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1. Horseshoe‐based Bayesian nonparametric estimation of effective population size trajectories

   https://doi.org/10.1111/biom.13276  

   Faulkner, James_R; Magee, Andrew_F; Shapiro, Beth; Minin, Vladimir_N (May 2020, Biometrics)

   Abstract Phylodynamics is an area of population genetics that uses genetic sequence data to estimate past population dynamics. Modern state‐of‐the‐art Bayesian nonparametric methods for recovering population size trajectories of unknown form use either change‐point models or Gaussian process priors. Change‐point models suffer from computational issues when the number of change‐points is unknown and needs to be estimated. Gaussian process‐based methods lack local adaptivity and cannot accurately recover trajectories that exhibit features such as abrupt changes in trend or varying levels of smoothness. We propose a novel, locally adaptive approach to Bayesian nonparametric phylodynamic inference that has the flexibility to accommodate a large class of functional behaviors. Local adaptivity results from modeling the log‐transformed effective population size a priori as a horseshoe Markov random field, a recently proposed statistical model that blends together the best properties of the change‐point and Gaussian process modeling paradigms. We use simulated data to assess model performance, and find that our proposed method results in reduced bias and increased precision when compared to contemporary methods. We also use our models to reconstruct past changes in genetic diversity of human hepatitis C virus in Egypt and to estimate population size changes of ancient and modern steppe bison. These analyses show that our new method captures features of the population size trajectories that were missed by the state‐of‐the‐art methods. 

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2. Late Pleistocene shrub expansion preceded megafauna turnover and extinctions in eastern Beringia

   https://doi.org/10.1073/pnas.2107977118  

   Monteath, Alistair J.; Gaglioti, Benjamin V.; Edwards, Mary E.; Froese, Duane (December 2021, Proceedings of the National Academy of Sciences)

   The collapse of the steppe-tundra biome (mammoth steppe) at the end of the Pleistocene is used as an important example of top-down ecosystem cascades, where human hunting of keystone species led to profound changes in vegetation across high latitudes in the Northern Hemisphere. Alternatively, it is argued that this biome transformation occurred through a bottom-up process, where climate-driven expansion of shrub tundra ( Betula , Salix spp.) replaced the steppe-tundra vegetation that grazing megafauna taxa relied on. In eastern Beringia, these differing hypotheses remain largely untested, in part because the precise timing and spatial pattern of Late Pleistocene shrub expansion remains poorly resolved. This uncertainty is caused by chronological ambiguity in many lake sediment records, which typically rely on radiocarbon ( 14 C) dates from bulk sediment or aquatic macrofossils—materials that are known to overestimate the age of sediment layers. Here, we reexamine Late Pleistocene pollen records for which 14 C dating of terrestrial macrofossils is available and augment these data with 14 C dates from arctic ground-squirrel middens and plant macrofossils. Comparing these paleovegetation data with a database of published 14 C dates from megafauna remains, we find the postglacial expansion of shrub tundra preceded the regional extinctions of horse ( Equus spp.) and mammoth ( Mammuthus primigenius ) and began during a period when the frequency of 14 C dates indicates large grazers were abundant. These results are not consistent with a model of top-down ecosystem cascades and support the hypothesis that climate-driven habitat loss preceded and contributed to turnover in mammal communities. 

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3. A sea of change: Tracing parasitic dynamics through the past millennia in the northern Adriatic, Italy

   https://doi.org/10.1130/G52187.1  

   Fitzgerald, Erin; Ryan, Delaney; Scarponi, Daniele; Huntley, John Warren (May 2024, Geology)

   Our study uses data from Holocene core samples and modern death assemblages to understand how human-induced environmental change in the northern Adriatic Sea (Italy) may have affected parasite-host dynamics in the economically important bivalve Chamelea gallina. Thirty-one radiocarbon dates confirm temporal distinctness between the periods before and after the onset of significant human influence and confirm that trematode prevalence has decreased by an order of magnitude over the past ∼2 k.y. The median number of parasite-induced pits per bivalve host and parasite aggregation has also decreased significantly, signaling a substantial decrease in the effective population size of digenean trematodes. Gaussian finite mixture modeling of pit size does not support the hypothesis of parasite extinction. Combined, these results indicate the (potentially ongoing) collapse of parasite-host interactions in C. gallina in concert with human influence on the Adriatic and its transition to an urban sea. 

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4. Kalman Filter Based Electricity Market States Forecasting: A State-Space Framework

   Rana, Md Masud; Bo, Rui; Choi, Bong Jun (August 2019, Proceedings of 9th IEEE International Conference on CYBER Technology in Automation, Control and Intelligent Systems (IEEE-CYBER 2019))

   Gaining money and high profit is the dream of electricity market investors; however, it requires accurate financial knowledge and price forecasting ability. Most of the investors are used the electricity market historical information for forecasting power generation, consumption, and utility price. Unfortunately, electricity market time-series profile is high volatility and change over time, so the factual data cannot accurately reflect the electricity market states such as power consumption and generation. In the literature, there is no systematic way or suitable models that can fit, analyze, and predict electricity market system states over time. Interestingly, this paper proposes an electricity market state-space model which is obtained by a set of electricity market differential equations. After simplifying of these equations, the continuous-time electricity market state-space model is derived. Using discrete-time step size parameter, the continuous-time system is discretised. Furthermore, the noisy measurements are obtained by a set of smart sensors. Finally, the Kalmna filter based electricity market state forecasting algorithm is developed based on noisy measurements. Simulation results show that the proposed algorithm can properly forecast the electricity market states. Consequently, this kind of model and algorithm can help to develop the electricity market simulator and assist investor to participate/invest electricity market regardless of the world economic downtown. 

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5. Spatiotemporal Dynamics of Prehistoric Human Population Growth: Radiocarbon Dates as Data and Population Ecology Models.

   https://doi.org/doi.org/10.1016/j.jas.2018.11.006  

   Robinson, E. (April 2019, Journal of archaeological science)

   Archaeologists now routinely use summed radiocarbon dates as a measure of past population size, yet few have coupled these measures to theoretical expectations about social organization. To help move the ‘dates as data’ approach from description to explanation, this paper proposes a new integrative theory and method for quan- titative analyses of radiocarbon summed probability distributions (SPDs) in space. We present this new approach to ‘SPDs in space’ with a case study of 3571 geo-referenced radiocarbon dates from Wyoming, USA. We develop a SPD for the Holocene in Wyoming, then analyze the spatial distribution of the SPD as a function of time using a standard nearest-neighbor statistic. We compare population growth and decline throughout the Holocene with expectations for different Ideal Distribution Models from population ecology that predict the relationship be- tween habitat quality and population density. Results suggest that populations in Wyoming were initially clustered and then became increasingly dispersed through the course of the Holocene. These results suggest that Allee-like benefits to aggregation, rather than ideal free-driven dispersion patterns, explain settlement decisions in response to growing populations. Our approach is a first step in constructing a method and theory for de- scribing relationships between social organization and population growth trends derived from archaeological radiocarbon time-series. 

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