Abstract
Afforestation is proposed as one of the most effective climate solutions for carbon sequestration. As a majority of threatened species are linked to forests, afforestation can also contribute to mitigate the biodiversity crisis. There is however a caveat: the agricultural legacy (high nutrient availability, altered soil biota structure and function) of new forests constrains the development of forest-adapted species, affects tree growth and stability, and delays environmental benefits from afforestation.
We hypothesize that inoculation of former arable land with soil (including microbiome, fauna and seeds/rhizomes of understory vegetation) from old forests along with targeted tree species mixtures will improve productivity and more rapidly restore forest-adapted communities. This will ultimately result in diverse, stable and resilient multifunctional forests.
We will test this hypothesis and develop applied inoculation methods by: i) exploring soil biota and benchmarking biodiversity in existing afforestation research Chronosequence platforms (chronosequences and sites with increasing distance to other forests); ii) conducting inoculation experiments in mesocosms to measure seedling performance and, above- and belowground linkages; iii) establishing field-scale inoculation experiments in new and existing afforestations to test short- and long-term inoculation success on forest productivity, biodiversity and soil functioning at the ecosystem scale; iv) incorporating the landscape context into guidelines and tools for spatially explicit prioritization of areas for assisted dispersal.
The aims are to resolve barriers for successful restoration and develop landscape-scale afforestation strategies that optimize productivity and biodiversity for the planning and implementation of green infrastructure; and produce basic knowledge on the tree, understory vegetation, soil fauna and microbiome nexus and its effect on forest productivity, biodiversity and soil functions (N-retention, C-sequestration, methane uptake).