DOI: 10.5061/dryad.rbnzs7h96
title: Environmental niche models improve species identification in DNA barcoding

description: Recent advances in DNA barcoding have immeasurably advanced global
 biodiversity research in the last two decades. However, inherent
 limitations in barcode sequences, such as hybridization, introgression, or
 incomplete lineage sorting can lead to misidentifications when relying
 solely on barcode sequences. Here, we propose a new
 Niche-model-Based Species Identification (NBSI) method based on the idea
 that species distribution information is a potential complement to DNA
 barcoding species identifications. NBSI performs species membership
 inference by incorporating niche modeling predictions and traditional DNA
 barcoding identifications. Systematic tests across diverse
 scenarios show significant improvements in species identification success
 rates under the newly proposed NBSI framework, where the largest increase
 is from 4.7% (95%CI: 3.51%-6.25%) to 94.8% (95%CI: 93.19%-96.06%).
 Additionally, obvious improvements were observed when using NBSI on
 potentially ambiguous sequences whose genetic nearest neighbors belong to
 another species or more than two species, commonly occurring with species
 represented by single or short DNA barcodes. These results
 support our assertion that environmental factors/variables are valuable
 complements to DNA sequence data for species identification by avoiding
 potential mis-identifications inferred from genetic information alone. The
 NBSI framework is currently implemented as a new R package,
 “NicheBarcoding”, that is open source under GNU General Public License and
 freely available
 from https://CRAN.R-project.org/package=NicheBarcoding.
description: # Environmental niche models improve species identification in DNA
 barcoding
 [https://doi.org/10.5061/dryad.rbnzs7h96](https://doi.org/10.5061/dryad.rbnzs7h96) ## Description of the data and file structure The software package and the supplementary materials of this research article: Environmental Niche Models Improve Species Identification in DNA Barcoding ### Files and variables #### File: Appendix\_S1\_Simulated\_datasets.zip **Description:** All generated simulated datasets. * Files starting with "Sim_": These are simulated datasets generated with species numbers of 20, 50, and 100, all with 100 individuals each. The fundamental coalescent parameter *θ* was set to 0.05, 0.01, and 0.2 for the respective datasets. Consequently, in the dataset with 20 species, each species contains 5 individuals; in the dataset with 50 species, each species contains 2 individuals; and in the dataset with 100 species, each species contains 1 individual. * Files starting with "SimAmbSeqs_": Based on the "Sim_*" files, potentially ambiguous sequences whose genetic nearest neighbors belong to another species or more than two species are extracted from each simulated dataset, forming new corresponding simulated datasets. #### File: Appendix\_S2\_Empirical\_datasets.zip **Description:** All collected empirical datasets. * Files starting with "Emp_": Empirical datasets (1-6) used for testing the NBSI method in the study. * Files starting with "EmpAmbSeqs_": Based on the "Emp_*" files, potentially ambiguous sequences whose genetic nearest neighbors belong to another species or more than two species are extracted from each simulated dataset, forming new corresponding simulated datasets. #### File: Appendix\_S3\_Simulated\_datasets\_-\_EnvSelected.zip **Description:** The variables retained for niche modeling in every single test of simulated datasets. * Files starting with "EnvSelected_": When testing the NBSI method using simulated datasets, to eliminate the impact of multicollinearity, we used the variance inflation factor (VIF) method to remove redundant variables. We recorded the variables that were retained in each model-building instance, corresponding to each simulated dataset. * Files starting with "EnvSelected_AmbSeqs_": When testing the NBSI method using simulated datasets composed of the ambiguous sequences, to eliminate the impact of multicollinearity, we used the variance inflation factor (VIF) method to remove redundant variables. We recorded the variables that were retained in each model-building instance, corresponding to each simulated dataset composed of the ambiguous sequences. #### File: Appendix\_S4\_Empirical\_datasets\_-\_EnvSelected.zip **Description:** The variables retained for niche modeling in every single test of empirical datasets. * Files starting with "EnvSelected_": When testing the NBSI method using empirical datasets, to eliminate the impact of multicollinearity, we used the variance inflation factor (VIF) method to remove redundant variables. We recorded the variables that were retained in each model-building instance, corresponding to each empirical dataset. * Files starting with "EnvSelected_AmbSeqs_": When testing the NBSI method using empirical datasets composed of the ambiguous sequences, to eliminate the impact of multicollinearity, we used the variance inflation factor (VIF) method to remove redundant variables. We recorded the variables that were retained in each model-building instance, corresponding to each empirical dataset composed of the ambiguous sequences. ## Code/software #### File: NicheBarcoding\_0.0.1.8000.tar.gz **Description:** The source code of the R package "NicheBarcoding". Users can directly install and use it via RStudio.