Understanding Late Pleistocene human land preference using ecological niche models in an Australasian test case. (20th October 2020)

• Record Type: Journal Article
• Authors: Zachwieja, Alexandra J.; Bacon, Anne-Marie; Nguyen, Thi Mai Huong; Nguyen, Anh Tuan; Westaway, Kira; Duringer, Philippe; Ponche, Jean-Luc; Patole-Edoumba, Élise; Sichanthongtip, Phonephanh; Sayavongkhamdy, Thongsa; Dunn, Tyler E.; Demeter, Fabrice; Shackelford, Laura L.
• Is Part Of: Quaternary international. Volume 563(2020)
• Page Start: 13
• Page End: 28
• Publication Date: 2020-10-20
• Subjects: Human land preference -- Ecological niche modeling -- Southeast Asia -- Competition -- Late pleistocene; Geology, Stratigraphic -- Quaternary -- Periodicals; Stratigraphie -- Quaternaire -- Périodiques; 551.79
• DOI: 10.1016/j.quaint.2020.09.026
• Languages: English
• ISSNs: 1040-6182

Abstract

Abstract: Ecological niche models (ENM) of species distributions and dispersal patterns are well established in the biological sciences. Their use in paleoanthropological reconstructions of hominin niches is relatively recent, successfully focusing on out of Africa dispersals and human land preference in Europe and Central Asia. These studies have suggested that some of the most important variables for predicting human site use in these regions are moderate annual temperature and rainfall. Here, we used ENM to combine these long-used abiotic predictors of human land preference with landform data (slope) and one potentially important biotic variable (human-carnivore competition quantified using a competition index) in an Australasian test case. We constructed ENMs in the program Maxent to investigate the impact of these abiotic and biotic variables on human land preference patterns in Late Pleistocene Australasia. Though calculated competition across test sites was high, models including this biotic data produced ill-fitting localized models (AUC = 0.695) that relied on mean annual temperature. Large-scale models including solely temperature and rainfall fit well (AUC = 0.84) but are poor predictors of land preference compared to models including slope in this mountainous region (AUC = 0.924) showcasing a discrepancy between accuracy and precision in abiotic models. While the biotic data included in these models was considered unimportant to predictions of human land preference, the inclusion of additional landform data in temperate ENMs should be pursued given the importance of slope as a predictor in large-scale models.