Article

Leafing patterns and drivers across seasonally dry tropical communities

Citation

Alberton B, Torres RdS, Silva TSF, da Rocha HR, Moura MSB & Morellato LPC (2019) Leafing patterns and drivers across seasonally dry tropical communities. Remote Sensing, 11 (19), Art. No.: 2267. https://doi.org/10.3390/rs11192267

Abstract
Investigating the timing of key phenological events across environments with variable seasonality is crucial to understand the drivers of ecosystem dynamics. Leaf production in the tropics is mainly constrained by water and light availability. Identifying the factors regulating leaf phenology patterns allows efficiently forecasting of climate change impacts. We conducted a novel phenological monitoring study across four Neotropical vegetation sites using leaf phenology time series obtained from digital repeated photographs (phenocameras). Seasonality differed among sites, from very seasonally dry climate in the caatinga dry scrubland with an eight-month long dry season to the less restrictive Cerrado vegetation with a six-month dry season. To unravel the main drivers of leaf phenology and understand how they influence seasonal dynamics (represented by the green color channel (Gcc) vegetation index), we applied Generalized Additive Mixed Models (GAMMs) to estimate the growing seasons, using water deficit and day length as covariates. Our results indicated that plant-water relationships are more important in the caatinga, while light (measured as day-length) was more relevant in explaining leafing patterns in Cerrado communities. Leafing behaviors and predictor-response relationships (distinct smooth functions) were more variable at the less seasonal Cerrado sites, suggesting that different life-forms (grasses, herbs, shrubs, and trees) are capable of overcoming drought through specific phenological strategies and associated functional traits, such as deep root systems in trees.

Keywords
vegetative phenology; deciduousness; greenness; caatinga; cerrado; savanna; seasonality; climate drivers; time series; near-surface remote phenology

Journal
Remote Sensing: Volume 11, Issue 19

StatusPublished
Publication date31/10/2019
Publication date online28/09/2019
Date accepted by journal25/09/2019
URLhttp://hdl.handle.net/1893/30433
eISSN2072-4292