Identifying interactions among reforestation success drivers: A case study from the Philippines

Published Date
24 November 2015, Vol.316:62–77, doi:10.1016/j.ecolmodel.2015.08.005

Author

• Hai Dinh Le ^a,b,,,
• Carl Smith ^b
• John Herbohn ^b,c

• aVietnam Forestry University, Xuan Mai, Chuong My, Hanoi, Viet Nam
• bSchool of Agriculture and Food Sciences, The University of Queensland, St. Lucia, Brisbane, Qld 4072, Australia
• cTropical Forests and People Centre, University of the Sunshine Coast, Maroochydore, Qld 4556, Australia

Received 30 January 2015. Revised 3 August 2015. Accepted 4 August 2015. Available online 27 August 2015.

Highlights

• Previous research has only documented independent effects of drivers on reforestation success.
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  Tested non-additive interactions of reforestation success drivers were found up to 90% of total.
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  Understanding non-additive interactions are needed for reforestation policy and planning.

Abstract 

Reforestation is an expensive undertaking. It is a long-term, complex, and trans-disciplinary process and it involves uncertainties and changing conditions. There is also a complex array of drivers (including biophysical, technical, socio-economic, institutional, and management drivers) that affect reforestation success. Previous research has documented the independent effects of biophysical and technical, environmental and socio-economic drivers on reforestation success. However, research over the last decade has revealed that the outcome of multiple factor interactions is commonly non-additive (i.e. synergies and antagonisms). Therefore, in order to provide better decision support for reforestation planning and policy setting it is necessary to understand the interactive effects that drivers have on reforestation success. To understand these interactive effects, we developed a Bayesian network model based on data collected from 43 reforestation projects on Leyte Island, the Philippines. Non-additive interactions among reforestation success drivers (i.e. synergies and antagonisms) were found to account for up to 90% of interactions tested. This result suggests an urgent need to account for these non-additive interactions in reforestation policy and planning in order to avoid unanticipated outcomes, wasted effort and missed opportunities.

Keywords

Bayesian networks

Synergistic interactions

Antagonistic interactions

Non-additive interactions

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  Corresponding author at: Vietnam Forestry University, Xuan Mai, Chuong My, Hanoi, Viet Nam. Tel.: +84 34840705; fax: +84 34840233.

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