DOI: 10.5061/dryad.m37pvmd9n
title: Paleobiogeographic insights gained from ecological niche models: progress and continued challenges

description: The spatial distribution of individuals within ecological assemblages, and
 their associated traits and behaviors, are key determinants of ecosystem
 structure and function. Consequently, determining the spatial distribution
 of species, and how distributions influence patterns of species richness
 across ecosystems today and in the past, helps us understand what factors
 act as fundamental controls on biodiversity. Here, we explore how
 ecological niche modeling has contributed to understanding the
 spatiotemporal distribution of past biodiversity, and past ecological and
 evolutionary processes. We first perform a semi-quantitative literature
 review to capture studies that applied ecological niche models (ENMs) in
 the past, identifying 668 studies. We coded each study according to focal
 taxonomic groups and whether and how the study used fossil evidence,
 whether it relied on evidence or methods in addition to ENMs, and spatial
 scale and temporal intervals. We used trends in publication patterns
 across categories to anchor discussion of recent technical advances in
 niche modeling, focusing on paleobiogeographic ENM applications. We then
 explored the contributions of ENMs to paleobiogeography, with a particular
 focus on examining patterns and associated drivers of range dynamics;
 phylogeography and within-lineage dynamics; macroevolutionary patterns and
 processes, including niche change, speciation, and extinction; drivers of
 community assembly; and conservation paleobiogeography. Overall, ENMs are
 powerful tools for elucidating paleobiogeographic patterns. ENMs are most
 commonly used to understand Quaternary dynamics, but an increasing number
 of studies use ENMs to gain important insight into both ecological and
 evolutionary processes in pre-Quaternary times. Deeper integration with
 traits and phylogenies may further extend those insights.
description: We conducted an initial search on 15 September 2023 for
 peer-reviewed articles, written in English, that applied ENMs to past time
 intervals, using both the Scopus and Web of Science databases with nearly
 identical search conditions (see Appendix 1 for full search terms). Our
 search and screening followed the PRISMA protocol for scoping reviews
 (Tricco et al. 2018). Article metadata was downloaded from each database
 (Scopus <em>n </em>= 16155, Web of Science <em>n
 </em>= 15600), and the two datasets were merged and duplicates
 removed (<em>n </em>= 22656). We screened article titles and
 abstracts to determine if they (a) projected an ENM to a point in time
 before 1800 A.D., and/or (b) included fossil occurrences in their ENM. We
 identified 668 studies that met our criteria, and randomly assigned these
 to the five authors to gather data on the ENM approaches therein. Data
 extracted from each article included taxonomic information (taxonomic
 description and resolution, and the number of taxonomic units analyzed),
 time periods for which data were modeled and projected, whether the fossil
 record was used for either model calibration or validation, whether
 additional data (e.g., molecular, isotopic, morphological, etc.) were
 used, and the geographic extent of the analysis. All data manipulation and
 analyses were performed in R (version 4.3.0; R Core Team 2014) using an
 RStudio interface (version 2023.06.1 Build 524 “Mountain Hydrangea”;
 Rstudio Team 2020). Data manipulations were carried out with
 <em>dplyr</em> (version 1.1.2; Wickham et al.
 2023<em>b</em>), <em>tidyr</em> (version 1.3.0;
 Wickham et al. 2023<em>a</em>), and
 <em>stringr</em> (version 1.5.0; Wickham 2023). Title and
 abstract screening was done through <em>revtools</em> (version
 0.4.1; Westgate 2019). <strong>Referenced
 Literature:</strong> R Core
 Team. 2014: R: A language and environment for statistical
 computing. Rstudio Team. 2020:
 RStudio: integrated development for R. 
 Tricco, A. C., E. Lillie, W. Zarin, K. K. O’Brien, H. Colquhoun,
 D. Levac, D. Moher, M. D. Peters, T. Horsley, and L. Weeks. 2018: PRISMA
 extension for scoping reviews (PRISMA-ScR): checklist and explanation.
 Annals of internal medicine 169:467–473. 
 Westgate, M. J. 2019: revtools: An R package to support article
 screening for evidence synthesis. Research synthesis methods
 10:606–614. Wickham, H. 2023:
 stringr: Simple, Consistent Wrappers for Common String
 Operations. Wickham, H., D.
 Vaughan, and M. Girlich. 2023a: tidyr: Tidy Messy Data.
 Wickham, H., R. François, L. Henry, K.
 Müller, and D. Vaughan. 2023b: dplyr: A Grammar of Data
 Manipulation.
description: # Paleobiogeographic insights gained from ecological niche models:
 progress and continued challenges
 [https://doi.org/10.5061/dryad.m37pvmd9n](https://doi.org/10.5061/dryad.m37pvmd9n) This dataset contains data resulting from a semi-quantitative literature review of peer-reviewed articles that applied ecological niche models to past time intervals. We searched Scopus and Web of Science to find relevant references, performing the search on 15 September 2023 and using search terms as described in Supplementary Appendix 1 (see Zenodo link). ## Description of the data and file structure We provide two spreadsheets in CSV format. The first spreadsheet is the primary data file and records the literature that resulted from the literature search. The second file contains metadata, including descriptions of the content of each column in the data file. ## Sharing/Access information Access to the data is also available through Zenodo. ## Code/Software Access to the full script to analyze the data provided here is available through Zenodo.