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1. The natural history of luck: A synthesis study of structured population models

   https://doi.org/10.1111/ele.14390  

   Hernández, Christina M; Ellner, Stephen P; Snyder, Robin E; Hooker, Giles (March 2024, Ecology Letters)

   Heino, Mikko (Ed.)

   Abstract: Chance pervades life. In turn, life histories are described by probabilities (e.g. survival and breeding) and averages across individuals (e.g. mean growth rate and age at maturity). In this study, we explored patterns of luck in lifetime outcomes by analysing structured population models for a wide array of plant and animal species. We calculated four response variables: variance and skewness in both lifespan and lifetime reproductive output (LRO), and partitioned them into contributions from different forms of luck. We examined relationships among response variables and a variety of life history traits. We found that variance in lifespan and variance in LRO were positively correlated across taxa, but that variance and skewness were negatively correlated for both lifespan and LRO. The most important life history trait was longevity, which shaped variance and skew in LRO through its effects on variance in lifespan. We found that luck in survival, growth, and fecundity all contributed to variance in LRO, but skew in LRO was overwhelmingly due to survival luck. Rapidly growing populations have larger variances in LRO and lifespan than shrinking populations. Our results indicate that luck‐induced genetic drift may be most severe in recovering populations of species with long mature lifespan and high iteroparity. 

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2. A flexible analytic model of cosmic variance in the first billion years

   https://doi.org/10.1093/mnras/staa2828  

   Trapp, A C; Furlanetto, Steven R (October 2020, Monthly Notices of the Royal Astronomical Society)

   null (Ed.)

   ABSTRACT Cosmic variance is the intrinsic scatter in the number density of galaxies due to fluctuations in the large-scale dark matter density field. In this work, we present a simple analytic model of cosmic variance in the high-redshift Universe (z ∼ 5–15). We assume that galaxies grow according to the evolution of the halo mass function, which we allow to vary with large-scale environment. Our model produces a reasonable match to the observed ultraviolet (UV) luminosity functions in this era by regulating star formation through stellar feedback and assuming that the UV luminosity function is dominated by recent star formation. We find that cosmic variance in the UV luminosity function is dominated by the variance in the underlying dark matter halo population, and not by differences in halo accretion or the specifics of our stellar feedback model. We also find that cosmic variance dominates over Poisson noise for future high-z surveys except for the brightest sources or at very high redshifts (z ≳ 12). We provide a linear approximation of cosmic variance for a variety of redshifts, magnitudes, and survey areas through the public python package galcv. Finally, we introduce a new method for incorporating priors on cosmic variance into estimates of the galaxy luminosity function and demonstrate that it significantly improves constraints on that important observable. 

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3. Effect of Satellite Spatial Resolution on Submesoscale Variance in Ocean Color and Temperature

   https://doi.org/10.1029/2024GL114266  

   Carberry, Luke; Siegel, David_A; Nidzieko, Nicholas_J (March 2025, Geophysical Research Letters)

   Abstract The capability of moderate‐spatial‐resolution satellites to accurately resolve submesoscale variations in surface tracers remains an open question, one with relevance to observing physical‐biological interactions in the surface ocean. In this study, we address this question by comparing the variance of two tracers, chlorophyll concentration (Chl) and sea surface temperature (SST), resolved by two satellites—MODIS Aqua, with a resolution of 1.5 km, and Landsat 8/9, with a resolution of 30 m. We quantify tracer variance resolved by both satellites on the submesoscale using spatial variance spectral slopes. We find that MODIS measures significantly higher variance compared to Landsat, in both Chl and SST. This is because, despite higher signal‐to‐noise ratio for MODIS per pixel, Landsat signal‐to‐noise ratio increases considerably when aggregating pixels. Furthermore, by comparing Chl to SST variance for each satellite we find Landsat to be better match to theory for resolving submesoscale physical‐biological interactions. 

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4. Investigating the Impact of Measurement Variance on Gene Circuit Model Parameterization

   https://doi.org/10.1101/2025.04.02.646175  

   Spartalis, Thales R; Tang, Wan; Tang, Xun (April 2025, bioRxiv)

   Summary Ordinary differential equation (ODE)-based modeling is a powerful tool in the design and characterization of synthetic gene circuits. Despite its popularity, identifying the model parameters based off experimental measurement is a nontrivial task. In this study, we leverage cell-free experimental measurement of two RNA-based regulators to investigate the impact and the incorporation of measurement variance in the pair-wise squared error objective function used for ODE-model parameterization. Our findings suggest that while unweighted objective function and weighting by the inverse variance can provide reasonably accurate parameter estimation, weighing the objective function with the inverse stabilized variance could further improve the parameterization, by also capturing the system variance with a mitigated prediction variance. 

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5. Efficient variance components analysis across millions of genomes

   https://doi.org/10.1038/s41467-020-17576-9  

   Pazokitoroudi, Ali; Wu, Yue; Burch, Kathryn S.; Hou, Kangcheng; Zhou, Aaron; Pasaniuc, Bogdan; Sankararaman, Sriram (August 2020, Nature Communications)

   Abstract While variance components analysis has emerged as a powerful tool in complex trait genetics, existing methods for fitting variance components do not scale well to large-scale datasets of genetic variation. Here, we present a method for variance components analysis that is accurate and efficient: capable of estimating one hundred variance components on a million individuals genotyped at a million SNPs in a few hours. We illustrate the utility of our method in estimating and partitioning variation in a trait explained by genotyped SNPs (SNP-heritability). Analyzing 22 traits with genotypes from 300,000 individuals across about 8 million common and low frequency SNPs, we observe that per-allele squared effect size increases with decreasing minor allele frequency (MAF) and linkage disequilibrium (LD) consistent with the action of negative selection. Partitioning heritability across 28 functional annotations, we observe enrichment of heritability in FANTOM5 enhancers in asthma, eczema, thyroid and autoimmune disorders. 

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