Taylor's law (TL), a commonly observed and applied pattern in ecology, describes variances of population densities as related to mean densities via log(variance) = log( The goal of this study was to provide tools to help fill this gap in understanding by providing Using numeric simulations and 82 multi‐decadal population datasets, we here propose, test and apply two proximate statistical determinants of TL slopes which we argue can become key tools for understanding the nature and ecological causes of TL slope variation. We find that measures based on population skewness, coefficient of variation and synchrony are effective proximate determinants. We demonstrate their potential for application by using them to help explain covariation in slopes of spatial and temporal TL (two common types of TL). This study provides tools for understanding TL, and demonstrates their usefulness.
- NSF-PAR ID:
- 10058739
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- ISSN:
- 0027-8424
- Page Range / eLocation ID:
- 201703593
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract a ) +b *log(mean). Variations among datasets in the slope,b , have been associated with multiple factors of central importance in ecology, including strength of competitive interactions and demographic rates. But these associations are not transparent, and the relative importance of these and other factors for TL slope variation is poorly studied. TL is thus a ubiquitously used indicator in ecology, the understanding of which is still opaque.proximate determinants of TL slopes , statistical quantities that are correlated to TL slopes but are simpler than the slope itself and are more readily linked to ecological factors. -
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Abstract Aim Understanding how spatial scale of study affects observed dispersal patterns can provide insights to spatiotemporal population dynamics, particularly in systems with significant long‐distance dispersal (LDD). We aimed to investigate the dispersal gradients of two rusts of wheat with spores of similar size, mass and shape, over multiple spatial scales. We hypothesized that a single dispersal kernel could fit the dispersal from all spatial scales well, and that it would be possible to obtain similar results in spatiotemporal increase of disease when modelling based on differing scales.
Location Central Oregon and St. Croix Island.
Taxa Puccinia striiformis f. sp.tritici, Puccinia graminis f. sp.tritici, Triticum aestivum .Methods We compared empirically derived primary disease gradients of cereal rust across three spatial scales: local (inoculum source and sampling unit = 0.0254 m, spatial extent = 1.52 m) field‐wide (inoculum source = 1.52 m, sampling unit = 0.305 m and spatial extent = 91.4 m) and regional (inoculum source and sampling unit = 152 m, spatial extent = 10.5 km). We then examined whether disease spread in spatially explicit simulations depended upon the scale at which data were collected by constructing a compartmental time‐step model.
Results The three data sets could be fit well by a single power law dispersal kernel. Simulating epidemic spread at different spatial resolutions resulted in similar patterns of spatiotemporal spread. Dispersal kernel data obtained at one spatial scale can be used to represent spatiotemporal disease spread at a larger spatial scale.
Main Conclusions Organisms spread by aerially dispersed small propagules that exhibit LDD may follow similar dispersal patterns over a several hundred‐ or thousand‐fold expanse of spatial scale. Given that the primary mechanisms driving aerial dispersal remains constant, it may be possible to extrapolate across scales when empirical data are unavailable at a scale of interest.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.1703593114
