Integrating life history traits into predictive phylogeography.
Mol Ecol
; 28(8): 2062-2073, 2019 04.
Article
em En
| MEDLINE
| ID: mdl-30667113
ABSTRACT
Predictive phylogeography seeks to aggregate genetic, environmental and taxonomic data from multiple species in order to make predictions about unsampled taxa using machine-learning techniques such as Random Forests. To date, organismal trait data have infrequently been incorporated into predictive frameworks due to difficulties inherent to the scoring of trait data across a taxonomically broad set of taxa. We refine predictive frameworks from two North American systems, the inland temperate rainforests of the Pacific Northwest and the Southwestern Arid Lands (SWAL), by incorporating a number of organismal trait variables. Our results indicate that incorporating life history traits as predictor variables improves the performance of the supervised machine-learning approach to predictive phylogeography, especially for the SWAL system, in which predictions made from only taxonomic and climate variables meets only moderate success. In particular, traits related to reproduction (e.g., reproductive mode; clutch size) and trophic level appear to be particularly informative to the predictive framework. Predictive frameworks offer an important mechanism for integration of organismal trait, environmental data, and genetic data in phylogeographic studies.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Classificação
/
Filogeografia
/
Floresta Úmida
/
Características de História de Vida
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
País/Região como assunto:
America do norte
Idioma:
En
Revista:
Mol Ecol
Assunto da revista:
BIOLOGIA MOLECULAR
/
SAUDE AMBIENTAL
Ano de publicação:
2019
Tipo de documento:
Article