test
 set; and `step7_future_predict.R` is used to predict global distribution
 of HPB richness and abundance in the future. * Folder ending in `remove`
 or `remove_abun`: Remove the commensal. * `map_remove` and
 `map_remove_abun` are used to predict the global distribution of richness
 and abundance of HPB excluding commensal. There are six code
 files: `step1_select_sample.R` is used for data
 preprocessing; `step2_vif.R` is used to calculate variance inflation
 factor; `step3_feature_selection.R` is used to identify the optimal
 feature set; `step4_hyperparameter_tuning.R` is used to optimize the
 model's hyperparameters; `step5_test_model.R` is used to validate
 model on the test set; and `step6_modelling.R` is used for model
 construction. `dat_remove.csv` is data used for global distribution of HPB
 richness and `dat_remove_abun.csv` is data used for global distribution of
 HPB abundance.  * `future_remove` and `future_remove_abun` are used to
 analyze the impact of climate change on HPB excluding commensal. There are
 seven code files: `step1_extract_variables.R` is used to extract climate
 variables corresponding to each location; `step2_data_process.R` is used
 for data preprocessing; `step3_MESS.R` is used to calculate multivariate
 environmental similarity surface; `step4_feature_selection.R` is used
 to identify the optimal feature set; `step5_modelling.R` is used for model
 construction; `step6_test_model.R` is used to validate model on the test
 set; and `step7_future_predict.R` is used to predict global distribution
 of HPB richness and abundance in the
 future. `df_412_terra_nohuman.csv` and `df0_add_nohuman.csv` are data used
 for climate change analysis of HPB richness and abundance. * Folder ending
 in `EMP`: Analyze using the Earth Microbiome Project (EMP). * `map_EMP`
 are used to predict the global distribution of HPB. There are six code
 files: `step1_select_sample.R` is used for data
 preprocessing; `step2_vif.R` is used to calculate variance inflation
 factor; `step3_feature_selection.R` is used to identify the optimal
 feature set; `step4_hyperparameter_tuning.R` is used to optimize the
 model's hyperparameters; `step5_test_model.R` is used to validate
 model on the test set; and `step6_modelling.R` is used for model
 construction. `train_EMP.csv` is data used for global distribution of HPB
 richness and `location.csv` is data related to location information. *
 `future_EMP` are used to analyze the impact of climate change on
 HPB. There are seven code files: `step1_extract_variables.R` is used to
 extract climate variables corresponding to each
 location; `step2_data_process.R` is used for data
 preprocessing; `step3_MESS.R` is used to calculate multivariate
 environmental similarity surface; `step4_feature_selection.R` is used
 to identify the optimal feature set; `step5_modelling.R` is used for model
 construction; `step6_test_model.R` is used to validate model on the test
 set; and `step7_future_predict.R` is used to predict global distribution
 of HPB richness and abundance in the
 future. `df_412_terra_nohuman.csv` and `df0_add_nohuman.csv` are data used
 for climate change analysis of HPB richness and abundance. ##
 Code/Software The data analysis was mainly conducted using R (version
 4.3.3). ## Please note Please download the map data for analysis from
 **[http://bioinfo.qd.sdu.edu.cn/kegg/map_data/map_data.zip](http://bioinfo.qd.sdu.edu.cn/kegg/map_data/map_data.zip)** using a browser or **ftp://202.194.20.63/** by FTP download software, such as `FileZilla`. You can also use the `File Explorer` on the Windows system to download data.