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David A. Coomes

Researcher at University of Cambridge

Publications -  284
Citations -  28104

David A. Coomes is an academic researcher from University of Cambridge. The author has contributed to research in topics: Biodiversity & Canopy. The author has an hindex of 74, co-authored 256 publications receiving 22360 citations. Previous affiliations of David A. Coomes include Canterbury of New Zealand & Imperial College London.

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Towards a worldwide wood economics spectrum

Jérôme Chave, +6 more
- 01 Apr 2009 - 
TL;DR: It is suggested that, similar to the manifold that tree species leaf traits cluster around the 'leaf economics spectrum', a similar 'wood economics spectrum' may be defined.
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TRY - a global database of plant traits

Jens Kattge, +136 more
TL;DR: TRY as discussed by the authors is a global database of plant traits, including morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs, which can be used for a wide range of research from evolutionary biology, community and functional ecology to biogeography.
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Biodiversity conservation: challenges beyond 2010.

Michael R. W. Rands, +12 more
- 10 Sep 2010 - 
TL;DR: It is argued that more radical changes are required that recognize biodiversity as a global public good, that integrate biodiversity conservation into policies and decision frameworks for resource production and consumption, and that focus on wider institutional and societal changes to enable more effective implementation of policy.
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Positive biodiversity-productivity relationship predominant in global forests

Jingjing Liang, +92 more
- 14 Oct 2016 - 
TL;DR: A consistent positive concave-down effect of biodiversity on forest productivity across the world is revealed, showing that a continued biodiversity loss would result in an accelerating decline in forest productivity worldwide.
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TRY plant trait database : Enhanced coverage and open access

Jens Kattge, +754 more
- 01 Jan 2020 - 
TL;DR: The extent of the trait data compiled in TRY is evaluated and emerging patterns of data coverage and representativeness are analyzed to conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements.