Spatial and Temporal Distribution Patterns of the Bean Leaf Beetle,
Cerotoma trifurcata
(Forster)
1
, on Soybeans in Illinois
2

Kogan, Marcos; Ruesink, William G.; McDowell, Kenneth

doi:10.1093/ee/3.4.607

Cited by 27 publications

(17 citation statements)

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“…This work indicates that changed synchrony will modify the slope and possibly the validity of TL, with ramifications for applications of TL in many areas, including resource management (3), conservation (11), human demography (6), tornado outbreaks (8), and agriculture (2,12,13). Given the ubiquity of synchrony in nature (22), it seems highly likely that synchrony often affects values of TL slopes in real populations, as Hokkaido voles showed.…”

Section: Discussionmentioning

confidence: 92%

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Synchrony affects Taylor’s law in theory and data

Reuman

Zhao

Sheppard

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Taylor's law (TL) is a widely observed empirical pattern that relates the variances to the means of groups of nonnegative measurements via an approximate power law: variance g ≈ a × mean g b , where g indexes the group of measurements. When each group of measurements is distributed in space, the exponent b of this power law is conjectured to reflect aggregation in the spatial distribution. TL has had practical application in many areas since its initial demonstrations for the population density of spatially distributed species in population ecology. Another widely observed aspect of populations is spatial synchrony, which is the tendency for time series of population densities measured in different locations to be correlated through time. Recent studies showed that patterns of population synchrony are changing, possibly as a consequence of climate change. We use mathematical, numerical, and empirical approaches to show that synchrony affects the validity and parameters of TL. Greater synchrony typically decreases the exponent b of TL. Synchrony influenced TL in essentially all of our analytic, numerical, randomization-based, and empirical examples. Given the near ubiquity of synchrony in nature, it seems likely that synchrony influences the exponent of TL widely in ecologically and economically important systems.

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Section: Discussionmentioning

confidence: 92%

“…In physics, TL is sometimes called "fluctuation scaling." TL has been generalized (10) and applied or proposed for application to fisheries management (3,4), estimation of species persistence times (11), and agriculture (2,12,13). Potential mechanisms of TL have been explored extensively (9,14,15).…”

mentioning

confidence: 99%

Synchrony affects Taylor’s law in theory and data

Reuman

Zhao

Sheppard

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…Viruliferous beetles can potentially transmit BPMV to soybeans at early growth stages, which maximizes reduction in yield and seed quality (70). Phenological studies show the BLB develops one generation per year in much of Minnesota (48), two generations per year in Iowa (83), Illinois (39), and Nebraska (97), and three generations per year in Arkansas and South Carolina (20,38).…”

Section: Insect Vectors Of Bpmvmentioning

confidence: 99%

Bean pod mottle virus: A Threat to U.S. Soybean Production

et al. 2002

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“…Recently, examples of TL were found in bacterial microcosms (5, 6), forest trees (7, 8), human populations (9), coral reef fish populations (10), and barnacles (11,12). TL has been used practically in the design of sampling plans for the control of insect pests of soybeans (13,14) and cotton (15).Scientific studies of TL largely focus on the power-law exponent b (or slope b in the linear form), which Taylor believed to contain information about how populations of a species aggregate in space (1). Empirically, b often lies between 1 and 2 (16).…”

mentioning

confidence: 99%

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confidence: 99%

Random sampling of skewed distributions implies Taylor’s power law of fluctuation scaling

Cohen

Meng

2015

Proc. Natl. Acad. Sci. U.S.A.

110

122

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Taylor's law (TL), a widely verified quantitative pattern in ecology and other sciences, describes the variance in a species' population density (or other nonnegative quantity) as a power-law function of the mean density (or other nonnegative quantity): Approximately, variance = a(mean) b , a > 0. Multiple mechanisms have been proposed to explain and interpret TL. Here, we show analytically that observations randomly sampled in blocks from any skewed frequency distribution with four finite moments give rise to TL. We do not claim this is the only way TL arises. We give approximate formulae for the TL parameters and their uncertainty. In computer simulations and an empirical example using basal area densities of red oak trees from Black Rock Forest, our formulae agree with the estimates obtained by least-squares regression. Our results show that the correlated sampling variation of the mean and variance of skewed distributions is statistically sufficient to explain TL under random sampling, without the intervention of any biological or behavioral mechanisms. This finding connects TL with the underlying distribution of population density (or other nonnegative quantity) and provides a baseline against which more complex mechanisms of TL can be compared. or equivalently as a linear function when mean and variance are logarithmically transformed:Eqs. 1 and 2 may be exact if the mean and variance are population moments calculated from certain parametric families of probability distributions (e.g., a = 1 and b = 1 for a Poisson distribution). Eqs. 1 and 2 may be approximate if the mean and variance are sample moments based on finite random samples of observations. Most empirical tests of TL have not specified the random error associated with Eqs. 1 or 2. TL has been verified for hundreds of biological species and nonbiological quantities in more than a thousand papers in ecology, epidemiology, biomedical sciences, and other fields (2-4). Recently, examples of TL were found in bacterial microcosms (5, 6), forest trees (7, 8), human populations (9), coral reef fish populations (10), and barnacles (11,12). TL has been used practically in the design of sampling plans for the control of insect pests of soybeans (13,14) and cotton (15).Scientific studies of TL largely focus on the power-law exponent b (or slope b in the linear form), which Taylor believed to contain information about how populations of a species aggregate in space (1). Empirically, b often lies between 1 and 2 (16). Ballantyne and Kerkhoff (17) suggested that individuals' reproductive correlation determines the size of b. Ballantyne (18) proposed that b = 2 is a consequence of deterministic population growth. Cohen (19) showed that b = 2 arose from exponentially growing, noninteracting clones. Kilpatrick and Ives (20) proposed that interspecific competition could reduce the value of b. Other models that implied TL were the exponential dispersion model (21-23), models of spatially distributed colonies (24, 25), a stochastic version of logistic population dyn...

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Spatial and Temporal Distribution Patterns of the Bean Leaf Beetle, Cerotoma trifurcata (Forster) 1 , on Soybeans in Illinois 2

Cited by 27 publications

References 0 publications

Synchrony affects Taylor’s law in theory and data

Synchrony affects Taylor’s law in theory and data

Bean pod mottle virus: A Threat to U.S. Soybean Production

Random sampling of skewed distributions implies Taylor’s power law of fluctuation scaling

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