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Cited by 368

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

Predictive Models of Forest Dynamics

Purves, Drew, Pacala, Stephen·2008·Science

Abstract

Dynamic global vegetation models (DGVMs) have shown that forest dynamics could dramatically alter the response of the global climate system to increased atmospheric carbon dioxide over the next century. But there is little agreement between different DGVMs, making forest dynamics one of the greatest sources of uncertainty in predicting future climate. DGVM predictions could be strengthened by integrating the ecological realities of biodiversity and height-structured competition for light, facilitated by recent advances in the mathematics of forest modeling, ecological understanding of diverse forest communities, and the availability of forest inventory data.

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Crossref · 10.1126/science.1155359

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Metadata

Authors
Purves, D., & Pacala, S.
Venue
Science
Published
2008-06-13
Volume
320
Issue
5882
Pages
1452-1453
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W2085749928
Cited by
368
References
21
FWCI
19.85
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Predictive Models of Forest Dynamics. https://app.cochat.ai/papers/10.1126/science.1155359
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Purves, D., & Pacala, S. (2008). Predictive Models of Forest Dynamics. Science. https://doi.org/10.1126/science.1155359

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.