Das Leibniz-Institut zur Analyse des Biodiversitätswandels

ist ein Forschungsmuseum der Leibniz Gemeinschaft

‘phyloraster’: an R package to calculate measures of endemism and evolutionary diversity for rasters

AutorInnen: 
Alves-Ferreira, G., Mota, F. M. M., Talora, D. C., Oliveira, C. V., Solé, M., Heming, N. M.
Erscheinungsjahr: 
2024
Vollständiger Titel: 
‘phyloraster’: an R package to calculate measures of endemism and evolutionary diversity for rasters
Publiziert in: 
Ecography
Publikationstyp: 
Elektronische Publikation
DOI Name: 
10.1111/ecog.06902
Keywords: 
GIS, macroecology, spatial ecology, spatial patterns
Bibliographische Angaben: 
Alves-Ferreira, G., Mota, F. M. M., Talora, D. C., Oliveira, C. V., Solé, M., Heming, N. M. (2024): ‘phyloraster’: an R package to calculate measures of endemism and evolutionary diversity for rasters. - Ecography; DOI: 10.1111/ecog.06902
Abstract: 

 The spatial exploration of richness, endemism, and evolutionary diversity patterns has become an important part of biogeographic research and conservation planning. As the volume and complexity of biogeographical and phylogenetic data increase, the need for efficient tools to manipulate and analyze these datasets becomes essential. The 'phyloraster' package addresses this need by facilitating the analysis of evolution­ary diversity and endemism for rasters. Our package offers a set of functions to sup­port the linkage of species distribution models (SDMs) with phylogenies, providing then an understanding of the spatial distribution of biodiversity. It covers three main stages: pre-processing, processing, and post-processing of macroecological and phylo­genetic data. During the pre-processing step, basic functions are provided to prepare the data. The processing step combines functions to calculate indices including species richness, Faith's phylogenetic diversity, phylogenetic endemism, weighted endemism, and evolutionary distinctiveness. Additionally, this step includes functions to compute the standardized effect size for each metric using spatial and phylogenetic randomiza­tion methods, ensuring proper control for richness effects. The post-processing stage includes functions to calculate the change of metrics between different times (e.g. pres­ent and future). In relation to processing in our functions, we show that 'phyloraster' takes up considerably less RAM than the other packages when computing the same metrics (weighted endemism). Lower RAM usage minimizes the hardware require­ments to work with high-resolution datasets from local to global scales. This broadens user accessibility of the spatialized measures of endemism and evolutionary diversity.

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ehemaliger Humboldt-Stipendiat
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