DOI: 10.5061/dryad.0vt4b8h8k
title: Code and data from: Global patterns in plant environmental breadths

description: The latitudinal gradient in plant diversity is one of the most famous
 patterns in ecology. It is hypothesised that narrow niche breadths and
 restricted geographic ranges in the tropics allow more species to coexist
 with minimal overlap, relative to high-latitude regions. Although a wealth
 of studies have investigated these questions across different regions and
 taxonomic groups, these have consistently yielded contradictory results,
 leading to the continued persistence of numerous ecological explanations.
 Here, using a global occurrence database containing over 100,000 plant
 species, we provide the first globally standardised investigation into the
 geographic relationships among latitudinal range, environmental breath,
 and latitudinal median. We find limited evidence for a global latitudinal
 gradient in species’ ranges and environmental breadths, with results
 varying between hemispheres and along latitude within each hemisphere. In
 agreement with previous observations, we show consistent support for a
 latitudinal gradient in environmental breadth and latitudinal range, but
 only for trees in the northern hemisphere and for tropical species. In the
 southern hemisphere, conversely, these trends are inverted for
 non-tropical species, with latitudinal range and environmental breadth
 decreasing with distance from the equator. Moreover, these relationships
 are even weaker with environmental breadth, even though there is a strong
 relationship between environmental breadth and latitudinal range. By
 applying standardised methods at the global scale, these results
 illustrate that variation in species’ ranges is largely a by-product of
 biogeographic patterns rather than niche processes. Collectively, this
 work suggests that existing ecological “rules” linking niche breadth to
 latitude predominantly reflect regional sampling biases and a historical
 focus on the northern hemisphere and certain taxonomic groups.
description: # Global patterns in plant environmental breadths The contents of the zip
 file are: **1_original_data** The folder containing the raw data
 backbone_taxonomy: Includes the GBIF backbone taxonomy with all
 information, reduced to the Drosera genus (sample) backbone: Includes
 taxonomy files Taxon.csv: Taxonomy of the Drosera genus — taxonID: unique
 identifier — acceptedNameUsageID: unique identifier based on GBIF taxonomy
 — phylum/kingdom/family/specificEpithet: the taxonomic designation of that
 species — scientificName: the taxonomic name and authority —
 taxonomicStatus: whether that species is accepted, or a synonym of another
 species based on the backbone eml.xml: Metadata sample_envs: Includes
 .tiff files of the environmental variables. As a sample, they are reduced
 to a resolution of 1 degree (~100 x 100 km) — CHELSA_adj_1 to
 CHELSA_adj_19: Climatic variables from the CHELSA dataset, representing
 bioclimatic factors such as temperature, precipitation, and seasonality,
 following the CHELSA v2.1 numbering scheme, adjusted by downsampling to 1
 degree. — SG_Absolute_depth_to_bedrock: Depth to bedrock in meters —
 SG_CEC_005cm: Cation Exchange Capacity (CEC) at 0-5 cm soil depth —
 SG_Coarse_fragments_005cm: Proportion of coarse fragments at 0-5 cm soil
 depth — SG_Silt_Content_005cm: Percentage of silt content in soil at 0-5
 cm depth — SG_Soil_pH_H2O_005cm: Soil pH measured in H₂O at 0-5 cm depth
 world_shapefile: Includes a shapefile of the world administrative
 boundaries at the level 0 ``` — world_adm0.dbf: The dBASE table that
 stores the attribute information of features, — world_adm0.prj: The file
 that stores the coordinate system information. ``` — world_adm0.sbn: The
 files that store the spatial index of the features. — world_adm0.sbx: The
 files that store the spatial index of the features. — world_adm0.shp: The
 main file that stores the feature geometry. — world_adm0.shx: The index
 file that stores the index of the feature geometry. sample_occs.csv:
 Sample occurrences of the Drosera genus — Species: the species name —
 occID: unique occurrence identifier — latitude/longitude: geographic
 coordinates — country: country of o—ccurrence tree_species.csv: List of
 tree species (Beech et al. 2017) — accepted_name_aggr: the accepted name
 for each tree species **2_scripts:** Folder including the R scripts
 1_standardise_clean_thin.R: Script used for the cleaning and
 standardization of the species occurrences 2_modelling_maxent.R: Script
 used for generating the Species Distribution Models and calculating
 env.breadth 3-1_MESS_global.R: Script used for the MESS analysis at a
 global scale 3-2_MESS_perHemisphere.R: Script used for the MESS analyses
 per hemisphere 4-1_merging_results.R: Script used for merging the results
 from the different analyses (env.breadth, MESS) but also the metrics
 directly computed from the cleaned oCcurrences, such as latitudinal range
 4-2_merging_mess_values_perHemisphere.R: Script used for merging the
 results from the MESS analyses performed on distinct hemispheres
 5-1_coefficients_bootstrap.R: Script used for calculating the coefficients
 (i.e. slope of the relationships) 6 and 7: Scripts used to generate the
 relative figures in the manuscript z_my_maxent_funcs.R: Config file of
 slightly modified functions **3_generated_data:** Folder containing the
 data generated from the analyses The idea is, that this folder is used as
 output also when testing the scripts, i.e. sample outputs. not_sample:
 Contains the files generated by the author all_non-tree_medianLat.csv:
 List of all the non-tree species (including those prior cleaning) and
 their median latitude — sp: the species name — lat_median: median latitude
 for that species — growthform: an indicator of tree or non-tree
 all_tree_medianLat.csv: List of all the tree species (including those
 prior cleaning) and their median latitude — sp: the species name —
 lat_median: median latitude for that species — growthform: indicator of
 tree or non-tree area_perspecies.csv: List of all species and their
 geographic area — Species: name of species — mean_area_km2: the mean area
 in km2 coefficients_eblr.csv: Data frame of coefficients relative to the
 "Environmental Breadth vs. Latitudinal Range" relationship —
 model: modelled relationship, in this case "env.breadth vs lat
 range" — type: Type: Four different types of data subsets: — Global:
 Includes all data without hemisphere separation — Separate Hemispheres:
 Treats northern and southern hemispheres independently — Hemisphere
 Endemic: Only includes species endemic to one hemisphere — Hemisphere
 Specific MESS: Uses environmental breadth specific to each hemisphere —
 zone: Two latitude-based categories: — tropical: Latitudes close to the
 equator — else: Latitudes farther from the equator — hemisphere: Either
 the North or South hemisphere — indicator of tree or non-tree (tree or
 herb) — valci: Bootstrap-estimated coefficient of the regression model —
 valse: Standard error of the estimated coefficient — lower_bci: Lower
 bound of the bootstrap confidence interval — upper_bci: Upper bound of the
 bootstrap confidence interval — b: Regression coefficient, representing
 the relationship between environmental breadth and latitudinal range
 coefficients_lmeb.csv: Data frame of coefficients relative to the
 "Latitudinal Median vs. Environmental Breadth" relationship —
 model: modelled relationship, in this case "lat median vs
 env.breadth" — type: Type: Four different types of data subsets: —
 Global: Includes all data without hemisphere separation — Separate
 Hemispheres: Treats northern and southern hemispheres independently —
 Hemisphere Endemic: Only includes species endemic to one hemisphere —
 Hemisphere Specific MESS: Uses environmental breadth specific to each
 hemisphere — zone: Two latitude-based categories: — tropical: Latitudes
 close to the equator — else: Latitudes farther from the equator —
 hemisphere: Either the North or South hemisphere — indicator of tree or
 non-tree (tree or herb) — valci: Bootstrap-estimated coefficient of the
 regression model — valse: Standard error of the estimated coefficient —
 lower_bci: Lower bound of the bootstrap confidence interval — upper_bci:
 Upper bound of the bootstrap confidence interval — b: Regression
 coefficient, representing the relationship between latitudinal median and
 environmental breadth coefficients_lmlr.csv: Data frame of coefficients
 relative to the "Latitudinal Median vs. Latitudinal Range"
 relationship — model: modelled relationship, in this case "lat median
 vs lat range" — type: Type: Four different types of data subsets: —
 Global: Includes all data without hemisphere separation — Separate
 Hemispheres: Treats northern and southern hemispheres independently —
 Hemisphere Endemic: Only includes species endemic to one hemisphere —
 Hemisphere Specific MESS: Uses environmental breadth specific to each
 hemisphere — zone: Two latitude-based categories: — tropical: Latitudes
 close to the equator — else: Latitudes farther from the equator —
 hemisphere: Either the North or South hemisphere — growthform: indicator
 of tree or non-tree (tree or herb) — valci: Bootstrap-estimated
 coefficient of the regression model — valse: Standard error of the
 estimated coefficient — lower_bci: Lower bound of the bootstrap confidence
 interval — upper_bci: Upper bound of the bootstrap confidence interval —
 b: Regression coefficient, representing the relationship between
 latitudinal median and latitudinal range mess_values_perHemisphere.csv:
 Data frame of MESS values computed per hemisphere — species: name of
 species — mess_n: the MESS for the northern hemisphere (unitless) —
 mess_s: the MESS for the souther hemisphere (unitless) — growthform:
 indicator of tree or non-tree niche_data_summarized.csv: Most important
 data frame containing the main results. Data frame of species niche and
 model performance metrics. Output from the 4-1_merging_results.R script. —
 Species: the species name — env_breadth: Environmental breadth estimated
 from the species distribution model — n_inc_obs: Number of included
 observations after data filtering — fc: Feature class used in the species
 distribution model — rm: Regularization multiplier used in the model —
 tune.args: Parameters used for tuning the model — auc.train: Area Under
 the Curve (AUC) of the model during training — cbi.train: Continuous Boyce
 Index (CBI) during training — auc.diff.avg: Average difference between
 training and validation AUC — auc.diff.sd: Standard deviation of the AUC
 difference — auc.val.avg: Average AUC for validation data — auc.val.sd:
 Standard deviation of validation AUC — cbi.val.avg: Average CBI for
 validation data — cbi.val.sd: Standard deviation of validation CBI —
 or.10p.avg: Average omission rate at the 10th percentile threshold —
 or.10p.sd: Standard deviation of omission rate at the 10th percentile —
 or.mtp.avg: Average omission rate at the minimum training presence
 threshold — or.mtp.sd: Standard deviation of omission rate at the minimum
 training presence — AICc: Corrected Akaike Information Criterion for model
 selection — delta.AICc: Difference in AICc values relative to the best
 model — w.AIC: Model weight based on AICc — ncoef: Number of model
 coefficients — n_obs_total: Total number of observations available for the
 species — growthform: indicator of tree or non-tree (tree or herb) — mess:
 Multivariate Environmental Similarity Surface (MESS) value, indicating how
 well the species’ environment matches the training conditions —
 lat_range_sd_n: Standard deviation of latitudinal range in the Northern
 Hemisphere — lat_range_mad_n: Median absolute deviation of latitudinal
 range in the Northern Hemisphere — lat_median_n: Median latitude of
 occurrences in the Northern Hemisphere — lat_range_sd_s: Standard
 deviation of latitudinal range in the Southern Hemisphere —
 lat_range_mad_s: Median absolute deviation of latitudinal range in the
 Southern Hemisphere — lat_median_s: Median latitude of occurrences in the
 Southern Hemisphere — lat_range_sd_g: Standard deviation of latitudinal
 range globally — lat_range_mad_g: Median absolute deviation of latitudinal
 range globally — lat_median_g: Median latitude of occurrences globally
 raw_niche_data_mess.csv: Intermediate dataframe in the
 4-1_merging_results.R script. For variable description see
 niche_data_summarized.csv used_variables_nontree.csv: Dataframe containing
 the variables used for the modelling of the distribution of each species
 of non-tree — species: the species name Climatic variables (from CHELSA) —
 CHELSA_adj_1 to CHELSA_adj_19: Climatic variables from the CHELSA dataset,
 representing bioclimatic factors such as temperature, precipitation, and
 seasonality, following the CHELSA v2.1 numbering scheme, adjusted by
 downsampling to 1 degree. — I(CHELSA_adj_X^2): Squared transformations of
 the respective CHELSA-adjusted variables, used for modeling non-linear
 relationships. Soil variables (from SoilGrids) —
 SG_Absolute_depth_to_bedrock: Depth to bedrock in meters — SG_CEC_005cm:
 Cation Exchange Capacity (CEC) at 0-5 cm soil depth —
 SG_Coarse_fragments_005cm: Proportion of coarse fragments at 0-5 cm soil
 depth — SG_Silt_Content_005cm: Percentage of silt content in soil at 0-5
 cm depth — SG_Soil_pH_H2O_005cm: Soil pH measured in H₂O at 0-5 cm depth —
 I(SG_Absolute_depth_to_bedrock^2): Squared transformation of depth to
 bedrock — I(SG_CEC_005cm^2): Squared transformation of CEC —
 I(SG_Coarse_fragments_005cm^2): Squared transformation of coarse fragment
 proportion — I(SG_Silt_Content_005cm^2): Squared transformation of silt
 content — I(SG_Soil_pH_H2O_005cm^2): Squared transformation of soil pH —
 growthform: indicator of tree or non-tree (tree or herb), in this case
 only "herb" used_variables\_ tree.csv: Dataframe containing the
 variables used for the mmodelingof the distribution of each species of
 tree (For variable description see used_variables\_ tree.csv) zones.csv:
 Data frame relating to the main zone of distribution (tropical vs. else)
 within hemispheres per species — Species: the species name — zone_n:
 Latitudinal zone classification for the species in the Northern Hemisphere
 — zone_s: Latitudinal zone classification for the species in the Southern
 Hemisphere — zone_g: Latitudinal zone classification for the species
 globally, considering occurrences across both hemispheres The contents of
 one additional file outside of the folders:
 ODMAP_Anonymous_2024-11-14.csv: A file reporting the ODMAP SDM protocol
 details — section: the type of information reported — subsection: the
 subset of information being reported — element: the specific attribute
 being reported — value: the specific value reported