DOI: 10.5061/dryad.t1g1jwt3c
title: Identifying traits that enable lizard adaptation to different habitats

description: Aim: Species adapt differently to contrasting environments, such as open
 habitats with sparse vegetation and forested habitats with dense forest
 cover. We investigated colonization patterns in the open and forested
 environments in the Diagonal of Open Formations and surrounding rain
 forests (i.e., Amazon and Atlantic Forest) in Brazil, tested whether the
 diversification rates were affected by the environmental conditions, and
 identified traits that enabled species to persist in those environments.
 Location: South America, Brazil. Taxon: Squamata, Lizards Methods: We
 estimated ancestral ranges to identify range shifts relative to
 traditional open and forested habitats for all species. We used
 phylogenetic information and the current distribution of species in open
 and forested environments. To evaluate whether these environments
 influenced species diversification, we tested 12 models using a Hidden
 Geographic State Speciation and Extinction analysis. Finally, we combined
 phylogenetic relatedness and species traits in a machine learning
 framework to identify the traits permitting adaptation in those
 contrasting environments. Results: We identified 41 total transitions
 between open and forested habitats, of which 80% were from the forested
 habitats to the open habitats. Widely distributed species had lower
 speciation and extinction rates than species in forested or open habitats,
 with the latter having the higher overall rates. Mean body temperature,
 microhabitat, female SVL, and diet were identified as putative traits that
 enabled adaptation to different environments, and phylogenetic relatedness
 was an important predictor of species occurrence. Main conclusions: Our
 results indicate that transitions from forested to open habitats are most
 common. The combination of phylogenetic reconstruction of ancestral
 distributions and the machine learning framework enables us to integrate
 organismal trait data, environmental data, and evolutionary history in a
 manner that could be applied on a global scale.
description: ReadMe This repository contains the scripts
 and data used to conduct the analyses performed by Lanna et al.<br>
 For more details on the methodology, see the Lanna et al.
 manuscript. Data: 1. Meiri 2018
 (https://doi.org/10.1111/geb.12773) trait dataset:
 "Meiri_2018_traits_copy.csv". This file is a copy of the Meiri
 2018 trait dataset, used to get most of the traits used in the random
 forest analysis. 2.
 "Names_reptile_database_to_merge.csv" contains the species names
 used in the random forest analysis and the habitat that each species
 occupy. This file was used to collect the traits for the same species in
 the Meiri 2018 trait dataset. The script "Merge_csv_files.py"
 was used to merge the two files mentioned here. 3.
 "Merged_Data.csv" is the raw trait dataset that, after a careful
 filtering process became the file
 "Species_Open_or_Forested_traits_with_citations.csv", and then
 the file "For_RF_raw_data_2.csv", that was used in the random
 forest analysis. 4. Raw trait dataset with citations:
 "Species_Open_or_Forested_traits_with_citations.csv".<br>
 This file contains the raw data that was used to create the trait dataset
 used in the random forest analyses. The traits were collected from
 https://doi.org/10.1111/geb.12773 (Meiri 2018). Traits added from
 different sources have the source citation. 5. Trait
 dataset: "For_RF_raw_data_2.csv" includes all 13 traits for the
 235 species used in the random forest analysis.<br> The traits are
 described in Table 1 of the manuscript.<br> The habitats are coded
 as: open = 0, forested = 1.<br> Values for the categorical traits
 are:<br> Leg development: - Four-legged = 0<br>               
   - Leg-reduced = 1<br>                  - Hind limbs only =
 2<br>                  - Limbless = 3<br> Activity time: -
 Diurnal = 0<br>                - Nocturnal = 1<br>           
     - Cathemeral = 2<br> Microhabitat: - Terrestrial = 0<br>
               - Arboreal = 1<br>               - Fossorial =
 2<br>               - Saxicolous = 3<br>               -
 Semi-Arboreal = 4<br>               - Semi-Aquatic = 5<br>
 Diet: - Carnivorous = 0<br>       - Herbivorous = 1<br>     
  - Omnivorous = 2<br> Foraging mode: - Active foraging = 0<br>
                - Sit and wait = 1<br>                - Mixed =
 2<br> Reproductive mode: - Oviparous = 0<br>                -
 Viviparous = 1 6. Phylogenetic tree:
 "Corrected_names_Binary_forest_open_tree_from_Tonini9755_tree_minBL.newick" was used in all the analyses.<br> The original tree can be found at Tonini et al. 2016 paper (https://doi.org/10.1016/j.biocon.2016.03.039). 7. "Correct_names_2.txt" was used in the script "Keep_tip_for_244spp_2.R" to update the names used on the tree according to Reptile database lizard names (http://www.reptile-database.org/) 8. "all_244spp_2_habitats_2_BioGeoBEARS.txt" was used to run the BioGeoBEARS analysis.  9. "244spp_2_habitats_GEOHiSSE_2.txt" was used to run the GeoHiSSE analysis.<br> Description:<br> Range 0 = both habitats<br> Range 1 = open habitats<br> Range 2 = forested habitats 10. "Pairwise_distance_matrix_all_species_2.csv" represents the pairwise phylogenetic distance matrix among all the species present in the phylogenetic tree. This file was later edited to contain only the species used in the random forest analysis (see file 11 description). 11. "Pairwise_dist_matrix_235spp.csv" was used in the random forest analysis and represents the pairwise phylogenetic distance matrix among the 235 species used in this analysis. 12. "transitions_count_per_family.xlsx" was used to create figure 2. <br> It has information about the number of transitions that occurred from open to forested or from forested to open habitats within each family. Supporting information: 1. "Supporting_information.docx" contains information not used in the main file that can be found here "manuscript link". Scripts: 1. "Map.R" was used to create the map of figure 1. 2. "Keep_tip_for_244spp_2.R" was used to edit the original consensus phylogenetic tree from Tonini et al. 2016 (https://doi.org/10.1016/j.biocon.2016.03.039) to contain only the species used in this study.<br> The script also corrected the polytomies and changed the outdated species names for the names according to Reptile Database (http://www.reptile-database.org/). 3. "Script_BioGeoBEARS_2.R" was used to run the BioGeoBEARS analysis, adapted from http://phylo.wikidot.com/biogeobears#script<br> It was also used to create figure S1. 4. "geom_bar_histogram_transitions.R" was used to create figure 2. 5. "GeoHiSSE_12_models_NEW_SCRIPT.R" was used to run the GeoHiSSE analysis, adapted from Caetano et al. 2018 (https://doi.org/10.1111/evo.13602).<br> It was also used to create figure 3. 6. "Compute_phylo_distance_from_tree.R" was used to create the "Pairwise_distance_matrix_all_species_2.csv" and the "Pairwise_dist_matrix_235spp.csv" files. 7. "Merge_excel_files.py" was used in Python 3 to merge the Meiri 2018 trait dataset with the "Names_reptile_database_to_merge.csv" file. The .csv file resultant of this merging is the "Merged_Data.csv". 8. "RF_model_type_1_with_loop.R" was used to run the first three models of the random forest classification analysis to identify important traits to predict species occurrence in open and forested habitats. <br> It was also used to create the plots corresponding to models 1, 2, and 3 in figure 4 and figure S2. 9. "missForest_Boruta_RF_235spp_allTraits.R" was used to run models 4 and 5 of the random forest classification analysis.<br> In this script, we imputed the data using the missForest function before running the random forest classification algorithm.<br> It was also used to create the plots corresponding to models 4 and 5 in figure 4 and figure S2. 10. "Important_traits_RF.R" was used to check for trait differences between open and forested habitats and how the variation is distributed.<br> It was also used to create figure 5.