Natural forest regeneration through fire protection is a less imminent threat for truly stable savannas than afforestation.

African bistable savannas have important biodiversity value and merit conservation. At the same time, forest restoration is a nature-based solution that can be used to increase biodiversity, carbon stocks, and human well-being. Here we describe an experiment based on natural forest regeneration through the exclusion of anthropogenic fire. We show that it is easier to let nature do its work instead of channeling it into an artificial man-made ecosystem through human-induced burning or planting. We emphasize that nature-based solutions must be biome-appropriate and the choice between restoring forests or protecting savannas requires a thorough understanding of the local context.

A framework for tracing timber following the Ukraine invasion.

Scientific testing including stable isotope ratio analysis (SIRA) and trace element analysis (TEA) is critical for establishing plant origin, tackling deforestation and enforcing economic sanctions. Yet methods combining SIRA and TEA into robust models for origin verification and determination are lacking. Here we report a (1) large Eastern European timber reference database (Betula, Fagus, Pinus, Quercus) tailored to sanctioned products following the Ukraine invasion; (2) statistical test to verify samples against a claimed origin; (3) probabilistic model of SIRA, TEA and genus distribution data, using Gaussian processes, to determine timber harvest location. Our verification method rejects 40-60% of simulated false claims, depending on the spatial scale of the claim, and maintains a low probability of rejecting correct origin claims. Our determination method predicts harvest location within 180 to 230 km of true location. Our results showcase the power of combining data types with probabilistic modelling to identify and scrutinize timber harvest location claims.

Protecting an artificial savanna as a nature-based solution to restore carbon and biodiversity in the Democratic Republic of the Congo.

A large share of the global forest restoration potential is situated in artificial 'unstable' mesic African savannas, which could be restored to higher carbon and biodiversity states if protected from human-induced burning. However, uncertainty on recovery rates in protected unstable savannas impedes science-informed forest restoration initiatives. Here, we quantify the forest restoration success of anthropogenic fire exclusion within an 88-ha mesic artificial savanna patch in the Kongo Central province of the Democratic Republic of the Congo (DR Congo). We found that aboveground carbon recovery after 17 years was on average 11.40 ± 0.85 Mg C ha-1 . Using a statistical model, we found that aboveground carbon stocks take 112 ± 3 years to recover to 90% of aboveground carbon stocks in old-growth forests. Assuming that this recovery trajectory would be representative for all unstable savannas, we estimate that they could have a total carbon uptake potential of 12.13 ± 2.25 Gt C by 2100 across DR Congo, Congo and Angola. Species richness recovered to 33.17% after 17 years, and we predicted a 90% recovery at 54 ± 2 years. In contrast, we predicted that species composition would recover to 90% of old-growth forest composition only after 124 ± 3 years. We conclude that the relatively simple and cost-efficient measure of fire exclusion in artificial savannas is an effective nature-based solution to climate change and biodiversity loss. However, more long-term and in situ monitoring efforts are needed to quantify variation in long-term carbon and diversity recovery pathways. Particular uncertainties are spatial variability in socio-economics and growing conditions as well as the effects of projected climate change.

SmartWoodID-an image collection of large end-grain surfaces to support wood identification systems.

Wood identification is a key step in the enforcement of laws and regulations aimed at combatting illegal timber trade. Robust wood identification tools, capable of distinguishing a large number of timbers, depend on a solid database of reference material. Reference material for wood identification is typically curated in botanical collections dedicated to wood consisting of samples of secondary xylem of lignified plants. Specimens from the Tervuren Wood Collection, one of the large institutional wood collections around the world, are used as a source of tree species data with potential application as timber. Here, we present SmartWoodID, a database of high-resolution optical scans of the end-grain surfaces enriched with expert wood anatomical descriptions of macroscopic features. These can serve as annotated training data to develop interactive identification keys and artificial intelligence for computer vision-based wood identification. The first edition of the database consists of images of 1190 taxa, with a focus on potential timber species from the Democratic Republic of the Congo with at least four different specimens per species included. Database URL https://hdl.handle.net/20.500.12624/SmartWoodID_first_edition.

Improved wood species identification based on multi-view imagery of the three anatomical planes.

BACKGROUND: The identification of tropical African wood species based on microscopic imagery is a challenging problem due to the heterogeneous nature of the composition of wood combined with the vast number of candidate species. Image classification methods that rely on machine learning can facilitate this identification, provided that sufficient training material is available. Despite the fact that the three main anatomical sections contain information that is relevant for species identification, current methods only rely on transverse sections. Additionally, commonly used procedures for evaluating the performance of these methods neglect the fact that multiple images often originate from the same tree, leading to an overly optimistic estimate of the performance. RESULTS: We introduce a new image dataset containing microscopic images of the three main anatomical sections of 77 Congolese wood species. A dedicated multi-view image classification method is developed and obtains an accuracy (computed using the naive but common approach) of 95%, outperforming the single-view methods by a large margin. An in-depth analysis shows that naive accuracy estimates can lead to a dramatic over-prediction, of up to 60%, of the accuracy. CONCLUSIONS: Additional images from non-transverse sections can boost the performance of machine-learning-based wood species identification methods. Additionally, care should be taken when evaluating the performance of machine-learning-based wood species identification methods to avoid an overestimation of the performance.

Opportunities in phytochemistry, ecophysiology and wood research via laser ablation direct analysis in real time imaging-mass spectrometry.

Analysis of wood transects in a manner that preserves the spatial distribution of the metabolites present is highly desirable to among other things: (1) facilitate ecophysiology studies that reveal the association between chemical make-up and environmental factors or climatic events over time; and (2) investigate the mechanisms of the synthesis and trafficking of small molecules within specialised tissues. While a variety of techniques could be applied to achieve these goals, most remain challenging and impractical. Laser ablation direct analysis in real time imaging-mass spectrometry (LADI-MS) was successfully used to survey the chemical profile of wood, while also preserving the small-molecule spatial distributions. The tree species Entandrophragma candollei Harms, Millettia laurentii DeWild., Pericopsis elata (Harms) Meeuwen, Dalbergia nigra (Vell.) Benth. and Dalbergia normandii Bosser & R.Rabev were analysed. Several compounds were associated with anatomical features. A greater diversity was detected in the vessels and parenchyma compared with the fibres. Analysis of single vessels revealed that the chemical fingerprint used for timber identification is mainly determined by vessel content. Laser ablation direct analysis in real time imaging-mass spectrometry offers unprecedented opportunities to investigate the distribution of metabolites within wood samples, while circumventing the issues associated with previous methods. This technique opens up new vistas for the discovery of small-molecule biomarkers that are linked to environmental events.

When xylarium and herbarium meet: linking Tervuren xylarium wood samples with their herbarium specimens at Meise Botanic Garden.

BACKGROUND: The current data paper aims to interlink the African plant collections of the Meise Botanic Garden Herbarium (BR) and the Royal Museum for Central Africa Xylarium (Tw). Complementing both collections strengthens the reference value of each institutional collection, as more complete metadata are made available and it enables increased quality control for the identification of wood specimens. Furthermore, the renewed connection enables the linking of available wood trait data with data on phenology, leaf morphology or even molecular information for many tree species, allowing assessments of performance of individual trees. In addition to studies at the interspecific level, comparisons at the intraspecific level become possible, which could lead to important new insights into resilience to and impact of global change, as well as biodiversity conservation or forest management of Central African forest ecosystems. NEW INFORMATION: By interlinking the Tervuren Xylarium Wood database with the recently digitised herbarium of Meise Botanic Garden, we were able to establish a link between 6,621 xylarium and 9,641 herbarium records for 6,953 plant specimens. Both institutional databases were complemented with reliable specimen metadata. The Tervuren xylarium now profits from taxonomic revisions made by botanists at Meise Botanic Garden and a list of phenotypical features for woody African species can be extended with wood anatomical descriptors. New metadata from the Tw xylarium records were used to add the country of collection to 50 linked BR herbarium specimens for which this information was missing. Furthermore, metadata available from the labels on digitised BR herbarium specimens was used to update Tw xylarium records with the date of collection (817 records), collection locality (698 records), coordinates (482 records) and altitude (817 records). In conclusion, we created a reference database with reliable botanic identities which can be used in a range of studies, such as modelling analyses, community assessments or trait analyses, all framed in a spatiotemporal context. Furthermore, the linked collections hold historical reference data and specimens that can be studied in the context of global changes.

The persistence of carbon in the African forest understory.

Quantifying carbon dynamics in forests is critical for understanding their role in long-term climate regulation1-4. Yet little is known about tree longevity in tropical forests3,5-8, a factor that is vital for estimating carbon persistence3,4. Here we calculate mean carbon age (the period that carbon is fixed in trees7) in different strata of African tropical forests using (1) growth-ring records with a unique timestamp accurately demarcating 66 years of growth in one site and (2) measurements of diameter increments from the African Tropical Rainforest Observation Network (23 sites). We find that in spite of their much smaller size, in understory trees mean carbon age (74 years) is greater than in sub-canopy (54 years) and canopy (57 years) trees and similar to carbon age in emergent trees (66 years). The remarkable carbon longevity in the understory results from slow and aperiodic growth as an adaptation to limited resource availability9-11. Our analysis also reveals that while the understory represents a small share (11%) of the carbon stock12,13, it contributes disproportionally to the forest carbon sink (20%). We conclude that accounting for the diversity of carbon age and carbon sequestration among different forest strata is critical for effective conservation management14-16 and for accurate modelling of carbon cycling4.