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Background We previously described the KINSSHIP syndrome, an autosomal dominant disorder associated with intellectual disability (ID), mesomelic dysplasia and horseshoe kidney, caused by de novo variants in the degron of AFF3. Mouse knock-ins and overexpression in zebrafish provided evidence for a dominant-negative mode of action, wherein an increased level of AFF3 resulted in pathological effects. Methods Evolutionary constraints suggest that other modes-of-inheritance could be at play. We challenged this hypothesis by screening ID cohorts for individuals with predicted-to-be damaging variants in AFF3. We used both animal and cellular models to assess the deleteriousness of the identified variants. Results We identified an individual with a KINSSHIP-like phenotype carrying a de novo partial duplication of AFF3 further strengthening the hypothesis that an increased level of AFF3 is pathological. We also detected seventeen individuals displaying a milder syndrome with either heterozygous Loss-of-Function (LoF) or biallelic missense variants in AFF3. Consistent with semi-dominance, we discovered three patients with homozygous LoF and one compound heterozygote for a LoF and a missense variant, who presented more severe phenotypes than their heterozygous parents. Matching zebrafish knockdowns exhibit neurological defects that could be rescued by expressing human AFF3 mRNA, confirming their association with the ablation of aff3. Conversely, some of the human AFF3 mRNAs carrying missense variants identified in affected individuals did not rescue these phenotypes. Overexpression of mutated AFF3 mRNAs in zebrafish embryos produced a significant increase of abnormal larvae compared to wild-type overexpression further demonstrating deleteriousness. To further assess the effect of AFF3 variation, we profiled the transcriptome of fibroblasts from affected individuals and engineered isogenic cells harboring + / + , KINSSHIP/KINSSHIP, LoF/ + , LoF/LoF or KINSSHIP/LoF AFF3 genotyp

Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple-scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing-based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process-based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process-based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale.

Forests contribute to climate change mitigation through carbon storage and uptake, but the extent to which this carbon pool varies in space and time is still poorly known. Several Earth Observation missions have been specifically designed to address this issue, for example, NASA's GEDI, NASA-ISRO's NISAR and ESA's BIOMASS. Yet, all these missions' products require independent and consistent validation. A permanent, global, in situ, site-based forest biomass reference measurement system relying on ground data of the highest possible quality is therefore needed. Here, we have assembled a list of almost 200 high-quality sites through an in-depth review of the literature and expert knowledge. In this study, we explore how representative these sites are in terms of their coverage of environmental conditions, geographical space and biomass-related forest structure, compared to those experienced by forests worldwide. This work also aims at identifying which sites are the most representative, and where to invest to improve the representativeness of the proposed system. We show that the environmental coverage of the system does not seem to improve after at least the 175 most representative sites are included, but geographical and structural coverages continue to improve as more sites are added. We highlight the areas of poor environmental, geographical, or structural coverage, including, but not limited to, Canada, the western half of the USA, Mexico, Patagonia, Angola, Zambia, eastern Russia, and tropical and subtropical highlands (e.g. in Colombia, the Himalayas, Borneo, Papua). For the proposed system to succeed, we stress that (1) data must be collected and processed applying the same standards across all countries and continents; (2) system establishment and management must be inclusive and equitable, with careful consideration of working conditions; and (3) training and site partner involvement in downstream activities should be mandatory.

Above-ground biomass (AGB) is an important metric used to quantify the mass of carbon stored in terrestrial ecosystems. For forests, this is routinely estimated at the plot scale (typically 1 ha) using inventory measurements and allometry. In recent years, terrestrial laser scanning (TLS) has appeared as a disruptive technology that can generate a more accurate assessment of tree and plot scale AGB; however, operationalising TLS methods has had to overcome a number of challenges. One such challenge is the segmentation of individual trees from plot level point clouds that are required to estimate woody volume, this is often done manually (e.g. with interactive point cloud editing software) and can be very time consuming. Here we present TLS2trees, an automated processing pipeline and set of Python command line tools that aims to redress this processing bottleneck. TLS2trees consists of existing and new methods and is specifically designed to be horizontally scalable. The processing pipeline is demonstrated on 7.5 ha of TLS data captured across 10 plots of seven forest types; from open savanna to dense tropical rainforest. A total of 10,557 trees are segmented with TLS2trees: these are compared to 1281 manually segmented trees. Results indicate that TLS2trees performs well, particularly for larger trees (i.e. the cohort of largest trees that comprise 50% of total plot volume), where plot-wise tree volume bias is ±0.4 m3 and %RMSE is 60%. Segmentation performance decreases for smaller trees, for example where DBH ≤10 cm; a number of reasons are suggested including performance of semantic segmentation step. The volume and scale of TLS data captured in forest plots is increasing. It is suggested that to fully utilise this data for activities such as monitoring, reporting and verification or as reference data for satellite missions an automated processing pipeline, such as TLS2trees, is required. To facilitate improvements to TLS2trees, as well as modification for other l

Earth Observation data are uniquely positioned to estimate forest aboveground biomass density (AGBD) in accordance with the United Nations Framework Convention on Climate Change (UNFCCC) principles of ‘transparency, accuracy, completeness, consistency and comparability’. However, the use of space-based AGBD maps for national-level reporting to the UNFCCC is nearly non-existent as of 2023, the end of the first global stocktake (GST). We conduct an evidence-based comparison of AGBD estimates from the NASA Global Ecosystem Dynamics Investigation and ESA Climate Change Initiative, describing differences between the products and National Forest Inventories (NFIs), and suggesting how science teams must align efforts to inform the next GST. Between the products, in the tropics, the largest differences in estimated AGBD are primarily in the Congolese lowlands and east/southeast Asia. Where NFI data were acquired (Peru, Mexico, Lao PDR and 30 regions of Spain), both products show strong correlation to NFI-estimated AGBD, with no systematic deviations. The AGBD-richest stratum of these, the Peruvian Amazon, is accurately estimated in both. These results are remarkably promising, and to support the operational use of AGB map products for policy reporting, we describe targeted ways to align products with Intergovernmental Panel on Climate Change (IPCC) guidelines. We recommend moving towards consistent statistical terminology, and aligning on a rigorous framework for uncertainty estimation, supported by the provision of open-science codes for large-area assessments that comprehensively report uncertainty. Further, we suggest the provision of objective and open-source guidance to integrate NFIs with multiple AGBD products, aiming to enhance the precision of national estimates. Finally, we describe and encourage the release of user-friendly product documentation, with tools that produce AGBD estimates directly applicable to the IPCC guideline methodologies. With these steps, spac

NASA's Global Ecosystem Dynamics Investigation (GEDI) mission offers data for temperate and pan-tropical estimates of aboveground forest biomass (AGB). The spaceborne, full-waveform LiDAR from GEDI provides sample footprints of canopy structure, expected to cover about 4% of the land area following two years of operation. Several options are available for estimating AGB at different geographical scales. Using GEDI sample data alone, gridded biomass predictions are based on hybrid inference which correctly propagates errors due to the modeling and accounts for sampling variability, but this method requires at least two GEDI tracks in the area of interest. However, there are significant gaps in GEDI coverage and in some areas of interest GEDI data may need to be combined with other wall-to-wall remotely sensed (RS) data, such as those from multispectral or SAR sensors. In these cases, we may employ hierarchical model-based (HMB) inference that correctly considers the additional model errors that result from relating GEDI data to the wall-to-wall data. Where predictions are possible from both hybrid and HMB inference the question arises which framework to choose, and under what circumstances? In this paper, we make progress towards answering these questions by comparing the performance of the two prediction frameworks under conditions relevant for the GEDI mission. Conventional model-based (MB) inference with wall-to-wall TanDEM-X data was applied as a baseline prediction framework, which does not involve GEDI data at all. An important feature of the study was the comparison of AGB predictors in terms of both standard deviation (SD: the square root of variance) and root mean square error (RMSE: the square root of mean square error – MSE). Since, in model-based inference, the true AGB in an area of interest is a random variable, comparisons of the performance of prediction frameworks should preferably be made in terms of their RMSEs. However, in practice only the SD can

This dataset was used to producee the figures and statistics of the publication "Estimating forest aboveground biomass with terrestrial laser scanning: current status and future directions". This dataset contains 391 entries. Each entry is a tree that was terrestrial laser scanned and consecutively harvested to assess its aboveground biomass (AGB). AGB was also obtained from allometric scaling equations. Several ancillary tree properties such as stem diameter, foliage conditions,... and scan metadata (type of scanner, pattern) are included. We refer to the tab 'headers' for an explanation and units of the respective columns.

We propose a framework to estimate high above ground biomass (AGB) from L-band SAR imagery leveraging spaceborne lidars such as GEDI or ICESat-2 and repeat-pass coherence. Our results indicate we are able to overcome model saturation typically associated with purely backscatter methodologies. We validate our approach using lidar-derived AGB maps from the AfriSAR datasets at Mondah, Ogooue, and Lope. We apply our framework to UAVSAR and ALOS-2 imagery to obtain 50 meter resolution biomass maps. We obtain < 60% nRMSE (in some cases much better) with negligible relative bias using a multiscale random forest model. We illustrate that the inclusion of coherence can significantly improve high AGB estimation particularly at the coastal site Mondah.

Carbon accounting in forests remains a large area of uncertainty in the global carbon cycle. Forest aboveground biomass is therefore an attribute of great interest for the forest management community, but the accuracy of aboveground biomass maps depends on the accuracy of the underlying field estimates used to calibrate models. These field estimates depend on the application of allometric models, which often have unknown and unreported uncertainties outside of the size class or environment in which they were developed. Here, we test three popular allometric approaches to field biomass estimation, and explore the implications of allometric model selection for county-level biomass mapping in Sonoma County, California. We test three allometric models: Jenkins et al. (For Sci 49(1): 12–35, 2003), Chojnacky et al. (Forestry 87(1): 129–151, 2014) and the US Forest Service’s Component Ratio Method (CRM). We found that Jenkins and Chojnacky models perform comparably, but that at both a field plot level and a total county level there was a ~ 20% difference between these estimates and the CRM estimates. Further, we show that discrepancies are greater in high biomass areas with high canopy covers and relatively moderate heights (25–45 m). The CRM models, although on average ~ 20% lower than Jenkins and Chojnacky, produce higher estimates in the tallest forests samples (> 60 m), while Jenkins generally produces higher estimates of biomass in forests < 50 m tall. Discrepancies do not continually increase with increasing forest height, suggesting that inclusion of height in allometric models is not primarily driving discrepancies. Models developed using all three allometric models underestimate high biomass and overestimate low biomass, as expected with random forest biomass modeling. However, these deviations were generally larger using the Jenkins and Chojnacky allometries, suggesting that the CRM approach may be more appropriate for biomass mapping with lidar. These results co