Urban Environments Reshape Reproductive Phenology in Plants Across the Tropics
Jha, R. R.; Simha, A.; Ita, R. E.; Rao, R.; Li, D.; Kandlikar, G.
Show abstract
Plant phenological responses to global change phenomena like urbanization remain understudied in the tropics, hindering predictions regarding the dynamics of tropical ecosystems amid rapid land use changes. Studies of tropical phenology are limited by complexities, like the limited availability of phenological data, especially in urbanized landscapes. Observations recorded on citizen science platforms can overcome this limitation by providing vast, spatially distributed data. In this study, we utilize iNaturalist data to evaluate plant reproductive phenology in tropical urban vs. rural habitats. We first compare iNaturalist data (111533 records) to herbarium collections (217991 records) in order to validate their use, and we then investigate urban-rural phenology differences within 25-km spatial grids for 238 species. Data from iNaturalist and herbaria yield complementary insights, with the former being uniformly distributed between urban and rural settings, and the latter biased towards rural observations. On average, we found species to have significantly longer reproductive duration ({beta} = 11.79 {+/-} 2.83 SE, t = 4.16, p < 10^4), and correspondingly weaker strength of seasonality in urban settings than in nearby rural localities. We also find trait-mediated variation, with seasonal, annual, and herbaceous plants showing more pronounced differences in reproductive duration and seasonality strength. These results suggest that urbanization in tropical landscapes might have important implications for plant demography, with potential consequences for community and ecosystem dynamics. Our work also points to the value of integrating insights from natural history collections with data from citizen science platforms for enabling broad-scale insights into ecological dynamics in tropical urban landscapes.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.