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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 semiquantitative literature review to capture studies that applied ecological niche models (ENMs) to the past, identifying 668 studies. We coded each study according to focal taxonomic group, whether and how the study used fossil evidence, whether it relied on evidence or methods in addition to ENMs, spatial scale of the study, and temporal intervals included in the ENMs. 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 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. 

Climate and ecosystem dynamics vary across timescales, but research into climate-driven vegetation dynamics usually focuses on singular timescales. We developed a spectral analysis–based approach that provides detailed estimates of the timescales at which vegetation tracks climate change, from 10¹to 10⁵years. We report dynamic similarity of vegetation and climate even at centennial frequencies (149⁻¹to 18,012⁻¹year⁻¹, that is, one cycle per 149 to 18,012 years). A breakpoint in vegetation turnover (797⁻¹year⁻¹) matches a breakpoint between stochastic and autocorrelated climate processes, suggesting that ecological dynamics are governed by climate across these frequencies. Heightened vegetation turnover at millennial frequencies (4650⁻¹year⁻¹) highlights the risk of abrupt responses to climate change, whereas vegetation-climate decoupling at frequencies >149⁻¹year⁻¹may indicate long-lasting consequences of anthropogenic climate change for ecosystem function and biodiversity. 

Abstract Simulations are playing an increasingly important role in paleobiology. When designing a simulation study, many decisions have to be made and common challenges will be encountered along the way. Here, we outline seven rules for executing a good simulation study. We cover topics including the choice of study question, the empirical data used as a basis for the study, statistical and methodological concerns, how to validate the study, and how to ensure it can be reproduced and extended by others. We hope that these rules and the accompanying examples will guide paleobiologists when using simulation tools to address fundamental questions about the evolution of life. 

Analyses of morphological disparity have been used to characterize and investigate the evolution of variation in the anatomy, function and ecology of organisms since the 1980s. While a diversity of methods have been employed, it is unclear whether they provide equivalent insights. Here, we review the most commonly used approaches for characterizing and analysing morphological disparity, all of which have associated limitations that, if ignored, can lead to misinterpretation. We propose best practice guidelines for disparity analyses, while noting that there can be no ‘one-size-fits-all’ approach. The available tools should always be used in the context of a specific biological question that will determine data and method selection at every stage of the analysis.