Interspecific variation in leaf phenology and its relationship with plant traits in a seasonal tropical forest

Leaf phenology plays an important role in plant life history strategies
and in determining ecosystem-level fluxes of carbon and water. In seasonal
tropical forests, leaf phenology is highly variable among tree species,
but limited quantitative data have hindered our understanding of leaf
phenology, and its relationship with other plant traits. We investigated
leaf phenology and its relationship to plant traits in 48 tree species in
a seasonally dry tropical forest. Our study combined image time-series
acquired by uncrewed aerial vehicles (UAVs) with ground-based forest
inventories and plant functional and life-history traits. Quantitative
metrics of phenology revealed wide variation along two dimensions –the
degree and timing of deciduousness – with large interspecific variation.
We categorized each species to one of three phenological groups: evergreen
(25 species, leaf cover always > 60%), early deciduous (six
species), and late deciduous (17 species). Early deciduous species were
deciduous near the beginning of the dry season and for a relatively short
period (mean 22 days); late deciduous species were deciduous later in the
dry season and for a longer period (mean 63 days). In multiple regression
models, plant traits explained roughly 50% of the interspecific variation
in phenology metrics. The best predictors in these models were xylem
vessel area (species with larger vessels tended to have earlier start
dates of deciduousness), LMA (negative relationship with the degree of
deciduousness), and leaf phosphorus content (positive relationship with
the degree of deciduousness). Synthesis – Co-occurring tree species in a
seasonal tropical forest vary widely in the degree and timing of
deciduousness, with natural breaks in this variation defining three
phenological groups – evergreen, early deciduous, and late deciduous – and
continuous variation in phenological traits within the deciduous groups.
Interspecific variation in the degree and timing of deciduousness is
associated with other plant traits. Combining UAV imagery with
ground-based forest inventory and trait data can lead to improved
understanding of the complex phenology of tropical forests, which is
crucial for accurately predicting carbon and water fluxes under changing
climates.