Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella

Journal of Evolutionary Biology

Volumn 25, Issue 6, 2012, Pages 1039-1052

  Hether, T D ^a,b   Hoffman, E A ^a  

^a UNIVERSITY OF CENTRAL FLORIDA   (United States)

^b UNIVERSITY OF IDAHO   (United States)

Author keywords

Amphibian; Clusters; Ensemble learning; Gene flow; Landscape genetics; Random Forest; Southeastern USA

Indexed keywords

MICROSATELLITE DNA;

ALGORITHM; CLUSTER ANALYSIS; CONNECTIVITY; DECIDUOUS FOREST; FROG; GENE FLOW; GENETIC DIFFERENTIATION; GENETIC MARKER; GENETIC STRUCTURE; HABITAT QUALITY; HABITAT TYPE; HABITAT USE; LANDSCAPE ECOLOGY; URBAN AREA;

ALGORITHM; ANIMAL; ANURA; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; CLUSTER ANALYSIS; COMPUTER SIMULATION; ECOSYSTEM; GENETIC VARIABILITY; GENETICS; METHODOLOGY; PHYSIOLOGY; POPULATION DENSITY; POPULATION GENETICS; SPECIES DIFFERENCE; UNITED STATES;

ALGORITHMS; ANIMALS; ANURA; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMPUTER SIMULATION; ECOSYSTEM; GENETIC VARIATION; GENETICS, POPULATION; MICROSATELLITE REPEATS; MODELS, GENETIC; POPULATION DENSITY; SOUTHEASTERN UNITED STATES; SPECIES SPECIFICITY;

UNITED STATES;

AMPHIBIA; ANURA; HYLA; HYLA SQUIRELLA;

EID: 84861193656     PISSN: 1010061X     EISSN: 14209101     Source Type: Journal    
DOI: 10.1111/j.1420-9101.2012.02497.x     Document Type: Article

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