Earth system impacts of the European arrival and Great Dying in the Americas after 1492

Human impacts prior to the Industrial Revolution are not well constrained. We investigate whether the decline in global atmospheric CO2 concentration by 7–10 ppm in the late 1500s and early 1600s which globally lowered surface air temperatures by 0.15∘C, were generated by natural forcing or were a result of the large-scale depopulation of the Americas after European arrival, subsequent land use change and secondary succession. We quantitatively review the evidence for (i) the pre-Columbian population size, (ii) their per capita land use, (iii) the post-1492 population loss, (iv) the resulting carbon uptake of the abandoned anthropogenic landscapes, and then compare these to potential natural drivers of global carbon declines of 7–10 ppm. From 119 published regional population estimates we calculate a pre-1492 CE population of 60.5 million (interquartile range, IQR 44.8–78.2 million), utilizing 1.04 ha land per capita (IQR 0.98–1.11). European epidemics removed 90% (IQR 87–92%) of the indigenous population over the next century. This resulted in secondary succession of 55.8 Mha (IQR 39.0–78.4 Mha) of abandoned land, sequestering 7.4 Pg C (IQR 4.9–10.8 Pg C), equivalent to a decline in atmospheric CO2 of 3.5 ppm (IQR 2.3–5.1 ppm CO2). Accounting for carbon cycle feedbacks plus LUC outside the Americas gives a total 5 ppm CO2 additional uptake into the land surface in the 1500s compared to the 1400s, 47–67% of the atmospheric CO2 decline. Furthermore, we show that the global carbon budget of the 1500s cannot be balanced until large-scale vegetation regeneration in the Americas is included. The Great Dying of the Indigenous Peoples of the Americas resulted in a human-driven global impact on the Earth System in the two centuries prior to the Industrial Revolution

Meta-Analysis of Genome-Wide Linkage Studies in Celiac Disease.

OBJECTIVE: A meta-analysis of genome-wide linkage studies allows us to summarize the extensive information available from family-based studies, as the field moves into genome-wide association studies. METHODS: Here we apply the genome scan meta-analysis (GSMA) method, a rank-based, model-free approach, to combine results across eight independent genome-wide linkages performed on celiac disease (CD), including 554 families with over 1,500 affected individuals. We also investigate the agreement between signals we identified from this meta-analysis of linkage studies and those identified from genome-wide association analysis using a hypergeometric distribution. RESULTS: Not surprisingly, the most significant result was obtained in the HLA region. Outside the HLA region, suggestive evidence for linkage was obtained at the telomeric region of chromosome 10 (10q26.12-qter; p = 0.00366), and on chromosome 8 (8q22.2-q24.21; p = 0.00491). Testing signals of association and linkage within bins showed no significant evidence for co-localization of results. CONCLUSION: This meta-analysis allowed us to pool the results from available genome-wide linkage studies and to identify novel regions potentially harboring predisposing genetic variation contributing to CD. This study also shows that linkage and association studies may identify different types of disease-predisposing variants

Comment on "The global tree restoration potential"

Bastin et al. (Reports, 5 July 2019, p. 76) state that the restoration potential of new forests globally is 205 gigatonnes of carbon, conclude that “global tree restoration is our most effective climate change solution to date,” and state that climate change will drive the loss of 450 million hectares of existing tropical forest by 2050. Here we show that these three statements are incorrect

Assessment of Bias in Pan-Tropical Biomass Predictions

Above-ground biomass (AGB) is an essential descriptor of forests, of use in ecological and climate-related research. At tree- and stand-scale, destructive but direct measurements of AGB are replaced with predictions from allometric models characterizing the correlational relationship between AGB, and predictor variables including stem diameter, tree height and wood density. These models are constructed from harvested calibration data, usually via linear regression. Here, we assess systematic error in out-of-sample predictions of AGB introduced during measurement, compilation and modeling of in-sample calibration data. Various conventional bivariate and multivariate models are constructed from open access data of tropical forests. Metadata analysis, fit diagnostics and cross-validation results suggest several model misspecifications: chiefly, unaccounted for inconsistent measurement error in predictor variables between in- and out-of-sample data. Simulations demonstrate conservative inconsistencies can introduce significant bias into tree- and stand-scale AGB predictions. When tree height and wood density are included as predictors, models should be modified to correct for bias. Finally, we explore a fundamental assumption of conventional allometry, that model parameters are independent of tree size. That is, the same model can provide predictions of consistent trueness irrespective of size-class. Most observations in current calibration datasets are from smaller trees, meaning the existence of a size dependency would bias predictions for larger trees. We determine that detecting the absence or presence of a size dependency is currently prevented by model misspecifications and calibration data imbalances. We call for the collection of additional harvest data, specifically under-represented larger trees

Allometric equation for Raphia laurentii De Wild, the commonest palm in the central Congo peatlands

The world's largest tropical peatland lies in the central Congo Basin. Raphia laurentii De Wild, the most abundant palm in these peatlands, forms dominant to mono-dominant stands across approximately 45% of the peatland area. R. laurentii is a trunkless palm with fronds up to 20 m long. Owing to its morphology, there is currently no allometric equation which can be applied to R. laurentii. Therefore it is currently excluded from aboveground biomass (AGB) estimates for the Congo Basin peatlands. Here we develop allometric equations for R. laurentii, by destructively sampling 90 individuals in a peat swamp forest, in the Republic of the Congo. Prior to destructive sampling, stem base diameter, petiole mean diameter, the sum of petiole diameters, total palm height, and number of palm fronds were measured. After destructive sampling, each individual was separated into stem, sheath, petiole, rachis, and leaflet categories, then dried and weighed. We found that palm fronds represented at least 77% of the total AGB in R. laurentii and that the sum of petiole diameters was the best single predictor variable of AGB. The best overall allometric equation, however, combined the sum of petiole diameters (SDp), total palm height (H), and tissue density (TD): AGB = Exp(-2.691 + 1.425 × ln(SDp) + 0.695 × ln(H) + 0.395 × ln(TD)). We applied one of our allometric equations to data from two nearby 1-hectare forest plots, one dominated by R. laurentii, where R. laurentii accounted for 41% of the total forest AGB (with hardwood tree AGB estimated using the Chave et al. 2014 allometric equation), and one dominated by hardwood species, where R. laurentii accounted for 8% of total AGB. Across the entire region we estimate that R. laurentii stores around 2 million tonnes of carbon aboveground. The inclusion of R. laurentii in AGB estimates, will drastically improve overall AGB, and therefore carbon stock estimates for the Congo Basin peatlands

The association between loneliness and depressive symptoms among adults aged 50 years and older: a 12-year population-based cohort study

Background: Loneliness is experienced by a third of older adults in the UK and is a modifiable potential risk factor for depressive symptoms. It is unclear how the association between loneliness and depressive symptoms persists over time, and whether it is independent of related social constructs and genetic confounders. We aimed to investigate the association between loneliness and depressive symptoms, assessed on multiple occasions during 12 years of follow-up, in a large, nationally representative cohort of adults aged 50 years and older in England. / Methods: We did a longitudinal study using seven waves of data that were collected once every 2 years between 2004 and 2017, from adults aged 50 years and older in the English Longitudinal Study of Ageing (ELSA). The exposure was loneliness at baseline (wave two), measured with the short 1980 revision of the University of California, Los Angeles Loneliness Scale (R-UCLA). The primary outcome was a score indicating severity of depression measured at six subsequent timepoints (waves three to eight), using the eight-item version of the Centre for Epidemiologic Studies Depression Scale (CES-D). Analyses were linear multilevel regressions, before and after adjusting for social isolation, social support, polygenic risk scores, and other sociodemographic and health-related confounders. The secondary outcome was depression diagnosis, measured using a binary version of the CES-D. / Findings: 4211 (46%) of 9171 eligible participants had complete data on exposure, outcome, and confounders, and were included in our complete case sample. After all adjustments, a 1-point increase in loneliness score was associated with a 0·16 (95% CI 0·13–0·19) increase in depressive symptom severity score (averaged across all follow-ups). We estimated a population attributable fraction for depression associated with loneliness of 18% (95% CI 12–24) at 1 year of follow-up and 11% (3–19) at the final follow-up (wave eight), suggesting that 11–18% of cases of depression could potentially be prevented if loneliness were eliminated. Associations between loneliness and depressive symptoms remained after 12 years of follow-up, although effect sizes were smaller with longer follow-up. / Interpretation: Irrespective of other social experiences, higher loneliness scores at baseline were associated with higher depression symptom severity scores during 12 years of follow-up among adults aged 50 years and older. Interventions that reduce loneliness could prevent or reduce depression in older adults, which presents a growing public health problem worldwide. / Funding: National Institute on Aging and a consortium of UK Government departments coordinated by the National Institute for Health Research

Restoring natural forests is the best way to remove atmospheric carbon

Plans to triple the area of plantations will not meet 1.5 °C climate goals. New natural forests can, argue Simon L. Lewis, Charlotte E. Wheeler and colleagues