DOI: 10.6084/m9.figshare.7801334.v1
title: Data

description: Contains 3 folders:<br><b>1. filtered_data: </b><br><i>1. </i><i>all_sps_environmental_test_data.csv</i>: A dataset representing georeferenced data used to test the niche-centroid distance's models; it has 21 variables <br>"folder": Folder with the occurrence and abundance data for a species"Species": Species ID "Latitud": Decimal latitude"Longitud": Decimal longitude"Abundance": Number of individuals"n_train": number of data used to train the models"PC1": Value for the 1 Principal Component as extracted from Principal Components Raster Layers "PC10": Value for the 10 Principal Component as extracted from Principal Components Raster Layers "PC11": Value for the 11 Principal Component as extracted from Principal Components Raster Layers "PC12": Value for the 12 Principal Component as extracted from Principal Components Raster Layers "PC13": Value for the 13 Principal Component as extracted from Principal Components Raster Layers"PC14": Value for the 14 Principal Component as extracted from Principal Components Raster Layers"PC15": Value for the 15 Principal Component as extracted from Principal Components Raster Layers"PC2": Value for the 2 Principal Component as extracted from Principal Components Raster Layers"PC3": Value for the 3 Principal Component as extracted from Principal Components Raster Layers "PC4": Value for the 4 Principal Component as extracted from Principal Components Raster Layers"PC5": Value for the 5 Principal Component as extracted from Principal Components Raster Layers "PC6": Value for the 6 Principal Component as extracted from Principal Components Raster Layers "PC7": Value for the 7 Principal Component as extracted from Principal Components Raster Layers"PC8": Value for the 8 Principal Component as extracted from Principal Components Raster Layers "PC9": Value for the 9 Principal Component as extracted from Principal Components Raster Layers.<br><br><i>2. all_sps_environmental_train_data.csv: </i> A dataset representing georeferenced data used to train the niche-centroid distance's models; it has 20 variables <br>"folder": Folder which the occurrence and abundance data for a species"Species": Species ID "Latitud": Decimal latitude"Longitud": Decimal longitude"n_train": number of data used to train the models"PC1": Value for the 1 Principal Component as extracted from Principal Components Raster Layers "PC10": Value for the 10 Principal Component as extracted from Principal Components Raster Layers "PC11": Value for the 11 Principal Component as extracted from Principal Components Raster Layers "PC12": Value for the 12 Principal Component as extracted from Principal Components Raster Layers "PC13": Value for the 13 Principal Component as extracted from Principal Components Raster Layers"PC14": Value for the 14 Principal Component as extracted from Principal Components Raster Layers"PC15": Value for the 15 Principal Component as extracted from Principal Components Raster Layers"PC2": Value for the 2 Principal Component as extracted from Principal Components Raster Layers"PC3": Value for the 3 Principal Component as extracted from Principal Components Raster Layers "PC4": Value for the 4 Principal Component as extracted from Principal Components Raster Layers"PC5": Value for the 5 Principal Component as extracted from Principal Components Raster Layers "PC6": Value for the 6 Principal Component as extracted from Principal Components Raster Layers "PC7": Value for the 7 Principal Component as extracted from Principal Components Raster Layers"PC8": Value for the 8 Principal Component as extracted from Principal Components Raster Layers "PC9": Value for the 9 Principal Component as extracted from Principal Components Raster Layers.<br><i>3. </i><i>sps_traits_CleanAllInclude.csv: </i>Dataset with 10 variables representing bird species traits <br>"folder": Folder with the occurrence and abundance data for a species,"Species": Species ID"SciName": Scientific name"n_train": Number of data points to train the niche-centroid distance models."n_abundance": Number of abundance points to test the relationship between population abundance and niche-centroid distance."Include": Does the species was included in the analysis "Aquatic": Is it aquatic"Migratory": Is it migratory"BodyMass": Body Mass"PercentBBS": Percent of the distribution sampled in the Bird Breathing Survive.<br><i>4. sps_traits_corsPCAParallel.csv:</i> Correlation results of the relationship between population abundance and niche-centroid distance<br>"Species": Species ID"SciName": Scientific name"n_train": Number of data points to train the niche-centroid distance models."n_abundance": Number of abundance points to test the relationship between population abundance and niche-centroid distance."Aquatic": Is it aquatic"Migratory": Is it migratory"BodyMass": Body Mass"PercentBBS": Percent of the distribution sampled in the Bird Breathing Survive."folder": Folder with the occurrence and abundance data for a species"Longitud": Decimal longitude"Latitud": Decimal latitude"n_test": Number of abundance data to test model predictions."PC1": Value for the 1 Principal Componet as extracted from Principal Components Raster Layers "PC2": Value for the 2 Principal Componet as extracted from Principal Components Raster Layers "PC3": Value for the 3 Principal Componet as extracted from Principal Components Raster Layers "in_convex3d": inside the 3-dimensional convex hull "covex_3d_euc": Ecuclidean distence to niche-centroid using 3 variables (using the first 3 PCs) "in_convex2d": inside the 2-dimensional convex hull "covex_2d_euc": Ecuclidean distence using 2 variables (using the first 2 PCs) "mahala_dist_3d": Mahalanobis distance to niche-centroid (using the first 3 PCs) "in_ellipsoid_2d": inside the 2-dimensional ellipsoid"ellipsoid3d_volume": 3 dimensional Ellipsoid volume (niche width)"mahala_dist_2d": Mahalanobis distance to niche-centroid (using the first 2 PCs) "in_ellipsoid_3d": inside the 2-dimensional ellipsoid"Abundance": Population abundance"rho_convex_3d": Rho value of the correlation between Euclidean distance (using the first 3 PCs) to niche-centroid and population abundance "pval_convex_3d": P-value of the correlation between Euclidean diarance (using the first 3 PCs) to niche-centroid and population abundance."rho_convex_2d": Rho value of the correlation between Euclidean distance (using the first 2 PCs) to niche-centroid and population abundance "pval_convex_2d": P-value of the correlation between Euclidean distance (using the first 3 PCs) to niche-centroid and population abundance."rho_mahalanobis_3d": Rho value of the correlation between Mahalanobis distance (using the first 3 PCs) to niche-centroid and population abundance "pval_mahalanobis_3d":P-value value of the correlation between Mahalanobis distance (using the first 3 PCs) to niche-centroid and population abundance "rho_mahalanobis_2d": Rho value of the correlation between Mahalanobis distance (using the first 2 PCs) to niche-centroid and population abundance "pval_mahalanobis_2d": P-value of the correlation between Mahalanobis distance (using the first 2 PCs) to niche-centroid and population abundance <br><i>5. Cal_mel_PresentAbundance.tif:</i> Predicted abundance in the present scenario for the Lark Bunting Calamospiza melanocorys. <br><i>6. Cal_mel_FutureAbundance.tif: </i>Predicted abundance in the future scenario for the Lark Bunting Calamospiza melanocorys. PCA equations to the bioclimatic layers for the representative concentration pathway 8.5 for the CCSM4 general circulation model available from (Hijmans et al. 2005a).<br><br><b>2. PCA_future: </b>A folder with the projected Principal Components (PCs) layers of the bioclimatic variables of WorldClim (without bio 8, 9, 18, and 19). The layers that were used to project the PCs were the ones for the representative concentration pathway 8.5 for the CCSM4 general circulation model available from (Hijmans et al. 2005a).<br><b>3. PCA_present: </b>A folder withPrincipal Components (PCs) layers of the of the bioclimatic variables of WorldClim (without bio 8, 9, 18, and 19). It also has the scree plot of the variance explained variance by each component.<br>