Applications of Species Distribution Modeling for Palaeontological Fossil Detection: Late Pleistocene Models of Saiga (Artiodactyla: Bovidae, Saiga Tatarica)
Few studies utilise modern species distribution data and modeling to make predictions for examining potential fossil localities. Instead, species distribution modeling is often used for palaeoenvironmental interpretations. Using palaeoclimate data to model potential past distributions for a species provides a prediction showing areas where its fossil remains may be found. In this study, the current, Last Glacial Maximum, and Last Interglacial potential distributions of the arid steppe-obligate saiga antelope (Artiodactyla: Bovidae, Saiga tatarica) were modeled using the species distribution model Maxent. Few fossil records exist, but available fossil locality records were used to validate both palaeo models, resulting in speculative predictions about where the saiga may have lived. Known fossil localities of saiga from the Last Glacial Maximum time period were located within predicted moderately suitable environments, while four of seven Last Interglacial fossil localities were located within predicted moderately suitable environments, suggesting that models can accurately identify areas where fossils for the saiga can be found. Specifically, these models suggest saiga fossils may be located in northwestern and northeastern China, the western and central regions of the Middle East, and southern Alaska. The predicted areas in northeastern China are of particular interest because saiga fossils have not been identified in this region, but some palaeontologists theorize that northeast China may have been suitable for saiga in the past. The models lend credence to this argument.
Jurestovsky, Derek; and Joyner, T. Andrew. 2018. Applications of Species Distribution Modeling for Palaeontological Fossil Detection: Late Pleistocene Models of Saiga (Artiodactyla: Bovidae, Saiga Tatarica). Palaeobiodiversity and Palaeoenvironments. Vol.98(2). 277-285. https://doi.org/10.1007/s12549-017-0298-8 ISSN: 1867-1594