DOI: 10.5061/dryad.x95x69phs
title: Data from: Taxonomic identification bias does not drive patterns of abundance and diversity in theropod dinosaurs

description: The ability of palaeontologists to correctly diagnose and classify new
 fossil species from incomplete morphological data is fundamental to our
 understanding of evolution. Different parts of the vertebrate skeleton
 have different likelihoods of fossil preservation and varying amounts of
 taxonomic information, which could bias our interpretations of fossil
 material. Substantial previous research has focused on the diversity and
 macroevolution of non-avian theropod dinosaurs. Theropods provide a rich
 dataset for analysis of the interactions between taxonomic diagnosability
 and fossil preservation. We use specimen data and formal taxonomic
 diagnoses to create a new metric, the Likelihood of Diagnosis (LoD), which
 quantifies the diagnostic likelihood of fossil species in relation to bone
 preservation potential. We use this to assess whether a taxonomic
 identification bias impacts the non-avian theropod fossil record. We find
 the patterns of differential species abundance and clade diversity are not
 a consequence of their relative diagnosability. Although there are other
 factors that bias the theropod fossil record, our results suggest patterns
 of relative abundance and diversity for theropods might be more
 representative of Mesozoic ecology than often considered.
description: <strong>Collected data</strong>:
 <em>Theropod_Completeness_data</em>: Raw completeness
 data updated from Cashmore and Butler (2019). doi.org/10.5061/dryad.37c840g. Completeness
 data were primarily gathered from figures and descriptive text in the
 literature, supplemented by additional online sources, museum catalogues,
 first-hand observation of specimens, and via personal
 communications. 
 <em>Theropod_Bone_occ_specimen_data</em>: The number
 of individual occurrences of each bone per theropod specimen. Due to the
 nature of the completeness data collection these estimations had to be
 projected from raw completeness scores based on the expected number of
 elements per continuous series (teeth, vertebrae, ribs, digits)
 <em>Theropod_Bone_occ_species_data</em>: The number
 of individual occurrences of each bone per theropod species, projected
 from raw completeness scores.
 <em>Theropod_Diagnoses_data</em>: The number of
 autapomorphies and 'unique combination' of characters,
 identified for each skeletal element for all valid theropod species from
 taxonomic diagnoses within published literature. The total ‘unique
 combination’ of characters was regarded as equivalent to a single
 autapomorphy. Therefore, for each species with such diagnoses, the
 individual characters were scored as a proportion of the sum of all the
 characters (i.e., for a ‘unique combination’ of four characters, each
 character represents 25% of an autapomorphy)
   <strong>R
 inputs:</strong>
 <em>R_input_bone_occs_species</em>: Bone occurrences
 per species taken from
 <em>Theropod_Bone_occ_species_data</em>
 <em>R_input_bone_occs_species_0-1</em>: Bone
 presence/absence per species taken from
 <em>Theropod_Bone_occ_species_data</em>
 <em>R_input_bone_occs_specimens</em>: Bone
 occurrences per individual specimen taken from
 <em>Theropod_Bone_occ_specimen_data</em>
 <em>R_input_bone_occs_specimens_0-1</em>: Bone
 presence/absence per individual specimen taken from
 <em>Theropod_Bone_occ_specimen_data</em>
 <em>R_input_characters_autaps&UCs</em>:
 Diagnostic characters identified for each skeletal element for all valid
 theropod species taken
 from <em>Theropod_Diagnoses_data</em>
 <em>R_input_pbdb_data_all_taxa</em>: Non-avian
 theropod dinosaur occurrence data downloaded from the Paleobiology
 Database (PBDB) and manually cleaned and manipulated for key data
 extraction    <strong>Output
 data reused in analyses: </strong>
 <em>R_output_diag_df_LoD: </em>species specific data
 including calculated LoD scores and abundance proxies. Used for
 species-specific statistical comparisons
 <em>R_output_subgroup_data:</em> subgroup
 data including calculated mean LoD scores and summarised abundance
 proxies, derived from personal data collection.
 <em>R_output_subgroup_data2: </em>subgroup data
 including calculated mean LoD scores and summarised abundance proxies with
 additional PBDB data included. Used for subgroup statistical
 comparisons
 <em>R_output_temporal_data_pers: </em>temporal data
 included calculated mean LoD scores and summarised abundance proxies,
 derived from personal data collection. Used for temporal statistical
 comparisons
 <em>R_output_temporal_data_pbdb</em>: temporal data
 included calculated mean LoD scores and summarised abundance proxies,
 derived from PBDB data. Used for temporal statistical
 comparisons
 <em>Formations_data_tables</em>: Raw species-specific
 input data (personal and PBDB data) and Formation specific outputs
 manipulated from R, and used in formation based statistical comparisons.
 Formation data workflow in file.   
 <strong>R code: </strong><em>R code. LoD
 calculation; </em><em>R code. Taxonomic comparisons; R code.
 Formation comparisons; R code. Temporal comparisons</em>
   <strong>Analytical supporting
 results:</strong> <em>Support results
 table. Ljung-Box_tests_pbdb_data_residuals</em>: Results from
 Ljung-Box residual autocorrelation tests of GLS models comparing temporal
 changes in mean LoD to abundance proxies derived from PBDB data.
 <em>Support results table.
 Ljung-Box_tests_personal_data_residuals: </em>Results from Ljung-Box
 residual autocorrelation tests of GLS models comparing temporal changes in
 mean LoD to abundance proxies derived from personal data
 collection. <em>Support results table.
 Shapiro-Wilk_tests_pbdb_temporal_data_residuals</em>: Results from
 Shapiro-Wilk's normality tests of residuals from GLS models comparing
 temporal changes in mean LoD to abundance proxies derived from PBDB
 data. <em>Support results table.
 Shapiro-Wilk_tests_personal_temporal_data_residuals</em>: Results
 from Shapiro-Wilk's normality tests of residuals from GLS models
 comparing temporal changes in mean LoD to abundance proxies derived from
 personal data collection. <em>Support results
 table. Shapiro-Wilk_tests_Formation_LoD_dist: </em>Results of
 Shapiro-Wilk's normality tests of the distribution of LoD scores for
 each of the most specious theropod formations.
 <em>Support results table.
 Shapiro-Wilk_tests_Stage_LoD_dist: </em>Results of
 Shapiro-Wilk's normality tests of the distribution of LoD scores for
 each Mesozoic geological stage. <em>Support
 results table. Shapiro-Wilk_tests_Subgroup_dist: </em>Results of
 Shapiro-Wilk's normality tests of the distribution of  LoD scores for
 each Mesozoic geological stage. <em>Support
 results table. Subgroup_comparisons_mean,trim,wins</em>: Results of
 comparisons between the Trimmed and Winsorized mean LoD and species
 richness and select abundance proxies across taxonomic subgroups using GLS
 models. <em>Support results table.
 Temporal_comparisons_mean,trim,wins</em>: Results of comparisons
 between the Trimmed and Winsorized mean LoD and species richness and
 select abundance proxies through geological time using GLS
 models.