The ecological drivers of variation in global language diversity

Hua, Xia; Greenhill, Simon J.; Cardillo, Marcel; Schneemann, Hilde; Bromham, Lindell

doi:10.1038/s41467-019-09842-2

Cited by 54 publications

(57 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fact that countries are geographically clustered implies interdependence among them. Interdependence of countries can arise from processes as diverse as cultural transmission facilitated by spatial proximity, nearby geographical location producing comparable environmental conditions or language relatedness [30]. Empirical evidence shows that central variables of our interest, democracy [31,32] and pathogens [33], are geographically clustered.…”

Section: Previous Misconceptions In Levels Of Analysismentioning

confidence: 95%

Multi-level modelling of time-series cross-sectional data reveals the dynamic interaction between ecological threats and democratic development

Kusano

Kemmelmeier

2020

R. Soc. open sci.

View full text Add to dashboard Cite

What is the relationship between environment and democracy? The framework of cultural evolution suggests that societal development is an adaptation to ecological threats. Pertinent theories assume that democracy emerges as societies adapt to ecological factors such as higher economic wealth, lower pathogen threats, less demanding climates and fewer natural disasters. However, previous research confused within-country processes with between-country processes and erroneously interpreted between-country findings as if they generalize to within-country mechanisms. We analyse a time-series cross-sectional dataset to study the dynamic relationship between environment and democracy (1949–2016), accounting for previous misconceptions in levels of analysis. By separating within-country processes from between-country processes, we find that the relationship between environment and democracy not only differs by country but also depends on the level of analysis. Economic wealth predicts increasing levels of democracy in between-country comparisons, but within-country comparisons show that democracy declines in years when countries become wealthier. This relationship is only prevalent among historically wealthy countries but not among historically poor countries, whose wealth also increased over time. By contrast, pathogen prevalence predicts lower levels of democracy in both between-country and within-country comparisons. Multi-level modelling also confirms that the within-country effect of pathogen prevalence remains robust even after considering a region-level analysis. Longitudinal analyses identifying temporal precedence reveal that not only reductions in pathogen prevalence drive future democracy, but also democracy reduces future pathogen prevalence and increases future wealth. These nuanced results contrast with previous analyses using narrow, cross-sectional data. Overall, our findings illuminate the dynamic process by which environment and democracy shape each other.

show abstract

Section: Previous Misconceptions In Levels Of Analysismentioning

confidence: 95%

Multi-level modelling of time-series cross-sectional data reveals the dynamic interaction between ecological threats and democratic development

Kusano

Kemmelmeier

2020

R. Soc. open sci.

View full text Add to dashboard Cite

show abstract

“…However, after accounting for spatial and phylogenetic autocorrelation using the phylo-spatial GLS method, only phylogenetic relatedness (MPD ses ) and FDis α were associated significantly with richness above their covariation with other predictor variables ( Table 2). The difference between R 2 values of the OLS (0.67) and GLS (0.32) models indicates that approximately 35% of the variation in species richness explained by the predictors is due to phylogenetic and spatial autocorrelation in the data (Hua et al 2019). The distribution of predictor and response variables across ecoregions can be seen in Figure 5.…”

Section: Richnessmentioning

confidence: 99%

“…Because ecoregions share species with adjacent ecoregions, spatial autocorrelation in species richness makes spatial units non-independent data points, and can inflate the influence of widespread species on analyses. To account for this, we used a method that accounts for both phylogenetic and spatial autocorrelation in the data (Hua et al 2019), which is similar to a model described by Freckleton & Jetz (2009). This method works by constraining the residual correlation in the response (log(species richness)) between ecoregions to be a linear function of spatial and phylogenetic proximity, measured as the pairwise geographic distance between the centroid of each ecoregion, and phylogenetic variance-covariances.…”

Section: Richnessmentioning

confidence: 99%

Equilibrium and non‐equilibrium phases in the radiation of Hakea and the drivers of diversity in Mediterranean‐type ecosystems

Skeels

Cardillo

2019

Evolution

Self Cite

View full text Add to dashboard Cite

Mediterranean‐type ecosystems (MTEs) contain exceptional plant diversity. Explanations for this diversity are usually classed as either “equilibrium,” with elevated MTE diversity resulting from greater ecological carrying capacities, or “non‐equilibrium,” with MTEs having a greater accumulation of diversity over time than other types of ecosystems. These models have typically been considered as mutually exclusive. Here, we present a trait‐based explanatory framework that incorporates both equilibrium and non‐equilibrium dynamics. Using a large continental Australian plant radiation (Hakea) as a case study, we identify traits associated with niche partitioning in coexisting species (α‐traits) and with environmental filtering (β‐traits), and reconstruct the mode and relative timing of diversification of these traits. Our results point to a radiation with an early non‐equilibrium phase marked by divergence of β‐traits as Hakea diversified exponentially and expanded from the southwest Australian MTE into biomes across the Australian continent. This was followed from seven million years ago by an equilibrium phase, marked by diversification of α‐traits and a slowdown in lineage diversification as MTE‐niches became saturated. These results suggest that processes consistent with both equilibrium and non‐equilibrium models have been important during different stages of the radiation of Hakea, and together they provide a richer explanation of present‐day diversity patterns.

show abstract

“…The uneven distribution of languages across the globe has interested researchers for decades, e.g. [1][2][3][4][5][6][7][8][9][10]. Cavalli-Sforza and colleagues [3,11] showed that a strong correlation exists between linguistic and genetic phyla, which they linked to prehistoric patterns of human migration.…”

Section: Introductionmentioning

confidence: 99%

“…Languages are thereby seen as being adaptive to specific niches determined by the social, technological, physical and ecological aspects of the environment in which they are learned and used [8,14,15]. Especially intriguing is the correlation, stressed in several studies [4,9,10,16], between language distributions and environmental variables. They mainly reflect the finding that the highest linguistic diversity exists in tropical areas and gradually declines towards polar regions [4,9,10].…”

Section: Introductionmentioning

confidence: 99%

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

Antunes¹,

Schiefenhövel²,

d’Errico³

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

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.

show abstract

The ecological drivers of variation in global language diversity

Cited by 54 publications

References 48 publications

Multi-level modelling of time-series cross-sectional data reveals the dynamic interaction between ecological threats and democratic development

Multi-level modelling of time-series cross-sectional data reveals the dynamic interaction between ecological threats and democratic development

Equilibrium and non‐equilibrium phases in the radiation of Hakea and the drivers of diversity in Mediterranean‐type ecosystems

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

Contact Info

Product

Resources

About