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  1. ; ; ; ; ; ( , Briefings in Bioinformatics)
    Abstract Background

    Estimation of genetic relatedness, or kinship, is used occasionally for recreational purposes and in forensic applications. While numerous methods were developed to estimate kinship, they suffer from high computational requirements and often make an untenable assumption of homogeneous population ancestry of the samples. Moreover, genetic privacy is generally overlooked in the usage of kinship estimation methods. There can be ethical concerns about finding unknown familial relationships in third-party databases. Similar ethical concerns may arise while estimating and reporting sensitive population-level statistics such as inbreeding coefficients for the concerns around marginalization and stigmatization.

     Results

    Here, we present SIGFRIED, which makes use of existing reference panels with a projection-based approach that simplifies kinship estimation in the admixed populations. We use simulated and real datasets to demonstrate the accuracy and efficiency of kinship estimation. We present a secure federated kinship estimation framework and implement a secure kinship estimator using homomorphic encryption-based primitives for computing relatedness between samples in two different sites while genotype data are kept confidential. Source code and documentation for our methods can be found at https://doi.org/10.5281/zenodo.7053352.

     Conclusions

    Analysis of relatedness is fundamentally important for identifying relatives, in association studies, and for estimation of population-level estimates of inbreeding. As the awareness of individual and group genomic privacy is growing, privacy-preserving methods for the estimation of relatedness are needed. Presented methods alleviate the ethical and privacy concerns in the analysis of relatedness in admixed, historically isolated and underrepresented populations.

     Short Abstract

    Genetic relatedness is a central quantity used for finding relatives in databases, correcting biases in genome wide association studies and for estimating population-level statistics. Methods for estimating genetic relatedness have high computational requirements, and occasionally do not consider individuals from admixed ancestries. Furthermore, the ethical concerns around using genetic data and calculating relatedness are not considered. We present a projection-based approach that can efficiently and accurately estimate kinship. We implement our method using encryption-based techniques that provide provable security guarantees to protect genetic data while kinship statistics are computed among multiple sites.

     
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  2. ; ; ; ; ; ; ; ; ; ; et al ( , Frontiers in Ecology and the Environment)
    The physical structure of vegetation is thought to be closely related to ecosystem function, but little is known of its pertinence across geographic regions. Here, we used data from over three million trees in continental North America to evaluate structural diversity – the volumetric capacity and physical arrangement of biotic components in ecosystems – as a predictor of productivity. We show that structural diversity is a robust predictor of forest productivity and consistently outperforms the traditional measure – species diversity – across climate conditions in North America. Moreover, structural diversity appears to be a better surrogate of niche occupancy because it captures variation in size that can be used to measure realized niche space. Structural diversity offers an easily measured metric to direct restoration and management decision making to maximize ecosystem productivity and carbon sequestration. 
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  3. ; ; ( , ACS Applied Materials & Interfaces)
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  4. ; ; ; ( , Geoderma)
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  5. ; ; ; ; ( , Journal of Climate)
    Abstract This study examines historical simulations of ENSO in the E3SM-1-0, CESM2, and GFDL-CM4 climate models, provided by three leading U.S. modeling centers as part of the Coupled Model Intercomparison Project phase 6 (CMIP6). These new models have made substantial progress in simulating ENSO’s key features, including: amplitude; timescale; spatial patterns; phase-locking; spring persistence barrier; and recharge oscillator dynamics. However, some important features of ENSO are still a challenge to simulate. In the central and eastern equatorial Pacific, the models’ weaker-than-observed subsurface zonal current anomalies and zonal temperature gradient anomalies serve to weaken the nonlinear zonal advection of subsurface temperatures, leading to insufficient warm/cold asymmetry of ENSO’s sea surface temperature anomalies (SSTA). In the western equatorial Pacific, the models’ excessive simulated zonal SST gradients amplify their zonal temperature advection, causing their SSTA to extend farther west than observed. The models underestimate both ENSO’s positive dynamic feedbacks (due to insufficient zonal wind stress responses to SSTA) and its thermodynamic damping (due to insufficient convective cloud shading of eastern Pacific SSTA during warm events); compensation between these biases leads to realistic linear growth rates for ENSO, but for somewhat unrealistic reasons. The models also exhibit stronger-than-observed feedbacks onto eastern equatorial Pacific SSTAs from thermocline depth anomalies, which accelerates the transitions between events and shortens the simulated ENSO period relative to observations. Implications for diagnosing and simulating ENSO in climate models are discussed. 
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  6. ; ; ; ; ( , Science of The Total Environment)
    null (Ed.)
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  7. ; ( , Journal of Climate)
    null (Ed.)
    Abstract The characteristics of El-Niño-Southern Oscillation (ENSO) phase-locking in observations and CMIP5 and CMIP6 models are examined in this study. Two metrics based on the peaking month histogram for all El Niño and La Niña events are adopted to delineate the basic features of ENSO phase-locking in terms of the preferred calendar month and strength of this preference. It turns out that most models are poor at simulating the ENSO phase-locking, either showing little peak strengths or peaking at the wrong seasons. By deriving ENSO’s linear dynamics based on the conceptual recharge oscillator (RO) framework through the seasonal linear inverse model (sLIM) approach, various simulated phase-locking behaviors of CMIP models are systematically investigated in comparison with observations. In observations, phase-locking is mainly attributed to the seasonal modulation of ENSO’s SST growth rate. In contrast, in a significant portion of CMIP models, phase-locking is co-determined by the seasonal modulations of both SST growth and phase-transition rates. Further study of the joint effects of SST growth and phase-transition rates suggests that for simulating realistic winter peak ENSO phase-locking with the right dynamics, climate models need to have four key factors in the right combination: (1) correct phase of SST growth rate modulation peaking at the fall; (2) large enough amplitude for the annual cycle in growth rate; (3) amplitude of semi-annual cycle in growth rate needs to be small; and (4) amplitude of seasonal modulation in SST phase-transition rate needs to be small. 
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  8. ; ; ; ; ; ( , Global Ecology and Biogeography)
    Xu, Xiaofeng (Ed.)
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  9. ; ; ; ; ; ; ; ; ; ( , Global Ecology and Biogeography)
    Michaletz, Sean (Ed.)
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  10. ; ; ; ; ( , Nature Sustainability)
    null (Ed.)
    Soil nitrogen (N) availability is of critical importance to the productivity of terrestrial ecosystems worldwide. Plant diversity continues to decline globally due to habitat conversion and degradation, but its influence on soil N remains uncertain. By conducting a global meta-analysis of 1,650 paired observations of soil N in plant species mixtures and monocultures from 149 studies, we show that, on average across observations, soil total N is 6.1% higher in species mixtures. This mixture effect on total N becomes more positive with the number of species in mixtures and with stand age. The mixture effects on net N mineralization rate and inorganic N concentrations shift from negative in young stands to positive in older stands with greater positive effects in more-diverse mixtures. These effects of mixtures were consistent among cropland, forest and grassland ecosystems and held across climate zones. Our results suggest that plant diversity conservation not only enhances the productivity of current vegetation but also increases soil N retention that will sustain the productivity of future vegetation. 
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