Abstract:
Mangroves and the environments where they grow are highly dynamic, making it essential to
understand the seasonality of their flowering and fruiting for effective restoration approaches. This
study aimed to assess the monthly variation in flowering and fruiting of Rhizophora mucronata and
Rhizophora apiculata, identifying their peak seasons. Three mature, healthy trees of each species under
similar conditions were tagged and monitored monthly from August 2025 to April 2026. The flowering
stages (buds, open flowers, withered flowers) and fruiting stages (immature, semi-mature, mature and
fallen) of each plant were visually observed and their abundance was recorded using five percentagebased categories, and score values ranging from 1 to 5 were assigned (1: absent, 2: 1-10%, 3: 11-30%,
4: 31-50%, and 5: >50%). The highest mean score for each stage in each month was calculated to
identify the peak months and the Kruskal-Wallis test was used to determine significant differences
among months. Statistical results revealed that there were no significant monthly differences in any of
the tested flowering or fruiting stages (p=0.4289) in both species. R. mucronata exhibited an extended
flowering peak from August to September (flower buds: >50%; open and withered flowers: 31-50%),
whereas R. apiculata peaked in August with flower buds: >50%; open flowers: 1-10%. R. mucronata
showed peak fruiting in September with immature fruits: 11-30% and mature fruits: 1-10%, while R.
apiculata peaked in December (immature and semi-mature fruits: 1-10%). Both species exhibited
concurrent flowering peaks, whereas fruiting peaks showed species-specific variation. Although R.
apiculata exhibited a fruiting peak in December, fruit development was lower than in R. mucronata.
Flowering stages showed higher intensity, while fruit set percentages were comparatively lower in both
species. This study recommends the continuation of long-term monitoring and integration of
phenological data with environmental variables to better capture and explain seasonal variations.
