Quantitative methods demonstrate that environment alone is an insufficient predictor of present-day language distributions in New Guinea

Abstract

Environmental parameters constrain the distributions of plant and animal species. A key question is to what extent does environment influence human behavior. Decreasing linguistic diversity from the equator towards the poles suggests that ecological factors influence linguistic geography. However, attempts to quantify the role of environmental factors in shaping linguistic diversity remain inconclusive. To this end, we apply Ecological Niche Modelling methods to present-day language diversity in New Guinea. We define an Eco-Linguistic Niche (ELN) as the range of environmental conditions present in the territory of a population speaking a specific language or group of languages characterized by common language traits. In order to reconstruct the ELNs, we used Papuan and Austronesian language groups, transformed their geographical distributions into occurrence data, assembled available environmental data for New Guinea, and applied predictive architectures developed in the field of ecology to these data. We find no clear relationship between linguistic diversity and ELNs. This is particularly true when linguistic diversity is examined at the level of language groups. Language groups are variably dependent on environment and generally share their ELN with other language groups. This variability suggests that population dynamics, migration, linguistic drift, and socio-cultural mechanisms must be taken into consideration in order to better understand the myriad factors that shape language diversity.

Population inter-connectivity over the past 120,000 years explains distribution and diversity of Central African hunter-gatherers

“…We first asked whether the current distribution and density of Central African hunter-gatherers (CAHG) is a product of long-term adaptation to life in the rainforest or instead a recent product of the Bantu Expansion. To this purpose, we compiled ethnographic data on the geographical location of 749 camps from 11 CAHG populations, and then applied the Maximum Entropy ( MaxEnt ) machine-learning algorithm (10) to determine the relative influence of several bioclimatic and ecological factors on the distribution of CAHG (8,9) (Fig. 1A, 1B)(see Materials and Methods for alternative model fitting algorithms).…”

“…They mathematically relate occurrences of a particular species (in this case “species” corresponds to georeferenced hunter-gatherer camps) and the bioclimatic or ecological features of the areas it inhabits to produce a model that by identifying the “realised niche” of the species (70) can predict its potential geographic distribution based on suitable environmental conditions. Increasingly, ENMs have started being used to also understand the non-random distribution of human populations or cultural traditions in the present and in the past (8,9,32).…”

“…To address those questions, we first compiled ethnographic data on the distribution of 749 camps from 11 hunter-gatherer groups extending from West to East Central Africa. We used them as inputs for environmental niche models (ENMs) to determine the relative influence of several bioclimatic and ecological factors, as well as the presence of farming populations, on the distribution and abundance of Central African Hunter-Gatherers (CAHG) (13–14). We further assess the relationship between the Bantu expansion and CAHG demography by comparing the historical effective population sizes of 9 CAHG populations (N=265 individuals) with those of 15 Bantu-speaking communities (N=677 individuals) that have settled in the region from 20 to 150 generations ago (~600-4,500 years).…”

Field Effectiveness of Drones to Identify Potential Aedes aegypti Breeding Sites in Household Environments from Tapachula, a Dengue-Endemic City in Southern Mexico

“…Once the aerial photographs were obtained, the limits of each property were verified by colony cartography with ArcGIS 10.3 software. The images were saved in JPEG format and processed on the free software GNU Image Manipulation Programme (GIMP ® 2.8) [28][29][30]. The potential breeding sites were manually searched and counted using GIMP software [28].…”

Population interconnectivity over the past 120,000 years explains distribution and diversity of Central African hunter-gatherers

“…To address those questions, we first compiled ethnographic data on the distribution of 749 camps from 11 hunter-gatherer groups extending from West to East Central Africa. We used them as inputs for environmental niche models (ENMs) to determine the relative influence of several bioclimatic and ecological factors, as well as the presence of farming populations, on the distribution and abundance of CAHG ( 13 , 14 ). Then, we used high-resolution paleoclimatic reconstructions and topographic maps to make continuous predictions about where CAHG could have lived over the past 120,000 years and the potential extension of their interaction networks.…”