Seed rain composition responds to climate change in a subtropical forest.
Sci Total Environ
; 903: 166772, 2023 Dec 10.
Article
em En
| MEDLINE
| ID: mdl-37666333
ABSTRACT
Recent climate change has been shown to alter aspects of forest plant demography, such as growth and mortality, but less attention has been focused on how climate change alters the reproduction of plant populations through time. We hypothesized that the plant seed production would respond to climate change, and that the response would differ according to plant life form and functional traits. We tested this hypothesis by examining climate change from 2005 to 2020 and by determining the temporal trends of seed rain and seed production from plants with different life forms (e.g., herbs, vines, trees, palms) and of tree species with different statures as well as leaf, seed and wood traits during 2014-2020. We also tested the correlation between meteorological variables and time series of seed rain using cross correlation analysis. We found increasing wetness (lower vapor pressure deficit) through time but with decreasing minimum relative humidity, which is a pattern consistent with trends seen in many other parts of the world. During the study period, seed production of shrubs and relative contribution of woody vines to total seed rain decreased, while relative contribution of palms to total seed rain and tree species with more conservative leaf traits increased their contribution to total seed rain. Overall, these trends were well explained by the trends of meteorological variables and the responses of these life forms to climate change in previous studies. Additionally, the increasingly conservative leaf traits were also consistent with shifts in traits following recovery from disturbance. Our results suggest that a trait-based approach may help to unveil trends that are not readily apparent by examining seed counts alone. The compositional change found in the seed rain may indicate future shifts in forest species composition and should be incorporated into future studies of forest modelling and projections under climate change.
Texto completo:
1
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article