Paleobiogeographic insights gained from ecological niche models: progress and continued challenges

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.

Methods
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 n = 16155, Web of Science n = 15600), and the two datasets were merged and duplicates removed (n = 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 dplyr (version 1.1.2; Wickham et al. 2023b), tidyr (version 1.3.0; Wickham et al. 2023a), and stringr (version 1.5.0; Wickham 2023). Title and abstract screening was done through revtools (version 0.4.1; Westgate 2019).

Referenced Literature:

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.