Tropical phenology: Recent advances and perspectives

Shoko Sakai*, Kaoru Kitajima

*Corresponding author for this work

    Research output: Contribution to journalJournal articlepeer-review

    48 Citations (Scopus)

    Abstract

    Tropical phenology is characterized by its high diversity. Lacking a cool season that restricts growth, phenological cycles vary from species that reproduce multiple times per year to those that reproduce only once in several years even within a community. As such, environmental cues of phenological events are more diverse among species and communities of tropical organisms compared with those in higher latitudes. Community-wide phenological patterns differ among regions that differ in climate patterns and biogeographical backgrounds. These patterns are increasingly revealed as long-term phenology data accumulate especially for tree species at long-term monitoring sites. Advances in analytical methods applied to sufficiently long-term data sets generate novel insights. Long-term data are also critically important for understanding how climate changes affect phenological patterns and consequently species interactions and biological diversity. Particularly important is to understand how changes in drought regimes, both in terms of frequency and intensity, may affect plant phenology, and consequently have cascading impacts on tropical forest communities. To effectively link phenology studies and management of tropical forests and their ecosystem services in future studies, we should not only continue observation at existing sites, but also expand monitoring sites across regions, including ecosystems modified by human activities.

    Original languageEnglish
    Pages (from-to)50-54
    Number of pages5
    JournalEcological Research
    Volume34
    Issue number1
    DOIs
    Publication statusPublished - Jan 2019

    Scopus Subject Areas

    • Ecology, Evolution, Behavior and Systematics

    User-Defined Keywords

    • climate change
    • drought
    • long-term monitoring
    • time-series data
    • tropical phenology

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