update: Recent advances in using species distributional models to understand past distributions

Abstract

The first wave of climate‐driven, paleoecological, species distribution models (SDMs) focused pri‐ marily on testing global climate models (GCMs; e.g., Bartlein et al. 1986). Recently, a second wave of research, using increasingly sophisticated SDMs, expanded the use of paleo‐SDMs into bio‐ geographic questions. This new focus is contribut‐ ing substantially to discussions about outstanding questions in biogeography, such as stability of niches through time and fundamental spatiotem‐ poral controls on geographic patterns of biodiver‐ sity. The value of paleo‐data in biogeographically oriented distributional models gained prominence when Nogués‐Bravo (2009) highlighted the use of SDMs for investigating past distributions of spe‐ cies. Nogués‐Bravo (2009) outlined areas where paleo‐data can help understand and test some of the assumptions underlying SDMs, highlighted methodological considerations when creating and projecting SDMs through time, and recommended a set of best practices when extending SDMs to include paleo‐data. Since then, many new studies have utilized paleo‐SDMs, alone or in combination with other approaches, and the number of papers relying on paleo SDMs has grown rapidly (e.g. 27 studies reviewed by Nogués‐Bravo 2009; 82 stud‐ ies reviewed by Svenning et al. 2011). Two recent review papers (Svenning et al. 2011; Varela et al. 2011) deepen the discussion about paleo‐SDMs by synthesizing this new research, discussing ad‐ vances made in paleo‐biogeography, and outlining the challenges going forward. While there are many points of agreement between the two re‐ views, reading them in parallel makes obvious some areas of disagreement that point to unre‐ solved issues with paleo‐SDMs. SDMs based on modern distribution data are influenced by a number of different uncertain‐ ties, including incomplete distribution informa‐ tion, simplified or uncertain mechanistic relation‐ ships between distribution and climate, and ex‐ trapolation into no‐analog climates, to name a few (e.g. Guisan & Zimmermann 2000; Guisan & Thuiller 2005; Elith et al. 2006; Jiménez‐Valverde et al. 2008). Paleo‐SDMs are subject to the same limitations, but often to a greater extent. Many of these technical issues are discussed in detail by Varela et al. (2011), with the goal of establishing a " robust and scientifically‐based theoretical and methodological framework " for the use of SDMs in paleobiology. They focus largely on the particu‐ lars of paleontological data, and highlight issues of spatial, temporal, taphonomic, and collection bias that should be considered when modeling and interpreting paleo‐SDMs. Varela et al. (2011) ad‐ vocate cautious use of paleo‐SDMs, arguing that paleo‐SDMs are promising but that key gaps in knowledge currently limit their widespread appli‐ cation. The use of SDMs in paleobiology has grown rapidly despite these limitations. Svenning et al. (2011) synthesize the many ways that SDMs have been and could be applied to outstanding ques‐ tions in paleoecology. Their review is broader (82 papers vs. 42 reviewed by Varela et al. 2011) and focuses in particular on the integration of SDMs with genetic data. They outline four primary appli‐ cations of SDMs, including the use of paleo‐SDMs to test hypotheses about glacial refugia, the end‐ Pleistocene megafaunal extinctions, Holocene pa‐ leoecology, and deep‐time biogeography. Sven‐ ning et al. (2011) provide a more optimistic view of the use of SDMs in paleobiology, perhaps be‐ cause they focus more on applications of paleo‐ SDMs and less on potential issues with the under‐ lying data. However, some recommendations by Varela et al. (2011) imply that work highlighted by Svenning et al. (2011) should not be attempted at large spatial or temporal scales at this time. As one example, both groups correctly argue for cau‐ tion when using and interpreting statistically‐ downscaled climate simulations. However, Varela et al. (2011) take a highly conservative approach and argue that statistical downscaling should not news and update