Using ecological niche modeling for quantitative biogeographic analysis: a case study of Miocene and Pliocene Equinae in the Great Plains

Abstract

The subfamily Equinae in the Great Plains region of North America underwent a dramatic radiation and subsequent decline as climate changed from warm and humid in the middle Miocene to cooler and more arid conditions during the late Miocene. Here we use ecological niche modeling (ENM), specifically the GARP (Genetic Algorithm using Rule-set Prediction) modeling system, to reconstruct the geographic distribution of individual species during two time slices from the middle Miocene through early Pliocene. This method combines known species occurrence points with environmental parameters inferred from sedimentological variables to model each species' fundamental niche. The geographic range of each species is then predicted to occupy the geographic area within the study region wherever the set of environmental parameters that constrain the fundamental niche occurs. We analyze changes in the predicted distributions of individual species between time slices in relation to Miocene/Pliocene climate change. Specifically, we examine and compare distribution patterns for two time slices that span the period from the mid-Miocene (Barstovian) Climatic Optimum into the early Pliocene (Blancan) to determine whether habitat fragmentation led to speciation within the clade and whether species survival was related to geographic range size. Patchy geographic distributions were more common in the middle Miocene when speciation rates were high. During the late Miocene, when speciation rates were lower, continuous geographic ranges were more common. Equid species tracked their preferred habitat within the Great Plains region as well as regionally throughout North America. Species with larger predicted ranges preferentially survived the initial cooling event better than species with small geographic ranges. As climate continued to deteriorate in the late Miocene, however, range size became irrelevant to survival, and extinction rates increased for species of all range sizes. This is the first use of ENM and GARP in the continental fossil record. This powerful quantitative biogeographic method offers great promise for studies of other taxa and geologic intervals.

Sharks that pass in the night: using Geographical Information Systems to investigate competition in the Cretaceous Western Interior Seaway

“…[24,50,51]). However, most data represent even higher resolution at the 1 mile 2 township, range and section.…”

Historical Biogeography: Evolution in Time and Space

“…Phylogenetic biogeographic methods such as modified BPA (Lieberman 2000) address this problem by inferring two different area cladograms, one reflecting the vicariance (geographic division) events and the other the geodispersal (area collision) events. If the vicariance and geodispersal cladograms exhibit congruent topologies, it is assumed that cyclical events had produced the observed biogeographic pattern (Maguire and Stigall 2009). …”

“…Smith and Donoghue (2010) were the first to combine parametric biogeographic methods with paleoclimate data and ecological niche models of extant taxa as a way to understand how past climates and land connections have shaped the biogeographic distribution of lineages over time. Similarly, Stigall andLieberman (2005, 2006) pioneered the integration of ENM models into paleobiogeographic analysis, through the combination niche models with the fossil record of extinct lineages (e.g., Maguire and Stigall 2009). The advantage of this approach over ENM models based on extant taxa (Smith and Donoghue 2010) is that fossil-based ENM reconstructions do not assume that ecological niches are stable over time (this might be true for ecological time scales but not over geological scales of millions of years), so these models can be used to track patterns of niche conservatism and evolution over longer time scales (Stigall 2012).…”

Evaluating the Significance of Paleophylogeographic Species Distribution Models in Reconstructing Quaternary Range-Shifts of Nearctic Chelonians

“…[18], [23], [26]–[27], [43]–[45]. Furthermore, SDMs constructed from fossil occurrences and paleoenvironmental data have often successfully predicted modern geographic distributions [46]–[47]. However, there are notable exceptions in which SDMs grossly failed to agree with fossil data or molecular phylogeographic data [27], [30], [43], [48]–[49].…”