Literature DB >> 27912025

Population dynamics, synchrony, and environmental quality of Hokkaido voles lead to temporal and spatial Taylor's laws.

Joel E Cohen1,2,3, Takashi Saitoh4.   

Abstract

Taylor's law (TL) asserts that the variance in a species' population density is a power-law function of its mean population density: log(variance) = a + b × log(mean). TL is widely verified. We show here that empirical time series of density of the Hokkaido gray-sided vole, Myodes rufocanus, sampled 1962-1992 at 85 locations, satisfied temporal and spatial forms of TL. The slopes (b ± standard error) of the temporal and spatial TL were estimated to be 1.613 ± 0.141 and 1.430 ± 0.132, respectively. A previously verified autoregressive Gompertz model of the dynamics of these populations generated time series of density which reproduced the form of temporal and spatial TLs, but with slopes that were significantly steeper than the slopes estimated from data. The density-dependent components of the Gompertz model were essential for the temporal TL. Adding to the Gompertz model assumptions that populations with higher mean density have reduced variance of density-independent perturbations and that density-independent perturbations are spatially correlated among populations yielded simulated time series that satisfactorily reproduced the slopes from data. The slopes (b ± standard error) of the enhanced simulations were 1.619 ± 0.199 for temporal TL and 1.575 ± 0.204 for spatial TL.
© 2016 by the Ecological Society of America.

Entities:  

Keywords:  Gompertz model; Taylor's law; autoregressive time series; density dependence; population dynamics; rodents; spatial correlation; synchrony; voles

Mesh:

Year:  2016        PMID: 27912025     DOI: 10.1002/ecy.1575

Source DB:  PubMed          Journal:  Ecology        ISSN: 0012-9658            Impact factor:   5.499


  5 in total

1.  Synchrony affects Taylor's law in theory and data.

Authors:  Daniel C Reuman; Lei Zhao; Lawrence W Sheppard; Philip C Reid; Joel E Cohen
Journal:  Proc Natl Acad Sci U S A       Date:  2017-05-30       Impact factor: 11.205

2.  Power law analysis of the human milk microbiome.

Authors:  Bin Yi; Hongju Chen
Journal:  Arch Microbiol       Date:  2022-09-01       Impact factor: 2.667

3.  Individual size variation reduces spatial variation in abundance of tree community assemblage, not of tree populations.

Authors:  Hua-Feng Wang; Meng Xu
Journal:  Ecol Evol       Date:  2017-11-09       Impact factor: 2.912

4.  Assessing and Interpreting the Metagenome Heterogeneity With Power Law.

Authors:  Zhanshan Sam Ma
Journal:  Front Microbiol       Date:  2020-05-06       Impact factor: 5.640

5.  Spatial and temporal autocorrelations affect Taylor's law for US county populations: Descriptive and predictive models.

Authors:  Meng Xu; Joel E Cohen
Journal:  PLoS One       Date:  2021-01-07       Impact factor: 3.240

  5 in total

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