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Top candidate models that predict mean maximum sap velocity and mean annual basal area increment across seven tropical lowland rainforest tree species in Daintree, northeast Australia.

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Figshare2015-12-03 更新2026-04-29 收录
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https://figshare.com/articles/dataset/_Top_candidate_models_that_predict_mean_maximum_sap_velocity_and_mean_annual_basal_area_increment_across_seven_tropical_lowland_rainforest_tree_species_in_Daintree_northeast_Australia_/1454189
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AICc refers to Akaike Information criterion corrected for small sample size, ∆AICc to the difference between each model’s AICc and the minimum AICc found, w to Akaike weights, and w1/wi to evidence ratios where w1 is the Akaike weight of the best fitting model. Akaike weights may be interpreted as relative model probabilities. Models with a higher evidence ratio are less likely to be the best model. The summed weights (Σw) for predictors is the relative likelihood that the predictor should form part of the model [48]. The top candidate models include ≥95% of Akaike weights.Top candidate models that predict mean maximum sap velocity and mean annual basal area increment across seven tropical lowland rainforest tree species in Daintree, northeast Australia.
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2015-12-03
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