z-logo
Premium
Growing biodiverse carbon‐rich forests
Author(s) -
Pichancourt JeanBaptiste,
Firn Jennifer,
Chadès Iadine,
Martin Tara G.
Publication year - 2014
Publication title -
global change biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.146
H-Index - 255
eISSN - 1365-2486
pISSN - 1354-1013
DOI - 10.1111/gcb.12345
Subject(s) - biodiversity , carbon sequestration , ecosystem services , climate change , deforestation (computer science) , environmental resource management , climate change mitigation , environmental science , soil carbon , agroforestry , restoration ecology , reducing emissions from deforestation and forest degradation , ecosystem , ecology , geography , carbon stock , computer science , biology , carbon dioxide , soil water , programming language
Regrowing forests on cleared land is a key strategy to achieve both biodiversity conservation and climate change mitigation globally. Maximizing these co‐benefits, however, remains theoretically and technically challenging because of the complex relationship between carbon sequestration and biodiversity in forests, the strong influence of climate variability and landscape position on forest development, the large number of restoration strategies possible, and long time‐frames needed to declare success. Through the synthesis of three decades of knowledge on forest dynamics and plant functional traits combined with decision science, we demonstrate that we cannot always maximize carbon sequestration by simply increasing the functional trait diversity of trees planted. The relationships between plant functional diversity, carbon sequestration rates above ground and in the soil are dependent on climate and landscape positions. We show how to manage ‘identities’ and ‘complementarities’ between plant functional traits to achieve systematically maximal cobenefits in various climate and landscape contexts. We provide examples of optimal planting and thinning rules that satisfy this ecological strategy and guide the restoration of forests that are rich in both carbon and plant functional diversity. Our framework provides the first mechanistic approach for generating decision‐makingrules that can be used to manage forests for multiple objectives, and supports joined carbon credit and biodiversity conservation initiatives, such as Reducing Emissions from Deforestation and forest Degradation REDD+ . The decision framework can also be linked to species distribution models and socio‐economic models to find restoration solutions that maximize simultaneously biodiversity, carbon stocks, and other ecosystem services across landscapes. Our study provides the foundation for developing and testing cost‐effective and adaptable forest management rules to achieve biodiversity, carbon sequestration, and other socio‐economic co‐benefits under global change.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here