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Data from: A Bayesian approach to social structure uncovers cryptic regulation of group dynamics in Drosophila melanogaster
负责人:
关键词:
Modeling: stochastic spatial;Evolution: social;Behavior: social;Mate choice;Social structure;Modeling: individual based;drosophila melanogaster
DOI:
doi:10.5061/dryad.49r20
摘要:
of social interactions. Modeling this process is challenging, because all individuals simultaneously behave in ways that shape their social environments—a process called
Data from: The coalescent in boundary-limited range expansions
负责人:
关键词:
Genetic Variation;Coalescent Theory;genetic drift;gene flow;population genetics
DOI:
doi:10.5061/dryad.mk6g1
摘要:
en have drastic population genetic effects, such as a stochastic resampling of the gene pool through the "surfing" phenomenon. Most models assume tha
Data from:A method for estimating Hill function-based dynamic models of gene regulatory networks
负责人:
关键词:
gene regulatory networks;optimization;Escherichia coli;parameter estimation
DOI:
doi:10.5061/dryad.ht047
摘要:
. Different types of models, such as logical, continuous, and stochastic models, can be used to describe GRNs. In this paper, we present a new approach to identi
Data from: Age-dependent speciation can explain the shape of empirical phylogenies
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关键词:
R package TreeSimGM;Macroevolution;parameter estimation;birth-death process;Methodology testing;Simulated trees;stochastic models;diversification;Empirical trees
DOI:
doi:10.5061/dryad.31227
摘要:
uces the imbalance seen in empirical trees is a challenging problem, to which none of the existing models provide a satisfying answer. Here we propose a simple
Data from: Modeling effects of nonbreeders on population growth estimates
负责人:
Lee, Aline M.
关键词:
demographic stochasticity floaters intermittent breeding matrix model nonbreeding population dynamics reproductive skipping sensitivity analysis
DOI:
doi:10.5061/dryad.t56cn
摘要:
ally impeding projection of deterministic and stochastic population growth rates. We present and analyse a general modelling framework for systems wher
Data from: Rethinking ‘normal’: the role of stochasticity in the phenology of a synchronously breeding seabird
负责人:
Youngflesh, Casey
关键词:
Adélie penguin Bayesian hierarchical model climate change coloniality phenological mismatch stochasticity synchrony Phenology
DOI:
doi:10.5061/dryad.23sv1
摘要:
) and temporal variability (inter-annual) can make it difficult to separate intrinsic, extrinsic, and stochastic drivers of phenological variability. The goal of this study
Data from: Phylogenetic analysis using Lévy processes: finding jumps in the evolution of continuous traits
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关键词:
Primates;continuous trait evolution;saltational evolution;Bayesian inference;Lévy processes
DOI:
doi:10.5061/dryad.0n761
摘要:
Gaussian processes, a class of stochastic processes including Brownian motion and the Ornstein–Uhlenbeck process, are widely used to model conti
Data from: Incorporating uncertainty into forest management planning: timber harvest, wildfire and climate change in the boreal forest
负责人:
关键词:
disturbance;Populus tremuloides;stochastic;risk;Model;Pinus banksiana;spatial;Holocene;Picea mariana;Landscape
DOI:
doi:10.5061/dryad.0rb1b
摘要:
parameterizations of a stochastic, spatially-explicit state-and-transition simulation model (STSM). We then use Monte Carlo simulations with the STSM to “stress
Data from: Matching habitat choice promotes species persistence under climate change
负责人:
关键词:
species range shift;Adaptation;Dispersal
DOI:
doi:10.5061/dryad.rm93865
摘要:
ty. We also found that environmental stochasticity resulted in suboptimal matching habitat choice, decreasing the benefits of this dispersal mode under
Data from: How random is social behaviour? Disentangling social complexity through the study of a wild house mouse population
负责人:
Perony, Nicolas
关键词:
animal behaviour habitat use home range social structure self-organisation stochastic modelling
DOI:
doi:10.5061/dryad.c2b53
摘要:
s of a population of wild house mice (Mus domesticus) in their natural environment can be explained by a simple stochastic model. We first introduce the noti

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