Abstract

Climate influences geographic differences of vegetation phenology through both contemporary and historical variability. The latter effect is embodied in vegetation heterogeneity underlain by spatially varied genotype and species compositions tied to climatic adaptation. Such long-term climatic effects are difficult to map and therefore often neglected in evaluating spatially explicit phenological responses to climate change. In this study we demonstrate a way to indirectly infer the portion of land surface phenology variation that is potentially contributed by underlying genotypic differences across space. The method undertaken normalized remotely sensed vegetation start-of-season (or greenup onset) with a cloned plants-based phenological model. As the geography of phenological model prediction (first leaf) represents the instantaneous effect of contemporary climate, the normalized land surface phenology potentially reveals vegetation heterogeneity that is related to climatic adaptation. The study was done at the continental scale for the conterminous U.S., with a focus on the eastern humid temperate domain. Our findings suggest that, in an analogous scenario, if a uniform contemporary climate existed everywhere, spring vegetation greenup would occur earlier in the north than in the south. This is in accordance with known species-level clinal variations—for many temperate plant species, populations adapted to colder climates require less thermal forcing to initiate growth than those in warmer climates. This study, for the first time, shows that such geographic adaption relationships are supported at the ecosystem level. Mapping large-scale vegetation climate adaptation patterns contributes to our ability to better track geographically varied phenological responses to climate change.

Document Type

Article

Publication Date

4-19-2016

Notes/Citation Information

Published in Climate, v. 4, issue 2, 24, p. 1-12.

© 2016 by the authors; licensee MDPI, Basel, Switzerland.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

Digital Object Identifier (DOI)

https://doi.org/10.3390/cli4020024

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