Node-based analysis of species distributions
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Standard
Node-based analysis of species distributions. / Borregaard, Michael Krabbe; Rahbek, Carsten; Fjeldså, Jon; Parra, Juan L.; Whittaker, Robert James; Graham, Catherine H.
I: Methods in Ecology and Evolution, Bind 5, Nr. 11, 2014, s. 1225-1235.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Node-based analysis of species distributions
AU - Borregaard, Michael Krabbe
AU - Rahbek, Carsten
AU - Fjeldså, Jon
AU - Parra, Juan L.
AU - Whittaker, Robert James
AU - Graham, Catherine H.
PY - 2014
Y1 - 2014
N2 - The integration of species distributions and evolutionary relationships is one of the most rapidly moving research fields today and has led to considerable advances in our understanding of the processes underlying biogeographical patterns. Here, we develop a set of metrics, the specific overrepresentation score (SOS) and the geographic node divergence (GND) score, which together combine ecological and evolutionary patterns into a single framework and avoids many of the problems that characterize community phylogenetic methods in current use.This approach goes through each node in the phylogeny and compares the distributions of descendant clades to a null model. The method employs a balanced null model, is independent of phylogeny size, and allows an intuitive visualization of the results.We demonstrate how this novel implementation can be used to generate hypotheses for biogeographical patterns with case studies on two groups with well-described biogeographical histories: a local-scale community data set of hummingbirds in the North Andes, and a large-scale data set of the distribution of all species of New World flycatchers. The node-based analysis of these two groups generates a set of intuitively interpretable patterns that are consistent with current biogeographical knowledge.Importantly, the results are statistically tractable, opening many possibilities for their use in analyses of evolutionary, historical and spatial patterns of species diversity. The method is implemented as an upcoming R package nodiv, which makes it accessible and easy to use.
AB - The integration of species distributions and evolutionary relationships is one of the most rapidly moving research fields today and has led to considerable advances in our understanding of the processes underlying biogeographical patterns. Here, we develop a set of metrics, the specific overrepresentation score (SOS) and the geographic node divergence (GND) score, which together combine ecological and evolutionary patterns into a single framework and avoids many of the problems that characterize community phylogenetic methods in current use.This approach goes through each node in the phylogeny and compares the distributions of descendant clades to a null model. The method employs a balanced null model, is independent of phylogeny size, and allows an intuitive visualization of the results.We demonstrate how this novel implementation can be used to generate hypotheses for biogeographical patterns with case studies on two groups with well-described biogeographical histories: a local-scale community data set of hummingbirds in the North Andes, and a large-scale data set of the distribution of all species of New World flycatchers. The node-based analysis of these two groups generates a set of intuitively interpretable patterns that are consistent with current biogeographical knowledge.Importantly, the results are statistically tractable, opening many possibilities for their use in analyses of evolutionary, historical and spatial patterns of species diversity. The method is implemented as an upcoming R package nodiv, which makes it accessible and easy to use.
U2 - 10.1111/2041-210X.12283
DO - 10.1111/2041-210X.12283
M3 - Journal article
VL - 5
SP - 1225
EP - 1235
JO - Methods in Ecology and Evolution
JF - Methods in Ecology and Evolution
SN - 2041-210X
IS - 11
ER -
ID: 138901107