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Data from: Subtle individual variation in indeterminate growth leads to major variation in survival and lifetime reproductive output in a long-li
负责人:
Armstrong, Doug
关键词:
Bayesian hierarchical modelling indeterminate growh individual variation life-history evolution long-lived ectotherms survival modelling von Bertalanffy growth model
DOI:
doi:10.5061/dryad.2j05h
摘要:
owed substantial individual variation in growth trajectories, and hierarchical models fitted to clutch-mass data and recapture histories showed tha
Data from: The architecture of river networks can drive the evolutionary dynamics of aquatic populations
负责人:
关键词:
confluence position;downstream migration;dendritic shape;riverscapes;agent-based model (ABM);Genetic structure
DOI:
doi:10.5061/dryad.7rs2d
摘要:
how riverscapes, with their complex architectures, affect patterns of neutral genetic diversity. Using a spatially explicit agent-based modeling (ABM) approach
Data from: Incorporating interspecific competition into species-distribution mapping by upward scaling of small-scale model projections
负责人:
关键词:
biotic interactions;JABOWA-III;Species distribution model;forest-gap model
DOI:
doi:10.5061/dryad.tf7f7
摘要:
timeframe with a robust forest gap model and scaled up to the landscape using a forestland classification technique. To demonstrate the method, we appli
Data from: Mechanical reproductive isolation facilitates parallel speciation in western North American scincid lizards
负责人:
Richmond, Jonathan Q.
关键词:
Hybridization Mate choice Reproductive isolation Speciation: ecological lizards Vertebrates
DOI:
doi:10.5061/dryad.51d7k
摘要:
reproductive isolation in most animal groups is largely unknown and rarely tested. In this study, we used hierarchical Bayesian modeling of mate compatibility exper
Data from: The effects of model choice and mitigating bias on the ribosomal tree of life
负责人:
关键词:
Trichoplax adhaerens;Cyanophora paradoxa;Desulfurispirillum indicum;Chlamydomonas reinhardtii;Bigelowiella natans;Cenarchaeum symbiosum;Cryptosporidium muris;Zymomonas mobilis;Nitrosoarchaeum limnia;Lactobacillus fermentum;Aspergillus flavus;Oscillibacter valericigenes;Arachnula sp;Archaeoglobus fulgidus;single-matrix model;Thermoplasma acidophilum;Paenibacillus sp;Perkinsus marinus;Corynebacterium pseudotuberculosis;Natromonas pharaonis;Staphylothermus marinus;two-domain tree;Diplonema sp;Prevotella denticola;Cyanothece sp;Ribosomal tree of life;Candida albicans;Anaerolinea thermophila;Methanocaldococcus jannaschii;Phaeodactylum tricornutum;Methylacidiphilum infernorum;Methanosarcina mazei;Dictyostelium discoideum;Chlorobium limicola;Thermotoga maritima;Methanosphaera stadtmanae;Babesia bovis;Thermodesulfatator indicus;Thermovibrio ammonificans;Flamella sp;Korarchaeum cryptofilum;Syntrophus aciditrophicus;Methanobrevibacter smithii;Deinococcus deserti;Gemmatimonas aurantiaca;Dyadobacter fermentans;Methanococcus aeolicus;Tribonema sp;Toxoplasma gondii;Acetobacter pasteurianus;Methanococcoides burtonii;Leishmania major;Thalassiosira pseudonana;Aeropyrum pernix;Nitrosomonas europaea;mixture model;Thermococcus kodakarensis;Arabidopsis thaliana;Apis mellifera;Nitrosopumilis sp;Halobacterium salinarum;Acidilobus saccharovorans;Pyrobaculum aerophilium;Fibrobacter succinogenes;Acanthamoeba sp;Idiomarina loihiensis;Ciona intestinalis;Meiothermus silvanus;Thermocrinis albus;Neisseria meningitidis;Spirochaeta coccoides;Methanopyrus kandleri;Xylella fastidiosa;Bodo sp;Ignicoccus hospitalis;Physarum polycephalum;Desulfurococcus mucosus;Danio rerio;Compositional heterogeneity;Treponema brennaborense;Sulfolobus solfataricus;Hypterthermus butylicus;Picrophilus torridus;Plasmodium vivax;Denitrovibrio acetiphilus;Haloarcula marismortui;three-domain tree;Metallosphaera sedula;Phytophthora infestans;Planctomyces brasiliensis;Thermovirga lienii;Trypanosoma brucei;Dictyoglomus thermophilum;Cryptobacterium curtum;Trimastix pyriformis;Cryptococcus gattii;Dehalococcoides ethenogenes;Pyrolobus fumarii;Guillardia theta;Methanoculleus marisingri;Thermus scotoductus;Syntrophothermus lipocalidus;Coraliomargarita akajimensis;Desulfobacca acetoxidans;Nitrosospira multiformis;Heterosigma sp;Prochlorococcus marinus;Fusobacterium nucleatum;Rhodopirellula baltica;Emiliana huxleyi;Nanoarchaeum equitans;Pyrococcus horikoshii;Ostreococcus tauri;Euglena sp
DOI:
doi:10.5061/dryad.7785h
摘要:
conflicting phylogenetic signals from HGT and/or paralogy. Thus, we tested several models of varying sophistication on three different datasets, per
Data from: A novel growth model evaluating age-size effect on long-term trends in tree growth.
负责人:
关键词:
Metabolic theory;21st century;age-related physiological constraints;20th century;Cryptomeria japonica;hierarchical Bayes;neighbourhood analysis;Genetic Variation;competition;climate;conifer
DOI:
doi:10.5061/dryad.7qc3s
摘要:
r drivers, and to predict forests’ responses to environmental changes reliably. 2.To address this issue, we present a novel tree growth model
Data from: Hierarchy in adaptive radiation: a case study using the Carnivora (Mammalia)
负责人:
关键词:
Macroevolution;Carnivora;Mammalia;early burst;phylogenetic comparative methods;Mammals;Cenozoic;Traits
DOI:
doi:10.5061/dryad.nv3846b
摘要:
Simpson’s “early burst” model of adaptive radiation was intended to explain the early proliferation of morphological and functional var
Data from: Causes and short-term consequences of variation in milk composition in wild sheep
负责人:
Renaud, Limoilou-Amelie
关键词:
Bayesian modeling ecophysiology hierarchical levels individual differences lactation maternal strategy multivariate analyses
DOI:
doi:10.5061/dryad.5682v7j
摘要:
of multivariate models – Multivariate Hierarchical Modeling (MHM) with latent variables ? which allow to statistically estimate unstructured covariances/correlations amo
Data from: Improving estimates of environmental change using multilevel regression models of Ellenberg indicator values
负责人:
关键词:
DOI:
doi:10.5061/dryad.r8k26cd
摘要:
species lists in some or all sample sites. Hierarchical modelling led to more accurate and precise estimates of site-level differences in mean EIV scores bet
Data from: Accounting for genotype uncertainty in the estimation of allele frequencies in autopolyploids
负责人:
关键词:
hierarchical Bayesian modeling;genotyping by sequencing;polyploidy;population genomics;RADseq;allelic dosage uncertainty
DOI:
doi:10.5061/dryad.t297p
摘要:
lele frequency estimation in a unified inferential framework using a hierarchical Bayesian model to sum over genotype uncertainty. Simulated data sets were generated under var

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