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Population

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Population - Free Register to Access Experts & Abstracts

Alison F Feder - One of the best experts on this subject based on the ideXlab platform.

  • evolutionary dynamics in structured Populations under strong Population genetic forces
    G3: Genes Genomes Genetics, 2019
    Co-Authors: Alison F Feder, Pleuni S Pennings, Joachim Hermisson, Dmitri A Petrov
    Abstract:

    In the long-term neutral equilibrium, high rates of migration between subPopulations result in little Population differentiation. However, in the short-term, even very abundant migration may not be enough for subPopulations to equilibrate immediately. In this study, we investigate dynamical patterns of short-term Population differentiation in adapting Populations via stochastic and analytical modeling through time. We characterize a regime in which selection and migration interact to create non-monotonic patterns of Population differentiation over time when migration is weaker than selection, but stronger than drift. We demonstrate how these patterns can be leveraged to estimate high migration rates using approximate Bayesian computation. We apply this approach to estimate fast migration in a rapidly adapting intra-host Simian-HIV Population sampled from different anatomical locations. We find differences in estimated migration rates between different compartments, even though all are above N e m = 1. This work demonstrates how studying demographic processes on the timescale of selective sweeps illuminates processes too fast to leave signatures on neutral timescales.

William F. Fagan - One of the best experts on this subject based on the ideXlab platform.

  • Survivorship patterns in captive mammalian Populations: implications for estimating Population growth rates
    Ecological Applications, 2010
    Co-Authors: Heather J. Lynch, Sara L. Zeigler, Leslie Wells, Jonathan D. Ballou, William F. Fagan
    Abstract:

    For species of conservation concern, ecologists often need to estimate potential Population growth rates with minimal life history data. We use a survivorship database for captive mammals to show that, although survivorship scale (i.e., longevity) varies widely across mammals, survivorship shape (i.e., the age-specific pattern of mortality once survivorship has been scaled to maximum longevity) varies little. Consequently, reasonable estimates of Population growth rate can be achieved for diverse taxa using a model of survivorship shape along with an estimate of longevity. In addition, we find that the parameters of survivorship shape are related to taxonomic group, a fact that may be used to further improve estimates of survivorship when full life history data are unavailable. Finally, we compare survivorship shape in captive and wild Populations of the same species and find higher adult survivorship in captive Populations but no corresponding increase in juvenile survivorship. These differences likely reflect a convolution of true differences in captive vs. wild survivorship and the difficulty of observing juvenile mortality in field studies.

Douglas A. Bell - One of the best experts on this subject based on the ideXlab platform.

  • assessing predictions of Population viability analysis peregrine falcon Populations in california
    Ecological Applications, 2014
    Co-Authors: Timothy J Wootton, Douglas A. Bell
    Abstract:

    Population viability analysis (PVA) has been an important tool for evaluating species extinction risk and alternative management strategies, but there is little information on how well PVA predicts Population trajectories following changes in management actions. We tested previously published predictions from a stage-structured PVA of Peregrine Falcons (Falco peregrinus) in California, USA (Wootton and Bell 1992), against Population trajectories following the 1992 termination of statewide, active management (Population supplementation of captive-reared young). In the absence of extensive post-management monitoring, we developed surrogate estimates of breeding Population size by calibrating several citizen science data sets (Christmas Bird Count, CBC; and North American Breeding Bird Survey, BBS) to intensive Population surveys taken primarily during the active management period. CBC abundance data standardized by observer effort exhibited a strong relationship to intensive survey data (r2 = 0.971), indica...

Richard A. Hinrichsen - One of the best experts on this subject based on the ideXlab platform.

  • Population viability analysis for several Populations using multivariate state-space models
    Ecological Modelling, 2009
    Co-Authors: Richard A. Hinrichsen
    Abstract:

    The International Union for the Conservation of Nature and Natural Resources (IUCN), the world's largest and most important global conservation network, has listed approximately 16,000 species worldwide as threatened. The most important tool for recognizing and listing species as threatened is Population viability analysis (PVA), which estimates the probability of extinction of a Population or species over a specified time horizon. The most common PVA approach is to apply it to single time series of Population abundance. This approach to Population viability analysis ignores covariability of local Populations. Covariability can be important because high synchrony of local Populations reduces the effective number of local Populations and leads to greater extinction risk. Needed is a way of extending PVA to model correlation structure among multiple local Populations. Multivariate state-space modeling is applied to this problem and alternative estimation methods are compared. The multivariate state-space technique is applied to endangered Populations of pacific salmon, USA. Simulations demonstrated that the correlation structure can strongly influence Population viability and is best estimated using restricted maximum likelihood instead of maximum likelihood.

Worden K. - One of the best experts on this subject based on the ideXlab platform.

  • Foundations of Population-based SHM, Part I : homogeneous Populations and forms
    'Elsevier BV', 2021
    Co-Authors: Bull L.a., Gardner P.a., Gosliga J., Rogers T.j., Dervilis N., Cross E.j., Papatheou E., Maguire A.e., Campos C., Worden K.
    Abstract:

    In Structural Health Monitoring (SHM), measured data that correspond to an extensive set of operational and damage conditions (for a given structure) are rarely available. One potential solution considers that information might be transferred, in some sense, between similar systems. A Population-based approach to SHM looks to both model and transfer this missing information, by considering data collected from groups of similar structures. Specifically, in this work, a framework is proposed to model a Population of nominally-identical systems, such that (complete) datasets are only available from a subset of members. The SHM strategy defines a general model, referred to as the Population form, which is used to monitor a homogeneous group of systems. First, the framework is demonstrated through applications to a simulated Population, with one experimental (test-rig) member; the form is then adapted and applied to signals recorded from an operational wind farm

  • Foundations of Population-based SHM, part III : heterogeneous Populations – mapping and transfer
    'Elsevier BV', 2021
    Co-Authors: Gardner P., Bull L.a., Gosliga J., Dervilis N., Worden K.
    Abstract:

    This is the third and final paper in a series laying foundations for a theory/methodology of Population-Based Structural Health Monitoring (PBSHM). PBSHM involves utilising knowledge from one set of structures in a Population and applying it to a different set, such that predictions about the health states of each member in the Population can be performed and improved. Central ideas behind PBSHM are those of knowledge transfer and mapping. In the context of PBSHM, knowledge transfer involves using information from a source domain structure, where labels are known for given feature sets, and mapping these onto the unlabelled feature space of a different, target domain structure. This mapping means a classifier trained on the transformed source domain data will generalise to the unlabelled target domain data; i.e. a classifier built on one structure will generalise to another, making Structural Heath Monitoring (SHM) cost-effective and applicable to a wide range of challenging industrial scenarios. This process of mapping features and labels across source and target domains is defined here via domain adaptation, a subcategory of transfer learning. A mathematical underpinning for when domain adaptation is possible in a structural dynamics context is provided, with reference to topology within a graphical representation of structures. Subsequently, a novel procedure for performing domain adaptation on topologically different structures is outlined