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Such a functional trait approach would also help to solve the issue of intraspecific tree trait variations due to e.g. ontogenetic changes.
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Next, we tested whether the prevalence of interactions was influenced by host tree traits.
Several tree traits were non-random distributed in the human-dominated landscape.
The first phase is an off-line learning process, where semantic tree traits perceived by humans are learned.
For all three groups, the difference in prevalence of interaction across modules was determined by a gradient in interaction intimacy (i.e. host tree specialization), driven by host tree traits.
We used one-way analysis of variance (ANOVA) to compare tree traits across the four size-fecundity groups.
We asked: (1) What is the relationship between tree traits, namely stem diameter and fecundity (seed crop size), and patterns of seed dispersal?
In an African rain forest, fecundity was poorly explained by individual tree traits like crown area, height and diameter for nine species including three that were wind-dispersed [22].
Stepwise multiple linear regressions using a backward elimination process were performed to determine which tree traits best predicted total number of seeds downwind and their mean dispersal distances, and to gauge seed shadow extent and total number of seeds exceeding 60 m distance downwind (SW, W, NW).
Dependence on host tree traits is reduced if epiphytes establish in ant gardens (Benzing 1990).
The effects of the tree traits that potentially influence epiphyte performance are likely to be modulated by climate.
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