DOI: 10.5061/dryad.wm37pvmrm
title: Data from: Avoiding impacts of phylogenetic tip-state-errors on dispersal and extirpation rates in alpine plant biogeography

description: Aim: Many biogeographic analyses require some form of automated state
 assignment to tips of phylogenetic trees, reflecting a species presence or
 absence in a particular area, e.g., a biome. As datasets get exponentially
 larger, such procedures may increasingly induce errors (here called
 tip-state-error), but the specific algorithmic cause and consequence on
 downstream estimation of dispersal and extinction rates remains poorly
 known. We aim to improve automated tip-scoring methods in the context of
 the alpine biome by leveraging elevation information. We document the
 profound effect of tip-state-errors on Dispersal-Extirpation-Cladogenesis
 (DEC) models. Location: The European Alpine Arc. Taxon: 3’317 vascular
 plant species, emphasizing six focal clades: Campanula, Carex, Festuca,
 Ranunculus, Saxifraga, and Viola. Methods: We use GBIF data to classify
 whether species occur above the upper climatic treeline using a newly
 developed algorithm ElevDistr or a gridded landscape model of thermal
 belts, under various filtering thresholds. We compared classification
 performance using the Flora Alpina as validation data. To determine if
 tip-state-error biases the dispersal and extirpation rate estimation, we
 fit DEC models for selected clades using tip-states from different
 classification models. Results: ElevDistr is less error prone than other
 approaches. Filtering thresholds lower the false positive rate but
 increase the false negative rate. Inflated false positive rates bias the
 dispersal rate estimation upward, while inflated false negative rates lead
 to upward bias in extirpation rate estimation. Main conclusions: Even
 moderate tip-state-error may lead to profound systematic bias in dispersal
 and extinction rate estimation if an unbalanced ratio between false
 positive and false negative rates occurs. Therefore, careful validation is
 imperative, though ElevDistr alleviates this problem in the context of the
 alpine environment. Overall, our results document contrasting rates of
 alpine biome shifts across the studied genera and have major implications
 for studies addressing the likelihood of niche evolution versus geographic
 dispersal.
description: # Data from: Avoiding impacts of phylogenetic tip-state-errors on
 dispersal and extirpation rates in alpine plant biogeography --- This data
 archive contains the input data, the scripts, and the output data to run
 24 Dispersal-Extirpation-Cladogenesis (DEC) models in RevBayes. Detailed
 description of how GBIF data was processed to receive the input data, can
 be found in the sections 2.2 2.4 of the manuscript and processing of the
 final log files in section 2.6. ## Description of the data and file
 structure **data.zip** - This archive contains the directory holding all
 the input data. And contains the following element: *
 a_classification.csv: contains all the classification of the 3317 species.
 Columns presenting species, number of available occurrence observations,
 proportion of species classified as alpine respectively non-alpine by the
 Flora Alpina. Followed by the proportion of species that are classified as
 alpine or non-alpine by the polygon method or ElevDistr. *
 b_oneTwoTree_output: this folder contains for every of the six genera the
 phylogeny from the pipeline oneTwoTree. * The files GENUS_METHOD.nex are
 the input files for the DEC model generated by hand from
 a_classification.csv. **scripts.zip** - This archive contains the scripts
 used for processing the data. And contains the following element: *
 1_date_tree.r: A r script used to convert the phylogram (oneTwoTree
 output) into a chronogram. The script uses as an input the files
 data/b_oneTwoTree_output/GENUS/Result_Tree_1XXXXXXXXX.tre. An generates
 the output files output/GENUS.tre * 2_array.sh: Is a bash script that
 executes a array job. The script is coded to run on the sciCORE high
 performance cluster from the University of Basel. It runs the
 XX_simple_run.Rev scripts in a array. * The files XX_simple_run.Rev
 contain the code to run one DEC model. It represents the combination of
 all six genera and the four classification methods. Input files are: data/
 GENUS_METHOD.nex and output/GENUS.tre. The MCMC results are stored in
 output/ GENUS_METHOD.params.log **output.zip** - This archive contains the
 directory holding all the output data. And contains the following element:
 * GENUS.tre: Contains the time calibrated phylogeny of every genus. *
 GENUS_METHOD.params.log: These files contain the DEC model output from
 RevBayes. Every log file contains Iteration: posterior, likelihood, prior,
 extirpation rate and dispersal rate, sampled during the MCMC run. ##
 Sharing/Access information Here I link to the data sources or the pipeline
 to collect the data Links to other publicly accessible locations of the
 data: * "Raw" GBIF data:
 [https://www.gbif.org/occurrence/download/0262521-210914110416597](https://www.gbif.org/occurrence/download/0262521-210914110416597) Data was derived from the following sources: * Flora Alpina: Aeschimann, D., Lauber, K., Moser, D. M., & Theurillat, J. P. (2004). Flora alpina: atlas des 4500 plantes vasculaires des Alpes. Belin. * OneTwoTree: Drori, M., Rice, A., Einhorn, M., Chay, O., Glick, L., & Mayrose, I. (2018). OneTwoTree: An online tool for phylogeny reconstruction. *Molecular Ecology Resources*, *18*(6), 1492-1499.