Simulating Populations Obeying Taylor’s Power Law

1992 ◽  
pp. 295-311
Author(s):  
R. A. J. Taylor
Keyword(s):  
Power Law ◽  
Taylor’S Power Law ◽  
Taylor's Power Law

scholarly journals Predictions of Taylor's power law, density dependence and pink noise from a neutrally modeled time series

2010 ◽  
Vol 265 (1) ◽  
pp. 78-86 ◽  
Author(s):  
Petr Keil ◽  
Tomáš Herben ◽  
James Rosindell ◽  
David Storch
Keyword(s):  
Time Series ◽  
Density Dependence ◽  
Power Law ◽  
Pink Noise ◽  
Taylor’S Power Law ◽  
Taylor's Power Law

Fitting Taylor's Power Law

Oikos ◽  
1992 ◽  
Vol 65 (3) ◽  
pp. 538 ◽  
Author(s):  
Joe N. Perry ◽  
Ian P. Woiwod
Keyword(s):  
Power Law ◽  
Taylor’S Power Law ◽  
Taylor's Power Law

scholarly journals Influence of the physical dimension of leaf size measures on the goodness of fit for Taylor's power law using 101 bamboo taxa

2019 ◽  
Vol 19 ◽  
pp. e00657 ◽  
Author(s):  
Peijian Shi ◽  
Lei Zhao ◽  
David A. Ratkowsky ◽  
Karl J. Niklas ◽  
Weiwei Huang ◽  
...  
Keyword(s):  
Power Law ◽  
Goodness Of Fit ◽  
Leaf Size ◽  
Taylor’S Power Law ◽  
Taylor's Power Law

Spatial distribution and sequential sampling of Nysius spp. (Hemiptera: Lygaeidae) on sunflowers

1988 ◽  
Vol 28 (2) ◽  
pp. 279 ◽  
Author(s):  
PG Allsopp ◽  
S Iwao ◽  
LR Taylor
Keyword(s):  
Power Law ◽  
Negative Binomial ◽  
Sequential Sampling ◽  
Binomial Model ◽  
Sequential Decision ◽  
Taylor’S Power Law ◽  
Crop Development ◽  
Precision Level ◽  
Taylor's Power Law ◽  
Mixed Populations

Counts of adults of mixed populations of Nysius vinitor Bergroth and N. clevelandensis Evans on preflowering and postflowering sunflowers did not conform to the Poisson distribution because of overdispersion. Preflowering samples did not conform to the negative binomial model, but postflowering samples did with a common k of 3.78. Both sets of samples fitted significantly (P<0.01) Iwao's patchiness regression and Taylor's power law, but with significantly (P<0.01) different intercepts and slopes, respectively. Relationships to determine sample sizes for fixed levels of precision and fixed-precision-level stop lines are developed for both stages of crop development using Taylor's power law. Sequential decision plans based on Iwao's regression are developed for use in the management of Nysius spp. on preflowering and postflowering sunflowers.

Taylor’s power law and fixed-precision sampling: application to abundance of fish sampled by gillnets in an African lake

2017 ◽  
Vol 74 (1) ◽  
pp. 87-100 ◽  
Author(s):  
Meng Xu ◽  
Jeppe Kolding ◽  
Joel E. Cohen
Keyword(s):  
Power Law ◽  
Fish Assemblage ◽  
Mesh Size ◽  
Stock Management ◽  
Taylor’S Power Law ◽  
Scaling Relationship ◽  
Temporal Variance ◽  
Taylor's Power Law ◽  
Power Law Function ◽  
The Mean

Taylor’s power law (TPL) describes the variance of population abundance as a power-law function of the mean abundance for a single or a group of species. Using consistently sampled long-term (1958–2001) multimesh capture data of Lake Kariba in Africa, we showed that TPL robustly described the relationship between the temporal mean and the temporal variance of the captured fish assemblage abundance (regardless of species), separately when abundance was measured by numbers of individuals and by aggregate weight. The strong correlation between the mean of abundance and the variance of abundance was not altered after adding other abiotic or biotic variables into the TPL model. We analytically connected the parameters of TPL when abundance was measured separately by the aggregate weight and by the aggregate number, using a weight–number scaling relationship. We utilized TPL to find the number of samples required for fixed-precision sampling and compared the number of samples when sampling was performed with a single gillnet mesh size and with multiple mesh sizes. These results facilitate optimizing the sampling design to estimate fish assemblage abundance with specified precision, as needed in stock management and conservation.

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