Community phylogeny of the globally critically imperiled pine rockland ecosystem

Autor: Lauren B. Trotta, Benjamin Baiser, Jennifer Possley, Daijiang Li, James Lange, Sarah Martin, Emily B. Sessa
Rok vydání: 2018
Předmět:
Zdroj: American Journal of Botany. 105:1735-1747
ISSN: 0002-9122
DOI: 10.1002/ajb2.1168
Popis: PREMISE OF THE STUDY Community phylogenetic methods incorporate information on evolutionary relationships into studies of organismal assemblages. We used a community phylogenetic framework to investigate relationships and biogeographic affinities and to calculate phylogenetic signal of endemism and invasiveness for the flora of the pine rocklands-a globally critically imperiled ecosystem with a significant portion of its distribution in South Florida, United States. METHODS We reconstructed phylogenetic relationships of 538 vascular plant taxa, which represent 92.28% of the vascular flora of the pine rocklands. We estimated phylogenetic signal for endemism and invasiveness using phylogenetic generalized linear mixed models. We determined the native range for each species in the data set and calculated the total number of species sourced from each region and all possible combinations of these regions. KEY RESULTS The pine rockland flora includes representatives of all major vascular plant lineages, and most species have native ranges in the New World. There was strong phylogenetic signal for endemism, but not for invasiveness. CONCLUSIONS Community phylogenetics has high potential value for conservation planning, particularly for fragmented and endangered ecosystems like the pine rockland. Strong phylogenetic signal for endemic species in our data set, which also tend to be threatened or endangered, can help to identify species at risk, as well as fragments where those species occur, highlighting conservation priorities. Our results indicate, at least in the pine rockland ecosystem, no phylogenetic signal for invasive species, and thus other information must be used to predict the potential for invasiveness.
Databáze: OpenAIRE