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The optimal range of subplot widths for predicting canopy status and obtaining the correct mean dynamic rates was 25�C50?m. This range provided the best fit between observed and predicted percentage of trees in the canopy per d.b.h. class (Fig.?5). At all subplot widths, the probability of predicting the correct trees in the understorey was >95%. However, only for subplot widths between 25 and 50?m was the percentage of trees observed in the canopy when predicted in the canopy and the percentage of trees predicted in the canopy when observed in the canopy �� 60% (Fig.?5, Appendix?S5). Similarly, the differences in understorey growth and mortality rates were ��?10% at all subplot widths. However, only for subplot widths between 31.25 and 50?m was the difference in observed versus predicted canopy growth rates http://www.selleckchem.com/products/Trichostatin-A.html CAI was between 2.5 http://www.selleckchem.com/products/pd-0332991-palbociclib-isethionate.html and 3.5 for 77% of the subplots (Appendix?S6). The mean maximum height of the forest was 33?m, and the mean lower boundary of the canopy layer ( ) was 17?m, indicating the average canopy layer depth was 15?m (Table?3). The variance in CAI among subplots was low. Although observed variance among subplots in tree density was much higher than in the randomizations, the observed variance in CAI was lower than 95% of the randomizations (Table?3). There was little variation in CAI among habitats (Appendix?S6). The number of trees and the maximum height per subplot had significant positive correlations http://www.selleck.cn/products/ve-822.html with CAI per subplot (Appendix?S6). A multiple regression showed these two variables explained 48% of variation in CAI. The mean CAI for the BCI 50-ha plot closely matched the mean number of leaf strata reported by Clark et?al. (2008) for La Selva, Costa Rica (Table?4). The coefficient of variations were similar, as well as the strength of the correlation between number of layers/strata and maximum height (Table?4). The PPA crown layer model, despite its simplicity, accurately predicted whether trees were in the canopy or understorey, explained important variation in dynamic rates and gave insight into forest structuring. It provides a framework to define cohorts of trees with similar light environment and dynamic rates that could serve as the basis for landscape-scale dynamic modelling in tropical forests. The crown model was successful in predicting subplot-level characteristics of canopy structure. The model reproduced observations from data sets using different methods (aerial photographs versus ground observations) from two separate years (1995 and 2000). The PPA model fit observations better than a spatially explicit rigid crown model, which highlights the importance of crown displacement in structuring canopies. The rigid model designated too many trees, especially small trees, into the canopy.