Details

Autor(en) / Beteiligte

• Chapman, Colin A.
• Valenta, Kim
• Bonnell, Tyler R.
• Brown, Kevin A.
• Chapman, Lauren J.

Titel

Solar radiation and ENSO predict fruiting phenology patterns in a 15-year record from Kibale National Park, Uganda

Ist Teil von

• Biotropica, 2018-05, Vol.50 (3), p.384-395

Ort / Verlag

Hoboken: Wiley

Erscheinungsjahr

2018

Link zum Volltext

Quelle

Wiley Online Library All Journals

Beschreibungen/Notizen

• Fruiting, flowering, and leaf set patterns influence many aspects of tropical forest communities, but there are few long-term studies examining potential drivers of these patterns, particularly in Africa. We evaluated a 15-year dataset of tree phenology in Kibale National Park, Uganda, to identify abiotic predictors of fruit phenological patterns and discuss our findings in light of climate change. We quantified fruiting for 326 trees from 43 species and evaluated these patterns in relation to solar radiance, rainfall, and monthly temperature. We used time-lagged variables based on seasonality in linear regression models to assess the effect of abiotic variables on the proportion of fruiting trees. Annual fruiting varied over 3.8-fold, and inter-annual variation in fruiting is associated with the extent of fruiting in the peak period, not variation in time of fruit set. While temperature and rainfall showed positive effects on fruiting, solar radiance in the two-year period encompassing a given year and the previous year was the strongest predictor of fruiting. As solar irradiance was the strongest predictor of fruiting, the projected increase in rainfall associated with climate change, and coincident increase in cloud cover suggest that climate change will lead to a decrease in fruiting. ENSO in the prior 24-month period was also significantly associated with annual ripe fruit production, and ENSO is also affected by climate change. Predicting changes in phenology demands understanding inter-annual variation in fruit dynamics in light of potential abiotic drivers, patterns that will only emerge with long-term data.