Building statistical models to analyze species distributions

Ecological Applications

Volumn 16, Issue 1, 2006, Pages 33-50

  Latimer, Andrew M ^a   Wu, Shanshan ^a   Gelfand, Alan E ^b   Silander Jr , John A ^a  

^a UNIVERSITY OF CONNECTICUT   (United States)

^b DUKE UNIVERSITY   (United States)

Author keywords

Bayesian; Cape Floristic Region; Hierarchical; Proteaceae; Spatial dependence; Spatial random effects; Spatially explicit; Species distribution model; Uncertainty

Indexed keywords

BAYESIAN ANALYSIS; CAPE FLORISTIC REGION; GEOGRAPHICAL DISTRIBUTION; HIERARCHICAL SYSTEM;

BAYES THEOREM; BIOLOGY; COMPUTER SIMULATION; CONFERENCE PAPER; ECOLOGY; ECOSYSTEM; EMPIRICAL RESEARCH; ENVIRONMENTAL MONITORING; GENETICS; HUMAN; METHODOLOGY; PHYSIOLOGY; POPULATION DYNAMICS; POPULATION GENETICS; PROBABILITY; PROTEACEAE; REGRESSION ANALYSIS; SOUTH AFRICA; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICS;

BAYES THEOREM; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; ECOLOGY; ECOSYSTEM; EMPIRICAL RESEARCH; ENVIRONMENTAL MONITORING; GENETICS, POPULATION; HUMANS; MODELS, STATISTICAL; POPULATION DYNAMICS; PROBABILITY; PROTEACEAE; REGRESSION ANALYSIS; SOUTH AFRICA;

PROTEACEAE;

EID: 33645097847     PISSN: 10510761     EISSN: 10510761     Source Type: Journal    
DOI: 10.1890/04-0609     Document Type: Conference Paper

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