Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Data Sheet 1_Investigating pre-Columbian floristic legacy effects using machine learning in the southern Atlantic Forest, Brazil.docx

Domaine:

environment and energygeospatial

Type de record:

dataset
Créateur:
BarHerJonAnt
Hôte:avatar

The scope and scale of past human impacts on both historic and current vegetation is of widespread interest in the historical sciences. In the Atlantic Forest of southern Brazil (Portuguese: Mata Atlântica), previous work has identified Amerindian settlement and land-use as a probable driver of the extent and composition of forest cover, with time-extended legacies that remain detectable in modern floristic inventories. Previously published investigations into the ecological history of the southern Atlantic Forest have either eschewed the role of humans or, where anthropogenic drivers are explicitly examined, utilized spatially restricted environmental datasets, necessarily limiting the generalizability their conclusions. This study aims to redress this gap, and to quantify the impact of past Amerindian Pre-Columbian settlement and associated land use on the modern-day distribution of several key plant species across the entire southern Atlantic Forest. We fit Maxent species distribution models (SDMs) using Indigenous archaeological site locations (Tupi-Guarani and southern Jê) and modern plant species occurrence data (35 unique species) in a comparative analytical framework to investigate Indigenous influence on the likelihood of occurrence of culturally significant or medicinal plant species. Our results indicate that (i) the inclusion of archaeological settlement location data and SDM predictions as covariates can improve the performance of contemporary floristic species distribution modeling and should be incorporated into ecological models of plant species in landscapes with long-standing human presence, especially when they are used to inform policy that explicitly aims to preserve “natural” biomes and; (ii) a synanthropic relationship can be demonstrated between the southern Jê and Araucaria angustifolia, a finding that complements previously published phylogeographic and palaeoenvironmental studies exploring the same link.

Visit

figshare.com

Tags

ArchaeologySDMAtlantic ForestgeospatialarchaeoecologyMaxent

Licenses

CC BY 4.0

Similaires

The Legacy of Spatial Apartheid Machine Learning Challenge Data Version 1The Legacy of Spatial Apartheid Machine Learning Challenge Data Version 2The Legacy of Spatial Apartheid Machine Learning Challenge Data Archival VersionData Sheet 1_Mapping soil salinity using machine learning and remote sensing data in semi-arid croplands.docxMohammadMaaz6229/Algeria-Forest-Fire-Prediction-using-Machine-LearningHumaimaRiaz47/Algerian-Forest-Fire-Prediction-Using-Machine-Learning

The Legacy of Spatial Apartheid Machine Learning Challenge Data Version 1

The dataset was created from data classifying neighbourhoods in South Africa According to 4 neighbou

The Legacy of Spatial Apartheid Machine Learning Challenge Data Version 2

The dataset was created from data classifying neighbourhoods in South Africa According to 4 neighbou

The Legacy of Spatial Apartheid Machine Learning Challenge Data Archival Version

The dataset was created from data classifying neighbourhoods in South Africa According to 4 neighbou

Data Sheet 1_Mapping soil salinity using machine learning and remote sensing data in semi-arid croplands.docx

Soil salinity significantly constrains agricultural productivity and land sustainability, particu

MohammadMaaz6229/Algeria-Forest-Fire-Prediction-using-Machine-Learning

machine-learning data-science python scikit-learn exploratory-data-analysis eda forest-fire regressi

HumaimaRiaz47/Algerian-Forest-Fire-Prediction-Using-Machine-Learning

A Flask-based web application that predicts the Fire Weather Index (FWI) — an indicator of forest fi