TRIDENT -Surface sampling as diet discrimination enhancement

• Main Authors: Francisco, Arthur, Brunetière, Noël, Berlioz, Emilie, Ramdarshan, Anusha, Merceron, Gildas
• Other Authors: Institut Pprime (PPRIME), Université de Poitiers-ENSMA-Centre National de la Recherche Scientifique (CNRS), Laboratoire de paléontologie, évolution, paléoécosystèmes, paléoprimatologie (PALEVOPRIM ), Université de Poitiers-Centre National de la Recherche Scientifique (CNRS), oznan University of Technology, Poland
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2016
• Subjects: dental tribology; tooth wear; surface texture analysis; [PHYS]Physics [physics]; [SDE]Environmental Sciences; Trident; Alces alces
• Online Access: https://hal.science/hal-03908793; https://hal.science/hal-03908793/document; https://hal.science/hal-03908793/file/Francisco2016-5th%20ICSM.pdf

International audience Since almost three decades, dental microwear has been used as a dietary indicator, Walker et al. (1978). It is commonly accepted that scratches are the result of abrasive grassy diets, that pits mainly result from the consumption of fruit/seeds and that leaves and soft diets produce lower enamel wear. Thanks to progress in microscopy and interferometry, smaller surface details are caught, which makes more accurate fractal analyzes possible. Scott et al. (2006) have proposed several fractal parameters whereas Schulz et al. (2010) have tested the more “industrial” parameters from the ISO 25178 norm in conjunction with the fractal parameters. In the present work, the authors propose a complementary approach using anenlarged set of parameters: height parameters, spatial parameters and fractal parameters. The dental facets of modern ungulates including the African grazing hartebeest antelope (Alcelaphus buselaphus; N=15), the European leaf browsing moose (Alces alces; N=15) and the African fruit browsing duiker (Cephalophus silvicultor; N=15) were scanned (200 × 280μm) using the Leica DCM8 confocal and interferometric optical profiler.Instead of solely performing the surface texture analysis over the whole scan surface, we also generate a set of 1024 [512×512 points = 66×66 μm] areas for every scan. Thus, for the 45 individuals and for all parameters mentioned above, we generate Mean, Median, 5%- and 95%-quantiles, Minimal and Maximal values and the Mean of the 10 highest values and Mean of the 10 lowest values over the 1024 automatically generated areas. Surfaces were first treated using a 2 nd order polynomial process or using an 8 th order polynomial process. From the 286 generated parameters, a one-way analysis of variance identifies only 12 as discriminating at best the three species. Most of these 12 parameters are not means but minimum, maximum, value of 5 and 95% quantiles of the field parameters. When generating a Principal Component Analysis with these 12variables, we identified ...