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| Publications [#329831] of Brian R. Silliman
Papers Published
- Fariña, JM; He, Q; Silliman, BR; Bertness, MD, Biogeography of salt marsh plant zonation on the Pacific coast of South America,
Journal of Biogeography, vol. 45 no. 1
(January, 2018),
pp. 238-247, WILEY [doi]
(last updated on 2023/06/01)
Abstract:
Aim: The aim of this study was to investigate the biogeography of plant zonation in salt marshes on the Pacific coast of South America; to examine whether salt marsh plant zonation varies with latitude; and to explore the relative importance of climatic, tidal, edaphic and disturbance factors in explaining large-scale variation in salt marsh plant community structure. Location: A 2,000-km latitudinal gradient on the Pacific coast in Chile, with a climate shift from hyper-arid at low to hyper-humid at high latitudes. Methods: Plant zonation was quantified in field surveys of ten marshes. Climate, tidal regimes, edaphic factors and disturbances (tsunami and rainfall floods) were determined. We used multivariate analyses to explore their relative importance in explaining large-scale variation in salt marsh plant community structure. Results: Across latitude, marshes were dominated by distinct plant communities in different climate regions, especially at the extreme dry and wet latitudes. Intertidal plant species zonation was present in hyper-arid and semi-arid climates, but not in arid, humid and hyper-humid climates. Latitudinal variation in low-marsh plant communities (regularly flooded at high tide) was largely a function of precipitation, while at high marshes (never flooded at high tide) latitudinal variation was explained with precipitation, temperature, tidal cycles, soil salinity and disturbances. Main conclusions: Salt marshes on the Pacific coast of South America belong to Dry Coast and Temperate biogeographic types. Salt marsh plant zonation varies across latitude, and is explained by climatic, tidal, edaphic and disturbance factors. These patterns appear to be mechanistically explained by extrapolating experimentally generated community assembly models and have implications for predicting responses to climate change.
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