Reconstruction of tree-line vegetation response to long-term climate change View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1989-10

AUTHORS

Serge Payette, Louise Filion, Ann Delwaide, Christian Bégin

ABSTRACT

KNOWLEDGE of the vegetation response to climate change is necessary to assess and predict realistic ecosystem development in the anticipated, CO2-induced warmer world, particularly at high latitudes where greater warming is expected1–3. Reconstruction of vegetation development over the past 1,000 years may be helpful in this respect, because this period was characterized by contrasting climatic conditions4–9. Here we report the reconstruction of wind-exposed, tree-line vegetation associated with long-term climate change in northern Canada, using tree-ring and growth-form analyses of spruce subfossils. Three major types of growth form within the exposed, but stable, lichen–spruce community successively predominated in response to climate forcing: high krummholz (dwarf spruce, <2-m high) with scarce small (<2-m high) trees (AD 1305–1435, cool period), trees >2–3 m high) and high krummholz (AD 1435–1570, warm period) and low krummholz ( ≲50 cm) (little ice age to present: AD 1570 onwards, cold period and present climate, respectively). Whereas the expansion of a marginal lichen–spruce woodland climaxed during the late Middle Ages (AD 1435–1570), present development of a low-krummholz vegetation at these sites seems to be out of phase with the twentieth century warming. This suggests that ecosystem recovery to global warming is not straightforward, depending on the nature of vegetation structure present at the time climate change occurred. The implications of such ecosystem resilience for the detection and monitoring of the expected CO2-induced warming is discussed, particularly for the climate-sensitive arctic and subarctic regions. More... »

PAGES

429-432

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/341429a0

DOI

http://dx.doi.org/10.1038/341429a0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1012340353


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