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38,519 Research products, page 1 of 3,852

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  • 2012-2021
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  • Open Access
    Authors: 
    SXS Collaboration;
    Publisher: Zenodo
    Project: NSERC , NSF | Gravitational Radiation a... (1708213), NSF | Sustained-Petascale In Ac... (1238993), NSF | Maximizing Science Output... (1708212), NSF | Maximizing Scientific Out... (1806665), NSF | Leadership Class Scientif... (0725070), NWO | Precision Gravity: black ... (29769), NSF | Collaborative Research: P... (1713694)

    Simulation of a black-hole binary system evolved by the SpEC code.

  • English
    Authors: 
    ATLAS Collaboration;
    Publisher: HEPData
    Project: NSERC

    C_N^sub(eta_1, eta_2) for Pb+Pb, pT>0.2GeV, (160<=Nch<180) No data abstract available.

  • Open Access
    Authors: 
    Field, Daniel; Hsiang, Allison;
    Publisher: figshare
    Project: NSERC

    Nexus file for phylogenetic analysis. (ZIP 7 kb)

  • Open Access
    Authors: 
    Tian, Bo; Tianquan Lu; Xu, Yang; Ruling Wang; Guanqun Chen;
    Publisher: figshare
    Project: NSERC

    Additional file 3: Table S2. Summary of the Illumina sequencing data.

  • Research data . Image . 2021
    Open Access
    Authors: 
    (:Unkn) Unknown;
    Publisher: Electronic version published by Vancouver Island University
    Country: Canada

    Moving library cabinets

  • Open Access
    Authors: 
    SXS Collaboration;
    Publisher: Zenodo
    Project: NSERC , NSF | Collaborative Research: T... (1333129), NSF | Gravitational Radiation a... (1708213), NSF | Gravitational Radiation a... (1404569), NSF | Collaborative Research: P... (1440083), NSF | Maximizing Science Output... (1708212), NSF | CAREER: General Relativis... (1151197), NSF | Leadership Class Scientif... (0725070), NSF | MRI-R2: Acquisition of a ... (0960291), NSF | Sustained-Petascale In Ac... (1238993),...

    Simulation of a black-hole binary system evolved by the SpEC code.

  • English
    Authors: 
    Mills, Michelle B.; Michalowicz, Carolyn A.; Song, Ellen; Maahs, Adam C.; Newman, Julia A.; Cawte, Taylr; Lovnicki, Jessica M.; Soldatov, Dmitriy V.; Preuss, Kathryn E.;
    Publisher: Cambridge Crystallographic Data Centre
    Project: NSERC

    Related Article: Michelle B. Mills, Carolyn A. Michalowicz, Ellen Song, Adam C. Maahs, Julia A. Newman, Taylr Cawte, Jessica M. Lovnicki, Dmitriy V. Soldatov, Kathryn E. Preuss|2021|Cryst.Growth Des.|21|5897|doi:10.1021/acs.cgd.1c00791

  • Research data . Image . 2013
    Open Access English
    Authors: 
    Barron, George;
    Country: Canada
  • Open Access
    Authors: 
    SXS Collaboration;
    Publisher: Zenodo
    Project: NSF | Maximizing Science Output... (1708212), NSERC , NSF | Gravitational Radiation a... (1708213), NSF | Leadership Class Scientif... (0725070), NSF | Sustained-Petascale In Ac... (1238993), NSF | Collaborative Research: P... (1713694), NWO | Precision Gravity: black ... (29769), NSF | Maximizing Scientific Out... (1806665)

    Simulation of a black-hole binary system evolved by the SpEC code.

  • Open Access English
    Authors: 
    Hargreaves, Anna L.; Bailey, Susan F.; Laird, Robert A.;
    Publisher: Dryad
    Project: NSERC

    Fig 2 (heatmap) data files and R codeData and R code needed to create Fig 2 in Hargreaves et al (2015) J Evol Biol. One data file for each of the 6 figure panels. Each file contains evolved D across the range in each of 500 generations of stable climate followed by 1000 generations of climate change.Fig 2 (heatmap).zipFig 3 (D lines) data and R codeData and R code needed to create Fig 3 in Hargreaves et al (2015) J Evol Biol. One data file for each of the 6 models shown. Each file contains evolved D across the range after 500 generations of stable climate and after 1000 generations of climate change, averaged across 10 runs per cost per model.Fig 3 (D lines).zipFig 4 (delta.D) data and R codeData and R code needed to create Fig 4 in Hargreaves et al (2015) J Evol Biol. One data file for each of the 4 models (ie figure rows) shown. Each file contains evolved D across the range after 500 generations of stable climate and after 1000 generations of climate change for 30 runs per model.Fig 4 (delta.D).zipFig 6 (D vs density) data and R codeData and R code needed to create Fig 6 in Hargreaves et al (2015) J Evol Biol. Two data files (one for evolved D and one for density) for each of 2 model runs, one with dispersal (dispersal distance =1 as normal) and one run without dispersal (dispersal distance =0).Fig 6 (D vs density).zipAppendix S1 data and R code for each figureData and R code needed to create figures in Appendix S1 in Hargreaves et al (2015) J Evol Biol. All figures remake Fig 3 while varying one parameter. Fig S1.1 shows murate = .005; Fig S1.2 shows avshift = .01, .05, .2; Fig. S1.3 shows K=10; Fig. S1.4 shows effect of eliminating kin selection by randomizing individuals within columns before each dispersal event. For each figure there is 1 data file per model. Each data file contains evolved D across the range after 500 generations of stable climate and after 1000 generations of climate change, for 10 runs per cost.Appendix S1.zipModel code Matlab fileCode to run the model simulations.rangeshift (for dryad).mFig 5 (extinction threshold) Matlab codeMatlab code to run the simulations necessary to determine the relationship between the speed of climate change (avshift) and probability of extinction.rangeshift_thresh (for dryad).m Dispersal ability will largely determine whether species track their climatic niches during climate change, a process especially important for populations at contracting (low-latitude/low-elevation) range limits that otherwise risk extinction. We investigate whether dispersal evolution at contracting range limits is facilitated by two processes that potentially enable edge populations to experience and adjust to the effects of climate deterioration before they cause extinction: (i) climate-induced fitness declines towards range limits and (ii) local adaptation to a shifting climate gradient. We simulate a species distributed continuously along a temperature gradient using a spatially explicit, individual-based model. We compare range-wide dispersal evolution during climate stability vs. directional climate change, with uniform fitness vs. fitness that declines towards range limits (RLs), and for a single climate genotype vs. multiple genotypes locally adapted to temperature. During climate stability, dispersal decreased towards RLs when fitness was uniform, but increased when fitness declined towards RLs, due to highly dispersive genotypes maintaining sink populations at RLs, increased kin selection in smaller populations, and an emergent fitness asymmetry that favoured dispersal in low-quality habitat. However, this initial dispersal advantage at low-fitness RLs did not facilitate climate tracking, as it was outweighed by an increased probability of extinction. Locally adapted genotypes benefited from staying close to their climate optima; this selected against dispersal under stable climates but for increased dispersal throughout shifting ranges, compared to cases without local adaptation. Dispersal increased at expanding RLs in most scenarios, but only increased at the range centre and contracting RLs given local adaptation to climate.

search
Include:
The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
38,519 Research products, page 1 of 3,852
  • Open Access
    Authors: 
    SXS Collaboration;
    Publisher: Zenodo
    Project: NSERC , NSF | Gravitational Radiation a... (1708213), NSF | Sustained-Petascale In Ac... (1238993), NSF | Maximizing Science Output... (1708212), NSF | Maximizing Scientific Out... (1806665), NSF | Leadership Class Scientif... (0725070), NWO | Precision Gravity: black ... (29769), NSF | Collaborative Research: P... (1713694)

    Simulation of a black-hole binary system evolved by the SpEC code.

  • English
    Authors: 
    ATLAS Collaboration;
    Publisher: HEPData
    Project: NSERC

    C_N^sub(eta_1, eta_2) for Pb+Pb, pT>0.2GeV, (160<=Nch<180) No data abstract available.

  • Open Access
    Authors: 
    Field, Daniel; Hsiang, Allison;
    Publisher: figshare
    Project: NSERC

    Nexus file for phylogenetic analysis. (ZIP 7 kb)

  • Open Access
    Authors: 
    Tian, Bo; Tianquan Lu; Xu, Yang; Ruling Wang; Guanqun Chen;
    Publisher: figshare
    Project: NSERC

    Additional file 3: Table S2. Summary of the Illumina sequencing data.

  • Research data . Image . 2021
    Open Access
    Authors: 
    (:Unkn) Unknown;
    Publisher: Electronic version published by Vancouver Island University
    Country: Canada

    Moving library cabinets

  • Open Access
    Authors: 
    SXS Collaboration;
    Publisher: Zenodo
    Project: NSERC , NSF | Collaborative Research: T... (1333129), NSF | Gravitational Radiation a... (1708213), NSF | Gravitational Radiation a... (1404569), NSF | Collaborative Research: P... (1440083), NSF | Maximizing Science Output... (1708212), NSF | CAREER: General Relativis... (1151197), NSF | Leadership Class Scientif... (0725070), NSF | MRI-R2: Acquisition of a ... (0960291), NSF | Sustained-Petascale In Ac... (1238993),...

    Simulation of a black-hole binary system evolved by the SpEC code.

  • English
    Authors: 
    Mills, Michelle B.; Michalowicz, Carolyn A.; Song, Ellen; Maahs, Adam C.; Newman, Julia A.; Cawte, Taylr; Lovnicki, Jessica M.; Soldatov, Dmitriy V.; Preuss, Kathryn E.;
    Publisher: Cambridge Crystallographic Data Centre
    Project: NSERC

    Related Article: Michelle B. Mills, Carolyn A. Michalowicz, Ellen Song, Adam C. Maahs, Julia A. Newman, Taylr Cawte, Jessica M. Lovnicki, Dmitriy V. Soldatov, Kathryn E. Preuss|2021|Cryst.Growth Des.|21|5897|doi:10.1021/acs.cgd.1c00791

  • Research data . Image . 2013
    Open Access English
    Authors: 
    Barron, George;
    Country: Canada
  • Open Access
    Authors: 
    SXS Collaboration;
    Publisher: Zenodo
    Project: NSF | Maximizing Science Output... (1708212), NSERC , NSF | Gravitational Radiation a... (1708213), NSF | Leadership Class Scientif... (0725070), NSF | Sustained-Petascale In Ac... (1238993), NSF | Collaborative Research: P... (1713694), NWO | Precision Gravity: black ... (29769), NSF | Maximizing Scientific Out... (1806665)

    Simulation of a black-hole binary system evolved by the SpEC code.

  • Open Access English
    Authors: 
    Hargreaves, Anna L.; Bailey, Susan F.; Laird, Robert A.;
    Publisher: Dryad
    Project: NSERC

    Fig 2 (heatmap) data files and R codeData and R code needed to create Fig 2 in Hargreaves et al (2015) J Evol Biol. One data file for each of the 6 figure panels. Each file contains evolved D across the range in each of 500 generations of stable climate followed by 1000 generations of climate change.Fig 2 (heatmap).zipFig 3 (D lines) data and R codeData and R code needed to create Fig 3 in Hargreaves et al (2015) J Evol Biol. One data file for each of the 6 models shown. Each file contains evolved D across the range after 500 generations of stable climate and after 1000 generations of climate change, averaged across 10 runs per cost per model.Fig 3 (D lines).zipFig 4 (delta.D) data and R codeData and R code needed to create Fig 4 in Hargreaves et al (2015) J Evol Biol. One data file for each of the 4 models (ie figure rows) shown. Each file contains evolved D across the range after 500 generations of stable climate and after 1000 generations of climate change for 30 runs per model.Fig 4 (delta.D).zipFig 6 (D vs density) data and R codeData and R code needed to create Fig 6 in Hargreaves et al (2015) J Evol Biol. Two data files (one for evolved D and one for density) for each of 2 model runs, one with dispersal (dispersal distance =1 as normal) and one run without dispersal (dispersal distance =0).Fig 6 (D vs density).zipAppendix S1 data and R code for each figureData and R code needed to create figures in Appendix S1 in Hargreaves et al (2015) J Evol Biol. All figures remake Fig 3 while varying one parameter. Fig S1.1 shows murate = .005; Fig S1.2 shows avshift = .01, .05, .2; Fig. S1.3 shows K=10; Fig. S1.4 shows effect of eliminating kin selection by randomizing individuals within columns before each dispersal event. For each figure there is 1 data file per model. Each data file contains evolved D across the range after 500 generations of stable climate and after 1000 generations of climate change, for 10 runs per cost.Appendix S1.zipModel code Matlab fileCode to run the model simulations.rangeshift (for dryad).mFig 5 (extinction threshold) Matlab codeMatlab code to run the simulations necessary to determine the relationship between the speed of climate change (avshift) and probability of extinction.rangeshift_thresh (for dryad).m Dispersal ability will largely determine whether species track their climatic niches during climate change, a process especially important for populations at contracting (low-latitude/low-elevation) range limits that otherwise risk extinction. We investigate whether dispersal evolution at contracting range limits is facilitated by two processes that potentially enable edge populations to experience and adjust to the effects of climate deterioration before they cause extinction: (i) climate-induced fitness declines towards range limits and (ii) local adaptation to a shifting climate gradient. We simulate a species distributed continuously along a temperature gradient using a spatially explicit, individual-based model. We compare range-wide dispersal evolution during climate stability vs. directional climate change, with uniform fitness vs. fitness that declines towards range limits (RLs), and for a single climate genotype vs. multiple genotypes locally adapted to temperature. During climate stability, dispersal decreased towards RLs when fitness was uniform, but increased when fitness declined towards RLs, due to highly dispersive genotypes maintaining sink populations at RLs, increased kin selection in smaller populations, and an emergent fitness asymmetry that favoured dispersal in low-quality habitat. However, this initial dispersal advantage at low-fitness RLs did not facilitate climate tracking, as it was outweighed by an increased probability of extinction. Locally adapted genotypes benefited from staying close to their climate optima; this selected against dispersal under stable climates but for increased dispersal throughout shifting ranges, compared to cases without local adaptation. Dispersal increased at expanding RLs in most scenarios, but only increased at the range centre and contracting RLs given local adaptation to climate.