A process-independent explanation for the general form of taylor’s law

American Naturalist

Volumn 186, Issue 2, 2015, Pages E51-E60

  Xiao, Xiao ^a   Locey, Kenneth J ^b   White, Ethan P ^a,c  

^a UTAH STATE UNIVERSITY   (United States)

^b INDIANA UNIVERSITY   (United States)

^c UNIVERSITY OF FLORIDA   (United States)

Author keywords

Constraints; Feasible set; Integer compositions; Integer partitions; Observable variation; Taylor s law

Indexed keywords

ECOLOGICAL APPROACH; FEASIBILITY STUDY; NUMERICAL MODEL; OBSERVATIONAL METHOD; POWER LAW; SPECIES-AREA RELATIONSHIP; VARIANCE ANALYSIS;

BIOLOGICAL MODEL; ECOLOGY; EMPIRICAL RESEARCH; POPULATION DYNAMICS;

ECOLOGY; EMPIRICAL RESEARCH; MODELS, BIOLOGICAL; POPULATION DYNAMICS;

EID: 84934996215     PISSN: 00030147     EISSN: 15375323     Source Type: Journal    
DOI: 10.1086/682050     Document Type: Article

References (43)

1. Azevedo, R. B., and A. M. Leroi. 2001. A power law for cells. Proceedings of the National Academy of Sciences of the USA 98:5699–5704.
2. Ballantyne, F., IV. 2005. The upper limit for the exponent of Taylor’s power law is a consequence of deterministic population growth. Evolutionary Ecology Research 7:1213–1220.
3. Ballantyne, F., and A. J. Kerkhoff. 2007. The observed range for temporal mean-variance scaling exponents can be explained by reproductive correlation. Oikos 116:174–180.
4. Bona, M. 2006. A walk through combinatorics: an introduction to enumeration and graph theory. 2nd ed. World Scientific, Singapore.
5. Brown, J. H., J. F. Gillooly, A. P. Allen, V. M. Savage, and G. B. West. 2004. Towards a metabolic theory of ecology. Ecology 85:1771–1789.
6. Cohen, J. E., M. Xu, and W. S. F. Schuster. 2013. Stochastic multiplicative population growth predicts and interprets Taylor’s power law of fluctuation scaling. Proceedings of the Royal Society B: Biological Sciences 280:20122955.
7. De Menezes, M. A., and A.-L. Barabási. 2004. Fluctuations in network dynamics. Physical Review Letters 92:028701–028704.
8. Eisler, Z., I. Bartos, and J. Kertész. 2008. Fluctuation scaling in complex systems: Taylor’s law and beyond. Advances in Physics 57: 89–142.
9. Eisler, Z., and J. Kertész. 2006. Scaling theory of temporal correlations and size-dependent fluctuations in the traded value of stocks. Physical Review E 73:046109.
10. Frank, S. A. 2014. Generative models versus underlying symmetries to explain biological pattern. Journal of Evolutionary Biology 27: 1172–1178.
11. Fronczak, A., and P. Fronczak. 2010. Origins of Taylor’s power law for fluctuation scaling in complex systems. Physical Review E 81: 066112.
12. Haegeman, B., and R. S. Etienne. 2010. Entropy maximization and the spatial distribution of species. American Naturalist 175:E74–E90.
13. Haegeman, B., and M. Loreau. 2008. Limitations of entropy maximization in ecology. Oikos 117:1700–1710.
14. Hanski, I. 1980. Spatial patterns and movements in Coprophagous beetles. Oikos 34:293–310.
15. Harte, J. 2011. Maximum entropy and ecology: a theory of abundance, distribution, and energetics. Oxford University Press, Oxford.
16. He, F., and K. J. Gaston. 2003. Occupancy, spatial variance, and the abundance of species. American Naturalist 162:366–375.
17. Jørgensen, B., J. R. Martínez, and M. Tsao. 1994. Asymptotic behaviour of the variance function. Scandinavian Journal of Statistics 21:223–243.
18. Kaltz, O., P. Escobar-Páramo, M. E. Hochberg, and J. E. Cohen. 2012. Bacterial microcosms obey Taylor’s law: effects of abiotic and biotic stress and genetics on mean and variance of population density. Ecological Processes 1:5.
19. Kalyuzhny, M., Y. Schreiber, R. Chocron, C. H. Flather, R. Kadmon, D. A. Kessler, and N. M. Shnerb. 2014. Temporal fluctuation scaling in populations and communities. Ecology 95:1701–1709.
20. Kendal, W. S. 2003. An exponential dispersion model for the distribution of human single nucleotide polymorphisms. Molecular Biology and Evolution 20:579–590.
21. Kendal, W. S., and B. Jørgensen. 2011. Taylor’s power law and fluctuation scaling explained by a central-limit-like convergence. Physical Review E 83:066115.
22. Kerkhoff, A. J., and F. Ballantyne. 2003. The scaling of reproductive variability in trees. Ecology Letters 6:850–856.
23. Kilpatrick, A. M., and A. R. Ives. 2003. Species interactions can explain Taylor’s power law for ecological time series. Nature 422:65–68.
24. Kleiber, M. 1932. Body size and metabolism. Hilgardia 6:315–351.
25. Locey, K. J., and D. J. McGlinn. 2013. Efficient algorithms for sampling feasible sets of macroecological patterns. PeerJ PrePrints 1: e78v1.
26. Locey, K. J., and E. P. White. 2013. How species richness and total abundance constrain the distribution of abundance. Ecology Letters 16:1177–1185.
27. Perry, J. N. 1994. Chaotic dynamics can generate Taylor’s power law. Proceedings of the Royal Society B: Biological Sciences 257:221–226.
28. Rosenzweig, M. L. 1995. Species diversity in space and time. Cambridge University Press, Cambridge.
29. Severs, C., and J. A. White. 2010. On the homology of the real complement of the k-parabolic subspace arrangement. arXiv 1012.3387 (math.CO). Combinatorics.
30. Supp, S. R., X. Xiao, S. K. M. Ernest, and E. P. White. 2012. An experimental test of the response of macroecological patterns to altered species interactions. Ecology 93:2505–2511.
31. Taylor, L. R. 1961. Aggregation, variance and the mean. Nature 189:732–735.
32. Taylor, L. R., and R. A. J. Taylor. 1977. Aggregation, migration and population mechanics. Nature 265:415–421.
33. Taylor, L. R., R. A. J. Taylor, I. P. Woiwod, and J. N. Perry. 1983. Behavioural dynamics. Nature 303:801–804.
34. Taylor, L. R., and I. P. Woiwod. 1980. Temporal stability as a densitydependent species characteristic. Journal of Animal Ecology 49: 209–224.
35. Taylor, L. R., and I. P. Woiwod 1982. Comparative synoptic dynamics. I. Relationships between inter- and intra-specific spatial and temporal variance/mean population parameters. Journal of Animal Ecology 51:879–906.
36. Taylor, L. R., I. P. Woiwod, and J. N. Perry. 1978. The densitydependence of spatial behaviour and the rarity of randomness. Journal of Animal Ecology 47:383–406.
37. Taylor, R. A. J. 1981a. The behavioural basis of redistribution I. The D-model concept. Journal of Animal Ecology 50:573–586.
38. Taylor, R. A. J 1981b. The behavioural basis of redistribution. II. Simulations of the D-model. Journal of Animal Ecology 50:587–604.
39. Tweedie, M. C. K. 1984. An index which distinguishes between some important exponential families. Pages 579–604 in J. K. Ghosh and J. Roy, eds. Statistics: applications and new directions. Proceedings of the Indian Statistical Institute Golden Jubilee International Conference, Calcutta.
40. White, E. P., K. M. Thibault, and X. Xiao. 2012. Characterizing species abundance distributions across taxa and ecosystems using a simple maximum entropy model. Ecology 93:1772–1778.
41. Xiao, X., K. J. Locey, and E. P. White. 2015. Data from: A processindependent explanation for the general form of Taylor’s law. American Naturalist, Dryad Digital Repository, http://dx.doi.org/10.5061/dryad.h1c09.
42. Xiao, X., E. P. White, M. B. Hooten, and S. L. Durham. 2011. On the use of log-transformation vs. nonlinear regression for analyzing biological power laws. Ecology 92:1887–1894.
43. Yenni, G. M. 2013. Self-limitation as an explanation for species’ relative abundances and the long-term persistence of rare species. PhD diss. Utah State University, Logan. http://digitalcommons.usu.edu/cgi/viewcontent.cgi?articlep2944&contextpetd.