Tag: geographically weighted regression

Article

Spatial variation in direct and indirect effects of climate and productivity on species richness of terrestrial tetrapods

Posted on

Barreto et al. (2021) – Global Ecology and Biogeography

We aimed to dissect the spatial variation of the direct and indirect effects of climate and productivity on the global species richness of terrestrial tetrapods. We used a geographically weighted path analysis to estimate and map the direct and indirect effects of temperature, precipitation and primary productivity on species richness of terrestrial tetrapods across the globe. We found that all relationships shift in magnitude, and even in direction, among taxonomic groups, geographical regions and connecting paths. Direct effects of temperature and precipitation are generally stronger than both indirect effects mediated by productivity and direct effects of productivity. Richness gradients seem to be driven primarily by the effects of climate on organismal physiological limits and metabolic rates rather than by the amount of productive energy. Reptiles have the most distinct relationships across tetrapods, with a clear latitudinal pattern in the importance of temperature versus water.

http://www.doi.org/10.1111/geb.13357

Icon

Barreto et al. (2021) - GEB

1 file(s) 6.46 MB
Download
Article

Environmental factors explain the spatial mismatches between species richness and phylogenetic diversity of terrestrial mammals

Posted on

Barreto, Graham & Rangel (2019) – Global Ecology and Biogeography

We explored the spatial variation of the relationships between species richness (SR), phylogenetic diversity (PD) and environmental factors to infer the possible mechanisms underlying patterns of mammalian diversity in different regions of the globe. We used a hexagonal grid to map SR and PD of mammals and four environmental factors (temperature, productivity, elevation and climate‐change velocity since the Last Glacial Maximum). We related those variables through direct and indirect pathways using a novel combination of path analysis and geographically weighted regression to account for spatial non-stationarity of path coefficients. We found that species richness, PD and environmental factors relate differently across the geographical space, with most relationships varying in both magnitude and direction. Species richness is associated with lower PD in much of the tropics and in the Americas, which reflects the tropical origin and the recent diversification of some mammalian clades in these regions. Environmental effects on PD are predominantly mediated by their effects on SR. But once richness is controlled for, the relationships between environmental factors and PD (i.e., PDSR) highlight environmentally driven changes in species composition. Environmental–PDSR relationships suggest that the relative importance of different mechanisms driving biodiversity shifts spatially. Across most of the globe, temperature and productivity are the strongest predictors of richness, whereas PDSR is best predicted by temperature. In conclusion, richness explains most spatial variation in PD, but both dimensions of biodiversity respond differently to environmental conditions across the globe, as indicated by the spatial mismatches in the relationships between environmental factors and these two types of diversity. We show that accounting for spatial non‐stationarity and environmental effects on PD while controlling for richness uncovers a more complex scenario of drivers of biodiversity than previously observed.

https://doi.org/10.1111/geb.12999

Icon

Barreto et al. (2019) - GEB

1 file(s) 1.43 MB
Download

Article

Drivers of geographic patterns of North American language diversity

Posted on

Coelho et al. (2019) – Proceedings B

Although many hypotheses have been proposed to explain why humans speak so many languages and why languages are unevenly distributed across the globe, the factors that shape geographical patterns of cultural and linguistic diversity remain poorly understood. Prior research has tended to focus on identifying universal predictors of language diversity, without accounting for how local factors and multiple predictors interact. Here, we use a unique combination of path analysis, mechanistic simulation modelling, and geographically weighted regression to investigate the broadly described, but poorly understood, spatial pattern of language diversity in North America. We show that the ecological drivers of language diversity are not universal or entirely direct. The strongest associations imply a role for previously developed hypothesized drivers such as population density, resource diversity, and carrying capacity with group size limits. The predictive power of this web of factors varies over space from regions where our model predicts approximately 86% of the variation in diversity, to areas where less than 40% is explained.

http://dx.doi.org/10.1098/rspb.2019.0242

Icon

Coelho et al. (2019) Proceedings B

1 file(s) 1,021.17 KB
Download