The ECAT dataset: expert-validated distribution data of endemic and sub-endemic trees of Central Africa (Dem. Rep. Congo, Rwanda, Burundi)

In this data paper, we present a specimen-based occurrence dataset compiled in the framework of the Conservation of Endemic Central African Trees (ECAT) project with the aim of producing global conservation assessments for the IUCN Red List. The project targets all tree species endemic or sub-endemic to the Central African region comprising the Democratic Republic of the Congo (DR Congo), Rwanda, and Burundi. The dataset contains 6361 plant collection records with occurrences of 8910 specimens from 337 taxa belonging to 153 genera in 52 families. Many of these tree taxa have restricted geographic ranges and are only known from a small number of herbarium specimens. As assessments for such taxa can be compromised by inadequate data, we transcribed and geo-referenced specimen label information to obtain a more accurate and complete locality dataset. All specimen data were manually cleaned and verified by botanical experts, resulting in improved data quality and consistency.

Spatial patterns of light-demanding tree species in the Yangambi rainforest (Democratic Republic of Congo)

Most Central African rainforests are characterized by a remarkable abundance of light-demanding canopy species: long-lived pioneers (LLP) and non-pioneer light demanders (NPLD). A popular explanation is that these forests are still recovering from intense slash-and-burn farming activities, which abruptly ended in the 19th century. This "human disturbance" hypothesis has never been tested against spatial distribution patterns of these light demanders. Here, we focus on the 28 most abundant LLP and NPLD from 250 one-ha plots distributed along eight parallel transects (~50 km) in the Yangambi forest. Four species of short-lived pioneers (SLP) and a single abundant shade-tolerant species (Gilbertiodendron dewevrei) were used as reference because they are known to be strongly aggregated in recently disturbed patches (SLP) or along watercourses (G. dewevrei). Results show that SLP species are strongly aggregated with clear spatial autocorrelation of their diameter. This confirms that they colonized the patch following a one-time disturbance event. In contrast, LLP and NPLD species have random or weakly aggregated distribution, mostly without spatial autocorrelation of their diameter. This does not unambiguously confirm the "human disturbance" hypothesis. Alternatively, their abundance might be explained by their deciduousness, which gave them a competitive advantage during long-term drying of the late Holocene. Additionally, a canonical correspondence analysis showed that the observed LLP and NPLD distributions are not explained by environmental variables, strongly contrasting with the results for the reference species G. dewevrei, which is clearly aggregated along watercourses. We conclude that the abundance of LLP and NPLD species in Yangambi cannot be unambiguously attributed to past human disturbances or environmental variables. An alternative explanation is that present-day forest composition is a result of adaptation to late-Holocene drying. However, results are inconclusive and additional data are needed to confirm this alternative hypothesis.

The Forest Observation System, building a global reference dataset for remote sensing of forest biomass

Forest biomass is an essential indicator for monitoring the Earth's ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world's forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities.