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Grasses and fire pose a major challenge for forest restoration. Here we evaluate a case study of reforestation in an area invaded by the tall invasive grass Saccharum spontaneum in the Panama Canal Watershed. The project objectives were to (1) replace Saccharum with a forest, (2) restore a stratified mixed species forest and (3) sequester carbon. We aimed to compare the practice of forest restoration with a treatment grounded in theory. Therefore, the first species selection method followed business-as-usual: contractors planted any combination of 130 prescribed species. The second method followed the framework species approach, a mixture of 22 species was planted to ensure early shade, create a stratified forest over time, attract seed dispersers, and for their potential to fix N 2. Both treatments showed successful restoration trajectories 8.5 years after planting, they did not differ in structural characteristics (stem density, basal area, aboveground biomass, height, and amount of Saccharum). However, based on the species present, the framework approach shows more potential to become a stratified forest. As the framework approach also withstood fires much better than the business-as-usual approach, we conclude that it improves restoration success in this human-dominated landscape.

To effectively reduce illegal timber trade, law enforcers need forensic methods to independently verify claims of wood origin. Multi-element analysis of traded plant material has the potential to be used to trace the origin of commodities, but for timber it has not been tested at relevant large scales. Here we put this method to the test, by evaluating its tracing accuracy for three economically important tropical timbers: Azobé and Tali in Central Africa (22 sites) and Red Meranti on Borneo (9 sites). Wood samples from 991 trees were measured using Inductively Coupled Plasma Mass Spectrometry and element concentrations were analysed to chemically group similar sites (clustering) and assess accuracy of tracing samples to their origin (Random Forest models). For all three timbers, we found distinct spatial differences in chemical composition. In Central Africa, tracing accuracy was 86%–98% for regional clusters of chemically similar sites, with accuracy depending on the tracing question. These clusters were 50–800 km apart and tracing accuracy was highest when combining the two timbers. Tracing accuracy of Red Meranti on Borneo was 88% at the site level. This high accuracy at a small scale may be related to the short distances at which differences in soil type occur on Borneo. A blind sample analysis of 46 African timber samples correctly identified the origin of 70%–72% of the samples, but failed to exclude 70% of the samples obtained from different species or outside the study area. Overall, these results illustrate a high potential for multi-element analysis to be developed into a timber tracing tool which can identify origin for multiple species and can do so at a within-country scale. To reach this potential, reference databases need to cover wider geographic areas and represent more timbers.

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