Satellite-observed shifts in C<inf>3</inf>/C<inf>4</inf> abundance in Australian grasslands are associated with rainfall patterns
- Publisher:
- Elsevier
- Publication Type:
- Journal Article
- Citation:
- Remote Sensing of Environment: an interdisciplinary journal, 2022, 273, pp. 1-19
- Issue Date:
- 2022-05-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1-s2.0-S0034425722000979-main.pdf | 12.5 MB |
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Species composition is a key determinant of grassland ecosystem function and resilience. Climate change is predicted to alter the distribution of cool season (C3) and warm season (C4) grasses, however, the lack of spatial distributions and temporal variations of grass functional type information severely limits our understanding of climate impacts on grasslands. This study classified C3 and C4 grasses per pixel according to the peak of growing season generated from Enhanced Vegetation Index time series. From 2003 to 2017, the C3-C4 composition of Australian rain-fed grasslands and pastures was mapped at 500 m resolution on an annual basis across a wide geographical range (10°S – 45°S), and revealed extreme inter-annual fluctuations. Over the 15-year period, the satellite-derived ratio of C4 to C3 grasses significantly increased (p < 0.05), indicating a long-term shift in community composition that was confirmed with 182,911 Atlas of Living Australia ground observations. The most pronounced changes occurred in mid-latitude transitional areas where C3 and C4 grasses co-dominate. Our climate analysis indicated the inter-annual fluctuations of C4/C3 grass ratios were significantly associated (p < 0.05) with warm/cool season rainfall ratios, and not with temperature or annual rainfall. This suggests that an increase in the warm/cool season rainfall ratio favors C4 grasses and a decrease in the warm/cool season rainfall ratio favors C3 grasses. Our findings reveal spatially-detailed dynamics of grasslands and demonstrate large-scale grassland compositional changes over 15 years. The grass composition maps should help improve ecological forecasting of grass distributions and enable researches on grassland ecosystem responses to climate change that are relevant to both adaptation of rangeland agricultural and fire management practices. Our study should also help predict grass distribution under future climate conditions, and assist in the accurate modelling of global water, carbon, and energy exchanges between the land surface and atmosphere.
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