ScholarGate
Asistente

Comparar métodos

Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.

Número de especímenes identificados×Análisis de Textura de Micromarcas Dentales×
CampoArqueologíaArqueología
FamiliaProcess / pipelineProcess / pipeline
Año de origen19711988
Autor originalR. E. ChaplinPeter Teaford
TipoFaunal quantification methodDietary inference method
Fuente seminalChaplin, R. E. (1971). The Study of Animal Bones from Archaeological Sites. Seminar Press. link ↗Ungar, P. S. (2007). Evolution of the human diet: The known, the unknown, and the unknowable. Oxford University Press. link ↗
AliasNISP method, specimen countmicrowear analysis, dental wear analysis
Relacionados44
ResumenNumber of identified specimens (NISP) is a fundamental zooarchaeological method that quantifies the abundance of faunal remains by counting all identifiable bone fragments or specimens in an assemblage. Formalized by R. E. Chaplin and later refined by Donald Grayson and others, NISP is the most straightforward and widely used quantification metric in zooarchaeology. Despite its simplicity, NISP is sensitive to both cultural and taphonomic factors that affect preservation, fragmentation, and identification of bone assemblages.Dental microwear texture analysis (DMTA) is a method that reconstructs diet and dietary behavior from microscopic wear patterns on the surfaces of teeth. Pioneered by Mark Teaford in the 1980s, DMTA analyzes the three-dimensional texture of wear patterns produced as food is chewed. The method reflects short-term (last few months) dietary composition, complementing longer-term dietary information obtained from stable isotope analysis. DMTA has proven powerful for distinguishing diets rich in tough/fibrous foods from those dominated by hard/brittle foods.
ScholarGateConjunto de datos
  1. v1
  2. 3 Fuentes
  3. PUBLISHED
  1. v1
  2. 3 Fuentes
  3. PUBLISHED

Ir a la búsqueda Descargar diapositivas

ScholarGateComparar métodos: Number of Identified Specimens · Dental Microwear Texture Analysis. Recuperado el 2026-06-20 de https://scholargate.app/es/compare