Precision restoration: A necessary approach to foster forest recovery in the 21st century

Año Publicación:  2021
Responsable: J. Castro et al.
Journal, Volumen y páginas:
Restoration Ecology, 29, 7, e13421


J. Castro, F. Morales-Rueda, F. B. Navarro, M. Löf, G. Vacchiano & D. Alcaraz-Segura


Forest restoration is currently a primary objective in environmental management policies at a global scale, to the extent that impressive initiatives and commitments have been launched to plant billions of trees. However, resources are limited and the success of any restoration effort should be maximized. Thus, restoration programs should seek to guarantee that what is planted today will become an adult tree in the future, a simple fact that, however, usually receives little attention. Here, we advocate for the need to focus restoration efforts on an individual plant level to increase establishment success while reducing negative side effects by using an approach that we term “precision forest restoration” (PFR). The objective of PFR will be to ensure that planted seedlings or sowed seeds will become adult trees with the appropriate landscape configuration to create functional and self-regulating forest ecosystems while reducing the negative impacts of traditional massive reforestation actions. PFR can take advantage of ecological knowledge together with technologies and methodologies from the landscape scale to the individual-plant scale, and from the more traditional, low-tech approaches to the latest high-tech ones. PFR may be more expensive at the level of individual plants, but will be more cost-effective in the long term if it allows for the creation of resilient forests able to provide multiple ecosystem services. PFR was not feasible a few years ago due to the high cost and low precision of the available technologies, but it is currently an alternative that might reformulate a wide spectrum of ecosystem restoration activities.

Keywords: aerial unmanned vehicles, artificial intelligence, drones, ecological interactions, forests, remote sensing, seeding, sowing

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